tag:theconversation.com,2011:/us/topics/forecasting-47326/articlesForecasting – The Conversation2024-01-23T18:59:54Ztag:theconversation.com,2011:article/2214962024-01-23T18:59:54Z2024-01-23T18:59:54ZDid the BOM get it wrong on the hot, dry summer? No – predicting chaotic systems is probability, not certainty<figure><img src="https://images.theconversation.com/files/570789/original/file-20240123-15-btnti9.jpg?ixlib=rb-1.1.0&rect=37%2C6%2C4123%2C2763&q=45&auto=format&w=496&fit=clip" /><figcaption><span class="caption">
</span> <span class="attribution"><span class="source">Shutterstock</span></span></figcaption></figure><p>What happened to the <a href="https://media.bom.gov.au/releases/1205/the-bureau-forecasts-an-unusually-warm-summer/">scorching El Niño summer</a> we were bracing for? Why has the east coast of Australia been drenched while the north and west gets the heat? </p>
<p>For beachgoers, a wrong weather forecast is annoying. For farmers, it can be very expensive. And for northern Queensland residents surprised by flooding after Cyclone Jasper, it can be devastating. Small wonder there’s <a href="https://www.adelaidenow.com.au/news/south-australia/farmers-roast-bom-forecasts-but-it-says-its-top-five-in-the-world/news-story/c945a38830fd6ea189e253b91b03fd36">been plenty</a> of <a href="https://indaily.com.au/news/2023/12/20/bom-criticised-for-queensland-rain-forecasts/">criticism</a> levelled at the Bureau of Meteorology and other forecasting agencies this summer. </p>
<p>The criticism is understandable. But is it fair? No. The reason is that weather forecasting is inherently not about certainty but probability. Our atmosphere and oceans do not behave in simple, easily predictable ways. They are <a href="https://www.bbc.com/future/article/20230203-why-the-world-feels-so-unstable-right-now">non-linear, chaotic systems</a>. That means we can only predict large weather features such as highs and lows or bands of storms with relative certainty and even then only for a few days in advance. </p>
<h2>We want certainty – but we have to settle for probability</h2>
<p>Let’s say you check your weather app and see your location has a 60% chance of rain at midday. What does this actually mean? </p>
<p>It means if this forecast was issued 100 times, you should get wet 60 times and stay dry 40 times.</p>
<p>To forecast rainfall <a href="http://www.bom.gov.au/climate/outlooks/#/overview/summary">for a whole season ahead</a>, meteorologists generally calculate the chance of exceeding average conditions, rather than stating that we will have a dry or wet summer with certainty. </p>
<p>So if we predict a 25% chance of above-average rain during an El Niño summer, we would expect that one out of every four times we make this prediction, we would observe higher rainfall than the average. </p>
<p>So how then do we know if we are making good forecasts? Given that a 60% chance of rain can mean wet or dry, albeit with different odds, we certainly won’t be able to judge the forecast quality based on a single event. Instead, we assess many forecasts of 60% rain made in the past to see if the 60 to 40 split of wet and dry eventuated. If it did for this and all other possible probabilities, the forecasts work well. </p>
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<a href="https://images.theconversation.com/files/570791/original/file-20240123-19-axi289.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=1000&fit=clip"><img alt="storms entering sydney" src="https://images.theconversation.com/files/570791/original/file-20240123-19-axi289.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/570791/original/file-20240123-19-axi289.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=396&fit=crop&dpr=1 600w, https://images.theconversation.com/files/570791/original/file-20240123-19-axi289.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=396&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/570791/original/file-20240123-19-axi289.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=396&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/570791/original/file-20240123-19-axi289.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=498&fit=crop&dpr=1 754w, https://images.theconversation.com/files/570791/original/file-20240123-19-axi289.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=498&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/570791/original/file-20240123-19-axi289.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=498&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px"></a>
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<span class="caption">Big weather events such as bands of storms are easier to predict with some certainty. But other weather is much harder.</span>
<span class="attribution"><span class="source">Shutterstock</span></span>
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<p>This isn’t what we’d like. Many of us find probabilistic forecasts confusing. Intuitively, we would prefer to simplify them into absolute statements. </p>
<p>Take a picnic you have planned for tomorrow. If you read the statement “there will be thunderstorms at noon tomorrow at Picnic Spot,” you will feel confident it’s best to cancel the event. But the statement “there’s a 60% chance of thunderstorms at noon tomorrow at Picnic Spot” is far more accurate. The first gives false certainty, by vastly oversimplifying what we really know. </p>
<p>Let’s not forget, there is a 40% chance it will stay dry, which the first statement completely ignores. And if it does stay dry, how will your friends react to the cancelled picnic? How much risk are you willing to take?</p>
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Read more:
<a href="https://theconversation.com/curious-kids-how-do-people-know-what-the-weather-will-be-108295">Curious Kids: how do people know what the weather will be?</a>
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<p>When we criticise weather forecasts for their inaccuracy, we are usually being unfair. You can’t actually say a weather forecast was wrong if you experienced rain when the forecast was for a high chance of being dry. It’s simply not possible to tell from a single day or even a season how well our forecasts are working because of the nature of how our atmosphere and oceans behave. We’ve known about this <a href="https://journals.ametsoc.org/view/journals/atsc/20/2/1520-0469_1963_020_0130_dnf_2_0_co_2.xml">for 60 years</a>. </p>
<p>That is why the Bureau of Meteorology’s seasonal forecasts come in likelihoods, such as the <a href="http://www.bom.gov.au/climate/ahead/outlooks/archive/20230928-outlook.shtml">rainfall outlook for October to December</a> issued on September 28th. It predicted that “October to December rainfall was likely (60 to 80% chance) to be below median for much of Australia excluding most of central and northwestern WA and south-west Tasmania.” Note that the forecast had a 20-40% chance for the wetter than usual conditions which some parts of Australia ended up experiencing. </p>
<p>But beware: We can’t declare the success or failure of a likelihood forecast from a single season. What the likelihood gives us is the ability to make better decisions based on the best information we have. </p>
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<h2>Less than certain but far better than nothing</h2>
<p>Given these constraints, how can we best use probabilistic forecasts in making decisions? </p>
<p>Here, weather and climate forecasting alone cannot provide the answers. The use and value of a particular forecast strongly depend on what decisions need to be made, our values, and what economic circumstances decisions are made in. </p>
<p>A very simple example is to <a href="https://rmets.onlinelibrary.wiley.com/doi/10.1002/qj.49712757715">assess how much</a> it would cost to protect ourselves against, say, a flood, and the loss we would incur if we did not protect ourselves and then the event happened. </p>
<p>If the cost of protection is very low and the loss very large, the answer is simple: protect yourself all the time. High protection costs and low losses imply we should never protect ourselves. Both statements can be made without bringing in the forecast probability. But in the middle, it gets tricky. How much should you spend on a highly damaging event with a low probability of occurring? </p>
<p>Deterministic weather forecasts giving certainty are only possible for a week or two, and only for the large features of the weather. This means longer term forecasts and those for intense weather systems such as thunderstorms or tropical cyclones will only ever be possible by assessing how likely different outcomes are, and giving us a probability. </p>
<p>It’s fine to complain about the weather. But we can’t complain about the forecasting based on a single event. We want to know what’s coming our way, but the weather doesn’t work like that. We owe it to society to provide and use the best information we have to protect and save property and lives. There is too much at stake to keep it simple. </p>
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Read more:
<a href="https://theconversation.com/extreme-weather-is-outpacing-even-the-worst-case-scenarios-of-our-forecasting-models-216562">Extreme weather is outpacing even the worst-case scenarios of our forecasting models</a>
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<p class="fine-print"><em><span>Christian Jakob receives funding from the Australian Research Council. </span></em></p>We crave certainty in our weather forecasts. But that’s only possible for big weather events such as cyclones and major storms. Everything else is probability.Christian Jakob, Director, ARC Centre of Excellence for the Weather of the 21st Century, Monash UniversityLicensed as Creative Commons – attribution, no derivatives.tag:theconversation.com,2011:article/2153952023-10-12T12:30:45Z2023-10-12T12:30:45ZWhat is a strong El Niño? Meteorologists anticipate a big impact in winter 2023-2024, but the forecasts don’t all agree<figure><img src="https://images.theconversation.com/files/553361/original/file-20231011-29-363wak.png?ixlib=rb-1.1.0&rect=32%2C873%2C3211%2C2058&q=45&auto=format&w=496&fit=clip" /><figcaption><span class="caption">The El Niño pattern stands out in the warm sea surface temperature anomalies in the Pacific in 2023</span> <span class="attribution"><a class="source" href="https://www.climate.gov/maps-data/data-snapshots/data-source/sst-enso-region-monthly-difference-average">NOAA Climate.gov</a></span></figcaption></figure><p><em>Meteorologists have been talking for weeks about <a href="https://www.accuweather.com/en/winter-weather/us-winter-forecast-for-the-2023-2024-season/1583853">a snowy season ahead</a> in the southern Rockies and the Sierra Nevada. They anticipate <a href="https://www.powder.com/trending-news/el-nino-huge-snow-east">more storms</a> in the U.S. South and Northeast, and warmer, drier conditions across the already dry Pacific Northwest and the upper Midwest.</em></p>
<p><em>One phrase comes up repeatedly with these projections: <a href="https://www.wcpo.com/weather/weather-101/a-strong-el-nino-expected-this-winter-heres-what-that-means-for-our-weather">a strong El Niño</a> is coming.</em></p>
<p><em>It sounds ominous. But what does that actually mean? We asked <a href="https://scholar.google.com/citations?user=z7CXcXkAAAAJ&hl=en">Aaron Levine</a>, an atmospheric scientist at the University of Washington whose research focuses on El Niño.</em></p>
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<iframe width="440" height="260" src="https://www.youtube.com/embed/wVlfyhs64IY?wmode=transparent&start=0" frameborder="0" allowfullscreen=""></iframe>
<figcaption><span class="caption">NOAA explains in animations how El Niño forms.</span></figcaption>
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<h2>What is a strong El Niño?</h2>
<p>During a normal year, the warmest sea surface temperatures are in the western Pacific and the Indian Ocean, in what’s known as the <a href="https://doi.org/10.1186/s40562-016-0054-3">Indo-Western Pacific warm pool</a>.</p>
<p>But every few years, the trade winds that blow from east to west weaken, allowing that warm water to slosh eastward and <a href="https://www.pmel.noaa.gov/elnino/schematic-diagrams">pile up along the equator</a>. The warm water causes the air above it to warm and rise, fueling precipitation in the central Pacific and shifting atmospheric circulation patterns across the basin.</p>
<p>This pattern is <a href="https://www.pmel.noaa.gov/elnino/what-is-el-nino">known as El Niño</a>, and it can <a href="https://www.climate.gov/media/13628">affect weather around the world</a>.</p>
<figure class="align-center ">
<img alt="An animation shows how warm water builds up along the equator off South America. The box where temperatures are measured is south of Hawaii." src="https://images.theconversation.com/files/553048/original/file-20231010-23-c36xip.gif?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/553048/original/file-20231010-23-c36xip.gif?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=272&fit=crop&dpr=1 600w, https://images.theconversation.com/files/553048/original/file-20231010-23-c36xip.gif?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=272&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/553048/original/file-20231010-23-c36xip.gif?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=272&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/553048/original/file-20231010-23-c36xip.gif?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=342&fit=crop&dpr=1 754w, https://images.theconversation.com/files/553048/original/file-20231010-23-c36xip.gif?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=342&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/553048/original/file-20231010-23-c36xip.gif?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=342&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px">
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<span class="caption">The box shows the Niño 3.4 region as El Niño begins to develop in the tropical Pacific, from January to June 2023.</span>
<span class="attribution"><a class="source" href="https://www.climate.gov/">NOAA Climate.gov</a></span>
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<p>A strong El Niño, in the most basic definition, occurs once the average sea surface temperature in the equatorial Pacific is at least 1.5 degrees Celsius (2.7 Fahrenheit) warmer than normal. It’s measured in an imaginary box along the equator, roughly south of Hawaii, known as the <a href="https://svs.gsfc.nasa.gov/4695">Nino 3.4 Index</a>.</p>
<p>But El Niño is a coupled ocean-atmosphere phenomenon, and the atmosphere also plays a crucial role.</p>
<p>What has been surprising about this year’s El Niño – and still is – is that the atmosphere hasn’t responded as much as we would have expected based on the rising sea surface temperatures.</p>
<h2>Is that why El Niño didn’t affect the 2023 hurricane season the way forecasts expected?</h2>
<p>The 2023 Atlantic hurricane season is a good example. Forecasters often use El Niño as a predictor of <a href="https://www.weather.gov/ilx/swop-springtopics">wind shear</a>, which can tear apart Atlantic hurricanes. But with the atmosphere not responding to the warmer water right away, the impact on Atlantic hurricanes was lessened and it turned out to be a <a href="https://www.nytimes.com/article/tropical-storm-sean-hurricane.html">busy season</a>.</p>
<p><a href="https://doi.org/10.1175/JCLI-D-12-00097.1">The atmosphere is what transmits El Niño’s impact</a>. Heat from the warm ocean water causes the air above it to warm and rise, which fuels precipitation. That air sinks again over cooler water. </p>
<p>The rising and sinking creates giant loops in the atmosphere <a href="https://www.climate.gov/news-features/blogs/enso/walker-circulation-ensos-atmospheric-buddy">called the Walker Circulation</a>. When the warm pool’s water shifts eastward, that also shifts where the rising and sinking motions happen. The atmosphere reacts to this change like ripples in a pond when you throw a stone in. These ripples affect the jet stream, which steers weather patterns in the U.S.</p>
<p>This year, in comparison with other large El Niño events – such as <a href="https://www.climate.gov/news-features/blogs/enso/united-states-el-ni%C3%B1o-impacts-0">1982-83, 1997-98</a> and <a href="https://www.climate.gov/news-features/understanding-climate/2015-state-climate-el-ni%C3%B1o-came-saw-and-conquered">2015-16</a> – we’re not seeing the same change in where the precipitation is happening. It’s taking much longer to develop, and it’s not as strong.</p>
<p>Part of that, presumably, is related to the whole tropics being very, very warm. But <a href="https://doi.org/10.1029/2019GL086182">this is still an emerging field of research</a>.</p>
<p>How El Niño will change with global warming is a big and open question. El Niño <a href="https://www.climate.gov/news-features/understanding-climate/climate-variability-oceanic-nino-index">only happens every few years</a>, and there’s a fair amount of variability between events, so just getting a baseline is tough.</p>
<h2>What does a strong El Niño typically mean for US weather?</h2>
<p>During <a href="https://www.climate.gov/enso">a typical El Niño winter</a>, the U.S. South and Southwest are cooler and wetter, and the Northwest is warmer and drier. The upper Midwest tends to be drier, while the Northeast tends to be a little wetter. </p>
<p>The likelihood and the intensity generally scale with the strength of the El Niño event.</p>
<p>El Niño has traditionally been good for the mountain snowpack in California, which the state relies for a large percentage of its water. But it is often not so good for the Pacific Northwest snowpack.</p>
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<a href="https://images.theconversation.com/files/553424/original/file-20231012-15-3wfvdw.png?ixlib=rb-1.1.0&q=45&auto=format&w=1000&fit=clip"><img alt="Two maps showing wetter, cooler weather in the Southeast and drier warmer air in the north during El Nino." src="https://images.theconversation.com/files/553424/original/file-20231012-15-3wfvdw.png?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/553424/original/file-20231012-15-3wfvdw.png?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=834&fit=crop&dpr=1 600w, https://images.theconversation.com/files/553424/original/file-20231012-15-3wfvdw.png?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=834&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/553424/original/file-20231012-15-3wfvdw.png?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=834&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/553424/original/file-20231012-15-3wfvdw.png?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=1047&fit=crop&dpr=1 754w, https://images.theconversation.com/files/553424/original/file-20231012-15-3wfvdw.png?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=1047&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/553424/original/file-20231012-15-3wfvdw.png?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=1047&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px"></a>
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<span class="caption">The jet stream takes a very different path in a typical El Niño vs. La Niña winter weather pattern. But these patterns have a great deal of variability. Not every El Niño or La Niña year is the same.</span>
<span class="attribution"><a class="source" href="https://www.climate.gov/media/14484">NOAA Climate.gov</a></span>
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<p>The <a href="https://www.climate.gov/news-features/featured-images/how-el-ni%C3%B1o-and-la-ni%C3%B1a-affect-winter-jet-stream-and-us-climate">jet stream plays a role</a> in that shift. When the polar jet stream is either displaced very far northward or southward, storms that would normally move through Washington or British Columbia are steered to California and Oregon instead.</p>
<h2>What do the forecasts show for the months ahead?</h2>
<p>Whether forecasters think a strong El Niño will develop depends on whose forecast model they trust.</p>
<p>This past spring, the <a href="https://www.weather.gov/media/climateservices/NWS%20Climate%20Forecast%20and%20Tools.pdf">dynamical forecast models</a> were <a href="https://iri.columbia.edu/our-expertise/climate/forecasts/enso/current/?enso_tab=enso-sst_table">already very confident</a> about the potential for a strong El Niño developing. These are big models that solve basic physics equations, starting with current oceanic and atmospheric conditions. </p>
<p>However, statistical models, which use statistical predictors of El Niño calculated from historical observations, were less certain.</p>
<p>Even in the <a href="https://iri.columbia.edu/our-expertise/climate/forecasts/enso/current/?enso_tab=enso-sst_table">most recent forecast model outlook</a>, the dynamical forecast models were predicting a stronger El Niño than the statistical models were.</p>
<p>If you go by just a sea surface temperature-based El Niño index, the forecast is for a fairly strong El Niño. </p>
<p>But the indices that incorporate the atmosphere are not responding in the same way. We’ve seen <a href="https://psl.noaa.gov/enso/enso.current.html">atmospheric anomalies</a> – as measured by cloud height monitored by satellites or sea-level pressure at monitoring stations – on and off in the Pacific since May and June, <a href="https://images.theconversation.com/files/553327/original/file-20231011-15-kprvx9.png">but not in a very robust fashion</a>. Even in September, they were nowhere near as large as they were in 1982, in terms of overall magnitude.</p>
<p>We’ll see if the atmosphere catches up by wintertime, when El Niño peaks.</p>
<h2>How long do El Niños last?</h2>
<p>Often during El Niño events – particularly strong El Niño events – the sea surface temperature anomalies collapse really quickly during the Northern Hemisphere spring. Almost all end in April or May.</p>
<p>One reason is that El Niño sows the seeds of its own demise. When El Niño happens, it <a href="https://doi.org/10.1175/1520-0469(1997)054%3C0811:AEORPF%3E2.0.CO;2">uses up that warm water</a> and the warm water volume shrinks. <a href="https://doi.org/10.1175/1520-0442(2000)013%3C3551:OOWWVC%3E2.0.CO;2">Eventually, it has eroded its fuel</a>.</p>
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<p>The surface can stay warm for a while, but once the heat from the subsurface is gone and the trade winds return, the El Niño event collapses. At the end of past El Niño events, the sea surface anomaly dropped very fast and we saw conditions typically switch to La Niña – El Niño’s cooler opposite.</p><img src="https://counter.theconversation.com/content/215395/count.gif" alt="The Conversation" width="1" height="1" />
<p class="fine-print"><em><span>Aaron Levine receives funding from NOAA and has received funding in the past from the National Research Council. He is a member of the American Geophysical Union </span></em></p>An atmospheric scientist explains how El Niño works, this year’s oddities and why this phenomenon doesn’t last long.Aaron Levine, Atmospheric Research Scientist, CICOES, University of WashingtonLicensed as Creative Commons – attribution, no derivatives.tag:theconversation.com,2011:article/2150542023-10-05T19:03:36Z2023-10-05T19:03:36ZIs there really a 1 in 6 chance of human extinction this century?<figure><img src="https://images.theconversation.com/files/552227/original/file-20231005-23-pvcy99.jpeg?ixlib=rb-1.1.0&rect=9%2C0%2C4815%2C3106&q=45&auto=format&w=496&fit=clip" /><figcaption><span class="caption">
</span> <span class="attribution"><a class="source" href="https://www.shutterstock.com/image-illustration/asteroid-hits-earth-3d-rendering-1909820308">Shutterstock</a></span></figcaption></figure><p>In 2020, Oxford-based philosopher Toby Ord published a book called <a href="https://theprecipice.com/">The Precipice</a> about the risk of human extinction. He put the chances of “existential catastrophe” for our species during the next century at one in six.</p>
<p>It’s quite a specific number, and an alarming one. The claim drew <a href="https://www.dailystar.co.uk/news/latest-news/humans-have-1-6-chance-21960557">headlines</a> at the time, and has been influential since – most recently brought up by Australian politician Andrew Leigh in a <a href="https://www.andrewleigh.com/what_s_the_worst_that_could_happen_existential_risk_and_extreme_politics_speech">speech</a> in Melbourne.</p>
<p>It’s hard to disagree with the idea we face troubling prospects over the coming decades, from climate change, nuclear weapons and bio-engineered pathogens (all big issues in my view), to rogue AI and large asteroids (which I would see as less concerning).</p>
<p>But what about that number? Where does it come from? And what does it really mean?</p>
<h2>Coin flips and weather forecasts</h2>
<p>To answer those questions, we have to answer another first: what is probability?</p>
<p>The most traditional view of probability is called frequentism, and derives its name from its heritage in games of dice and cards. On this view, we know there is a one in six chance a fair die will come up with a three (for example) by observing the frequency of threes in a large number of rolls.</p>
<p>Or consider the more complicated case of weather forecasts. What does it mean when a weatherperson tells us there is a one in six (or 17%) chance of rain tomorrow?</p>
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Read more:
<a href="https://theconversation.com/the-science-of-weather-forecasting-what-it-takes-and-why-its-so-hard-to-get-right-175740">The science of weather forecasting: what it takes and why it’s so hard to get right</a>
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<p>It’s hard to believe the weatherperson means us to imagine a large collection of “tomorrows”, of which some proportion will experience precipitation. Instead, we need to look at a large number of such predictions and see what happened after them.</p>
<p>If the forecaster is good at their job, we should see that when they said “one in six chance of rain tomorrow”, it did in fact rain on the following day one time in every six.</p>
<p>So, traditional probability depends on observations and procedure. To calculate it, we need to have a collection of repeated events on which to base our estimate.</p>
<h2>Can we learn from the Moon?</h2>
<p>So what does this mean for the probability of human extinction? Well, such an event would be a one-off: after it happened, there would be no room for repeats.</p>
<p>Instead, we might find some parallel events to learn from. Indeed, in Ord’s book, he discusses a number of potential extinction events, some of which can potentially be examined in light of a history. </p>
<figure class="align-center zoomable">
<a href="https://images.theconversation.com/files/552236/original/file-20231005-29-kna72i.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=1000&fit=clip"><img alt="A photo of the Moon with craters highlighted." src="https://images.theconversation.com/files/552236/original/file-20231005-29-kna72i.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/552236/original/file-20231005-29-kna72i.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=600&fit=crop&dpr=1 600w, https://images.theconversation.com/files/552236/original/file-20231005-29-kna72i.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=600&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/552236/original/file-20231005-29-kna72i.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=600&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/552236/original/file-20231005-29-kna72i.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=754&fit=crop&dpr=1 754w, https://images.theconversation.com/files/552236/original/file-20231005-29-kna72i.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=754&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/552236/original/file-20231005-29-kna72i.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=754&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px"></a>
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<span class="caption">Counting craters on the Moon can gives us clues about the risk of asteroid impacts on Earth.</span>
<span class="attribution"><a class="source" href="https://svs.gsfc.nasa.gov/3662#media_group_352853">NASA</a></span>
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<p>For example, we can estimate the chances of an extinction-sized asteroid hitting Earth by examining how many such space rocks have hit the Moon over its history. A French scientist named Jean-Marc Salotti <a href="https://www.sciencedirect.com/science/article/pii/S0016328722000337">did this in 2022</a>, calculating the odds of an extinction-level hit in the next century at around one in 300 million. </p>
<p>Of course, such an estimate is fraught with uncertainty, but it is backed by something approaching an appropriate frequency calculation. Ord, by contrast, estimates the risk of extinction by asteroid at one in a million, though he does note a considerable degree of uncertainty.</p>
<h2>A ranking system for outcomes</h2>
<p>There is another way to think about probability, called Bayesianism after the English statistician Thomas Bayes. It focuses less on events themselves and more on what we know, expect and believe about them.</p>
<p>In very simple terms, we can say Bayesians see probabilities as a kind of ranking system. In this view, the specific number attached to a probability shouldn’t be taken directly, but rather compared to other probabilities to understand which outcomes are more and less likely.</p>
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Read more:
<a href="https://theconversation.com/bayes-theorem-the-maths-tool-we-probably-use-every-day-but-what-is-it-76140">Bayes' Theorem: the maths tool we probably use every day, but what is it?</a>
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<p>Ord’s book, for example, contains a table of potential extinction events and his personal estimates of their probability. From a Bayesian perspective, we can view these values as relative ranks. Ord thinks extinction from an asteroid strike (one in a million) is much less likely than extinction from climate change (one in a thousand), and both are far less likely than extinction from what he calls “unaligned artificial intelligence” (one in ten).</p>
<p>The difficulty here is that initial estimates of Bayesian probabilities (often called “priors”) are rather subjective (for instance, I would rank the chance of AI-based extinction much lower). Traditional Bayesian reasoning moves from “priors” to “posteriors” by again incorporating observational evidence of relevant outcomes to “update” probability values.</p>
<p>And once again, outcomes relevant to the probability of human extinction are thin on the ground. </p>
<h2>Subjective estimates</h2>
<p>There are two ways to think about the accuracy and usefulness of probability calculations: calibration and discrimination.</p>
<p>Calibration is the correctness of the actual values of the probabilities. We can’t determine this without appropriate observational information. Discrimination, on the other hand, simply refers to the relative rankings. </p>
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<a href="https://theconversation.com/longtermism-why-the-million-year-philosophy-cant-be-ignored-193538">Longtermism – why the million-year philosophy can't be ignored</a>
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<p>We don’t have a basis to think Ord’s values are properly calibrated. Of course, this is not likely to be his intent. He himself indicates they are mostly designed to give “order of magnitude” indications. </p>
<p>Even so, without any related observational confirmation, most of these estimates simply remain in the subjective domain of prior probabilities.</p>
<h2>Not well calibrated – but perhaps still useful</h2>
<p>So what are we to make of “one in six”? Experience suggests most people have a less than perfect understanding of probability (as evidenced by, among other things, the ongoing volume of lottery ticket sales). In this environment, if you’re making an argument in public, an estimate of “probability” doesn’t necessarily need to be well calibrated – it just needs to have the right sort of psychological impact. </p>
<p>From this perspective, I’d say “one in six” fits the bill nicely. “One in 100” might feel small enough to ignore, while “one in three” might drive panic or be dismissed as apocalyptic raving. </p>
<p>As a person concerned about the future, I hope risks like climate change and nuclear proliferation get the attention they deserve. But as a data scientist, I hope the careless use of probability gets left by the wayside and is replaced by widespread education on its true meaning and appropriate usage.</p>
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Read more:
<a href="https://theconversation.com/433-people-win-a-lottery-jackpot-impossible-probability-and-psychology-suggest-its-more-likely-than-youd-think-191946">433 people win a lottery jackpot – impossible? Probability and psychology suggest it's more likely than you’d think</a>
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<img src="https://counter.theconversation.com/content/215054/count.gif" alt="The Conversation" width="1" height="1" />
<p class="fine-print"><em><span>Steven Stern does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.</span></em></p>What are the odds of the end of humanity? There’s no real way to know.Steven Stern, Professor of Data Science, Bond UniversityLicensed as Creative Commons – attribution, no derivatives.tag:theconversation.com,2011:article/2138772023-09-19T11:52:50Z2023-09-19T11:52:50ZSouth Africa’s destructive storm surges: geoscientist reveals the 3 factors that drove them<figure><img src="https://images.theconversation.com/files/549052/original/file-20230919-27-26ykei.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=496&fit=clip" /><figcaption><span class="caption">Tidal surges can cause enormous damage.</span> <span class="attribution"><span class="source">Martha van der Westhuizen/500px</span></span></figcaption></figure><p><em>A series of powerful tidal surges <a href="https://www.news24.com/news24/southafrica/news/battered-coastal-areas-begin-mop-up-operations-after-spring-tide-damage-20230918">battered</a> coastal areas in South Africa’s Western Cape, Eastern Cape and KwaZulu-Natal provinces over the weekend of 16 September. <a href="https://www.news24.com/news24/southafrica/news/spring-tide-damage-woman-dies-after-waves-crash-into-george-car-park-another-dangerous-surge-expected-20230917">One person died</a>; cars, homes and businesses were damaged. The Conversation Africa asked Jasper Knight, a geoscientist who researches coastal processes, to explain what happened.</em></p>
<h2>What drove the flooding?</h2>
<p>A critical combination of three factors resulted in this significant flooding. First, a storm surge associated with low-pressure weather systems coming onshore. These happen very commonly but often don’t result in high amounts of flooding by themselves. </p>
<p>Second, low-pressure cells often result in strong onshore winds, and these can whip up the sea surface and create big waves which can potentially run further inland, especially when the sea surface is raised.</p>
<p>Third, the coincidence of the storm with the period of high tide (a monthly event) and equinoctial high-high tide (a seasonal event). It is this combination that is the cause here, not these individual factors in isolation.</p>
<h2>What is a storm surge?</h2>
<p>This is where the level of the sea surface near the coast is temporarily raised up and results in flooding along that coastal stretch. Storm surges are caused by a low pressure (cyclone) weather system approaching the coastline from the ocean. Low pressure causes the sea surface to bulge upwards below the centre or eye of the cyclone, and the magnitude of this disturbance is related to the severity of the low pressure system – the deeper the low pressure cell, the higher the elevation of the sea surface. This is usually on the order of tens of centimetres to one metre or so. </p>
<p>As the cyclone approaches land, the water surface along the coast rises.</p>
<h2>What is a spring tide?</h2>
<p>Despite their name, <a href="https://oceanservice.noaa.gov/facts/springtide.html">spring tides</a> are regular occurrences throughout the year. They take place when the sun, Earth and moon are in alignment, and this happens once every (lunar) month. In addition, there are also times of the year, around the <a href="https://education.nationalgeographic.org/resource/equinox/">equinoxes</a>, where spring tides are higher than average. </p>
<p>We are very near the spring (vernal) equinox in the southern hemisphere (which is on or about 22 September). This is a period when the sun is aligned overhead of Earth’s equator and so exerts a bigger tidal force on the oceans. This may have been a contributing factor to the higher water levels around the coast.</p>
<h2>Were people sufficiently warned?</h2>
<p>Tidal patterns are highly predictable and this data is widely available for ports or harbours along the coast. In this case, the South African Weather Service issued <a href="https://www.news24.com/news24/southafrica/news/saturdays-weather-damaging-winds-waves-and-a-storm-surge-warning-for-the-coast-20230915">a warning</a>. This information is particularly useful for boaters, fishermen and other coastal users. Weather patterns are also fairly predictable, which is what weather forecasting is all about, so we know when a big storm may be approaching. </p>
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Read more:
<a href="https://theconversation.com/the-science-of-weather-forecasting-what-it-takes-and-why-its-so-hard-to-get-right-175740">The science of weather forecasting: what it takes and why it’s so hard to get right</a>
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<p>However, the net result of all of these factors in combination is less predictable: although low-lying coastal areas are vulnerable to flooding, forecasters may not know exactly when or how high.</p>
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<p>The other issue here is not just areas being covered by water but also the speed at which the water waves are moving, which is a factor in their destructiveness. </p>
<h2>What kind of emergency measures could be put in place?</h2>
<p>Local communities need to be warned more clearly and effectively if there is a threat of a storm surge and of coastal flooding. People and assets (like cars, anything that is moveable) should be moved from the area or kept inside. People tend to want to go to the sea to watch the waves but this puts them at more risk. Roads should be closed off where possible to keep people safe and away from the area. Floodwater management through using sandbags and similar actions should be undertaken.</p>
<p>Exactly the same measures used in places like <a href="https://www.floridadisaster.org/planprepare/">Florida in the US</a> for reducing hurricane risk should be used in South Africa, such as boarding up windows, keeping assets indoors, evacuating people from high risk areas, and moving furniture in houses to the first floor to reduce flood impacts.</p>
<p>Storm surges – and <a href="https://www.ipcc.ch/srocc/chapter/chapter-4-sea-level-rise-and-implications-for-low-lying-islands-coasts-and-communities/">sea levels rising in future</a> – are not going to go away so we need to be prepared for them.</p><img src="https://counter.theconversation.com/content/213877/count.gif" alt="The Conversation" width="1" height="1" />
<p class="fine-print"><em><span>Jasper Knight does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.</span></em></p>Local communities need to be warned more clearly and effectively if there is a threat of a storm surge and of coastal flooding.Jasper Knight, Professor of Physical Geography, University of the WitwatersrandLicensed as Creative Commons – attribution, no derivatives.tag:theconversation.com,2011:article/2101702023-08-09T12:32:17Z2023-08-09T12:32:17ZAI can help forecast air quality, but freak events like 2023’s summer of wildfire smoke require traditional methods too<figure><img src="https://images.theconversation.com/files/541336/original/file-20230805-83673-xiqg41.jpg?ixlib=rb-1.1.0&rect=0%2C0%2C3494%2C2331&q=45&auto=format&w=496&fit=clip" /><figcaption><span class="caption">Thick smoke rolling in from Canada's 2023 wildfires was a wakeup call for several cities.</span> <span class="attribution"><a class="source" href="https://www.gettyimages.com/detail/news-photo/people-wear-masks-as-they-wait-for-the-tramway-to-roosevelt-news-photo/1258511415">Eduardo Munoz Alvarez/Getty Images</a></span></figcaption></figure><p>Wildfire smoke from <a href="https://twitter.com/_HannahRitchie/status/1685583683707682816">Canada’s extreme fire season</a> has left a lot of people thinking about air quality and wondering what to expect in the days ahead.</p>
<p>All air contains gaseous compounds and small particles. But as air quality gets worse, these gases and particles can <a href="https://theconversation.com/extreme-heat-and-air-pollution-can-be-deadly-with-the-health-risk-together-worse-than-either-alone-187422">trigger asthma</a> and <a href="https://theconversation.com/wildfire-smoke-can-harm-human-health-even-when-the-fire-is-burning-hundreds-of-miles-away-a-toxicologist-explains-why-206057">exacerbate heart and respiratory problems</a> as they enter the nose, throat and lungs and even circulate in the bloodstream. When wildfire smoke turned New York City’s skies orange in early June 2023, <a href="https://gothamist.com/news/nyc-hospitals-saw-twice-as-many-asthma-er-visits-as-bad-air-blanketed-city">emergency room visits</a> for asthma doubled.</p>
<p>In <a href="https://www.airnow.gov/">most cities</a>, it’s easy to find a daily <a href="https://www.lung.org/clean-air/outdoors/air-quality-index">air quality index score</a> that tells you when the air is considered unhealthy or even hazardous. However, predicting air quality in the days ahead isn’t so simple.</p>
<p>I work on air quality forecasting as a <a href="https://cee.utk.edu/people/joshua-s-fu/">professor of civil and environmental engineering</a>. Artificial intelligence has improved these forecasts, but research shows it’s much more useful when paired with traditional techniques. Here’s why:</p>
<h2>How scientists predict air quality</h2>
<p>To predict air quality in the near future – a few days ahead or longer – scientists generally rely on two <a href="https://www.airnow.gov/aqi/aqi-basics/using-air-quality-index/#forecasts">main methods</a>: a <a href="https://www.airnow.gov/sites/default/files/2020-06/aq-forecasting-guidance-1016.pdf">chemical transport model</a> or a machine-learning model. These two models generate results in totally different ways.</p>
<p>Chemical transport models use lots of known chemical and physical formulas to calculate the presence and production of air pollutants. They use data from emissions inventories reported by local agencies that list pollutants from known sources, such as wildfires, traffic <a href="https://www.epa.gov/air-emissions-inventories/2020-nei-supporting-data-and-summaries">or factories</a>, and data from meteorology that provides atmospheric information, such as wind, precipitation, temperature and solar radiation.</p>
<p>These models simulate the flow and chemical reactions of the air pollutants. However, their simulations involve multiple variables with huge uncertainties. Cloudiness, for example, changes the incoming solar radiation and thus the photochemistry. This can make the results less accurate.</p>
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<a href="https://images.theconversation.com/files/541950/original/file-20230809-15-9ddhgg.PNG?ixlib=rb-1.1.0&q=45&auto=format&w=1000&fit=clip"><img alt="A map shows many yellow dots through the Midwest. in particular, where wildfire smoke has been blowing in from Canada." src="https://images.theconversation.com/files/541950/original/file-20230809-15-9ddhgg.PNG?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/541950/original/file-20230809-15-9ddhgg.PNG?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=404&fit=crop&dpr=1 600w, https://images.theconversation.com/files/541950/original/file-20230809-15-9ddhgg.PNG?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=404&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/541950/original/file-20230809-15-9ddhgg.PNG?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=404&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/541950/original/file-20230809-15-9ddhgg.PNG?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=508&fit=crop&dpr=1 754w, https://images.theconversation.com/files/541950/original/file-20230809-15-9ddhgg.PNG?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=508&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/541950/original/file-20230809-15-9ddhgg.PNG?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=508&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px"></a>
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<span class="caption">The EPA’s AirNow air pollution forecasts use machine learning. During wildfire events, a smoke-transport and dispersion model helps to simulate the spread of smoke plumes. This map is the forecast for Aug. 9, 2023. Yellow indicates moderate risk; orange indicates unhealthy air for sensitive groups.</span>
<span class="attribution"><a class="source" href="https://gispub.epa.gov/airnow/index.html">AirNow.gov</a></span>
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<p>Machine-learning models instead learn patterns over time from historical data to predict future air quality for any given region, and then apply that knowledge to current conditions to predict the future. </p>
<p>The downside of machine-learning models is that they do not consider any chemical and physical mechanisms, as chemical transport models do. Also, the accuracy of machine-learning projections under extreme conditions, such as heat waves or wildfire events, can be off if the models weren’t trained on such data. So, while machine-learning models can show where and when high pollution levels are most likely, such as during rush hour near freeways, they generally cannot deal with more random events, like wildfire smoke blowing in from Canada. </p>
<h2>Which is better?</h2>
<p>Scientists have determined that neither model is accurate enough on its own, but using the <a href="https://doi.org/10.1016/j.atmosenv.2022.118961">best attributes of both</a> models together <a href="https://doi.org/10.1016/j.envint.2023.107969">can help better predict the quality</a> of the air we breathe. </p>
<p>This combined method, known as the machine-learning – measurement model fusion, or ML-MMF, has the ability to provide science-based predictions with <a href="https://doi.org/10.1016/j.envint.2023.107969">more than 90% accuracy</a>. It is based on known physical and chemical mechanisms and can simulate the whole process, from the air pollution source to your nose. Adding satellite data can help them inform the public on both air quality safety levels and the direction pollutants are traveling with greater accuracy. </p>
<p>We recently <a href="https://doi.org/10.1016/j.envint.2023.107969">compared predictions from all three models</a> with actual pollution measurements. The results were striking: The combined model was 66% more accurate than the chemical transport model and 12% more accurate than the machine-learning model alone.</p>
<p>The chemical transport model is still the most common method used today to predict air quality, but applications with machine-learning models are becoming more popular. The regular <a href="https://gispub.epa.gov/airnow/index.html">forecasting method</a> used by the U.S. Environmental Protection Agency’s <a href="https://www.airnow.gov/">AirNow.gov</a> relies on machine learning. The site also compiles air quality forecast results from state and local agencies, most of which use <a href="https://www.epa.gov/cmaq">chemical transport</a> <a href="https://www.camx.com/">models</a>.</p>
<p>As information sources become more reliable, the combined models will become more accurate ways to forecast hazardous air quality, particularly during unpredictable events like wildfire smoke.</p><img src="https://counter.theconversation.com/content/210170/count.gif" alt="The Conversation" width="1" height="1" />
<p class="fine-print"><em><span>Joshua S. Fu received funding from U. S. EPA for wildfire and human health studies. </span></em></p>Air quality forecasting is getting better, thanks in part to AI. That’s good, given the health impact of air pollution. An environmental engineer explains how systems warn of incoming smog or smoke.Joshua S. Fu, Chancellor's Professor in Engineering, Climate Change and Civil and Environmental Engineering, University of TennesseeLicensed as Creative Commons – attribution, no derivatives.tag:theconversation.com,2011:article/2087402023-07-13T12:38:15Z2023-07-13T12:38:15ZWeather forecast accuracy is crucial in a heat wave – 1 degree can mean the difference between life and death<figure><img src="https://images.theconversation.com/files/536937/original/file-20230711-26-jekcvf.jpg?ixlib=rb-1.1.0&rect=0%2C44%2C6000%2C3943&q=45&auto=format&w=496&fit=clip" /><figcaption><span class="caption">Extreme heat can put lives at risk, making accurate forecasts essential for people working outdoors.</span> <span class="attribution"><a class="source" href="https://www.gettyimages.com/detail/photo/exhausted-construction-worker-at-construction-site-royalty-free-image/1334826526">FG Trade/E+ via Getty Images</a></span></figcaption></figure><p>Weather forecasts have gotten quite good over the years, but their temperatures aren’t always spot on – and the result when they underplay extremes can be lethal. <a href="https://doi.org/10.3386/w31361">Even a 1-degree difference</a> in a forecast’s accuracy can be the difference between life and death, our research shows. </p>
<p>As <a href="https://scholar.google.com/citations?user=614D6AEAAAAJ&hl=en">economists</a>, <a href="https://scholar.google.com/citations?user=9VrtHcIAAAAJ&hl=en">we have</a> <a href="https://scholar.google.com/citations?user=o7NswrkAAAAJ&hl=en">studied</a> how people use forecasts to manage weather risks. In a new working paper for the National Bureau of Economic Research, we looked at how human survival <a href="https://doi.org/10.3386/w31361">depends on the accuracy of temperature forecasts</a>, particularly during heat waves like <a href="https://abcnews.go.com/US/37-million-americans-alert-dangerous-heat/story?id=100959311">large parts of the U.S.</a> have been experiencing in recent days.</p>
<p>We found that when the forecasts underplayed the risk, even small forecast errors led to more deaths.</p>
<p>Our results also show that improving forecasts pays off. They suggest that making forecasts 50% more accurate would save 2,200 lives per year across the country and would have a net value that’s nearly <a href="https://www.everycrsreport.com/files/2022-06-17_IF11914_d3e26afb6fbd9006c54580150fc833d6f1614231.pdf">twice the annual budget</a> of the National Weather Service.</p>
<h2>Forecasts that are too mild lead to more deaths</h2>
<p>In the U.S. alone, the National Oceanic and Atmospheric Administration issues <a href="https://www.noaa.gov/sites/default/files/2021-06/NOAABlueBook2022_final.pdf">1.5 million forecasts per year</a> and collects around 76 billion weather observations that help it and private companies make better forecasts.</p>
<p>We examined data on <a href="https://www.cdc.gov/">every day’s deaths</a>, weather and National Weather Service forecast in every U.S county from 2005 to 2017 to analyze the impact of those forecasts on human survival. </p>
<p>We then compared deaths in each county over the week following a day with accurate forecasts to deaths in the same county over the week following a day with inaccurate forecasts but the same weather. Because weather conditions were the same, any differences in mortality could be attributed to how people’s reactions to forecasts affected their chance of dying in that weather.</p>
<figure class="align-center ">
<img alt="Cars drive under a sign reading: Extreme heat. Save Power 4-9PM. Stay Cool" src="https://images.theconversation.com/files/536939/original/file-20230711-29-ur6l4r.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/536939/original/file-20230711-29-ur6l4r.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=400&fit=crop&dpr=1 600w, https://images.theconversation.com/files/536939/original/file-20230711-29-ur6l4r.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=400&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/536939/original/file-20230711-29-ur6l4r.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=400&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/536939/original/file-20230711-29-ur6l4r.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=502&fit=crop&dpr=1 754w, https://images.theconversation.com/files/536939/original/file-20230711-29-ur6l4r.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=502&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/536939/original/file-20230711-29-ur6l4r.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=502&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px">
<figcaption>
<span class="caption">Vehicles on the 110 Freeway pass warning signs on Sept. 2, 2022, during a heat wave with temperatures as high as 112 degrees Fahrenheit (44 Celsius) in the Los Angeles suburbs.</span>
<span class="attribution"><a class="source" href="https://www.gettyimages.com/detail/news-photo/vehicles-drive-past-a-sign-on-the-110-freeway-warning-of-news-photo/1242895722?adppopup=true">Patrick T. Fallon/AFP via Getty Images</a></span>
</figcaption>
</figure>
<p>We found similar results when the forecast was wrong on hot days with temperatures above 86 degrees Fahrenheit (30 Celsius) and on cold days with temperatures below freezing. Both summer days that were hotter than forecast and winter days that were colder <a href="https://www.nber.org/papers/w31361">had more deaths</a>. Forecasts that went the other way and overestimated the summer heat or winter cold had little impact. </p>
<p>That doesn’t mean forecasters should exaggerate their forecasts, however. If people find that their forecasts are consistently off by a degree or two, they might change how they use forecasts or come to trust them less, leaving people at even higher risk.</p>
<h2>People are paying attention</h2>
<p>People do pay attention to forecasts and adjust their activities.</p>
<p>The <a href="https://www.bls.gov/tus/overview.htm">American Time Use Survey</a>, conducted continuously for the U.S. Bureau of Labor Statistics, shows what Americans across the country are doing on any given day. We found that on days when the forecast called for temperatures to be milder than they turned out to be – either cooler on a hot day or warmer on a cold day – people in the survey spent more time on leisure and less in home or work settings.</p>
<p><a href="https://www.eia.gov/">Electricity use</a> also varies in sync with forecasts, suggesting that people’s use of air conditioning does not just respond to the weather outside but also depends on how they planned for the weather outside.</p>
<figure class="align-center ">
<img alt="A man holds something over his head to shield the sun from his forehead. Other people walking across the bridge on a bright, sunny day have umbrellas and hats." src="https://images.theconversation.com/files/536940/original/file-20230711-23-mdhw2x.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/536940/original/file-20230711-23-mdhw2x.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=400&fit=crop&dpr=1 600w, https://images.theconversation.com/files/536940/original/file-20230711-23-mdhw2x.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=400&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/536940/original/file-20230711-23-mdhw2x.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=400&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/536940/original/file-20230711-23-mdhw2x.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=503&fit=crop&dpr=1 754w, https://images.theconversation.com/files/536940/original/file-20230711-23-mdhw2x.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=503&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/536940/original/file-20230711-23-mdhw2x.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=503&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px">
<figcaption>
<span class="caption">A man shields his head from the Sun as he walks across New York’s Brooklyn Bridge on a hot summer day in 2018.</span>
<span class="attribution"><a class="source" href="https://www.gettyimages.com/detail/news-photo/man-shields-his-head-from-the-sun-as-he-walks-across-the-news-photo/987271692">Drew Angerer/Getty Images</a></span>
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</figure>
<p>However, forecasts are not used equally across society. Deaths among racial minorities are less sensitive to forecast errors, we found. That could be due in part to having less flexibility to act on forecasts, or not having access to forecasts. We will dig into this difference in future work, as the answer determines how the National Weather Service can best reach everyone.</p>
<h2>The value of better forecasts</h2>
<p>It’s clear that people use forecasts to make decisions that can matter for life and death – <a href="https://www.texasmonthly.com/travel/texas-hiking-heat-deaths-big-bend-palo-duro/">when to go hiking</a>, for example, or whether to encourage an elderly neighbor to <a href="https://theconversation.com/saving-lives-from-extreme-heat-lessons-from-the-deadly-2021-pacific-northwest-heat-wave-206737">go to a cooling center</a>.</p>
<p>So, what is the value of accurate forecasts?</p>
<p>We combined our theoretical model with <a href="https://www.epa.gov/environmental-economics/mortality-risk-valuation">federal cost-benefit estimates</a> of how people value improvements in their chances of survival. From those, we estimated people’s willingness to pay for better forecasts. That calculation accounts for the risk of dying from extreme weather and for the costs of using forecasts to reduce their risk of dying, such as the costs of altering work and play schedules or using electricity.</p>
<p>The result shows that 50% more accurate forecasts are <a href="https://doi.org/10.3386/w31361">worth at least US$2.1 billion per year</a> based on the mortality benefits alone. In comparison, the 2022 <a href="https://www.everycrsreport.com/files/2022-06-17_IF11914_d3e26afb6fbd9006c54580150fc833d6f1614231.pdf">budget of the National Weather Service</a> was less than $1.3 billion.</p>
<p>Weather forecasts have gotten steadily better over the past decades. About 68% of the next-day temperature forecasts now have an error of less than 1.8 degrees. Our results suggest investing in improved forecast accuracy would probably be worth the cost. </p>
<p>Past improvements have come from better models, better observations and better computers. Future improvements could come from similar channels or from applying recent innovations in <a href="https://theconversation.com/ai-and-machine-learning-are-improving-weather-forecasts-but-they-wont-replace-human-experts-182498">machine learning</a> and <a href="https://www.nature.com/articles/d41586-023-02084-9">artificial intelligence</a> to weather prediction and communication.</p>
<p>Climate change will <a href="https://nca2018.globalchange.gov/">increase the frequency of extremely hot days</a>, which are <a href="https://climate.nasa.gov/explore/ask-nasa-climate/3151/too-hot-to-handle-how-climate-change-may-make-some-places-too-hot-to-live/">especially important for human health</a> and survival to forecast accurately. Climate change will make the weather weirder, but weird weather can do less harm when we can see it coming.</p><img src="https://counter.theconversation.com/content/208740/count.gif" alt="The Conversation" width="1" height="1" />
<p class="fine-print"><em><span>Derek Lemoine receives funding from Global Research Institute, is a Research Associate at the National Bureau of Economic Research, and is an Associate Fellow at the Centre for Economic Policy Research.</span></em></p><p class="fine-print"><em><span>Jeff Shrader receives funding from the National Science Foundation. </span></em></p><p class="fine-print"><em><span>Laura Bakkensen does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.</span></em></p>Three economists looked at years of temperature and death data and calculated the costs when forecasts miss the mark.Derek Lemoine, Associate Professor of Economics, University of ArizonaJeffrey Shrader, Assistant Professor of International and Public Affairs, Columbia UniversityLaura Bakkensen, Associate Professor of Economics and Policy, University of ArizonaLicensed as Creative Commons – attribution, no derivatives.tag:theconversation.com,2011:article/2058422023-05-26T00:44:09Z2023-05-26T00:44:09ZResearchers built an analogue computer that uses water waves to forecast the chaotic future<figure><img src="https://images.theconversation.com/files/528189/original/file-20230525-15-7szay.jpg?ixlib=rb-1.1.0&rect=0%2C0%2C2598%2C1901&q=45&auto=format&w=496&fit=clip" /><figcaption><span class="caption">
</span> <span class="attribution"><span class="source">Shutterstock</span></span></figcaption></figure><p>Can a computer learn from the past and anticipate what will happen next, like a human? You might not be surprised to hear that some cutting-edge AI models could achieve this feat, but what about a computer that looks a little different – more like a tank of water?</p>
<p>We have built <a href="https://doi.org/10.1209/0295-5075/acd471">a small proof-of-concept computer</a> that uses running water instead of a traditional logical circuitry processor, and forecasts future events via an approach called “reservoir computing”.</p>
<p>In benchmark tests, our analogue computer did well at remembering input data and forecasting future events – and in some cases it even did better than a high-performance digital computer.</p>
<p>So how does it work?</p>
<h2>Throwing stones in the pond</h2>
<p>Imagine two kids, Alice and Bob, playing at the edge of a pond. Bob throws big and small stones into water one at a time, seemingly at random. </p>
<p>Big and small stones create water waves of different size. Alice watches the water waves created by the stones and learns to anticipate what the waves will do next – and from that, she can have an idea of which stone Bob will throw next. </p>
<figure class="align-center ">
<img alt="" src="https://images.theconversation.com/files/526742/original/file-20230517-17-2g9fwb.png?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/526742/original/file-20230517-17-2g9fwb.png?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=338&fit=crop&dpr=1 600w, https://images.theconversation.com/files/526742/original/file-20230517-17-2g9fwb.png?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=338&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/526742/original/file-20230517-17-2g9fwb.png?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=338&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/526742/original/file-20230517-17-2g9fwb.png?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=424&fit=crop&dpr=1 754w, https://images.theconversation.com/files/526742/original/file-20230517-17-2g9fwb.png?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=424&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/526742/original/file-20230517-17-2g9fwb.png?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=424&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px">
<figcaption>
<span class="caption">Bob throws rocks into the pond, while Alice watches the waves and tries to predict what’s coming next.</span>
<span class="attribution"><span class="source">Yaroslav Maksymov</span>, <span class="license">Author provided</span></span>
</figcaption>
</figure>
<p><a href="https://towardsdatascience.com/gentle-introduction-to-echo-state-networks-af99e5373c68">Reservoir computers</a> copy the reasoning process taking place in Alice’s brain. They can learn from past inputs to predict the future events.</p>
<p>Although reservoir computers were first proposed using neural networks – computer programs loosely based on the structure of neurons in the brain – they can also be built with <a href="https://link.springer.com/chapter/10.1007/978-3-540-39432-7_63">simple physical systems</a>.</p>
<p>Reservoir computers are analogue computers. An analogue computer represents data continuously, as opposed to digital computers which represent data as abruptly changing binary “zero” and “one” states. </p>
<p>Representing data in a continuous way <a href="https://collection.sciencemuseumgroup.org.uk/objects/co8428222/electronic-storm-surge-modelling-machine-storm-surge-model">enables</a> analogue computers to model certain natural events – ones that occur in a kind of unpredictable sequence called a “<a href="https://onlinelibrary.wiley.com/doi/abs/10.1002/ima.1850010213">chaotic time series</a>” – better than a digital computer.</p>
<h2>How to make predictions</h2>
<p>To understand how we can use a reservoir computer to make predictions, imagine you have a record of daily rainfall for the past year and a bucket full of water near you. The bucket will be our “computational reservoir”. </p>
<p>We input the daily rainfall record to the bucket by means of stone. For a day of light rain, we throw a small stone; for a day of heavy rain, a big stone. For a day of no rain, we throw no rock.</p>
<p>Each stone creates waves, which then slosh around the bucket and interact with waves created by other stones. </p>
<hr>
<p>
<em>
<strong>
Read more:
<a href="https://theconversation.com/theres-a-way-to-turn-almost-any-object-into-a-computer-and-it-could-cause-shockwaves-in-ai-62235">There's a way to turn almost any object into a computer – and it could cause shockwaves in AI</a>
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</em>
</p>
<hr>
<p>At the end of this process, the state of the water in the bucket gives us a prediction. If the interactions between waves create large new waves, we can say our reservoir computer predicts heavy rains. But if they are small then we should expect only light rain. </p>
<p>It is also possible that the waves will cancel one another, forming a still water surface. In that case we should not expect any rain. </p>
<p>The reservoir makes a weather forecast because the waves in the bucket and rainfall patterns evolve over time following the same laws of physics. </p>
<p>So do many other natural and socio-economic processes. This means a reservoir computer can also forecast <a href="https://towardsdatascience.com/predicting-stock-prices-with-echo-state-networks-f910809d23d4">financial markets</a> and even <a href="https://pubmed.ncbi.nlm.nih.gov/26422421/">certain kinds</a> of <a href="https://www.researchgate.net/publication/308952845_Temporal_Learning_Using_Echo_State_Network_for_Human_Activity_Recognition">human activity</a>.</p>
<h2>Longer-lasting waves</h2>
<p>The “<a href="https://autobencoder.com/2021-04-05-bucket/">bucket of water</a>” reservoir computer has its limits. For one thing, the waves are short-lived. To forecast complex processes such as climate change and population growth, we need a reservoir with more durable waves.</p>
<figure>
<iframe width="440" height="260" src="https://www.youtube.com/embed/w-oDnvbV8mY?wmode=transparent&start=0" frameborder="0" allowfullscreen=""></iframe>
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<p>One option is “solitons”. These are self-reinforcing waves that keep their shape and move for long distances.</p>
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<a href="https://images.theconversation.com/files/526932/original/file-20230518-22-tq8wpq.png?ixlib=rb-1.1.0&q=45&auto=format&w=1000&fit=clip"><img alt="A drinking fountain with water flowing down a metal slope, exhibiting waves." src="https://images.theconversation.com/files/526932/original/file-20230518-22-tq8wpq.png?ixlib=rb-1.1.0&q=45&auto=format&w=237&fit=clip" srcset="https://images.theconversation.com/files/526932/original/file-20230518-22-tq8wpq.png?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=705&fit=crop&dpr=1 600w, https://images.theconversation.com/files/526932/original/file-20230518-22-tq8wpq.png?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=705&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/526932/original/file-20230518-22-tq8wpq.png?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=705&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/526932/original/file-20230518-22-tq8wpq.png?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=886&fit=crop&dpr=1 754w, https://images.theconversation.com/files/526932/original/file-20230518-22-tq8wpq.png?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=886&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/526932/original/file-20230518-22-tq8wpq.png?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=886&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px"></a>
<figcaption>
<span class="caption">Our reservoir computer used solitary waves like those seen in drinking fountains.</span>
<span class="attribution"><span class="source">Ivan Maksymov</span>, <span class="license">Author provided</span></span>
</figcaption>
</figure>
<p>For our reservoir computer, we used compact soliton-like waves. You often see such waves in a bathroom sink or a drinking fountain. </p>
<p>In our computer, a thin layer of water flows over a slightly inclined metal plate. A small electric pump changes the speed of the flow and creates solitary waves. </p>
<p>We added a fluorescent material to make the water glow under ultraviolet light, to precisely measure the size of the waves. </p>
<figure>
<iframe width="440" height="260" src="https://www.youtube.com/embed/Zwu3KEo8f00?wmode=transparent&start=0" frameborder="0" allowfullscreen=""></iframe>
</figure>
<p>The pump plays the role of falling stones in the game played by Alice and Bob, but the solitary waves correspond to the waves on the water surface. Solitary waves move much faster and live longer than water waves in a bucket, which lets our computer process data at a higher speed. </p>
<h2>So, how does it perform?</h2>
<p>We <a href="https://doi.org/10.1209/0295-5075/acd471">tested</a> our computer’s ability to remember past inputs and to make forecasts for a benchmark set of chaotic and random data. Our computer not only executed all tasks exceptionally well but also outperformed a high-performance digital computer tasked with the same problem. </p>
<p>With my colleague <a href="https://www.swinburne.edu.au/research/our-research/access-our-research/find-a-researcher-or-supervisor/researcher-profile/?id=apototskyy">Andrey Pototsky</a>, we also created a mathematical model that enabled us to better understand the physical properties of the solitary waves.</p>
<p>Next, we plan to miniaturise our computer as a <a href="https://www.theregister.com/2021/09/15/microfluidic_processor/">microfluidic processor</a>. Water waves should be able to do computations inside a chip that operates similarly to the silicon chips used in every smartphone. </p>
<hr>
<p>
<em>
<strong>
Read more:
<a href="https://theconversation.com/what-was-the-first-computer-122164">What was the first computer?</a>
</strong>
</em>
</p>
<hr>
<p>In the future, our computer may be able to produce reliable long-term forecasts in areas such as climate change, bushfires and financial markets – with much <a href="https://www.nature.com/articles/d41586-022-03212-7">lower cost and wider availability</a> than current supercomputers.</p>
<p>Our computer is also naturally immune to cyber attacks because it does not use digital data. </p>
<p>Our <a href="https://research.csu.edu.au/our-profile/research-centres/aicf">vision</a> is that a soliton-based microfluidic reservoir computer will bring data science and machine learning to rural and remote communities worldwide. But for now, our research work continues.</p><img src="https://counter.theconversation.com/content/205842/count.gif" alt="The Conversation" width="1" height="1" />
<p class="fine-print"><em><span>Ivan Maksymov does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.</span></em></p>In the future, our computer may be able to produce long-term forecasts in areas such as climate change, bushfires and financial markets – while being cheaper and more accessible than supercomputers.Ivan Maksymov, Principal Research Fellow, Charles Sturt UniversityLicensed as Creative Commons – attribution, no derivatives.tag:theconversation.com,2011:article/2027042023-03-28T12:22:16Z2023-03-28T12:22:16ZWhy tornadoes are still hard to forecast – even though storm predictions are improving<figure><img src="https://images.theconversation.com/files/518829/original/file-20230331-18-ihfljb.jpg?ixlib=rb-1.1.0&rect=0%2C315%2C1649%2C1048&q=45&auto=format&w=496&fit=clip" /><figcaption><span class="caption">A tornado touches down.</span> <span class="attribution"><a class="source" href="https://www.flickr.com/photos/noaanssl/47953539496">Morgan Schneider/OU CIMMS/NOAA NSSL</a></span></figcaption></figure><p><em>Meteorologists <a href="https://www.spc.noaa.gov">began warning about severe weather</a> with the potential for tornadoes <a href="https://www.wpc.ncep.noaa.gov/threats/threats.php">several days before</a> storms tore across <a href="https://earthobservatory.nasa.gov/images/151138/tornado-leaves-path-of-destruction-in-mississippi">the Southeast</a> and the <a href="https://www.nytimes.com/2023/03/31/us/midwest-storms-flood-weather.html">Central U.S.</a> in late March 2023. At one point, <a href="https://twitter.com/NWS/status/1641890452562403328">more than 28 million people</a> were under a <a href="https://www.weather.gov/safety/tornado-ww">tornado watch</a>. But pinpointing exactly where a tornado will touch down – like the tornadoes that hit <a href="https://www.bbc.com/news/world-us-canada-65072195">Rolling Fork, Mississippi</a>, on March 24, and towns in <a href="https://apnews.com/article/tornado-arkansas-storm-concert-79fe2da8a6b8bd92970032530b760d20">Arkansas</a>, <a href="https://www.bbc.com/news/world-us-canada-65072195">Illinois</a> and <a href="https://www.spc.noaa.gov/climo/reports/230331_rpts.html">multiple other states</a> on March 31 – still relies heavily on seeing the storms developing on radar. <a href="https://atmo.tamu.edu/people/profiles/faculty/nowotarskichristopher.html">Chris Nowotarski</a>, an atmospheric scientist, explains why, and how forecast technology is improving.</em></p>
<h2>Why are tornadoes still so difficult to forecast?</h2>
<p>Meteorologists have gotten a lot better at forecasting the conditions that make tornadoes more likely. But predicting exactly which thunderstorms will produce a tornado and when is harder, and that’s where a lot of severe weather research is focused today.</p>
<p>Often, you’ll have a line of thunderstorms in an environment that looks favorable for tornadoes, and one storm might produce a tornado but the others don’t. </p>
<p>The differences between them could be due to small differences in meteorological variables that aren’t resolved by our current observing networks or computer models. Even changes in the land surface conditions – fields, forested regions or urban environments – could affect whether a tornado forms. These small changes in the storm environment can have large impacts on the processes within storms that can make or break a tornado.</p>
<figure class="align-center ">
<img alt="Scientists stand near a truck outfitted with measuring devices with a dramatic storm on the horizon." src="https://images.theconversation.com/files/517815/original/file-20230327-18-egyw14.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/517815/original/file-20230327-18-egyw14.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=450&fit=crop&dpr=1 600w, https://images.theconversation.com/files/517815/original/file-20230327-18-egyw14.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=450&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/517815/original/file-20230327-18-egyw14.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=450&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/517815/original/file-20230327-18-egyw14.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=566&fit=crop&dpr=1 754w, https://images.theconversation.com/files/517815/original/file-20230327-18-egyw14.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=566&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/517815/original/file-20230327-18-egyw14.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=566&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px">
<figcaption>
<span class="caption">One way scientists gather data for understanding tornadoes is by chasing storms.</span>
<span class="attribution"><a class="source" href="https://www.flickr.com/photos/noaanssl/52201476520/">Annette Price/CIWRO</a>, <a class="license" href="http://creativecommons.org/licenses/by/4.0/">CC BY</a></span>
</figcaption>
</figure>
<p>One of the strongest predictors of whether a thunderstorm produces a tornado relates to <a href="https://www.weather.gov/jetstream/tornado">vertical wind shear</a>, which is how the wind changes direction or speed with height in the atmosphere.</p>
<p>How wind shear interacts with rain-cooled air within storms, which we call “outflow,” and how much precipitation evaporates can influence whether a tornado forms. If you’ve ever been in a thunderstorm, you know that right before it starts to rain, you often get a gust of cold air surging out from the storm. The characteristics of that cold air outflow are important to whether a tornado can form, because tornadoes typically form in that cooler portion of the storm.</p>
<h2>How far in advance can you know if a tornado is likely to be large and powerful?</h2>
<p>The vast majority of violent tornadoes form from <a href="https://www.weather.gov/ama/supercell">supercells</a>, thunderstorms with a deep rotating updraft, called a “mesocyclone.” Vertical wind shear can enable the midlevels of the storm to rotate, and upward suction from this mesocyclone can intensify the rotation within the storm’s outflow into a tornado.</p>
<p>If you have a supercell on radar and it has strong rotation above the ground, that’s often a precursor to a tornado. Some research suggests that <a href="https://doi.org/10.1175/WAF-D-19-0099.1">a wider mesocyclone is more likely to create a stronger</a>, longer-lasting tornado than other storms.</p>
<p>Forecasters also look at the storm’s environmental conditions – temperature, humidity and wind shear. Those offer more clues that a storm is likely to produce a significant tornado.</p>
<figure>
<iframe width="440" height="260" src="https://www.youtube.com/embed/R7CD6MpTefs?wmode=transparent&start=0" frameborder="0" allowfullscreen=""></iframe>
<figcaption><span class="caption">What radar showed as a tornado headed toward Rolling Fork, Mississippi, on March 24, 2023.</span></figcaption>
</figure>
<p>The percentage of tornadoes that <a href="https://www.weather.gov/safety/tornado-ww">trigger a warning</a> has increased over recent decades, due to <a href="https://www.weather.gov/jetstream/how">Doppler radar</a>, improved modeling and better understanding of the storm environment. About <a href="https://doi.org/10.1175/WAF-D-19-0119.1">87% of deadly tornadoes</a> from 2003 to 2017 had an advance warning.</p>
<p>The lead time for warnings has also improved. In general, it’s <a href="https://community.fema.gov/ProtectiveActions/s/article/Tornado-Alerts-and-Warnings">about 10 to 15 minutes</a> now. That’s enough time to get to your basement or, if you’re in a trailer park or outside, to find a safe facility. Not every storm will have that much lead time, so it’s important to get to shelter fast.</p>
<h2>What are researchers discovering today about tornadoes that can help protect lives in the future?</h2>
<p>If you think back to the <a href="https://www.imdb.com/title/tt0117998/">movie “Twister</a>,” in the early 1990s we were starting to do more field work on tornadoes. We were taking radar out in trucks and driving vehicles with roof-mounted instruments into storms. That’s when we really started to appreciate what we call the storm-scale processes – the conditions inside the storm itself, how variations in temperature and humidity in outflow can influence the potential for tornadoes.</p>
<p>Scientists can’t launch a weather balloon or send instruments into every storm, though. So, we also use computers to model storms to understand what’s happening inside. Often, we’ll run several models, referred to as ensembles. For instance, if nine out of 10 models produce a tornado, we know there’s a good chance the storm will produce tornadoes.</p>
<p>The National Severe Storms Laboratory has recently been experimenting with tornado warnings based on these models, called <a href="https://www.nssl.noaa.gov/projects/wof/">Warn-on-Forecast</a>, to increase the lead time for tornado warnings.</p>
<figure class="align-center zoomable">
<a href="https://images.theconversation.com/files/517845/original/file-20230328-490-c5aoro.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=1000&fit=clip"><img alt="A destroyed home with just one wall standing and furniture strewn about in Rolling Fork, Mississippi, after the tornado March 24, 2023." src="https://images.theconversation.com/files/517845/original/file-20230328-490-c5aoro.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/517845/original/file-20230328-490-c5aoro.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=400&fit=crop&dpr=1 600w, https://images.theconversation.com/files/517845/original/file-20230328-490-c5aoro.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=400&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/517845/original/file-20230328-490-c5aoro.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=400&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/517845/original/file-20230328-490-c5aoro.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=503&fit=crop&dpr=1 754w, https://images.theconversation.com/files/517845/original/file-20230328-490-c5aoro.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=503&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/517845/original/file-20230328-490-c5aoro.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=503&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px"></a>
<figcaption>
<span class="caption">An early warning can be the difference between life and death for people in homes without basements or cellars.</span>
<span class="attribution"><a class="source" href="https://www.gettyimages.com/detail/news-photo/aerial-view-of-a-destroyed-neighborhood-in-rolling-fork-news-photo/1249647508">Chandan Khanna/AFP via Getty Images</a></span>
</figcaption>
</figure>
<p>There are a lot of other areas of research. For example, to better understand how storms form, <a href="http://people.tamu.edu/%7Ecjnowotarski/research.html">I do a lot of idealized computer modeling</a>. For that, I use a model with a simplified storm environment and make small changes to the environment to see how that changes the physics within the storm itself. </p>
<p>There are also new tools in storm chasing. There’s been an explosion in the use of drones – scientists are putting sensors into unmanned aerial vehicles and <a href="https://www.colorado.edu/aerospace/2021/12/08/designing-flying-ai-systems-study-supercell-thunderstorms-close">flying them close to</a> and sometimes into the storm.</p>
<p>The focus of tornado research has also shifted from the Great Plains – the traditional “tornado alley” – <a href="https://doi.org/10.1175/JAMC-D-15-0342.1">to the Southeast</a>.</p>
<p><div data-react-class="Tweet" data-react-props="{"tweetId":"1641899393971265537"}"></div></p>
<h2>What’s different about tornadoes in the Southeast?</h2>
<p>In the Southeast there are some different influences on storms compared with the Great Plains. The Southeast has more trees and more varied terrain, and also more moisture in the atmosphere because it’s close to the Gulf of Mexico. There tend to be <a href="https://doi.org/10.1175/2008WAF2222132.1">more fatalities</a> in the Southeast, too, because <a href="https://theconversation.com/tornadoes-that-strike-at-night-are-more-deadly-and-require-more-effective-warning-systems-132955">more tornadoes form at night</a>.</p>
<figure class="align-center ">
<img alt="US map showing highest number of tornadoes in Mississippi, Alabama and western Tennessee." src="https://images.theconversation.com/files/517812/original/file-20230327-18-9tncri.png?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/517812/original/file-20230327-18-9tncri.png?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=458&fit=crop&dpr=1 600w, https://images.theconversation.com/files/517812/original/file-20230327-18-9tncri.png?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=458&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/517812/original/file-20230327-18-9tncri.png?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=458&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/517812/original/file-20230327-18-9tncri.png?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=575&fit=crop&dpr=1 754w, https://images.theconversation.com/files/517812/original/file-20230327-18-9tncri.png?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=575&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/517812/original/file-20230327-18-9tncri.png?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=575&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px">
<figcaption>
<span class="caption">A map of severe tornado days from 1986 to 2015 shows a large number in the Southeast.</span>
<span class="attribution"><a class="source" href="https://www.spc.noaa.gov/">NOAA Storm Prediction Center</a></span>
</figcaption>
</figure>
<p>We tend to see more tornadoes in the Southeast that are in lines of thunderstorms called “quasi-linear convective systems.” The processes that lead to tornadoes in these storms can be different, and scientists are learning more about that.</p>
<p>Some research has also suggested the start of <a href="https://doi.org/10.1002/joc.5285">a climatological shift</a> in tornadoes toward the Southeast. It can be difficult to disentangle an increase in storms from better technology spotting more tornadoes, though. So, more research is needed.</p>
<p><em>This article was updated March 31, 2023, with tornadoes in Arkansas and the central U.S.</em></p><img src="https://counter.theconversation.com/content/202704/count.gif" alt="The Conversation" width="1" height="1" />
<p class="fine-print"><em><span>Chris Nowotarski receives funding from NSF, NOAA, DOE, and NASA.</span></em></p>Researchers are turning to computer models, drones and other methods to improve tornado forecasting.Chris Nowotarski, Associate Professor of Atmospheric Science, Texas A&M UniversityLicensed as Creative Commons – attribution, no derivatives.tag:theconversation.com,2011:article/2011302023-03-19T11:51:55Z2023-03-19T11:51:55ZThe limits of expert judgment: Lessons from social science forecasting during the pandemic<figure><img src="https://images.theconversation.com/files/515881/original/file-20230316-18-h2iq1i.jpg?ixlib=rb-1.1.0&rect=0%2C0%2C5604%2C3837&q=45&auto=format&w=496&fit=clip" /><figcaption><span class="caption">To find out how well social scientists can predict societal change, researchers ran the largest forecasting initiative in the field’s history. Here’s what they found.</span> <span class="attribution"><span class="source">(Shutterstock)</span></span></figcaption></figure><p>Imagine being a policymaker at the beginning of the COVID-19 pandemic. You have to decide which actions to recommend, how much risk to tolerate and what sacrifices to ask your citizens to bear. </p>
<p>Who would you turn to for an accurate prediction about how people would react? Many would recommend going to the experts — social scientists. But we are here to tell you this would be bad advice.</p>
<p>As psychological scientists with decades of combined experience studying <a href="https://doi.org/10.1016/j.tics.2019.04.005">decision-making</a>, <a href="https://doi.org/10.1177/1745691616672066">wisdom</a>, <a href="https://doi.org/10.1037/amp0000903">expert judgment</a> and <a href="https://doi.org/10.1037/amp0000898">societal change</a>, we hoped social scientists’ predictions would be accurate and useful. But we also had our doubts. </p>
<p>Our discipline has been undergoing a crisis due to <a href="https://doi.org/10.1126/science.aac4716">failed study replications</a> and <a href="https://doi.org/10.1177/0956797611417632">questionable research practices</a>. If basic findings can’t be reproduced in controlled experiments, how confident can we be that our theories can explain complex real-world outcomes?</p>
<h2>Predicting social change</h2>
<p>To find out how well social scientists could predict societal change, we ran the largest forecasting initiative in our field’s history using predictions about change in the first year of the COVID-19 pandemic as a test case. </p>
<p>To do this, we tested how well social scientists could predict societal change in two ways. First, we asked social scientists for quick guesses about how things would change over the next two years of the pandemic. </p>
<p>Second, we ran a competition where over 100 teams of social scientists with access to historical data made month-by-month forecasts. We formally assessed their predictions for a range of social sciences phenomena, including changes in prejudice, subjective well-being, violence, individualism and political polarization between May 2020 and May 2021. </p>
<figure class="align-right zoomable">
<a href="https://images.theconversation.com/files/515307/original/file-20230314-2482-pwibpx.JPG?ixlib=rb-1.1.0&q=45&auto=format&w=1000&fit=clip"><img alt="Forecasting errors when social scientists were predicting social and psychological consequences of COVID-19." src="https://images.theconversation.com/files/515307/original/file-20230314-2482-pwibpx.JPG?ixlib=rb-1.1.0&q=45&auto=format&w=237&fit=clip" srcset="https://images.theconversation.com/files/515307/original/file-20230314-2482-pwibpx.JPG?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=750&fit=crop&dpr=1 600w, https://images.theconversation.com/files/515307/original/file-20230314-2482-pwibpx.JPG?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=750&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/515307/original/file-20230314-2482-pwibpx.JPG?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=750&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/515307/original/file-20230314-2482-pwibpx.JPG?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=943&fit=crop&dpr=1 754w, https://images.theconversation.com/files/515307/original/file-20230314-2482-pwibpx.JPG?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=943&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/515307/original/file-20230314-2482-pwibpx.JPG?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=943&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px"></a>
<figcaption>
<span class="caption">Results of the social science forecasting tournaments by the Forecasting Collaborative conducted during the 2020-2021 years of the COVID-19 pandemic.</span>
<span class="attribution"><span class="source">(Igor Grossmann)</span></span>
</figcaption>
</figure>
<p>Our findings, detailed in peer-reviewed papers in <a href="https://doi.org/10.1038/s41562-022-01517-1"><em>Nature Human Behaviour</em></a> and in <a href="https://doi.org/10.31234/osf.io/g8f9s"><em>American Psychologist</em></a>, paint a sobering picture. Despite the causal nature of most theories in the social sciences, and the fields’ emphasis on prediction in controlled settings, social scientists’ forecasts were generally not very good.</p>
<p>In both papers, we found that experts’ predictions were generally no more accurate than those made by samples of the general public. Further, their predictions were often worse than predictions generated by simple statistical models.</p>
<h2>Improving predictions</h2>
<p>Our studies did still give us reasons to be optimistic. First, forecasts were more accurate when teams had specific expertise in the domain they were making predictions in. If someone was an expert in depression, for example, they were better at predicting societal trends in depression. </p>
<p>Second, when teams were made up of scientists from different fields working together, they tended to do better at forecasting. Finally, teams that used simpler models to generate their predictions and made use of past data generally outperformed those that didn’t. </p>
<p>These findings suggest that, despite the poor performance of the social scientists in our studies, there are steps scientists can take to improve their accuracy at this type of forecasting.</p>
<figure class="align-left zoomable">
<a href="https://images.theconversation.com/files/515306/original/file-20230314-24-v7vilk.JPG?ixlib=rb-1.1.0&q=45&auto=format&w=1000&fit=clip"><img alt="An infographic of the map of the world with blue dots indicating where participants in the World after COVID were from" src="https://images.theconversation.com/files/515306/original/file-20230314-24-v7vilk.JPG?ixlib=rb-1.1.0&q=45&auto=format&w=237&fit=clip" srcset="https://images.theconversation.com/files/515306/original/file-20230314-24-v7vilk.JPG?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=750&fit=crop&dpr=1 600w, https://images.theconversation.com/files/515306/original/file-20230314-24-v7vilk.JPG?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=750&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/515306/original/file-20230314-24-v7vilk.JPG?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=750&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/515306/original/file-20230314-24-v7vilk.JPG?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=943&fit=crop&dpr=1 754w, https://images.theconversation.com/files/515306/original/file-20230314-24-v7vilk.JPG?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=943&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/515306/original/file-20230314-24-v7vilk.JPG?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=943&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px"></a>
<figcaption>
<span class="caption">Results of the World after COVID project documenting the diversity and uncertainty in predictions of the social and psychological consequences of the pandemic among members of the world’s scientific community.</span>
<span class="attribution"><span class="source">(Igor Grossmann)</span></span>
</figcaption>
</figure>
<p>Our research also found that, compared to lay people, social scientists were more aware of the herculean nature of the task at hand. In our studies, they expressed uncertainty and less confidence than lay people when making forecasts. </p>
<p>Similarly, social scientists <a href="https://doi.org/10.1037/amp0000903">expressed uncertainty</a> in their open-ended predictions for the <a href="https://worldaftercovid.info/">World after COVID project</a>, a video series we conducted with eminent scholars in the first year of the pandemic. </p>
<p>Thus, social scientists still have some wisdom to offer, reminding us of the uncertainty <a href="https://doi.org/10.1038/s44159-022-00081-9">and the need for humility</a> when forecasting the future. </p>
<h2>A call to action</h2>
<p>Our work highlights the importance of developing reliable sources of data and suggests strategies that can improve the accuracy of such forecasts.</p>
<p>These results are a call to action for the scientific community to continue developing better methods for predicting societal change so the public can rely on scientists in times of crisis.</p>
<p>Our projects show that expert prediction of societal change during the COVID-19 pandemic was far from perfect. But they also suggest ways such predictions can be improved. By drawing on specific expertise, collaborating across disciplines and making data-driven models, social scientists can produce more accurate and useful forecasts for policymakers and the public. </p>
<p>The scientific community should strive to develop better methods for predicting societal change, while acknowledging the uncertainty and complexity involved. Policymakers should appreciate the value of expert insight, but also be aware of its limitations and potential biases. If we want to predict the future, or shape it for that matter, than a bit of humility would likely help.</p><img src="https://counter.theconversation.com/content/201130/count.gif" alt="The Conversation" width="1" height="1" />
<p class="fine-print"><em><span>Igor Grossmann receives funding from the Social Sciences and Humanities Research Council of Canada, Ontario Ministry of Research, Innovation and Science, The John Templeton Foundation, and the Templeton World Charity Foundation. </span></em></p><p class="fine-print"><em><span>Cendri Hutcherson receives funding from the Social Sciences and Humanities Research Council of Canada, the Natural Sciences and Engineering Research Council of Canada, the Canada Foundation for Innovation, the Ontario Ministry of Research and Innovation, and the National Institutes for Mental Health (USA).</span></em></p><p class="fine-print"><em><span>Michael Varnum has received funding from the National Science Foundation (USA), the US Fulbright Program, and the China Postdoctoral Science Foundation.</span></em></p>A sobering picture emerges from a study testing social scientists’ ability to predict societal change during the COVID-19 pandemic.Igor Grossmann, Professor of Psychology, University of WaterlooCendri Hutcherson, Associate Professor of Psychology, University of TorontoMichael Varnum, Associate Professor of Psychology, Arizona State UniversityLicensed as Creative Commons – attribution, no derivatives.tag:theconversation.com,2011:article/1975262023-01-11T19:07:50Z2023-01-11T19:07:50ZWhat does climate change mean for extreme waves? In 80% of the world, we don’t really know<figure><img src="https://images.theconversation.com/files/503958/original/file-20230111-448-g2wsq8.jpeg?ixlib=rb-1.1.0&rect=39%2C0%2C4409%2C2937&q=45&auto=format&w=496&fit=clip" /><figcaption><span class="caption">
</span> <span class="attribution"><span class="source">Shutterstock</span></span></figcaption></figure><p>Across much of the world’s oceans, waves are getting bigger. In the Southern Ocean, where storm-driven swell can propagate halfway across the world to California, the average wave has grown about 20cm in the past 30 years.</p>
<p>These changes are part of climate change, and are likely to continue well into the future. If you’re making long-term plans near the sea – like building ships, or constructing flood defences in coastal cities – you need more detail about how big those waves are going to get.</p>
<p>In a <a href="https://doi.org/10.1126/sciadv.ade3170">study</a> published today in Science Advances, we looked at the projected changes in the size of the very biggest waves around the globe. We found the uncertainties in the projections could be larger than the projected future changes themselves in about 80% of the world’s oceans and coastlines.</p>
<h2>The ‘wave climate’</h2>
<p>My group and I study the world’s “wave climate”: the size and distribution of ocean waves in different places, and how that has changed in the past and will change in the future.</p>
<p>We’re interested in the heights of average waves, but also the extreme conditions. As with floods or heatwaves, extreme waves are the ones that cause problems – so they’re often the ones we need to know about when we’re building near the sea.</p>
<p>From floating buoys and satellite radar, we have records of wave heights extending back 30 to 40 years. These data don’t cover the whole world, but we feed them into computer models that fill in the gaps. </p>
<figure class="align-center zoomable">
<a href="https://images.theconversation.com/files/503961/original/file-20230111-4890-1qpfqc.jpeg?ixlib=rb-1.1.0&q=45&auto=format&w=1000&fit=clip"><img alt="An aerial photo of waves rolling in to shore." src="https://images.theconversation.com/files/503961/original/file-20230111-4890-1qpfqc.jpeg?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/503961/original/file-20230111-4890-1qpfqc.jpeg?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=450&fit=crop&dpr=1 600w, https://images.theconversation.com/files/503961/original/file-20230111-4890-1qpfqc.jpeg?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=450&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/503961/original/file-20230111-4890-1qpfqc.jpeg?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=450&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/503961/original/file-20230111-4890-1qpfqc.jpeg?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=566&fit=crop&dpr=1 754w, https://images.theconversation.com/files/503961/original/file-20230111-4890-1qpfqc.jpeg?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=566&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/503961/original/file-20230111-4890-1qpfqc.jpeg?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=566&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px"></a>
<figcaption>
<span class="caption">Waves are driven by winds over the surface of the ocean.</span>
<span class="attribution"><span class="source">Dan Grinwis</span></span>
</figcaption>
</figure>
<p>Waves are created by the wind, so our models of waves are also tied to what we know about wind conditions. Taken all together, we have about 40 years of model data giving wave conditions for the whole world’s oceans (broken up into “pixels” about 25 kilometres across).</p>
<p>We also use a branch of statistics called extreme value analysis to calculate things like the biggest wave you can expect at a given location once in 100 years (the 100-year event).</p>
<h2>Why waves are changing</h2>
<p>As the climate changes, we expect that global wind patterns will change – so the world’s waves will change as well. </p>
<p>One change we are already seeing is that many low-pressure systems, which create high winds, are becoming more intense and moving away from the equator and towards the poles. </p>
<hr>
<p>
<em>
<strong>
Read more:
<a href="https://theconversation.com/climate-change-may-change-the-way-ocean-waves-impact-50-of-the-worlds-coastlines-121239">Climate change may change the way ocean waves impact 50% of the world’s coastlines</a>
</strong>
</em>
</p>
<hr>
<p>In the southern hemisphere, this means more high winds over the Southern Ocean, driving bigger waves. This swell in the Southern Ocean propagates out into the Pacific, Atlantic and Indian oceans – which means bigger waves across the southern hemisphere.</p>
<p>Indeed, we have observed that average wave heights in the Southern Ocean have increased by around 20cm over the past 30 years.</p>
<p>In the northern hemisphere, there is more land closer to the pole. So the high winds are now more often happening over land, and ocean waves are actually losing some height. </p>
<h2>A blurry future</h2>
<p>So what does all this mean for the future? In our new study, we tried to figure that out.</p>
<p>To get an idea of the future of waves, we start with wind projections from major climate models that are used to project future temperatures as carbon dioxide levels in the atmosphere increase. We then feed these winds into our wave models, and see what they predict.</p>
<p>There are many wind and wave models, all with slight differences in their assumptions and the way they model the physics, so they all produce somewhat different projections. We combined the results from an ensemble of a dozen models to get a clearer picture of the differences.</p>
<figure class="align-center zoomable">
<a href="https://images.theconversation.com/files/503959/original/file-20230111-4926-ngzmq6.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=1000&fit=clip"><img alt="An aerial photo of waves crashing on a coastline." src="https://images.theconversation.com/files/503959/original/file-20230111-4926-ngzmq6.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/503959/original/file-20230111-4926-ngzmq6.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=450&fit=crop&dpr=1 600w, https://images.theconversation.com/files/503959/original/file-20230111-4926-ngzmq6.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=450&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/503959/original/file-20230111-4926-ngzmq6.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=450&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/503959/original/file-20230111-4926-ngzmq6.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=565&fit=crop&dpr=1 754w, https://images.theconversation.com/files/503959/original/file-20230111-4926-ngzmq6.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=565&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/503959/original/file-20230111-4926-ngzmq6.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=565&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px"></a>
<figcaption>
<span class="caption">Waves shape coastlines through erosion.</span>
<span class="attribution"><span class="source">Dan Grinwis</span></span>
</figcaption>
</figure>
<p>On average, we found extreme wave heights in many places are likely to grow by between 5% and 8% by 2100.</p>
<p>However, there is a lot of uncertainty in those estimates. One source of uncertainty is how much carbon dioxide humans pump into the atmosphere over the coming decades. </p>
<p>Another source is the uncertainty in the models themselves. We found that in many cases the difference in estimates between different models was about the same size as the projected changes in wave height.</p>
<h2>A note of caution</h2>
<p>The upshot of our research is that there is still a lot of uncertainty in what will happen to the size of extreme waves in the coming decades. That means there is also a lot of uncertainty in our projections of coastal flooding and the erosion of beaches.</p>
<p>These uncertainties may not seem huge – perhaps 30-40cm by 2100 – but they’re big enough to determine whether or not a particular coastal property ends up underwater.</p>
<hr>
<p>
<em>
<strong>
Read more:
<a href="https://theconversation.com/curious-kids-why-are-there-waves-112015">Curious Kids: why are there waves?</a>
</strong>
</em>
</p>
<hr>
<p>So for anyone making plans near the sea – like engineers designing coastal structures, governments building flood defences, or local councils making development decisions – the message is that you should err on the side of caution in your decision-making. </p>
<p>For the rest of us, it’s a reminder that climate change doesn’t just mean rising temperatures: it means a transformation of the whole global climate system, in ways we still don’t fully understand.</p><img src="https://counter.theconversation.com/content/197526/count.gif" alt="The Conversation" width="1" height="1" />
<p class="fine-print"><em><span>Ian Young receives funding from the Australian Research Council, the Victorian Government through DELWP and the Integrated Marine Observing System. </span></em></p>Sea-level rise isn’t the only climate-related problem for our coasts – extreme waves that cause flooding and erosion are also changing, but exactly how is hard to predict.Ian Young, Kernot Professor of Engineering, The University of MelbourneLicensed as Creative Commons – attribution, no derivatives.tag:theconversation.com,2011:article/1951722023-01-02T12:40:42Z2023-01-02T12:40:42ZScientists dig deep and find a way to accurately predict snowmelt after droughts<figure><img src="https://images.theconversation.com/files/497775/original/file-20221128-25-6d5d8u.jpg?ixlib=rb-1.1.0&rect=6%2C324%2C2293%2C1207&q=45&auto=format&w=496&fit=clip" /><figcaption><span class="caption">Following historic drought in 2021, reservoir levels dropped down in the Hoover Dam on the Colorado River, which gets its waters from the melting snowpack from the Rocky Mountains of Colorado and Wyoming.</span> <span class="attribution"><a class="source" href="https://pxhere.com/en/photo/623841">(pxhere.com)</a>, <a class="license" href="http://creativecommons.org/licenses/by-sa/4.0/">CC BY-SA</a></span></figcaption></figure><iframe style="width: 100%; height: 100px; border: none; position: relative; z-index: 1;" allowtransparency="" allow="clipboard-read; clipboard-write" src="https://narrations.ad-auris.com/widget/the-conversation-canada/scientists-dig-deep-and-find-a-way-to-accurately-predict-snowmelt-after-droughts" width="100%" height="400"></iframe>
<p>Where does your water supply come from? </p>
<p>If you live near mountains, for instance in British Columbia, a lot of your water probably comes from mountain snowpack. Over <a href="https://doi.org/10.1038/s41586-019-1822-y">1.9 billion people</a> globally rely on the snow melting and running off from these mountain snowpacks for their water supply.</p>
<p>Accurate predictions of this annual trend is critical for water supply planning. And forecasting models often rely on the <a href="https://www.academia.edu/47863505/Principles_of_snow_hydrology">historical relationship between mountain snowpack and the subsequent water supply</a>. </p>
<p>However, in times of unprecedented drought and a changing climate, these forecasting models seem to no longer be reliable. Following an intense drought in California in 2021, <a href="https://www.theguardian.com/us-news/2021/jun/07/california-drought-oregon-west-climate-change">snowmelt from mountain snowpack delivered significantly less water than historical models predicted</a>, meaning that reservoirs remained drier than anticipated. For the first time in 100 years, water supply models were wrong. </p>
<p>In an attempt to address the gaps in the traditional model, we recently <a href="https://doi.org/10.1029/2022GL100505">developed an updated water supply forecasting model</a> that considers additional factors, like water storage deficits in the soil and bedrock. This new model significantly improves the accuracy of water supply forecasts following drought.</p>
<h2>What are existing water supply models missing?</h2>
<p><a href="https://doi.org/10.1017/CBO9780511535673">Models used for forecasting snowmelt </a>typically consider winter rain and snowpack. But it turns out that water absorbed by the ground matters too. The amount of water absorbed into the soil and bedrock varies from year to year and is especially impacted by drought.</p>
<p>When snow melts or rain falls, almost all of it goes underground first before <a href="https://www.usgs.gov/special-topics/water-science-school/science/runoff-surface-and-overland-water-runoff">heading downstream to water supply systems </a>. The water storage processes below the surface of the ground are key to understanding the ultimate fate of rain and snow in the mountains.</p>
<figure class="align-center ">
<img alt="Schematic diagram of runoff generation in the mountains." src="https://images.theconversation.com/files/497773/original/file-20221128-12-3ska04.png?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/497773/original/file-20221128-12-3ska04.png?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=460&fit=crop&dpr=1 600w, https://images.theconversation.com/files/497773/original/file-20221128-12-3ska04.png?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=460&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/497773/original/file-20221128-12-3ska04.png?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=460&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/497773/original/file-20221128-12-3ska04.png?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=578&fit=crop&dpr=1 754w, https://images.theconversation.com/files/497773/original/file-20221128-12-3ska04.png?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=578&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/497773/original/file-20221128-12-3ska04.png?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=578&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px">
<figcaption>
<span class="caption">A diagram showing how water gets from snowpack or rain to water supply systems. Rain and snowmelt seep into the ground. Plants draw water from this region. Once the subsurface is wet, the water flows downstream to water supply systems.</span>
<span class="attribution"><a class="source" href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022GL100505">(Dana Lapides)</a>, <span class="license">Author provided</span></span>
</figcaption>
</figure>
<p>The below ground environment is made up of complex layers of soil, fractures and weathered bedrock that can <a href="https://hydrology.usu.edu/rrp/pdfs/ch2.pdf">store, detain and transport water</a>. The details of these processes are complicated, but the overall effect can be likened to a giant sponge. </p>
<p>Over the summer, the ground dries out and it gets wet again with the arrival of rain and snowmelt in winter and spring. Once the ground is wet enough, it starts to drip. This dripping water enters the groundwater and streams and eventually goes into the water supply systems.</p>
<p>How much water drips depends on how much snowmelt and rain is received, which is included in forecasting models. It also depends on how dry the subsurface was to begin with, which is not traditionally included in forecasting models.</p>
<h2>Plants use a lot of water</h2>
<p>How dry the subsurface is this year can depend on how much water the plants used last year (or even over the last few years). In hotter, drier years, plants can use more water from underground, causing the subsurface to dry out more.</p>
<p>Recent studies show us that <a href="https://doi.org/10.1038/s41586-021-03761-3">trees routinely dry up not just soils but also weathered bedrock metres below the surface</a>.</p>
<p>Scientists are still struggling to identify how dry these mountain environments can get and how far below the surface they dry. With a drier subsurface at the start of the year, more snowmelt is needed before water starts to flow downstream to water supply systems. </p>
<p>As droughts become more frequent and intense with climate change, this process could become more important even in regions that historically haven’t faced much drought.</p>
<h2>Measuring the moisture underground</h2>
<p>Directly observing the moisture levels of the ground’s subsurface is difficult, especially when it’s stored in weathered bedrock, which can extend many metres below the ground surface and be challenging to observe. </p>
<p>In our research, we found the most accurate measurements by lowering geophysical instruments down boreholes and taking water content readings at different depths. By comparing these readings over time, we observe how <a href="https://doi.org/10.1073/pnas.1800141115">the subsurface dries out and gets wet again</a>.</p>
<figure class="align-center ">
<img alt="Researcher measures subsurface wetness conditions" src="https://images.theconversation.com/files/497767/original/file-20221128-20492-5u0m49.jpeg?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/497767/original/file-20221128-20492-5u0m49.jpeg?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=429&fit=crop&dpr=1 600w, https://images.theconversation.com/files/497767/original/file-20221128-20492-5u0m49.jpeg?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=429&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/497767/original/file-20221128-20492-5u0m49.jpeg?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=429&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/497767/original/file-20221128-20492-5u0m49.jpeg?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=539&fit=crop&dpr=1 754w, https://images.theconversation.com/files/497767/original/file-20221128-20492-5u0m49.jpeg?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=539&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/497767/original/file-20221128-20492-5u0m49.jpeg?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=539&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px">
<figcaption>
<span class="caption">A USDA Forest Service employee uses an instrument to measure the moisture conditions deep underground.</span>
<span class="attribution"><span class="source">(Jamie Hinrichs/USDA Forest Service)</span>, <span class="license">Author provided</span></span>
</figcaption>
</figure>
<p>However, this intensive monitoring is nearly impossible to do over large areas.</p>
<p>While we can’t look directly underground everywhere, <a href="https://hess.copernicus.org/articles/20/1459/2016/">we can track how much water enters (rain and snowmelt) and leaves (plant water use) the ground using satellite-derived data</a>.</p>
<p>By taking a running account of water going in and out of the ground, we can estimate how dry the subsurface is — a metric we call the water storage deficit. </p>
<h2>Water supply models must dig deeper</h2>
<p>Our <a href="https://doi.org/10.1038/s41586-021-03761-3">newly-developed water supply forecasting model</a> accounts for water storage deficits in both soil and bedrock. This has improved post-drought forecast accuracy substantially, taking the probability of error in the calculation of predictions from 60 per cent to about 20 per cent.</p>
<p>Since we can calculate deficits before spring snowmelts, they serve as an early warning sign and can aid water management strategies.</p>
<p>As the climate changes, the water supply challenges in California foreshadow issues that will become increasingly prevalent in British Columbia and other regions reliant on mountain snowpack. Using updated forecasting models in the future can help these regions better prepare for <a href="https://drought.ca.gov/current-drought-conditions/#overview-of-2021">continued water shortages even when snowpack seems normal</a>.</p><img src="https://counter.theconversation.com/content/195172/count.gif" alt="The Conversation" width="1" height="1" />
<p class="fine-print"><em><span>Dana Ariel Lapides receives funding from Simon Fraser University and the US Forest Service Southwest Pacific Research Station with funds administered through Oak Ridge Institute for Science and Engineering (ORISE). </span></em></p><p class="fine-print"><em><span>David Dralle receives funding from the US Forest Service Pacific Southwest Research Station. </span></em></p><p class="fine-print"><em><span>Jesse Hahm receives funding from Simon Fraser University, the Natural Sciences and Engineering Research Council of Canada, the Canada Foundation for Innovation, and the Pacific Institute for Climate Solutions.</span></em></p><p class="fine-print"><em><span>Daniella Rempe does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.</span></em></p>Unprecedented droughts leave the subsurface drier than usual, affecting water supply in subsequent years.Dana Ariel Lapides, Postdoctoral Research Fellow, Geography, Simon Fraser UniversityDaniella Rempe, Assistant professor, Jackson School of Geosciences, The University of Texas at AustinDavid Dralle, Research officer, Hydrology, University of California, BerkeleyJesse Hahm, Department of Geography, Assistant Professor, Simon Fraser UniversityLicensed as Creative Commons – attribution, no derivatives.tag:theconversation.com,2011:article/1924542022-11-03T16:55:08Z2022-11-03T16:55:08ZHow a quest for mathematical truth and complex models can lead to useless scientific predictions – new research<figure><img src="https://images.theconversation.com/files/492970/original/file-20221102-26-cdyl7i.jpg?ixlib=rb-1.1.0&rect=49%2C0%2C3278%2C2220&q=45&auto=format&w=496&fit=clip" /><figcaption><span class="caption">The mathematical concept of a fractal is a never-ending pattern. </span> <span class="attribution"><a class="source" href="https://www.flickr.com/photos/grantdaws/13789893314/in/photolist-n1yNRh-n1z7w7-QYKTYY-iU88qq-RFDKVW-qiVTgX-dnKfQE-i4txij-oQ1RaZ-eeY1sk-ehx91S-5NxbxP-anxkVJ-oyM8Wt-mYkFh8-qTEe6g-to6SvE-qNdx6M-pi785j-qNdwjM-qvVLjP-vJxJFP-BkTGbg-pysEuR-oMK9yf-bpwh9N-ef4KcL-dPsse9-pDnWnJ-qiNQEY-sQ7aJv-ADkYoV-dD7gvB-AvEQn3-urorA4-aixXPU-AC9x6L-YNgTpL-say4Bh-VeYB9j-AvMb3T-V6NYPM-ADkX7M-dDcCms-2fbRzRf-omEgin-qBozhn-gZHrGb-dDcDcJ-BqTzfs">G. DAWSON/Flickr</a>, <a class="license" href="http://creativecommons.org/licenses/by-sa/4.0/">CC BY-SA</a></span></figcaption></figure><p>A dominant view in science is that there is a mathematical truth structuring the universe. It is assumed that the scientist’s job is to decipher these mathematical relations: once understood, they can be translated into mathematical models. Running the resulting “silicon reality” in a computer may then provide us with useful insights into how the world works.</p>
<p>Since science keeps on revealing secrets, models keep getting bigger. They integrate discoveries and newly found mechanisms to better reflect the world around us. Many scholars assume that more detailed models <a href="https://www.nature.com/articles/515338a">produce sharper estimates</a> and better predictions because they are closer to reality. But our new research, <a href="https://www.science.org/doi/10.1126/sciadv.abn9450">published in Science Advances</a>, suggests they may have the opposite effect.</p>
<p>The assumption that “more detail is better” cuts across disciplinary fields. The ramifications are enormous. Universities get more and more powerful computers because they want to run bigger and bigger models, requiring an increasing amount of computing power. Recently, the European Commission invested €8bn euros (£6.9bn) to create a very detailed simulation of the Earth (with humans), <a href="https://www.science.org/content/article/europe-building-digital-twin-earth-revolutionize-climate-forecasts">dubbed a “digital twin”</a>, hoping to better address current social and ecological challenges.</p>
<p>In our latest research, we show that the pursuit of ever more complex models as tools to produce more accurate estimates and predictions may not work. Based on statistical theory and mathematical experiments, we ran hundreds of thousands of models with different configurations and measured how uncertain their estimations are. </p>
<p>We discovered that more complex models tended to produce more uncertain estimates. This is because new parameters and mechanisms are added. A new parameter, say the effect of chewing gum on the spread of a disease, needs to be measured – and is therefore subject to measurement errors and uncertainty. Modellers may also use different equations to describe the same phenomenon mathematically. </p>
<p>Once these new additions and their associated uncertainties are integrated into the model, they pile on top of the uncertainties already there. And uncertainties keep on expanding with every model upgrade, making the model output fuzzier at every step of the way – even if the model itself becomes more faithful to reality.</p>
<figure class="align-center ">
<img alt="Server room in the Supercomputing Center of Barcelona." src="https://images.theconversation.com/files/492971/original/file-20221102-28436-yrh578.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/492971/original/file-20221102-28436-yrh578.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=344&fit=crop&dpr=1 600w, https://images.theconversation.com/files/492971/original/file-20221102-28436-yrh578.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=344&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/492971/original/file-20221102-28436-yrh578.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=344&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/492971/original/file-20221102-28436-yrh578.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=432&fit=crop&dpr=1 754w, https://images.theconversation.com/files/492971/original/file-20221102-28436-yrh578.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=432&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/492971/original/file-20221102-28436-yrh578.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=432&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px">
<figcaption>
<span class="caption">Supercomputer.</span>
<span class="attribution"><a class="source" href="https://www.shutterstock.com/image-photo/barcelona-spain-january-16-2018-view-1268319157">BearFotos/Shutterstock</a></span>
</figcaption>
</figure>
<p>This affects all models that do not have appropriate validation or training data against which to check the accuracy of their output. This includes global models of climate change, hydrology (water flow), food production and epidemiology alike, as well as all models predicting future impacts. </p>
<h2>Fuzzy results</h2>
<p>In 2009, engineers created an algorithm called Google Flu Trends for predicting the proportion of flu-related doctor visits across the US. Despite being based on 50 million queries that people had typed into Google, the model wasn’t able to predict the 2009 swine flu outbreak. The engineers then made the model, which is no longer operating, even more complex. But it still wasn’t all that accurate. <a href="https://www.sciencedirect.com/science/article/abs/pii/S0169207020301928">Research led by German psychologist Gerd Gigerenzer</a> showed it consistently overestimated doctor visits in 2011–13, in some cases by more than 50%. </p>
<p>Gigerenzer discovered that a much simpler model could produce better results. His model predicted weekly flu rates based only on one teeny piece of data: how many people had seen their GP the previous week. </p>
<p>Another example is global hydrological models, which track how and where water moves and is stored. They started simple in the 1960s based on “evapotranspiration processes” (the amount of water that could evaporate and transpire from a landscape covered in plants) and soon got extended, taking into account domestic, industrial and agricultural water uses at the global scale. The next step for these models is to simulate water demands on Earth for every kilometre each hour.</p>
<p>And yet one wonders if this extra detail will not just make them even more convoluted. We <a href="https://www.nature.com/articles/s41467-021-24508-8">have shown</a> that estimates of the amount of water used in irrigation produced by eight global hydrological models can be calculated with a single parameter only - the extent of the irrigated area. </p>
<h2>Ways forward</h2>
<p>Why has the fact that more detail can make a model worse been overlooked until now? Many modellers do not submit their models to uncertainty and sensitivity analysis, methods that tell researchers how uncertainties in the model affect the final estimation. Many keep on adding detail without working out which elements in their model are most responsible for the uncertainty in the output. </p>
<p>It is concerning as modellers are interested in developing ever larger models – in fact, entire careers are built on complex models. That’s because they are harder to falsify: their complexity intimidates outsiders and complicates understanding what is going on inside the model.</p>
<p>There are remedies, however. We suggest ensuring that models don’t keep getting larger and larger for the sake of it. Even if scientists do perform an uncertainty and sensitivity analysis, their estimates risk getting so uncertain that they become useless for science and policymaking. Investing a lot of money in computing just to run models whose estimate is completely fuzzy makes little sense. </p>
<p>Modellers should instead ponder how uncertainty expands with every addition of detail into the model – and find the best trade-off between the level of model detail and uncertainty in the estimation. </p>
<p>To find this trade-off, one can use the concept of “effective dimensions” – a measure of the number of parameters which add uncertainty to the final output, taking into account how these parameters interact with each other – which we define in our paper. </p>
<p>By calculating a model’s effective dimensions after each upgrade, modellers can appraise whether the increase in uncertainty still makes the model suitable for policy – or, in contrast, if it makes the model’s output so uncertain as to be useless. This increases transparency and helps scientists design <a href="https://www.nature.com/articles/d41586-020-01812-9">models that better serve science and society</a>. </p>
<p>Some modellers may still argue that the addition of <a href="https://www.nature.com/articles/s41558-022-01384-8.">model detail can lead to more accurate estimates</a>. The burden of proof now lies with them.</p><img src="https://counter.theconversation.com/content/192454/count.gif" alt="The Conversation" width="1" height="1" />
<p class="fine-print"><em><span>Arnald Puy receives funding from the European Commission (Marie-Sklodowska Curie Global Fellowship, grant number 792178).</span></em></p>The assumption that more detail is better is questioned by a new study.Arnald Puy, Associate Professor in Social and Environmental Uncertainties, University of BirminghamLicensed as Creative Commons – attribution, no derivatives.tag:theconversation.com,2011:article/1913172022-09-28T12:32:14Z2022-09-28T12:32:14ZNobel Prizes, election outcomes and sports championships – prediction markets try to foresee the future<figure><img src="https://images.theconversation.com/files/486954/original/file-20220928-12-t78jok.jpg?ixlib=rb-1.1.0&rect=0%2C122%2C4184%2C3075&q=45&auto=format&w=496&fit=clip" /><figcaption><span class="caption">Who will be next to cross this stage and accept a Nobel Prize?</span> <span class="attribution"><a class="source" href="https://www.gettyimages.com/detail/news-photo/general-view-of-the-stage-during-the-nobel-prize-awards-news-photo/500796996">Pascal Le Segretain/Getty Images News via Getty Images</a></span></figcaption></figure><p>Who will win Nobel Prizes in 2022? Wikipedia posits a handful of contenders for <a href="https://en.wikipedia.org/wiki/2022_Nobel_Prize_in_Physiology_or_Medicine">Physiology or Medicine</a>, about 20 different possible winners for the <a href="https://en.wikipedia.org/wiki/2022_Nobel_Peace_Prize">Peace Prize</a> and several dozen potential winners of the <a href="https://en.wikipedia.org/wiki/2022_Nobel_Prize_in_Literature">Literature Prize</a>. But since the Swedish Academy never announces nominees in advance, there are few insights indicating who will win, or even if the eventual winner is on a given list.</p>
<p>Are there ways to predict the future winners?</p>
<p>The <a href="https://apps.dtic.mil/sti/citations/AD0690498">Delphi approach</a>, named after the oracle in ancient Greece, gathers multiple rounds of opinions from a group of experts <a href="https://doi.org/10.1016/j.dss.2013.07.001">to generate a prediction</a>. Gambling firms provide betting odds on the likelihood that specific competitors will win. <a href="https://doi.org/10.1016/j.ejor.2016.12.043">Crowdsourced competitions</a>, such as the Yahoo Soccer World Cup “Pick-Em,” have participants predict individual contest winners and then aggregate the results.</p>
<p>Another approach is a prediction market that provides insight into what people expect will happen in the future by creating a stock market-like environment to capture the “<a href="https://www.penguinrandomhouse.com/books/175380/the-wisdom-of-crowds-by-james-surowiecki/">wisdom of the crowd</a>.” <a href="https://doi.org/10.1287/mnsc.44.8.1049">Groups and crowds often are collectively smarter</a> than individuals when many independent opinions are combined. </p>
<p>As an accounting and information systems professor at the University of Southern California, <a href="https://scholar.google.com/citations?user=DVk7EKAAAAAJ&hl=en&oi=ao">I investigate issues related to the crowd</a> both in my research and in my teaching. Here’s how prediction markets harness what the crowd thinks to forecast the future.</p>
<h2>The wisdom of the market</h2>
<p>In prediction markets, participants buy and sell stocks. Each stock’s price is tied to a different event happening in the future. Information about the future is captured in the stock prices. </p>
<p>For instance, in a prediction market focused on the Nobel Peace Prize, maybe Greta Thunberg is trading at $0.10 while Pope Francis is trading at $0.15, and the stocks for the entire group of candidates add up to sum to $1. The prices reflect the traders’ aggregated beliefs about the probability of their winning – a higher price means a higher perceived likelihood of winning.</p>
<p><iframe id="S3IU3" class="tc-infographic-datawrapper" src="https://datawrapper.dwcdn.net/S3IU3/3/" height="400px" width="100%" style="border: none" frameborder="0"></iframe></p>
<p>Prediction markets have various ways of setting stock prices. The Iowa Electronic Markets took following approach during the 2020 U.S. presidential election:</p>
<ul>
<li>Stock DEM2020 pays off $1 if the Democratic candidate wins, and $0 otherwise,</li>
<li>Stock REP2020 pays off $1 if the Republican candidate wins, and $0 otherwise.</li>
</ul>
<p>The stock prices capture the probabilities of each candidate winning, in two mutually exclusive events. If the price of DEM2020 is $0.52, then that is treated as the probability of that event occurring – a 52% chance. If DEM2020 is $0.52, then REP2020 is $0.48.</p>
<p>Prediction markets may use real money, or they can use play money. Google’s market used what it called “Goobles,” while the Hollywood Stock Exchange uses Hollywood Dollars. The Iowa Electronic Markets and PredictIt, both sponsored by universities, use real money. Researchers have found that there are <a href="https://doi.org/10.1080/10196780500491303">no differences in the performance of markets</a> using real money versus those using play money.</p>
<p>Although using play money makes it possible for many people to participate, one potential challenge for prediction markets that don’t use real money is <a href="https://doi.org/10.1016/j.dss.2015.07.004">gaining and maintaining interested participants</a>. Despite using different devices to keep up engagement, such as leader boards indicating who has accumulated the biggest portfolio, there is literally no money on the table to keep participants interested in the market.</p>
<figure class="align-center zoomable">
<a href="https://images.theconversation.com/files/486959/original/file-20220928-12981-49d7y1.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=1000&fit=clip"><img alt="German players hold 2014 World Cup trophy aloft" src="https://images.theconversation.com/files/486959/original/file-20220928-12981-49d7y1.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/486959/original/file-20220928-12981-49d7y1.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=409&fit=crop&dpr=1 600w, https://images.theconversation.com/files/486959/original/file-20220928-12981-49d7y1.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=409&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/486959/original/file-20220928-12981-49d7y1.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=409&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/486959/original/file-20220928-12981-49d7y1.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=514&fit=crop&dpr=1 754w, https://images.theconversation.com/files/486959/original/file-20220928-12981-49d7y1.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=514&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/486959/original/file-20220928-12981-49d7y1.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=514&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px"></a>
<figcaption>
<span class="caption">Market participants who know more about the game might better predict winners.</span>
<span class="attribution"><a class="source" href="https://newsroom.ap.org/detail/SoccerFIFAWorldCupFormat/f538c07d27f542cfaacbada7906cdacf/photo?Query=Germany%20World%20Cup%202014&mediaType=photo&sortBy=arrivaldatetime:desc&dateRange=&totalCount=7375&currentItemNo=40">AP Photo/Natacha Pisarenko</a></span>
</figcaption>
</figure>
<h2>Participants bring their knowledge to the market</h2>
<p>Prediction markets and crowdsourcing do not function in a vacuum. </p>
<p>Researchers have found that information about events finds its way into the prediction processes from various sources. For example, when I <a href="https://doi.org/10.1016/j.ejor.2016.12.043">analyzed the relationship between the betting odds</a> and the Yahoo Pick-Em crowd’s guesses for the 2014 FIFA World Cup, I found that there was no statistical difference between the proportion of correct guesses in each. My conclusion is that either the crowd’s guesses incorporated the betting odds information or the crowd’s guesses added up to the same result by some other means.</p>
<p>Generally, prediction markets use play money or are run by non-profit universities to study markets, elections and human decision making. Although gambling houses can take bets for many activities, <a href="https://www.legalsportsreport.com/74880/end-of-predictit-election-betting-around-the-corner/">external prediction markets are more restricted</a> in the activities they can be used to investigate, and are typically limited to elections. However, internal prediction markets – run within a corporation, for instance – <a href="https://doi.org/10.1007/978-3-319-07179-4_26">can explore almost any topic of interest</a>.</p>
<p>Typically, <a href="https://doi.org/10.1108/S1477-4070(2011)0000008014">prediction markets function better with informed participants</a>. Although using so-called inside information is illegal in some markets, including the New York Stock Exchange, there generally are no such limitations in prediction markets, or other crowdsourcing approaches. If those with inside information were to participate in a prediction market, it would likely lead to more accurate stock prices, as insiders make trades informed by their knowledge. However, if others <a href="https://doi.org/10.1016/j.accinf.2012.02.003">find out that a participant has inside information</a>, then they may very well try to gain access to that info, follow the insider’s actions or even decide to leave the unfair market.</p>
<p>The <a href="https://doi.org/10.1257/0895330041371321">accuracy of prediction markets</a> depends on many factors, including who is in the market, what their biases are and how heterogeneous the participants are. Accuracy can also depend on how many people are in the market – more is generally better – and the extent to which they are informed about the events of interest.</p>
<p>Researchers have found that prediction markets have <a href="https://doi.org/10.1016/j.ijforecast.2008.03.007">outperformed polls in presidential elections</a> roughly 75% of the time. But accurate results are not guaranteed. For example, <a href="https://www.bloomberg.com/opinion/articles/2016-11-15/prediction-markets-didn-t-call-trump-s-win-either">prediction markets did not correctly predict</a> that Donald Trump would win the U.S. presidency in 2016.</p>
<h2>Who will be in Stockholm for the ceremony?</h2>
<p>In 2011, Harvard University economics faculty had a real-money prediction market site, referred to as “the world’s most accurate prediction market.” The site had been used for predicting the Nobel Prize in Economics, but <a href="https://freakonomics.com/2011/10/harvard-shuts-down-its-nobel-prize-pool/">Harvard advised the site to shut down</a>.</p>
<p>I couldn’t find any current public prediction markets active for the 2022 Nobel Prizes.</p>
<p>For the moment, perhaps the closest to participating in a Nobel prediction market would be to place a bet at one of the gambling houses that <a href="https://www.gamblingsites.com/entertainment-betting/nobel-prize/">takes bets on the Nobel Prizes</a>. Or find a Nobel Prize Pick-Em site, propose such an event to an existing prediction market or build your own prediction market using <a href="https://www.cipher-sys.com/market-research-services">some of the available software</a>.</p>
<p>If you know of one, let me know, I want to play.</p><img src="https://counter.theconversation.com/content/191317/count.gif" alt="The Conversation" width="1" height="1" />
<p class="fine-print"><em><span>Daniel O'Leary does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.</span></em></p>Buying and selling stocks – with real or play money – is a way to harness the wisdom of the crowd about questions like who is going to win a competition.Daniel O'Leary, Professor of Accounting and Information Systems, University of Southern CaliforniaLicensed as Creative Commons – attribution, no derivatives.tag:theconversation.com,2011:article/1816722022-04-21T12:18:01Z2022-04-21T12:18:01ZAchoo! 5 essential reads for pollen season<figure><img src="https://images.theconversation.com/files/458973/original/file-20220420-14894-m6e6re.jpg?ixlib=rb-1.1.0&rect=0%2C0%2C6016%2C4007&q=45&auto=format&w=496&fit=clip" /><figcaption><span class="caption">Common hazel dispersing pollen in early spring. </span> <span class="attribution"><a class="source" href="https://www.gettyimages.com/detail/news-photo/common-hazel-close-up-of-male-catkins-dispersing-pollen-in-news-photo/971552142">Arterra/Universal Images Group via Getty Images</a></span></figcaption></figure><p>As spring expands across North America, trees, shrubs and flowers are releasing <a href="https://www.britannica.com/science/pollen">pollen</a>. This fine, powdery substance is produced by the male structures of cone-bearing and flowering plants. When it’s carried to the plants’ female structures by wind, water or pollinators, fertilization happens. </p>
<p>As pollen travels, it also triggers allergies in <a href="https://www.aafa.org/allergy-facts/#">some 25 million Americans</a>. Pollen exposure can cause sneezing, coughing, itchy eyes, runny nose and postnasal drip – unwelcome signs of spring for sufferers. This roundup of articles from our archives describes recent findings on protecting pollinators and coping with pollen season.</p>
<h2>1. Hey pollinators, over here</h2>
<p>Since pollen grains carry the cells that fertilize plants, it’s critical for them to get where they need to go. Often wind or gravity is all it takes, but for many plants, a pollinator has to carry the pollen grains. Some plants offer nectar or edible pollen to attract insects, bats or other animals, which carry pollen from plant to plant as they forage. Many flowers also <a href="https://theconversation.com/why-do-flowers-smell-151672">lure pollinators with scent</a>. </p>
<figure class="align-right zoomable">
<a href="https://images.theconversation.com/files/458976/original/file-20220420-25-8bves7.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=1000&fit=clip"><img alt="Bee flying, coated with bright yellow particles." src="https://images.theconversation.com/files/458976/original/file-20220420-25-8bves7.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=237&fit=clip" srcset="https://images.theconversation.com/files/458976/original/file-20220420-25-8bves7.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=467&fit=crop&dpr=1 600w, https://images.theconversation.com/files/458976/original/file-20220420-25-8bves7.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=467&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/458976/original/file-20220420-25-8bves7.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=467&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/458976/original/file-20220420-25-8bves7.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=586&fit=crop&dpr=1 754w, https://images.theconversation.com/files/458976/original/file-20220420-25-8bves7.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=586&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/458976/original/file-20220420-25-8bves7.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=586&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px"></a>
<figcaption>
<span class="caption">A thistle long-horned bee (<em>Melissodes desponsa</em>) covered with flower pollen.</span>
<span class="attribution"><a class="source" href="https://flic.kr/p/D8E563">Dejen Mengis, USGS</a></span>
</figcaption>
</figure>
<p>“Similar to the perfumes at a department store counter, flower scents are made up from a large and diverse number of chemicals which evaporate easily and float through the air,” writes Mississippi State University horticulturalist <a href="https://scholar.google.com/citations?user=dJ8gD7MAAAAJ&hl=en">Richard L. Harkess</a>. “To differentiate itself from other flowers, each species’ flowers put out a unique scent to attract specific pollinators. … Once pollinated, the flower stops producing a floral scent and nectar and redirects its energy to the fertilized embryo that will become the seed.”</p>
<hr>
<p>
<em>
<strong>
Read more:
<a href="https://theconversation.com/why-do-flowers-smell-151672">Why do flowers smell?</a>
</strong>
</em>
</p>
<hr>
<h2>2. Bees at the buffet</h2>
<p>It’s well known that many species of insects have <a href="https://theconversation.com/insect-apocalypse-not-so-fast-at-least-in-north-america-141107">declined in recent years</a>. One big focus is <a href="https://www.aphis.usda.gov/aphis/ourfocus/planthealth/plant-pest-and-disease-programs/honey-bees/honeybees">honeybees</a> and <a href="https://theconversation.com/beyond-honey-bees-wild-bees-are-also-key-pollinators-and-some-species-are-disappearing-89214">other species of bees</a>, which pollinate many important crops. </p>
<p>In a 2021 study, University of Florida agricultural extension specialist <a href="https://scholar.google.com/citations?user=I8IjAnIAAAAJ&hl=en">Hamutahl Cohen</a> found that when bees visited fields where sunflowers, grown as crops, were blooming over many acres, they <a href="https://theconversation.com/planting-mixes-of-flowers-around-farm-fields-helps-keep-bees-healthy-170527">picked up parasites at a high rate</a>. In contrast, bees that foraged in hedgerows around crop fields and could choose from diverse types of flowers to feed on spread out farther and had lower rates of infection. </p>
<figure class="align-center zoomable">
<a href="https://images.theconversation.com/files/458979/original/file-20220420-18-33gaht.jpeg?ixlib=rb-1.1.0&q=45&auto=format&w=1000&fit=clip"><img alt="Diverse shrubs in a planted border with inset photos of beneficial insects that they attract." src="https://images.theconversation.com/files/458979/original/file-20220420-18-33gaht.jpeg?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/458979/original/file-20220420-18-33gaht.jpeg?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=447&fit=crop&dpr=1 600w, https://images.theconversation.com/files/458979/original/file-20220420-18-33gaht.jpeg?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=447&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/458979/original/file-20220420-18-33gaht.jpeg?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=447&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/458979/original/file-20220420-18-33gaht.jpeg?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=561&fit=crop&dpr=1 754w, https://images.theconversation.com/files/458979/original/file-20220420-18-33gaht.jpeg?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=561&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/458979/original/file-20220420-18-33gaht.jpeg?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=561&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px"></a>
<figcaption>
<span class="caption">Hedgerows like this one in California have been shown to increase the number of beneficial insects like (left to right) lady beetles, syrphid flies and their larvae, shown feeding on aphids.</span>
<span class="attribution"><a class="source" href="https://ucanr.edu/sites/calagjournal/archive/?image=img6504p200.jpg">UCANR</a>, <a class="license" href="http://creativecommons.org/licenses/by-nd/4.0/">CC BY-ND</a></span>
</figcaption>
</figure>
<p>“The more bees in sunflower fields, the more parasites,” Cohen observed. “Sunflower blooms were aggregating bees, which in turn was amplifying disease risk.” However, “in the presence of many flower types, bees disperse and spread across resources, reducing each individual bee’s likelihood of encountering an infected individual.” </p>
<hr>
<p>
<em>
<strong>
Read more:
<a href="https://theconversation.com/planting-mixes-of-flowers-around-farm-fields-helps-keep-bees-healthy-170527">Planting mixes of flowers around farm fields helps keep bees healthy</a>
</strong>
</em>
</p>
<hr>
<h2>3. Warmer weather means more pollen</h2>
<p>As climate change raises average temperatures across the U.S., growing seasons are starting earlier and ending later in the year. That’s <a href="https://theconversation.com/pollen-season-is-getting-longer-and-more-intense-with-climate-change-heres-what-allergy-sufferers-can-expect-in-the-future-179158">bad news for allergy sufferers</a>. </p>
<p>“The higher temperature will extend the growing season, giving plants more time to emit pollen and reproduce,” write University of Michigan atmospheric scientists <a href="https://clasp.engin.umich.edu/people/zhang-yingxiao/">Yingxiao Zhang</a> and <a href="https://scholar.google.com/citations?user=3dWPwz8AAAAJ&hl=en">Allison L. Steiner</a>. And by increasing the concentration of carbon dioxide in Earth’s atmosphere, climate change will make it possible for plants to grow larger and generate more pollen. </p>
<p>“Southeastern regions, including Florida, Georgia and South Carolina, can expect large grass and weed pollen increases in the future. The Pacific Northwest is likely to see peak pollen season a month earlier because of the early pollen season of alder,” Zhang and Steiner report.</p>
<hr>
<p>
<em>
<strong>
Read more:
<a href="https://theconversation.com/pollen-season-is-getting-longer-and-more-intense-with-climate-change-heres-what-allergy-sufferers-can-expect-in-the-future-179158">Pollen season is getting longer and more intense with climate change – here's what allergy sufferers can expect in the future</a>
</strong>
</em>
</p>
<hr>
<p><div data-react-class="Tweet" data-react-props="{"tweetId":"1511731348821745683"}"></div></p>
<h2>4. Providing better forecasts</h2>
<p>With all that pollen out there, how can allergy sufferers know when counts are high? Today the U.S. has only a rudimentary network of 90 pollen observation stations across the country, staffed by volunteers and run only during pollen season, so often there isn’t good information available when people need it.</p>
<p><a href="https://scholar.google.com/citations?user=sUwveOEAAAAJ&hl=en">Fiona Lo</a>, an environmental health scientist at the University of Washington, is working with colleagues to develop a model that can predict airborne pollen releases. “Our forecast can predict for specific pollen types because our model includes information about how each plant type interacts differently with the environment,” Lo reports.</p>
<p>So far, the model only predicts levels of four types of common pollen in areas where there are observation stations. Ultimately, though, Lo and her collaborators “want to provide a forecast every day during pollen season to give allergy sufferers the information they need to manage their symptoms. Allergies are often undertreated, and knowledge about self-care is limited, so a reliable pollen forecast that is easy to access – for example, via an app on your phone – along with education on allergy management, could really help allergy sufferers.”</p>
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<strong>
Read more:
<a href="https://theconversation.com/sunny-with-a-chance-of-sneezing-im-building-a-tool-to-forecast-pollen-levels-that-will-help-allergy-sufferers-know-when-its-safe-to-go-outside-162073">Sunny with a chance of sneezing – I'm building a tool to forecast pollen levels that will help allergy sufferers know when it's safe to go outside</a>
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<h2>5. Support pollinators in your garden</h2>
<p>Pollen season is also gardening season, since it’s when plants are blooming. West Virginia University mycologist <a href="https://www.researchgate.net/profile/Brian-Lovett">Brian Lovett</a> offers advice for gardeners who want to <a href="https://theconversation.com/to-help-insects-make-them-welcome-in-your-garden-heres-how-153609">attract beneficial insects to their yards</a> for pollination and other purposes. </p>
<p>One step is to replace grass with native wildflowers, which will provide pollen and nectar for insects like ants, bees and butterflies. “Just as you may have a favorite local restaurant, insects that live around you have a taste for the flowers that are native to their areas,” Lovett notes.</p>
<figure class="align-center zoomable">
<a href="https://images.theconversation.com/files/458980/original/file-20220420-13790-30cq5q.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=1000&fit=clip"><img alt="Striped black and yellow butterfly feeding on purple flower" src="https://images.theconversation.com/files/458980/original/file-20220420-13790-30cq5q.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/458980/original/file-20220420-13790-30cq5q.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=400&fit=crop&dpr=1 600w, https://images.theconversation.com/files/458980/original/file-20220420-13790-30cq5q.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=400&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/458980/original/file-20220420-13790-30cq5q.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=400&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/458980/original/file-20220420-13790-30cq5q.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=503&fit=crop&dpr=1 754w, https://images.theconversation.com/files/458980/original/file-20220420-13790-30cq5q.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=503&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/458980/original/file-20220420-13790-30cq5q.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=503&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px"></a>
<figcaption>
<span class="caption">Swallowtail butterflies, shown here on a liatris flower in Washington state, are efficient pollinators that can be attracted to home gardens.</span>
<span class="attribution"><a class="source" href="https://www.gettyimages.com/detail/news-photo/swallowtail-butterfly-on-a-liatris-spicata-flower-in-july-news-photo/624174230">Wolfgang Kaehler/LightRocket via Getty Images</a></span>
</figcaption>
</figure>
<p>Replacing white lightbulbs with yellow or warm-hued LED bulbs, and providing water in dishes or other containers, are also insect-friendly steps. Local university extension offices and gardening stores can offer other suggestions. </p>
<p>“In my view, humans all too often see ourselves as separate from nature, which leads us to relegate biodiversity to designated parks,” Lovett observes. “In fact, however, we are an important part of the natural world, and we need insects just as much as they need us.”</p>
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<p>
<em>
<strong>
Read more:
<a href="https://theconversation.com/to-help-insects-make-them-welcome-in-your-garden-heres-how-153609">To help insects, make them welcome in your garden – here's how</a>
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<p>[<em>Get fascinating science, health and technology news.</em> <a href="https://memberservices.theconversation.com/newsletters/?nl=science&source=inline-science-fascinating">Sign up for The Conversation’s weekly science newsletter</a>.]</p><img src="https://counter.theconversation.com/content/181672/count.gif" alt="The Conversation" width="1" height="1" />
Pollen brings seasonal misery to millions of Americans, but it serves a critical purpose: fertilizing many kinds of plants, including food crops.Jennifer Weeks, Senior Environment + Cities Editor, The ConversationLicensed as Creative Commons – attribution, no derivatives.tag:theconversation.com,2011:article/1815562022-04-20T10:22:25Z2022-04-20T10:22:25ZEarly warnings for floods in South Africa: engineering for future climate change<figure><img src="https://images.theconversation.com/files/458802/original/file-20220420-19-acpci7.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=496&fit=clip" /><figcaption><span class="caption">If the forecast system works, African cities need significant on the ground support.</span> <span class="attribution"><span class="source">Shutterstock</span></span></figcaption></figure><p>Severe weather, rain and flooding are at the forefront of the minds of many South Africans, especially those in KwaZulu-Natal. In April 2022 the province’s coast received heavy rain, with some areas recording over <a href="https://earthobservatory.nasa.gov/images/149720/deluge-in-south-africa">300mm</a> in 24 hours. This is about a third of the annual rainfall in KwaZulu-Natal. </p>
<p>The rain was caused by a strong cut-off low weather system off the east coast of southern Africa. <a href="https://tinyurl.com/38z4acd5">Cut-off lows</a> frequently occur off this coast during the autumn months. These systems can cause localised flooding as well as <a href="http://www.scielo.org.za/scielo.php?script=sci_arttext&pid=S1021-20192012000200005">large wave events</a>. </p>
<p>The port city of Durban (in the municipality of eThekwini) has experienced flooding events almost every year since 2016. Storms responsible for these floods typically dropped 100mm-150mm of rain in 24 hours, according to the eThekwini data portal. </p>
<p>Between 11-12 April 2022 a storm system dropped even more rain than that. It caused devastating floods, landslides, and <a href="https://www.news24.com/news24/southafrica/news/live-rain-flooding-hit-kwazulu-natal-20220412">loss of life</a>. It is natural to ask whether the floods in the area will occur more often within the context of climate change or whether they were simply freak events. It is not possible to state conclusively that the storm was a result of climate change. However, scientists believe these types of intense weather systems will occur more frequently in the <a href="https://link.springer.com/article/10.1007/s10584-015-1573-1">future</a>. </p>
<p>Perhaps more pertinent is the question: did anyone see this coming? There isn’t a simple answer. It’s something that has been challenging scientists and engineers around the world for decades. For example scientists like myself have been trying to apply the principles of fluid mechanics to address these types of <a href="https://www.sciencedirect.com/science/article/pii/S0098300421000212">questions</a>.</p>
<p>This is important because if we can predict the occurrence of an event, we can arm disaster management teams with life-saving information. </p>
<h2>Predicting flooding</h2>
<p>Predicting flooding is not new and consists of two methods. The first relies on historical rainfall and flood records that come from <a href="https://www.researchgate.net/publication/307809059_South_African_Weather_Service_Operational_Satellite_Based_Precipitation_Estimation_Technique_Applications_and_Improvements">weather stations</a> and <a href="https://www.dws.gov.za/Hydrology/">river gauges</a>. South Africa has many of these stations throughout the country. It is vital that these stations are properly maintained. </p>
<p>The second method involves computer modelling. It is difficult to predict where rain will fall, how much will fall and whether this will cause flooding. These processes depend on spatial gradients that are not resolved in regional climate models. For example wind moves from high pressure to low pressure, sometimes bringing with it rain. However the wind speed (and how quickly the rain arrives) depends on the difference between the high and low pressure. This is typically very difficult to model accurately. <a href="https://journals.aps.org/prfluids/abstract/10.1103/PhysRevFluids.6.020501">Advances</a> in computer power will likely be able to address this in the future. </p>
<figure class="align-center zoomable">
<a href="https://images.theconversation.com/files/458681/original/file-20220419-13790-hzx4t2.png?ixlib=rb-1.1.0&q=45&auto=format&w=1000&fit=clip"><img alt="Map showing red zones meaning heavy rain." src="https://images.theconversation.com/files/458681/original/file-20220419-13790-hzx4t2.png?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/458681/original/file-20220419-13790-hzx4t2.png?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=454&fit=crop&dpr=1 600w, https://images.theconversation.com/files/458681/original/file-20220419-13790-hzx4t2.png?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=454&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/458681/original/file-20220419-13790-hzx4t2.png?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=454&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/458681/original/file-20220419-13790-hzx4t2.png?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=571&fit=crop&dpr=1 754w, https://images.theconversation.com/files/458681/original/file-20220419-13790-hzx4t2.png?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=571&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/458681/original/file-20220419-13790-hzx4t2.png?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=571&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px"></a>
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<span class="caption">The rain was caused by a strong cut-off low weather system off the east coast of southern Africa.</span>
<span class="attribution"><span class="source">NASA</span></span>
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</figure>
<p>Predicting where the water will flow once it reaches the ground is also challenging. Some water infiltrates the soil and flows as groundwater, while some water runs along the surface (called surface runoff). Both the surface and groundwater runoff contribute to the water flowing in rivers. If the ground is saturated, there is less infiltration and more surface water will flow into rivers, causing more flooding. Increased surface runoff also contributes to landslides and erosion. Hardened impermeable surfaces in cities and residential areas also cause increasing surface runoff. Storm duration is another factor that can influence flooding. </p>
<p>All these factors can combine to drive significant flood events. </p>
<p>How do engineers and city managers develop plans to respond in real-time to these events? A possible answer lies in developing forecast early warning systems. There are good examples in the <a href="https://www.deltares.nl/en/software/flood-forecasting-system-delft-fews-2/">Netherlands</a>. </p>
<h2>Forecast early warning system</h2>
<p>The <a href="http://www.durban.gov.za/Pages/default.aspx">eThekwini</a> coastal, stormwater and catchment management department has developed a forecast early warning system in conjunction with the <a href="https://ukzn.ac.za/">University of KwaZulu-Natal</a> and the Dutch engineering research institute <a href="https://www.deltares.nl/en/">Deltares</a>. The design incorporates weather forecasting, flood prediction and coastal modelling. The goal of the system is to alert authorities to severe weather to provide them with temporal and spatial information to guide decision making. An example of a system like this is the <a href="https://www.deltares.nl/en/projects/global-storm-surge-information-system-glossis/">Global Storm Surge Information System</a>.</p>
<p>The department’s engineers and managers have deployed hundreds of rain, weather, water level and coastal monitoring gauges throughout the region that provide authorities with real-time information. The system is still in a developmental stage and requires resource investment from national government, South African universities and local authorities. </p>
<p>The early warning system works by downscaling output from global modelling centres such as the <a href="https://www.ecmwf.int">European Centre for Medium-Range Weather Forecasts</a> and <a href="https://www.ncei.noaa.gov/products/weather-climate-models/global-forecast">National Centres for Environmental Prediction</a>. This data is fed into numerical models that predict flooding and coastal processes at regional and local scales.</p>
<p>The European Centre model did predict severe rain, about three days before the flood in 2022. The National Centres model did not. This highlights the difficulties in predicting weather and any decision making that follows. </p>
<p>Observations from the recent flood event suggest there is still a long way to improve and further develop the system. This will rely on improved data sharing between government departments, universities and communities. Currently this isn’t easy in South Africa, for policy reasons. Furthermore, government and local authorities must invest in city engineering staff and technical decision-makers.</p>
<p>Lastly, even if the forecast system works, African cities of the future need significant “on the ground” support in the form of disaster management teams such as police, rescue workers, paramedics and places of shelter.</p><img src="https://counter.theconversation.com/content/181556/count.gif" alt="The Conversation" width="1" height="1" />
<p class="fine-print"><em><span>Justin Pringle is a Y-rated researcher and receives funding from the National Research Foundation, UID: 127316. </span></em></p>Could South Africa’s flooding in 2022 have been foreseen? It has long been a challenge for scientists and engineers around the world.Justin Pringle, Senior Lecturer in Environmental Fluid Mechanics, University of KwaZulu-NatalLicensed as Creative Commons – attribution, no derivatives.tag:theconversation.com,2011:article/1757402022-02-01T14:22:23Z2022-02-01T14:22:23ZThe science of weather forecasting: what it takes and why it’s so hard to get right<figure><img src="https://images.theconversation.com/files/442675/original/file-20220126-17-1i0g402.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=496&fit=clip" /><figcaption><span class="caption">
</span> <span class="attribution"><span class="source">IgorZh/Shutterstock</span></span></figcaption></figure><p>Weather forecasting is an important science. Accurate forecasting can help to <a href="https://public.wmo.int/en/media/news/new-study-shows-socio-economic-benefits-of-weather-observations">save lives</a> and minimise property damage. It’s also crucial for agriculture, allowing farmers to track when it’s best to plant or helping them protect their crops. </p>
<p>And it will only become more vital in the coming years. Severe weather events are becoming <a href="https://public.wmo.int/en/media/press-release/weather-related-disasters-increase-over-past-50-years-causing-more-damage-fewer">more frequent and more intense</a> because of climate change and variability.</p>
<p>I am <a href="https://scholar.google.com/citations?hl=en&user=gsHLQ2gAAAAJ&view_op=list_works&sortby=pubdate">a meteorologist</a> with specialities in forecasting weather and climate change – who wants to improve the quality of weather products and their applications to spur socioeconomic development across Africa. Doing so matters: the World Bank has <a href="https://www.worldbank.org/en/news/feature/2017/09/12/improving-weather-forecasts-can-reduce-losses-to-development-in-africa">pointed out</a> that better weather forecasts can bolster the continent’s development. </p>
<p>So, how does forecasting work? What does it take to produce accurate, reliable and timely forecasts? And how can African countries do better on this front?</p>
<h2>A complex process</h2>
<p>Weather forecasting is complex and challenging. The process entails <a href="https://www.weather.gov/car/weatherforecasting">three steps</a>: observation, analysis and communication. </p>
<p>For observation, forecasters work with atmospheric models. These are sets of equations that depict the state of the atmosphere. The models use information on the initial state (observations) of the atmosphere, land and ocean to forecast weather. Data from the models is combined with information drawn from weather stations which are set up at key points across a region or country to give the actual state of the atmosphere. This <a href="https://link.springer.com/chapter/10.1007/978-94-010-0029-1_2">data assimilation</a> produces a better forecast since it optimises forecasters’ understanding of the evolving weather system. </p>
<p>It’s easier to be accurate when giving a short-range forecast – one that covers hours to days – than it is when interpreting long-range (months or seasons) data. The atmospheric system is dynamic; the more time that passes, the less certain forecasters can be of its state.</p>
<p>Technological advances have greatly improved the general quality of weather forecasting. For instance, more observations are possible because of <a href="https://www.earthnetworks.com/resources/weather-facts/automated-weather-stations/">automated weather stations</a>. There’s also been an increase in the use of <a href="https://www.usgs.gov/advanced-research-computing/what-high-performance-computing#:%7E:text=High%20Performance%20Computing%20most%20generally,science%2C%20engineering%2C%20or%20business">high performance computing</a>. This allows for more data storage, faster processing, analysis, and visualisation of incoming data.</p>
<p>These datasets are key in diagnosing past and current weather to create a forecast. Unfortunately, the data observation network (both manual and automated stations) is still poor, especially in developing countries. That’s the result of limited investment into the sector. Forecasters in these countries are forced to use alternative datasets that are not very accurate. </p>
<p>One such alternative dataset is <a href="https://www.ncei.noaa.gov/products/weather-climate-models/numerical-weather-prediction">Numerical Weather Prediction</a>. It uses global deterministic models that are normally not detailed enough to realistically represent <a href="https://www.metoffice.gov.uk/weather/learn-about/weather/how-weather-works/what-is-convection">convection</a> at a local or regional level; forecasters using this data often can’t accurately predict rainfall, especially heavy rain. A lack of access to better historical data also means forecasters struggle to identify when an area’s seasonal rainfall will start and end because they can’t examine trends over years or decades.</p>
<p>It’s these variations in access to data and technology that mean some forecasts are more accurate than others. </p>
<p>Once forecasts have been collated, they are released in various forms. The way that weather products – apps, TV and radio bulletins or website updates – are packaged will differ depending on end users’ needs. Some people, like farmers, may be especially interested in seasonal forecasts and will seek these out. Athletes, for example, are more likely to use portals or services that focus on hourly and daily forecasts. </p>
<p>I would recommend that, whoever you are, you consider seasonal forecasts general information for broad planning purposes. But this should be interpreted together with monthly, weekly and daily forecasts for accuracy’s sake.</p>
<h2>Indigenous knowledge</h2>
<p>Some African countries also use another kind of data for their forecasts: <a href="http://www.unesco.org/new/en/natural-sciences/priority-areas/links/related-information/what-is-local-and-indigenous-knowledge">indigenous ecological knowledge</a>. This entails drawing from communities’ long held knowledge about their environments, and especially about long-term trends and shifts. Such knowledge can be blended with scientific processes during forecasting.</p>
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<p>
<em>
<strong>
Read more:
<a href="https://theconversation.com/how-satellites-are-helping-africa-improve-weather-forecasts-45548">How satellites are helping Africa improve weather forecasts</a>
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<p>The <a href="https://nation.africa/kenya/business/seeds-of-gold/weatherman-rainmakers-team-up-to-offer-crop-farmers-accurate-forecast-1178750?view=htmlamp">“rainmakers”</a> from the Nganyi community in western Kenya are a good example. These residents have deep historical knowledge about the area’s climate and weather patterns. They use plants and animals to understand what the weather is doing. They now work with meteorologists from <a href="https://meteo.go.ke/">Kenya’s Meteorological Department</a> to produce seasonal weather forecasts.</p>
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<iframe width="440" height="260" src="https://www.youtube.com/embed/vyg4gAPqi4s?wmode=transparent&start=0" frameborder="0" allowfullscreen=""></iframe>
<figcaption><span class="caption">A trailer for a documentary about the “rainmakers” of Nganyi.</span></figcaption>
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<p>Indigenous knowledge is under threat as the elders who are its custodians are perishing. Vital plants and animals used in their processes are going extinct, too. It would be a great pity if this resource were lost to forecasters. This knowledge plays an important role in local livelihoods and it supports efforts to forecast and make sense of seasonal climate state at local scale.</p>
<h2>Changes coming</h2>
<p>Some of the ways that weather is forecast today may change in the coming years. The <a href="https://public.wmo.int/en">World Meteorological Organisation</a> is encouraging national meteorological services to move from what the weather will <em>be</em> (forecasting weather) to what the weather will <em>do</em> – <a href="https://public.wmo.int/en/resources/bulletin/impact-based-forecasting-and-warning-weather-ready-nations">impact based forecasting-and-warning</a>. </p>
<p>There’s also a push to ensure forecasts reach the people who need them. A number of African countries, among them <a href="https://link.springer.com/chapter/10.1007/978-3-030-61160-6_5">Malawi</a> and <a href="https://www.climate-chance.org/en/best-pratices/participatory-scenario-planning-psp-an-approach-to-translate-seasonal-climate-predictions-into-information-adapted-to-the-local-context/">Chad</a>, have adopted what’s known as Participatory Scenario Planning. This collaborative approach designs and delivers user focused climate information services by taking the co-production process down to the sub-national level. It brings together producers and users of weather and climate information – meteorologists, indigenous knowledge experts, researchers, various sectors of local government, farmers, as well as NGOs and journalists.</p>
<p>Private firms that provide global weather forecasts are also emerging. This is commendable given that they supplement the services of countries with limited resources. But my advice is that, where the national meteorological and hydrological centres have capacity to produce weather forecasts, theirs should be considered first, ahead of those generated by private firms. This is because national bodies’ forecasts are based on the observed historical and observed data which they are custodians of rather than private institutions that rely mainly on model data.</p><img src="https://counter.theconversation.com/content/175740/count.gif" alt="The Conversation" width="1" height="1" />
<p class="fine-print"><em><span>Victor Ongoma does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.</span></em></p>Weather forecasting is complex and challenging. The process entails three steps: observation, analysis and communication.Victor Ongoma, Assistant Professor, Université Mohammed VI PolytechniqueLicensed as Creative Commons – attribution, no derivatives.tag:theconversation.com,2011:article/1739042021-12-16T06:36:01Z2021-12-16T06:36:01ZHurricane-force wind gusts in Colorado, dust storms in Kansas, tornadoes in Iowa in December – here’s what fueled a day of extreme storms<figure><img src="https://images.theconversation.com/files/437954/original/file-20211216-23-w5ytwd.jpg?ixlib=rb-1.1.0&rect=0%2C0%2C1559%2C1059&q=45&auto=format&w=496&fit=clip" /><figcaption><span class="caption">A satellite view on the night of Dec. 15, 2021, at the same time tornadoes were reported in Iowa.</span> <span class="attribution"><a class="source" href="https://www.star.nesdis.noaa.gov/GOES/conus.php?sat=G16">NOAA</a></span></figcaption></figure><p><em>Extremely powerful winds swept across a large part of the U.S. on Dec. 15, 2021, <a href="https://twitter.com/NWSSPC/status/1471333229537873924">hitting several states with hurricane-force gusts</a>. Record temperatures helped generate <a href="https://www.desmoinesregister.com/story/weather/2021/12/15/iowa-high-wind-tornado-map-weather-damage-power-outage-updates/8901263002/">tornadoes in Iowa</a>, winds spread <a href="https://twitter.com/NWSWichita/status/1471248332886470657">grass fires</a> and <a href="https://twitter.com/KWCH12/status/1471203794641997826">dust clouds</a> in Kansas, and wind damage was reported from <a href="https://www.thedenverchannel.com/weather/weather-news/see-the-highest-wind-gusts-recorded-during-colorados-wednesday-wind-storm">Colorado</a> through the Midwest. The National Weather Service described it as a “historical weather day” with a “<a href="https://twitter.com/NWS/status/1471117913767743495">never-before-seen storm outlook</a>.” A meteorologist with the service later <a href="https://www.radioiowa.com/2021/12/17/wednesdays-storm-is-derecho-part-two-for-iowa/">said it qualified as a “serial derecho”</a> – the powerful storm had winds over 80 mph but more spread out than the intense <a href="https://theconversation.com/what-is-a-derecho-an-atmospheric-scientist-explains-these-rare-but-dangerous-storm-systems-140319">derecho that hit Iowa in 2020</a>. At least <a href="https://apnews.com/article/tornadoes-iowa-nebraska-storms-kansas-ee9378aae1cb74911e0f9842a01ccaa1">five people died</a> in the storm, the Associated Press reported.</em></p>
<p><em>We asked <a href="https://ge-at.iastate.edu/directory/william-gallus/">atmospheric scientist William Gallus</a>, whose office at Iowa State University was at the heart of the storms, to explain what caused the extreme weather and why it was so unusual.</em></p>
<h2>What happened in the atmosphere to trigger such extreme weather over such a wide area?</h2>
<p>We were seeing very strong winds because of a very powerful disturbance in the <a href="https://www.weather.gov/jetstream/jet">jet stream</a>. That disturbance helped to create a very intense <a href="https://scijinks.gov/high-and-low-pressure-systems/">low-pressure system</a>, which creates strong winds and storms. But the low pressure wasn’t what made this event unusual.</p>
<p>It was unprecedented because an incredible amount of warm air got <a href="https://twitter.com/BMcNoldy/status/1471151049016688640/photo/1">pulled up from the south</a> ahead of the storm.</p>
<p>Here in Iowa, <a href="https://twitter.com/NWSWPC/status/1471307948945285123">temperatures were the hottest</a> they’ve ever been in December, with <a href="https://twitter.com/JerryWVTM13/status/1471221081000267783">temperatures in the mid 70s</a> on Dec. 15, and a very unusual amount of humidity came up with those temperatures. That’s why we were seeing <a href="https://twitter.com/NWSDesMoines/status/1471268524849078280">tornado warnings</a> <a href="https://twitter.com/NWSDesMoines/status/1471265167610572800">across the region</a> – and reports of <a href="https://www.desmoinesregister.com/story/weather/2021/12/15/iowa-high-wind-tornado-map-weather-damage-power-outage-updates/8901263002/">tornado damage</a>. </p>
<p>Tornadoes are <a href="https://twitter.com/capitalweather/status/1471281957569011713">extremely rare in Iowa in December</a>. Minnesota, which had never had a tornado in December, also had tornado warnings and <a href="https://twitter.com/NWSTwinCities/status/1471319877390508036">a possible sighting</a>.</p>
<p><div data-react-class="Tweet" data-react-props="{"tweetId":"1471258306903687169"}"></div></p>
<p>The wind speeds with this particular system were about as strong as we’ve seen. But it was all of the other weather parameters coming together in December that put this storm system off the scale.</p>
<p>With the warm moist air, we also had thunderstorms, and thunderstorms tend to make the winds even stronger. If you went up 1,000 feet in the sky, you would find it’s much <a href="https://news.stanford.edu/news/2009/june24/high-altitude-winds-062309.html">windier up there</a>. When you have thunderstorms, the rain helps create a current of wind that goes downward, which we call a downdraft. If you have this downdraft, it tends to carry the really strong winds down to the ground. Thunderstorms in the conditions we were seeing could bring winds that could easily get over 80 mph.</p>
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<p>
<em>
<strong>
Read more:
<a href="https://theconversation.com/what-is-a-derecho-an-atmospheric-scientist-explains-these-rare-but-dangerous-storm-systems-140319">What is a derecho? An atmospheric scientist explains these rare but dangerous storm systems</a>
</strong>
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</p>
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<h2>Colorado saw wind gusts over 100 mph. What happened there?</h2>
<p>In Colorado, the mountains also help accelerate the wind. </p>
<p>The winds have to rise over the Rocky Mountains. If you get a <a href="https://www.weather.gov/media/lzk/inversion101.pdf">temperature inversion</a>, where the temperature actually starts to go up rather than down as you get higher in the atmosphere just above the top of the mountains, it can act like a lid that traps the momentum of the wind going over the mountains. The wind can’t really spread out, so instead it rushes downward once you’re on the east side of the mountains.</p>
<p><div data-react-class="Tweet" data-react-props="{"tweetId":"1471158494787108866"}"></div></p>
<p>With anything going downward, gravity accelerates it, just like if you drop a ball from the top of a skyscraper. The <a href="https://manoa.hawaii.edu/exploringourfluidearth/physical/atmospheric-effects/wind-formation">same thing happens to these winds</a>. As they flow down the east face of the Rockies, they accelerate. </p>
<p>When you’re on the leeward side of a mountain range, like Denver and Boulder, winds in those areas can <a href="https://twitter.com/NWSWPC/status/1471226860499525635">get really strong</a> as they’re descending.</p>
<h2>What role does the jet stream play in a storm like this?</h2>
<p>When we get a low-pressure system, it’s because of wiggles that are happening in the <a href="https://www.weather.gov/jetstream/jet">jet steam</a>. We call them troughs in meteorology.</p>
<figure class="align-right ">
<img alt="Image of the jet stream, looking like a wave" src="https://images.theconversation.com/files/437930/original/file-20211216-25-u8aefo.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=237&fit=clip" srcset="https://images.theconversation.com/files/437930/original/file-20211216-25-u8aefo.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=480&fit=crop&dpr=1 600w, https://images.theconversation.com/files/437930/original/file-20211216-25-u8aefo.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=480&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/437930/original/file-20211216-25-u8aefo.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=480&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/437930/original/file-20211216-25-u8aefo.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=603&fit=crop&dpr=1 754w, https://images.theconversation.com/files/437930/original/file-20211216-25-u8aefo.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=603&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/437930/original/file-20211216-25-u8aefo.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=603&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px">
<figcaption>
<span class="caption">An visualization of the jet stream.</span>
<span class="attribution"><a class="source" href="https://svs.gsfc.nasa.gov/3864">Trent L. Schindler/NASA Scientific Visualization Studio</a></span>
</figcaption>
</figure>
<p>If you look at a map showing the jet stream, the jet stream <a href="https://svs.gsfc.nasa.gov/3864">looks like a roller coaster</a> – it oscillates up and down, from north to south. Any time you’re out ahead of one of these troughs, where the jet stream bends down toward the south and then back toward the north, the air must rise out ahead of it, and this results in a low-pressure system. The winds that blow around it can become very strong. </p>
<p>In this case, there was an especially sharp trough in the jet stream, almost in a “V” shape, that intensified the effect. </p>
<figure>
<iframe width="440" height="260" src="https://www.youtube.com/embed/o203JXAnSA0?wmode=transparent&start=0" frameborder="0" allowfullscreen=""></iframe>
<figcaption><span class="caption">What is the jet stream?</span></figcaption>
</figure>
<h2>Is there a connection between this storm and the deadly tornadoes that hit Kentucky and other states on Dec. 10-11?</h2>
<p>It’s hard to say if there was one trigger somewhere on the planet that managed to create these two different ripples in the jet stream.</p>
<p>What’s interesting is that there is <a href="https://www.climate.gov/news-features/blogs/enso/december-2021-la-ni%C3%B1a-update-visual-aids">La Niña going on in the Pacific Ocean</a>. When we have La Niña conditions, we often find that the far northern part of the United States ends up colder than normal and the south ends up warmer, so you have this bigger contrast in temperatures than normal and it often leads to a stronger jet stream.</p>
<hr>
<p>
<em>
<strong>
Read more:
<a href="https://theconversation.com/tornadoes-and-climate-change-what-a-warming-world-means-for-deadly-twisters-and-the-type-of-storms-that-spawn-them-173645">Tornadoes and climate change: What a warming world means for deadly twisters and the type of storms that spawn them</a>
</strong>
</em>
</p>
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<p>[<em>Over 140,000 readers rely on The Conversation’s newsletters to understand the world.</em> <a href="https://memberservices.theconversation.com/newsletters/?source=inline-140ksignup">Sign up today</a>.]</p>
<p><em>This article was updated with a National Weather Service meteorologist describing the storm as a “serial derecho.”</em></p><img src="https://counter.theconversation.com/content/173904/count.gif" alt="The Conversation" width="1" height="1" />
<p class="fine-print"><em><span>William Gallus does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.</span></em></p>Forecasters described it as a ‘historical weather day.’ An atmospheric scientist who was at the heart of the storms explains what happened.William Gallus, Professor of Atmospheric Science, Iowa State UniversityLicensed as Creative Commons – attribution, no derivatives.tag:theconversation.com,2011:article/1694232021-10-07T00:36:47Z2021-10-07T00:36:47ZWhat is chaos? A complex systems scientist explains<figure><img src="https://images.theconversation.com/files/425107/original/file-20211006-27-6s91r0.jpg?ixlib=rb-1.1.0&rect=241%2C0%2C7245%2C4765&q=45&auto=format&w=496&fit=clip" /><figcaption><span class="caption">Tiny changes, like a butterfly's wing flapping, can be amplified downstream in a chaotic system.</span> <span class="attribution"><a class="source" href="https://www.gettyimages.com/detail/photo/paper-butterflies-royalty-free-image/1225435005">Catherine Falls Commercial/Moment via Getty Images</a></span></figcaption></figure><p>Chaos evokes images of the dinosaurs running wild in Jurassic Park, or my friend’s toddler ravaging the living room.</p>
<p>In a chaotic world, you never know what to expect. Stuff is happening all the time, driven by any kind of random impulse.</p>
<p>But chaos has a deeper meaning in connection to physics and climate science, related to how certain systems – like the weather or the behavior of a toddler – are fundamentally unpredictable.</p>
<p>Scientists define chaos as the amplified effects of tiny changes in the present moment that lead to long-term unpredictability. Picture two almost identical storylines. In one version, two people bump into each other in a train station; but in the other, the train arrives 10 seconds earlier and the meeting never happens. From then on, the two plot lines might be totally different.</p>
<figure class="align-center zoomable">
<a href="https://images.theconversation.com/files/425131/original/file-20211006-23-fbedlp.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=1000&fit=clip"><img alt="busy indoor train terminal" src="https://images.theconversation.com/files/425131/original/file-20211006-23-fbedlp.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/425131/original/file-20211006-23-fbedlp.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=400&fit=crop&dpr=1 600w, https://images.theconversation.com/files/425131/original/file-20211006-23-fbedlp.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=400&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/425131/original/file-20211006-23-fbedlp.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=400&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/425131/original/file-20211006-23-fbedlp.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=503&fit=crop&dpr=1 754w, https://images.theconversation.com/files/425131/original/file-20211006-23-fbedlp.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=503&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/425131/original/file-20211006-23-fbedlp.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=503&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px"></a>
<figcaption>
<span class="caption">Who doesn’t meet in the crowd if the train arrives a few seconds sooner?</span>
<span class="attribution"><a class="source" href="https://www.gettyimages.com/detail/photo/motion-at-liverpool-street-station-royalty-free-image/157731956">urbancow/E+ via Getty Images</a></span>
</figcaption>
</figure>
<p>Usually those little details don’t matter, but sometimes tiny differences have consequences that keep compounding. And that compounding is what leads to chaos.</p>
<p>A shocking series of discoveries in the 1960s and ‘70s showed just how easy it is to create chaos. Nothing could be more predictable than the swinging pendulum of a grandfather clock. But if you separate a pendulum halfway down by adding another axle, the swinging becomes <a href="https://www.youtube.com/watch?v=d0Z8wLLPNE0">wildly unpredictable</a>.</p>
<h2>Chaos is different from random</h2>
<p>As <a href="https://scholar.google.com/citations?user=suSGxQ8AAAAJ&hl=en&oi=ao">a complex systems scientist</a>, I think a lot about <a href="https://www.theatlantic.com/science/archive/2017/11/drove-not-drived/544595/">what is random</a>.</p>
<p>What’s the difference between a pack of cards and the weather?</p>
<p>You can’t predict your next poker hand – if you could, they’d throw you out of the casino – whereas you can probably guess tomorrow’s weather. But what about the weather two weeks from now? Or a year from now?</p>
<p>Randomness, like cards or dice, is unpredictable because we just don’t have the right information. Chaos is somewhere between random and predictable. A hallmark of chaotic systems is predictability in the short term that breaks down quickly over time, as in river rapids or <a href="https://doi.org/10.2307/1940591">ecosystems</a>.</p>
<figure class="align-center zoomable">
<a href="https://images.theconversation.com/files/425109/original/file-20211006-23-182joa5.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=1000&fit=clip"><img alt="panels of a shaded road through four seasons" src="https://images.theconversation.com/files/425109/original/file-20211006-23-182joa5.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/425109/original/file-20211006-23-182joa5.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=210&fit=crop&dpr=1 600w, https://images.theconversation.com/files/425109/original/file-20211006-23-182joa5.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=210&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/425109/original/file-20211006-23-182joa5.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=210&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/425109/original/file-20211006-23-182joa5.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=264&fit=crop&dpr=1 754w, https://images.theconversation.com/files/425109/original/file-20211006-23-182joa5.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=264&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/425109/original/file-20211006-23-182joa5.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=264&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px"></a>
<figcaption>
<span class="caption">Chaos can explain why climate is predictable while weather isn’t.</span>
<span class="attribution"><a class="source" href="https://www.gettyimages.com/detail/photo/seasons-in-a-park-royalty-free-image/1160011332">Sören Lubitz Photography/Moment via Getty Images</a></span>
</figcaption>
</figure>
<h2>Why chaos theory matters</h2>
<p>Isaac Newton envisioned physics as a set of rules governing a <a href="https://en.wikipedia.org/wiki/Clockwork_universe">clockwork universe</a> – rules that, once set in motion, would lead to a predetermined outcome. But chaos theory proves that even the <a href="https://en.wikipedia.org/wiki/Butterfly_effect">strictest rules and nearly perfect information can lead</a> to unpredictable outcomes.</p>
<p>This realization has practical applications for deciding what kinds of things are predictable at all. Chaos is why no weather app can tell you the weather two weeks from now – it’s just impossible to know.</p>
<p>On the other hand, broader predictions can still be possible. We can’t forecast the weather a year from now, but we still know what the weather is like this time of year. That’s how <a href="https://theconversation.com/warming-is-clearly-visible-in-new-us-climate-normal-datasets-159684">climate can be predictable</a> even when the weather isn’t. Theories of chaos and randomness help scientists sort out which kinds of predictions make sense and which don’t.</p>
<p><em>Read other short accessible explanations of newsworthy subjects written by academics in their areas of expertise for The Conversation U.S. <a href="https://theconversation.com/us/topics/significant-terms-105996">here</a>.</em></p><img src="https://counter.theconversation.com/content/169423/count.gif" alt="The Conversation" width="1" height="1" />
<p class="fine-print"><em><span>Mitchell Newberry does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.</span></em></p>Part of the 2021 Nobel Prize in Physics was awarded for work modeling Earth’s climate using its chaotic, complex weather. To scientists, chaos lies in the gray zone between randomness and predictability.Mitchell Newberry, Assistant Professor of Complex Systems, University of MichiganLicensed as Creative Commons – attribution, no derivatives.tag:theconversation.com,2011:article/1616462021-05-31T08:12:15Z2021-05-31T08:12:15ZSuperforecasters: what pandemic planners can learn from the world’s best predictors<p>Experts got it catastrophically wrong, according to Dominic Cummings, UK prime minister Boris Johnson’s former chief adviser. <a href="https://theconversation.com/dominic-cummings-evidence-five-key-questions-that-must-be-investigated-161683">Cummings has argued</a> that the UK government’s official scientific advice in March 2020 hugely misunderstood how the pandemic would play out, leading to a delay in locking down that cost thousands of lives. </p>
<p>According to Cummings, it was certain specialists with less knowledge of pandemics or medicine – such as data scientist Ben Warner, artificial intelligence researcher Demis Hassabis of DeepMind, and mathematician Tim Gowers – who gave more accurate forecasts at this point. </p>
<iframe id="noa-web-audio-player" style="border: none" src="https://embed-player.newsoveraudio.com/v4?key=x84olp&id=https://theconversation.com/superforecasters-what-pandemic-planners-can-learn-from-the-worlds-best-predictors-161646&bgColor=F5F5F5&color=D8352A&playColor=D8352A" width="100%" height="110px"></iframe>
<p>Cummings is also known to be a <a href="https://www.economist.com/science-and-technology/2021/04/15/how-spooks-are-turning-to-superforecasting-in-the-cosmic-bazaar">fan</a> of <em>Superforecasting</em> by Philip Tetlock, a book about people who predict future events more reliably than most. Some superforecasters <a href="https://time.com/5848271/superforecasters-covid-19/">have been praised</a> for their predictions about the pandemic, while others have also been <a href="https://unherd.com/2020/10/how-the-experts-messed-up-on-covid/">critical of the experts’</a> record.</p>
<p>So should governments make greater use of superforecasters instead of relying on scientific experts? The evidence isn’t quite that clear cut. But there certainly seem to be things governments could learn from superforecasting.</p>
<p>In a <a href="https://journals.sagepub.com/doi/epub/10.1177/0956797614524255">famous American study</a> on superforecasters published in 2014, they were an elite crew. Only the top 2% of contenders performed well enough in a geopolitical forecasting tournament to win the title. Their task was to assign probabilities to possible answers to dozens of questions. </p>
<p>The researchers provide a few illustrative examples. Who would be the president of Russia in 2012? Will North Korea detonate another nuclear weapon in the next three months? How many refugees will flee Syria next year?</p>
<p>Of course, just because someone does well one year doesn’t prove that they’re more skilled than anyone else. Maybe they just got lucky. We have to look at how well they did in the following years to evaluate how “super” they really are.</p>
<p>Impressively, these superforecasters maintained their edge as the tournament wore on for three more years. In fact, after being combined into “superforecasting teams” containing only other top performers, their performance increased by a substantial margin. The researchers also found that working in teams and taking relevant training improved performance for other forecasters, compared to forecasters in a control condition.</p>
<h2>Teams and training</h2>
<p>Whether or not we take Cummings at his word that the UK’s pandemic planning suffered from a “classic group-think bubble”, we know that teams don’t always make wise decisions. What was it that made the teams more successful in the US study? </p>
<p>It’s hard to say for sure, but the researchers specifically encouraged the teams to ask precise questions to encourage clearer thinking about the evidence supporting a particular forecast, to “look for evidence that contradicts your current prediction”, and to constructively introduce alternative points of view. </p>
<p>Such debate may well <a href="https://www.nature.com/articles/s41562-017-0273-4">improve collective judgment</a> and <a href="https://journals.sagepub.com/doi/abs/10.1177/104973239700700407">guard against groupthink</a>. Nor were team members required to come to a consensus. Although they shared information and opinions, they still made individual predictions which were combined by algorithm. Superforecaster teams in particular were <a href="https://journals.sagepub.com/doi/full/10.1177/1745691615577794">highly engaged</a>, frequently sharing information with and asking questions of other team members. </p>
<p><a href="https://www.sas.upenn.edu/%7Ebaron/journal/16/16511/jdm16511.pdf">Another study</a> took a closer look at which specific training techniques seemed to help most. Three techniques were particularly associated with higher accuracy. The first was the use of so-called comparison classes. </p>
<p>For example, if I am trying to predict the probability that Benedict Cumberbatch and Sophie Hunter will still be together in five years, it can be helpful to think about other “classes” that are relevant – say, the class of celebrity marriages, or even marriages in general. This allows me to look to history to inform my predictions: What percentage of celebrity marriages end in any given five-year period? </p>
<p>The second was to make use of mathematical and statistical models, when available, to help inform one’s views. The third was to “select the right questions” – a recommendation to spend more time predicting answers to questions where you know more than others about the topic, or on which additional research is likely to pay off. However, the researchers stressed that all components of <a href="https://cpb-us-e1.wpmucdn.com/sites.dartmouth.edu/dist/0/433/files/2014/06/ISQ-Supplement-Revised-2-20-17.docx">the training</a> may have contributed holistically to better performance.</p>
<p>Research has also shown that <a href="https://www.semanticscholar.org/paper/The-effects-of-feedback-and-training-on-the-of-Benson-%C3%96nkal/ec3b6960526eeed863ff8c5af80130bc07f903f5">accuracy improves when we keep track of our past performance</a> – but the kind of feedback matters. Did outcomes you thought would happen 20% of the time actually happen 20% of the time? What about outcomes you thought would happen 90% of the time? Performance improves for those who receive this kind of information.</p>
<h2>Can governments do better?</h2>
<p>Could the UK government have done better on COVID-19 by soliciting input from teams of superforecasters? It’s possible. Superforecasters at <a href="https://www.gjopen.com/">Good Judgment Open</a> and practised forecasters at <a href="https://www.metaculus.com/questions/">Metaculus</a> (in which I’ve participated) each seem to have done well on COVID-19, with Metaculus claiming to have <a href="https://www.metaculus.com/news/2020/06/02/LRT/">outperformed experts</a> in June 2020. That said, in a <a href="https://github.com/computationalUncertaintyLab/aggStatModelsAndHumanJudgment_PUBL/blob/main/summaryreports/summaryReport03/MetaAndConsensusForecastOfCOVID-19Targets_Mar_v0.4.pdf">recent series</a> of COVID-19-related predictions, trained forecasters were not always more accurate than experts. The researchers behind the survey are experimenting with ways of combining predictions from domain experts and trained forecasters into a “consensus forecast”.</p>
<p>It also seems plausible that even the training that helped the non-supers make better forecasts would have been useful. For example, Cummings claimed that although there was much attention to epidemiological models, evidence that contradicted the models’ assumptions – such as data being reported by intensive care units – was ignored. It certainly seems plausible that someone trained to “look for evidence that contradicts your current prediction” might have spotted this earlier.</p>
<p>Of course, not all recommendations from the literature are practical in government settings. In theory, governments could test such recommendations for themselves, adopting any that seemed beneficial. Unfortunately, you can’t improve what you don’t measure. </p>
<p>In <em>Superforecasting</em>, Tetlock emphasises that any organisation serious about improving its forecasts must attach concrete numbers to them, at least internally. A phrase like “serious possibility” may mean a 20% chance to one person and an 80% chance to another. </p>
<p>This is almost certainly what Cummings was referring to <a href="https://youtu.be/8LFS3FaRs_s?t=4380">when he said</a>: “A guy called Phil Tetlock wrote a book and in that book he said that you should not use words like reasonable and probable and likely, because it confuses everybody.” Perhaps it shouldn’t surprise us if organisations that do not make forecasts in a way that they can be evaluated are not equipped to learn how to make them better. To improve, you first have to try.</p><img src="https://counter.theconversation.com/content/161646/count.gif" alt="The Conversation" width="1" height="1" />
<p class="fine-print"><em><span>Gabriel Recchia participates in forecasts on the Metaculus platform mentioned in the article.</span></em></p>Some so-called superforecasters are claimed to have predicted the course of the pandemic better than scientific experts.Gabriel Recchia, Research Associate, Winton Centre for Risk and Evidence Communication, University of CambridgeLicensed as Creative Commons – attribution, no derivatives.tag:theconversation.com,2011:article/1607652021-05-27T12:04:54Z2021-05-27T12:04:54ZSome coastal areas are more prone to devastating hurricanes – a meteorologist explains why<figure><img src="https://images.theconversation.com/files/402981/original/file-20210526-13-cgj0pz.jpg?ixlib=rb-1.1.0&rect=0%2C33%2C2041%2C1299&q=45&auto=format&w=496&fit=clip" /><figcaption><span class="caption">Preparing for a hurricane on North Carolina's vulnerable Outer Banks.</span> <span class="attribution"><a class="source" href="https://www.gettyimages.com/detail/news-photo/hurricane-veteran-todd-liston-measures-for-plywood-and-news-photo/2500150?adppopup=true">Paul J. Richards/AFP via Getty Images</a></span></figcaption></figure><p>Every coastline in the North Atlantic is vulnerable to tropical storms, but some areas are <a href="https://www.aoml.noaa.gov/hrd-faq/#landfalls-by-state">more susceptible to hurricane destruction</a> than others. </p>
<p>To understand why, let’s look more closely at how tropical storms form and what turns them into destructive monsters. </p>
<h2>Ingredients of a hurricane</h2>
<p>Three key ingredients are needed for a hurricane to form: warm sea surface water that’s at least about <a href="https://oceanexplorer.noaa.gov/facts/hurricanes.html">80 degrees Fahrenheit</a> (26.5 C), a thick layer of moisture extending from the sea surface to roughly 20,000 feet and minimal vertical <a href="https://www.wunderground.com/hurricane/articles/wind-shear-explainer">wind shear</a> so the thunderstorm can grow vertically without interruption. </p>
<p>These prime conditions are often found in the tropical waters off the west coast of Africa.</p>
<figure class="align-center zoomable">
<a href="https://images.theconversation.com/files/402449/original/file-20210524-21-2hp4op.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=1000&fit=clip"><img alt="Storm tracks show the curved track from Africa into the Gulf of Mexico and out to the ocean." src="https://images.theconversation.com/files/402449/original/file-20210524-21-2hp4op.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/402449/original/file-20210524-21-2hp4op.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=371&fit=crop&dpr=1 600w, https://images.theconversation.com/files/402449/original/file-20210524-21-2hp4op.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=371&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/402449/original/file-20210524-21-2hp4op.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=371&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/402449/original/file-20210524-21-2hp4op.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=466&fit=crop&dpr=1 754w, https://images.theconversation.com/files/402449/original/file-20210524-21-2hp4op.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=466&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/402449/original/file-20210524-21-2hp4op.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=466&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px"></a>
<figcaption>
<span class="caption">Twenty-five years of Atlantic tropical storm tracks, ranging from tropical depressions in dark blue to hurricanes in yellows and reds.</span>
<span class="attribution"><a class="source" href="https://commons.wikimedia.org/wiki/File:Atlantic_hurricane_tracks_1980-2005.jpg">Nilfanion</a></span>
</figcaption>
</figure>
<p>Hurricanes can also form in the Gulf of Mexico and the Caribbean, but the ones that start close to Africa have thousands of miles of warm water ahead that they can <a href="https://ocean.si.edu/planet-ocean/waves-storms-tsunamis/hurricanes-typhoons-and-cyclones">draw energy from</a> as they travel. That energy can help them grow into powerful hurricanes.</p>
<p>Wind currents set most tropical storms on a course westward from Africa toward the Caribbean, Florida and the Gulf of Mexico. Some drift northward into the midlatitudes, where the prevailing winds shift from west to east and cause them to curve back out into the Atlantic. </p>
<p>Others encounter cooler ocean temperatures that rob them of fuel, or high wind shear that breaks them apart. That’s why tropical cyclones rarely hit northern states or Europe, though it does happen.</p>
<figure class="align-center ">
<img alt="Map showing return periods for coastal counties" src="https://images.theconversation.com/files/402461/original/file-20210524-17-6m88i6.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/402461/original/file-20210524-17-6m88i6.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=377&fit=crop&dpr=1 600w, https://images.theconversation.com/files/402461/original/file-20210524-17-6m88i6.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=377&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/402461/original/file-20210524-17-6m88i6.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=377&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/402461/original/file-20210524-17-6m88i6.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=474&fit=crop&dpr=1 754w, https://images.theconversation.com/files/402461/original/file-20210524-17-6m88i6.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=474&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/402461/original/file-20210524-17-6m88i6.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=474&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px">
<figcaption>
<span class="caption">The numbers shown here reflect how often a hurricane would be expected within 50 nautical miles. The red dots suggest a hurricane every five to seven years.</span>
<span class="attribution"><a class="source" href="https://www.nhc.noaa.gov/climo/">NOAA</a></span>
</figcaption>
</figure>
<h2>Time of season also influences hurricane paths</h2>
<p><a href="https://www.nhc.noaa.gov/climo/">Early in the season</a>, in June and July, sea surface temperatures are still warming and atmospheric wind shear slowly decreases across the open Atlantic. Most early-season hurricanes develop in a small area of the Caribbean and Gulf of Mexico where prime conditions begin early. </p>
<p>They typically form close to land, so coastal residents don’t have much time to prepare, but these storms also don’t have ideal conditions to gain strength. Texas, Louisiana and Mississippi, as well as Central America, are more likely to see hurricane strikes early in the season, as the trade winds favor an east-to-west motion. </p>
<p><a href="https://www.nnvl.noaa.gov/view/globaldata.html#SSTA">As surface waters gain heat</a> over the summer, hurricane frequency and severity begin to increase, especially into the peak hurricane months of August through October. </p>
<p>Toward the end of the season, trade winds begin to shift from west to east, ocean temperatures start to fall, and cold fronts can help divert storms away from the western Gulf and <a href="https://www.nhc.noaa.gov/data/tcr/AL142018_Michael.pdf">push them toward the Florida Panhandle</a>.</p>
<figure class="align-center ">
<img alt="Maps of storm activity by month" src="https://images.theconversation.com/files/402456/original/file-20210524-23-6l9fq3.png?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/402456/original/file-20210524-23-6l9fq3.png?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=458&fit=crop&dpr=1 600w, https://images.theconversation.com/files/402456/original/file-20210524-23-6l9fq3.png?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=458&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/402456/original/file-20210524-23-6l9fq3.png?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=458&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/402456/original/file-20210524-23-6l9fq3.png?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=576&fit=crop&dpr=1 754w, https://images.theconversation.com/files/402456/original/file-20210524-23-6l9fq3.png?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=576&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/402456/original/file-20210524-23-6l9fq3.png?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=576&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px">
<figcaption>
<span class="caption">The busiest areas during each month of hurricane season.</span>
<span class="attribution"><a class="source" href="https://www.weather.gov/images/mob/climate/May2018Climate/TC_AreasofOrigin.PNG">NOAA</a></span>
</figcaption>
</figure>
<h2>Shape of the seafloor matters for destructiveness</h2>
<p>The shape of the seafloor can also play a role in how destructive hurricanes become.</p>
<p>Hurricane strength is currently measured solely on a storm’s <a href="https://www.nhc.noaa.gov/aboutsshws.php">maximum sustained wind speeds</a>. But hurricanes also displace ocean water, creating a surge of high water that their winds push toward shore ahead of the storm.</p>
<p><a href="https://www.nhc.noaa.gov/surge/?text">This storm surge</a> is <a href="https://doi.org/10.1175/BAMS-D-12-00074.1">often the greatest threat to life and property</a> from a hurricane, accounting for about 49% of all direct fatalities between 1963 and 2012. Hurricane Katrina (2005) is a prime example: An estimated 1,500 people lost their lives when Katrina hit New Orleans, many of them in the storm surge flooding.</p>
<p>If the continental shelf where the hurricane hits is shallow and slopes gently, it generally produces a greater storm surge than a steeper shelf.</p>
<figure>
<iframe width="440" height="260" src="https://www.youtube.com/embed/TbHO1mWHKq4?wmode=transparent&start=0" frameborder="0" allowfullscreen=""></iframe>
<figcaption><span class="caption">How the shape of the seafloor affects a hurricane storm surge.</span></figcaption>
</figure>
<p>As a result, a major hurricane hitting the Texas and Louisiana Gulf Coast – which has a very wide and shallow <a href="https://www.nhc.noaa.gov/surge/?text">continental shelf</a> – may produce a 20-foot storm surge. However, the same hurricane might produce only a 10-foot storm surge along the Atlantic coastline, where the continental shelf drops off very quickly.</p>
<h2>Where are the hurricane hot spots?</h2>
<p>A few years ago, the National Oceanic and Atmospheric Administration <a href="https://www.aoml.noaa.gov/hrd-faq/#chances-of-tcs">analyzed the probability of U.S. coastlines’</a> being hit by a tropical storm based on storm hits from 1944 and 1999.</p>
<p>It found that New Orleans had about a 40% chance each year of a tropical storm strike. The chances rose for Miami and Cape Hatteras, North Carolina, both at 48%. San Juan, Puerto Rico, which has seen some devastating storms in recent years, was at 42%.</p>
<p>Hurricanes, which have sustained wind speeds of at least 74 miles per hour, were also more frequent in the three U.S. locations. Miami and Cape Hatteras were found to have a 16% chance of a direct hit by a hurricane in any given year, and New Orleans’ chance was estimated at 12%.</p>
<p>Each of these locations is vulnerable to a hurricane because of its location, but also its shape. North Carolina and Florida “stick out like a sore thumb” and are often grazed by hurricanes that curve up the east coast of the U.S.</p>
<figure class="align-center zoomable">
<a href="https://images.theconversation.com/files/402459/original/file-20210524-13-1k54w9h.png?ixlib=rb-1.1.0&q=45&auto=format&w=1000&fit=clip"><img alt="A map of storm probabilities over the Atlantic coasts" src="https://images.theconversation.com/files/402459/original/file-20210524-13-1k54w9h.png?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/402459/original/file-20210524-13-1k54w9h.png?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=467&fit=crop&dpr=1 600w, https://images.theconversation.com/files/402459/original/file-20210524-13-1k54w9h.png?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=467&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/402459/original/file-20210524-13-1k54w9h.png?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=467&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/402459/original/file-20210524-13-1k54w9h.png?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=586&fit=crop&dpr=1 754w, https://images.theconversation.com/files/402459/original/file-20210524-13-1k54w9h.png?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=586&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/402459/original/file-20210524-13-1k54w9h.png?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=586&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px"></a>
<figcaption>
<span class="caption">The probability that a named storm tropical storm or hurricane will affect a location at some point during hurricane season.</span>
<span class="attribution"><a class="source" href="https://www.aoml.noaa.gov/hrd-faq/">Todd Kimberlain/AOML NOAA</a></span>
</figcaption>
</figure>
<h2>Climate change changes the risk</h2>
<p>As <a href="https://www.ncdc.noaa.gov/cag/global/time-series/globe/ocean/ytd/12/1880-2021">sea surface temperatures rise</a> with the warming of the planet, more areas outside of these usual hurricane regions may see more tropical storms.</p>
<p><a href="https://www.jou.ufl.edu/staff/athena-masson/">I</a> analyzed <a href="https://tspace.library.utoronto.ca/handle/1807/92099">tropical cyclones in the North Atlantic that made landfall</a> from 1972 to 2019 to look for changes over the past half-century.</p>
<p>During the first six years of that period, 1972-77, the Atlantic averaged four direct hits per year. Of those, 75% were in the usual hurricane-prone areas, such as the Southern United States, the Caribbean and Central America. Six storms made landfall elsewhere, including New England, Canada and the Azores.</p>
<p>By 2014-19, the Atlantic averaged 7.6 direct hits per year. While the U.S. took the majority of those hits, Europe has been showing a steady increase in cyclones making landfall. Major hurricanes – those with sustained wind speeds of 111 miles per hour and above – are also more common than they were in the 1970s and ‘80s.</p>
<p><iframe id="uZzs4" class="tc-infographic-datawrapper" src="https://datawrapper.dwcdn.net/uZzs4/8/" height="400px" width="100%" style="border: none" frameborder="0"></iframe></p>
<p>While southern coastal locations of the United States may be the most vulnerable to tropical cyclone impacts, it is important to understand that a devastating cyclone can hit anywhere along the Atlantic and Gulf coasts.</p>
<p>The National Hurricane Center <a href="https://www.noaa.gov/media-release/noaa-predicts-another-active-atlantic-hurricane-season">forecast another busy season in 2021</a>, though it is not expected to be as extreme as 2020’s record 30 named storms. Even if an area hasn’t experienced a hurricane in several years, residents are advised to prepare for the season as if their area will take a hit – just in case.</p>
<p>[<em>Over 100,000 readers rely on The Conversation’s newsletter to understand the world.</em> <a href="https://theconversation.com/us/newsletters/the-daily-3?utm_source=TCUS&utm_medium=inline-link&utm_campaign=newsletter-text&utm_content=100Ksignup">Sign up today</a>.]</p><img src="https://counter.theconversation.com/content/160765/count.gif" alt="The Conversation" width="1" height="1" />
<p class="fine-print"><em><span>Athena Masson does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.</span></em></p>New Orleans has about a 40% chance of getting hit by a tropical storm in any given year. Here’s how heat, winds and the shape of the seafloor raise the hurricane damage risk.Athena Masson, Meteorology instructor, University of FloridaLicensed as Creative Commons – attribution, no derivatives.tag:theconversation.com,2011:article/1442662020-08-14T12:13:15Z2020-08-14T12:13:15ZPolitical forecast models aren’t necessarily more accurate than polls – or the weather<figure><img src="https://images.theconversation.com/files/352767/original/file-20200813-14-llrk3q.jpg?ixlib=rb-1.1.0&rect=15%2C0%2C5101%2C3406&q=45&auto=format&w=496&fit=clip" /><figcaption><span class="caption">As the old joke goes, it's difficult to make predictions, especially about the future.</span> <span class="attribution"><a class="source" href="https://www.gettyimages.com/detail/photo/fortune-teller-royalty-free-image/97765404">Tetra Images via Getty Images</a></span></figcaption></figure><p>As the presidential election approaches, everyone wants to know who will win. </p>
<p>But nobody wants to wait until the election is actually over and the votes are all counted up and double-checked. </p>
<p>In an effort to predict the winner weeks, or even months, in advance, pollsters take to the phones and the internet, and academics take to spreadsheets of statistics.</p>
<p>Some of these analysts boast impressive track records, but take caution from a <a href="https://scholar.google.com/citations?user=3y3BVcEAAAAJ&hl=en&oi=ao">political scientist who delves into the data</a> frequently: These methods may not necessarily be more accurate than any other method of predicting the future. For some, it’s not so different from consulting Ouija boards and reading tea leaves.</p>
<h2>The next Nostradamus?</h2>
<p>Several political analysts have made names for themselves as predictors of election outcomes.</p>
<p>In the wake of the 2016 election, one political predictor, <a href="https://arts-sciences.buffalo.edu/political-science/faculty/department-faculty/james-e-campbell.html">James Campbell at the University at Buffalo</a>, a longtime professor of political science, said forecasting models had been more accurate than the widely swinging public opinion polls. He listed several examples, along with <a href="http://centerforpolitics.org/crystalball/articles/how-accurate-were-the-political-science-forecasts-of-the-2016-presidential-election/">how well they had predicted the election’s outcome</a>.</p>
<p>One of the people on his list was <a href="https://www.stonybrook.edu/commcms/polisci/people/_faculty/Norpoth_Helmut.php">Stony Brook University political scientist Helmut Norpoth</a>, who back in March 2016, eight months before Election Day, had declared there was a <a href="https://www.foxnews.com/politics/professor-doubles-down-on-prediction-model-showing-trump-having-91-percent-chance-of-winning-election-despite-polls">91% chance that Donald Trump would win</a>. He claims to have a system capable of predicting the winner of <a href="https://www.newsweek.com/professor-who-accurately-predicted-25-out-27-elections-predicts-trump-2020-win-1516609">every election outcome but two</a>, all the way back to 1912.</p>
<p>Instead of relying on polls, Norpoth’s analysis, called “<a href="http://primarymodel.com/">The Primary Model</a>,” looks at the results of primary elections. For 2020, he observes that Trump won the New Hampshire and South Carolina primaries by wide margins, and therefore predicts the <a href="https://www.newsweek.com/professor-who-accurately-predicted-25-out-27-elections-predicts-trump-2020-win-1516609">president will do better than Biden</a>, who split those primaries with Bernie Sanders.</p>
<p><a href="https://www.american.edu/cas/faculty/lichtman.cfm">American University historian Allan Lichtman</a> was another star political forecaster, who called the 2016 election for Trump <a href="https://www.realclearpolitics.com/video/2016/09/23/election_forecasting_guru_allan_lichtman_predicts_donald_trump_will_win_2016_election.html#!">in September 2016</a>. He has identified 13 factors he calls “<a href="https://www.american.edu/cas/news/13-keys-to-the-white-house.cfm">keys to the White House</a>,” which include whether one candidate is an incumbent, whether the nomination was contested, whether there is a third party challenge, and Lichtman’s own assessment of the national economic conditions, the presence of a major scandal or major policy changes, as well as his views of the candidates’ charisma. </p>
<p>Lichtman claims he has been <a href="https://www.cnbc.com/2020/08/05/biden-will-beat-trump-says-historian-who-predicted-every-race-since-1984.html">right about every presidential election since 1984</a>, and says he <a href="https://thehill.com/homenews/campaign/510754-professor-with-history-of-correctly-predicting-elections-forecasts-that">predicts Trump will lose to Biden in 2020</a>.</p>
<figure class="align-center zoomable">
<a href="https://images.theconversation.com/files/352805/original/file-20200813-18-v9x5re.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=1000&fit=clip"><img alt="A person standing in front of a lake with trees and mountains in the background." src="https://images.theconversation.com/files/352805/original/file-20200813-18-v9x5re.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/352805/original/file-20200813-18-v9x5re.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=373&fit=crop&dpr=1 600w, https://images.theconversation.com/files/352805/original/file-20200813-18-v9x5re.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=373&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/352805/original/file-20200813-18-v9x5re.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=373&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/352805/original/file-20200813-18-v9x5re.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=468&fit=crop&dpr=1 754w, https://images.theconversation.com/files/352805/original/file-20200813-18-v9x5re.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=468&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/352805/original/file-20200813-18-v9x5re.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=468&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px"></a>
<figcaption>
<span class="caption">Could the clouds have anything to do with who wins the presidency?</span>
<span class="attribution"><a class="source" href="https://www.gettyimages.com/detail/photo/hiker-in-grand-teton-national-park-usa-royalty-free-image/1055707216">benedek via Getty Images</a></span>
</figcaption>
</figure>
<h2>Is it a nice day in Wyoming?</h2>
<p><a href="https://www.pomona.edu/directory/people/gary-n-smith">Pomona College economist Gary Smith</a> warns that these sorts of methods are not necessarily as robust as they may seem. Statistically speaking, he notes, “<a href="https://mindmatters.ai/2020/07/election-models-predicting-the-past-is-easy-and-useless/">any 10 observations can always be predicted perfectly</a> … with nine … explanatory variables.”</p>
<p>To demonstrate this, he used <a href="https://mindmatters.ai/2020/07/election-models-predicting-the-past-is-easy-and-useless/">the high temperature on Election Day in five small cities</a> across the country to create a prediction for the 2016 election, which matched up very well – at least from 1980 to 2016.</p>
<p>That and other examples he provides are reminders that with enough data, “<a href="https://www.tylervigen.com/spurious-correlations">spurious correlations</a>” are everywhere – such as the famous example that from 2000 to 2009, the <a href="https://www.tylervigen.com/spurious-correlations">divorce rate in Maine</a> was very closely matched to the per-capita consumption of margarine in the U.S.</p>
<p>NFL fans may recall the “<a href="http://content.time.com/time/specials/packages/article/0,28804,1856094_1856096_1856102,00.html">Washington Rule</a>,” which claimed that if the Washington, D.C., football team won its last home game before Election Day, the party in the White House would keep it. Sportswriters claimed it would predict every election from 1940 to 2000 – but since then, it has only gotten the 2008 result correct, and has been largely discarded. </p>
<figure class="align-center zoomable">
<a href="https://images.theconversation.com/files/352820/original/file-20200813-14-w9v5zh.png?ixlib=rb-1.1.0&q=45&auto=format&w=1000&fit=clip"><img alt="A chart with two lines that don't match initially, but then match up quite closely." src="https://images.theconversation.com/files/352820/original/file-20200813-14-w9v5zh.png?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/352820/original/file-20200813-14-w9v5zh.png?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=438&fit=crop&dpr=1 600w, https://images.theconversation.com/files/352820/original/file-20200813-14-w9v5zh.png?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=438&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/352820/original/file-20200813-14-w9v5zh.png?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=438&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/352820/original/file-20200813-14-w9v5zh.png?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=550&fit=crop&dpr=1 754w, https://images.theconversation.com/files/352820/original/file-20200813-14-w9v5zh.png?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=550&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/352820/original/file-20200813-14-w9v5zh.png?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=550&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px"></a>
<figcaption>
<span class="caption">From 1980 to 2016, the average temperature in five particular U.S. cities on Election Day matched up very well with the percentage of the popular vote that the party currently in the White House received.</span>
<span class="attribution"><a class="source" href="https://mindmatters.ai/2020/07/election-models-predicting-the-past-is-easy-and-useless/">Gary Smith, Mind Matters</a></span>
</figcaption>
</figure>
<h2>Hindsight in 2020 is 20/20</h2>
<p>Of course, scholars’ political forecasting models do incorporate information that could be linked to the elections. For instance, I believe that party unity, economic performance, scandals and incumbency are some of the most important factors in how elections turn out.</p>
<p>[<em>Insight, in your inbox each day.</em> <a href="https://theconversation.com/us/newsletters/the-daily-3?utm_source=TCUS&utm_medium=inline-link&utm_campaign=newsletter-text&utm_content=insight">You can get it with The Conversation’s email newsletter</a>.]</p>
<p>But economist and weather-based prognosticator Smith is correct when he points out that some of these systems “<a href="https://mindmatters.ai/2020/07/election-models-predicting-the-past-is-easy-and-useless/">predict past elections astonishingly well</a> and then do poorly with new elections and must be tweaked, after the fact, to ‘correct’ for these mispredictions.” In fact, both Lichtman and Norpoth have <a href="https://news.stonybrook.edu/facultystaff/maverick-modeller-helmut-norpoth-predicts-another-win-for-trump/">made changes</a> to their analysis methods <a href="http://primarymodel.com/2008">over time</a>. </p>
<p>They may need more tweaks in 2020, in part because they leave out factors that haven’t been important in the past, but might be vital now. For instance, election officials across the country are expecting a flood of mail-in ballots and early voting. The New York Times finds that a <a href="https://www.nytimes.com/interactive/2020/08/11/us/politics/vote-by-mail-us-states.html">record 76% of Americans</a> can vote by mail, and Gallup polling says <a href="https://news.gallup.com/poll/310586/americans-favor-voting-mail-option-november.aspx">64% of Americans support voting by mail</a>. Those figures are far beyond even the <a href="https://fivethirtyeight.com/features/the-coronavirus-could-change-how-we-vote-in-2020-and-beyond/">40% of votes cast in those ways</a> in the 2016 election. In the past, when new methods of voting have emerged, <a href="https://time.com/4305508/paper-ballot-history/">outcomes have been harder to predict</a>.</p>
<p>The forecasts may be interesting, and – like the polls – often grab headlines, but you probably don’t want to bet too much money based on what they say.</p><img src="https://counter.theconversation.com/content/144266/count.gif" alt="The Conversation" width="1" height="1" />
<p class="fine-print"><em><span>John A. Tures does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.</span></em></p>Can political prediction models pick the election winner better than the polls, the weather or Washington’s football team?John A. Tures, Professor of Political Science, LaGrange CollegeLicensed as Creative Commons – attribution, no derivatives.tag:theconversation.com,2011:article/1402992020-06-30T12:27:18Z2020-06-30T12:27:18ZWhy short-term forecasts can be better than models for predicting how pandemics evolve<figure><img src="https://images.theconversation.com/files/344538/original/file-20200629-155345-y5p5p6.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=496&fit=clip" /><figcaption><span class="caption">
</span> <span class="attribution"><a class="source" href="https://www.shutterstock.com/image-photo/colorful-umbrellas-background-sky-street-decoration-397740379">Denijal photography/Shutterstock</a></span></figcaption></figure><p>Confirmed COVID-19 cases have now passed <a href="https://www.worldometers.info/coronavirus/">10 million</a>: what will they be next week, globally and in your country? </p>
<p>Having a good estimate can help health authorities with their responses and will guide governments as they ease lockdowns. To this end, we have been publishing <a href="http://www.doornik.com/COVID-19">real-time forecasts</a> for confirmed cases and deaths for many parts of the world on an almost daily basis since March 20. These have largely been reliable indicators of what can be expected to happen in the next week.</p>
<p>Many of the more formal models for predicting the pandemic – such as the well-publicised <a href="https://mrc-ide.github.io/covid19-short-term-forecasts/index.html">Imperial College London</a> model that guided the UK government’s response – use maths to try to <a href="https://theconversation.com/how-to-model-a-pandemic-134187">explain the underlying processes</a> of the outbreak, and do this by adopting a small number of interpretable parameters (such as the R number). They make predictions based on understanding how outbreaks work in general.</p>
<p>Our forecasts, on the other hand, don’t attempt to understand why changes occur. Instead, they are based purely on data from the current pandemic, looking at how it has already evolved and shifted to predict what will happen next. This often leads to more accurate predictions. </p>
<h2>Why epidemiological models can struggle</h2>
<p>Imagine you are travelling by road from Boston to California. Knowing from previous trips that California is your destination, we track your journey and try to forecast each day’s itinerary. When there are road closures, you briefly detour, so our forecasts go wrong for a while, then recover. Many models have such an in-built “reversion to the mean” that can handle these sorts of small changes. </p>
<p>Usually this model works well. But what if you hear about wildfires in California and decide to visit Canada instead? The forecasts become increasingly poor if we maintain that you are still going to California. The model needs to recover from such a “structural break”.</p>
<figure class="align-center ">
<img alt="" src="https://images.theconversation.com/files/344527/original/file-20200629-155303-101hgmj.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/344527/original/file-20200629-155303-101hgmj.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=400&fit=crop&dpr=1 600w, https://images.theconversation.com/files/344527/original/file-20200629-155303-101hgmj.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=400&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/344527/original/file-20200629-155303-101hgmj.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=400&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/344527/original/file-20200629-155303-101hgmj.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=502&fit=crop&dpr=1 754w, https://images.theconversation.com/files/344527/original/file-20200629-155303-101hgmj.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=502&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/344527/original/file-20200629-155303-101hgmj.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=502&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px">
<figcaption>
<span class="caption">Canada and California are thousands of kilometres apart – rerouting would be a big change.</span>
<span class="attribution"><a class="source" href="https://commons.wikimedia.org/wiki/File:Amboy_Crater_Area_of_Critical_Environmental_Concern,_California_(15665575311).jpg">Bureau of Land Management/Wikimedia Commons</a>, <a class="license" href="http://creativecommons.org/licenses/by-sa/4.0/">CC BY-SA</a></span>
</figcaption>
</figure>
<p>Most models in the social sciences and epidemiology have a theory behind them that’s based on available evidence from the past. This simple travel example shows why such models may not be good for making predictions: they risk being too highly driven by their <a href="https://theconversation.com/in-all-probability-economic-forecasts-are-probably-wrong-28694">theoretical formulations</a> – such as that you’re going to California. </p>
<p>The Office for Budget Responsibility’s predictions of UK productivity after the 2008 financial crisis are a great visual example of what happens when such models go wrong. See the lovely graphs available from their <a href="https://obr.uk/docs/dlm_uploads/Historical_Forecast_Database_Jan17.pdf">historical forecast database</a>. We call them hedgehog graphs, because the wildly erroneous forecasts look like spines going away from the confirmed data.</p>
<p>In epidemiology, most <a href="https://theconversation.com/how-to-model-a-pandemic-134187">models</a> have a sound theoretical basis. They take account of epidemics starting slowly, then exponentially increasing and eventually slowing. However, human behaviour and policy reactions can lead to abrupt changes that can be difficult to allow for (such as unexpectedly visiting Canada). Data may also suddenly shift in a pandemic – ramping up testing may reveal many new infections, or cases in care homes may suddenly join the dataset. To be effective in such settings, forecasting devices must be sufficiently robust to handle problems of changing trends and sudden shifts in outcomes and measurements. Our short-term forecasts can handle this in a way more formal models often can’t.</p>
<h2>How our forecasts work and perform</h2>
<p>To create our forecasts – say, for the total number of COVID-19 cases in a country – we first create trend lines based on the confirmed data that we have. Every time a new data point is added, this creates a new trend line – so there are as many trend lines as there are data points. A machine learning algorithm then selects the trends that matter out of all of those available, and those it chooses are averaged to show how the process has evolved over time (the trend in the data). Forecasts are derived from this underlying trend, as well as by looking at the gap between earlier forecasts and actual outcomes.</p>
<p>It may seem surprising, but this works. The graph below shows the forecast we made on <a href="https://www.doornik.com/COVID-19/index_2020-05-22.html">May 22</a> for how the UK’s total number of COVID-19 cases would increase over the next week or so (the solid red line). Our forecast for May 30 was just under 272,000. The reported outcome ended up being 272,826.</p>
<figure class="align-center ">
<img alt="" src="https://images.theconversation.com/files/344530/original/file-20200629-155349-mumqqg.png?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/344530/original/file-20200629-155349-mumqqg.png?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=405&fit=crop&dpr=1 600w, https://images.theconversation.com/files/344530/original/file-20200629-155349-mumqqg.png?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=405&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/344530/original/file-20200629-155349-mumqqg.png?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=405&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/344530/original/file-20200629-155349-mumqqg.png?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=509&fit=crop&dpr=1 754w, https://images.theconversation.com/files/344530/original/file-20200629-155349-mumqqg.png?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=509&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/344530/original/file-20200629-155349-mumqqg.png?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=509&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px">
<figcaption>
<span class="caption">The fall in the count on May 20 was due to revisions to the data. Data is from the Center for Systems Science and Engineering at Johns Hopkins University.</span>
<span class="attribution"><span class="license">Author provided</span></span>
</figcaption>
</figure>
<p>This second graph shows forecasts of EU COVID-19 deaths that we <a href="https://www.doornik.com/COVID-19/index_deaths_2020-04-20.html">made during March and April</a>. The successive forecasts made over time are shown in red, with the actual data points in grey. The overlap between the grey and red lines shows that the forecasting here was pretty accurate. Compare the close bunching of the lines here to the hedgehog graphs mentioned earlier!</p>
<figure class="align-center ">
<img alt="" src="https://images.theconversation.com/files/344558/original/file-20200629-155303-so5pu3.png?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/344558/original/file-20200629-155303-so5pu3.png?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=404&fit=crop&dpr=1 600w, https://images.theconversation.com/files/344558/original/file-20200629-155303-so5pu3.png?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=404&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/344558/original/file-20200629-155303-so5pu3.png?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=404&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/344558/original/file-20200629-155303-so5pu3.png?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=508&fit=crop&dpr=1 754w, https://images.theconversation.com/files/344558/original/file-20200629-155303-so5pu3.png?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=508&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/344558/original/file-20200629-155303-so5pu3.png?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=508&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px">
<figcaption>
<span class="caption">Data is from the Center for Systems Science and Engineering at Johns Hopkins University.</span>
<span class="attribution"><span class="license">Author provided</span></span>
</figcaption>
</figure>
<p>However, a more precise way of judging the accuracy of forecasts is to look at a measure called mean absolute error (MAE). Absolute errors are the numerical differences between predictions and what the actual values turn out to be; MAE is the average of these differences for a set period. MAE gives a general measure of how far off your predictions were.</p>
<p>Up to April 4, the MAE for our one-week-ahead forecasts for COVID-19 deaths across a number of mainly European countries was 629, whereas on average forecasts by the <a href="https://mrc-ide.github.io/covid19-short-term-forecasts/index.html">Imperial College London</a> COVID-19 Response Team for deaths in the same countries over the same period were out by 1,068. When incorporating the following week’s data, on average our forecasts were out by roughly the same amount – 678 – whereas Imperial’s MAE had grown to 1,912. After April 11, our MAE figures began to mirror one another’s, but at least in the early stages of the pandemic, our predictions seemed to be more accurate.</p>
<p>During the pandemic, these forecasts have provided useful insights for the week ahead, and now that Latin America is the epicenter of the outbreak, agencies like the Inter-American Development Bank are using them. Not only is our more robust way of forecasting playing a role in the current pandemic, we believe it may be essential in a second wave.</p><img src="https://counter.theconversation.com/content/140299/count.gif" alt="The Conversation" width="1" height="1" />
<p class="fine-print"><em><span>This research has received financial support from the Robertson Foundation (award 9907422), the Institute for New Economic Thinking (grant 20029822), and Nuffield College, University of Oxford. David F Hendry has a financial interest in the software OxMetrics 8.2 used for the computations.</span></em></p><p class="fine-print"><em><span>This research has received financial support from the Robertson Foundation (award 9907422), the Institute for New
Economic Thinking (grant 20029822) and the ERC (grant 694262, DisCont).
Jurgen A Doornik has a financial interest in the software OxMetrics 8.2 used for the computations.</span></em></p><p class="fine-print"><em><span>Jennifer L Castle does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.</span></em></p>Many of the more formal models for predicting the pandemic try to understand why changes happen – but often it can be more accurate to ignore the reasons and simply look at the data.David F Hendry, Senior Research Fellow, University of OxfordJennifer L Castle, Tutorial Fellow in Economics, University of OxfordJurgen A Doornik, Research Fellow in Climate Econometrics, University of OxfordLicensed as Creative Commons – attribution, no derivatives.tag:theconversation.com,2011:article/1382132020-05-09T00:25:10Z2020-05-09T00:25:10ZThe Reserve Bank thinks the recovery will look V-shaped. There are reasons to doubt it<figure><img src="https://images.theconversation.com/files/333768/original/file-20200508-49579-1g7d3tn.jpg?ixlib=rb-1.1.0&rect=113%2C23%2C3832%2C1832&q=45&auto=format&w=496&fit=clip" /><figcaption><span class="caption">
</span> <span class="attribution"><a class="source" href="https://www.rba.gov.au/publications/smp/2020/may/pdf/06-economic-outlook.pdf">Shutterstock/RBA</a></span></figcaption></figure><p>The Reserve Bank’s long-awaited two-year forecasts for jobs, wages and growth are <a href="https://www.rba.gov.au/publications/smp/2020/may/">frightening</a>, but I fear they are not frightening enough.</p>
<p>The bank looks two years ahead every three months. The last set of forecasts, released at the start of February, mentioned coronavirus mainly as a source of “uncertainty”.</p>
<p>That’s how much things have changed.</p>
<p>Back then economic growth was going to climb over time, consumers were going to start opening their wallets again (household spending had been <a href="https://theconversation.com/gdp-update-spending-dips-and-saving-soars-as-we-stash-rather-than-spend-our-tax-cuts-128297">incredibly weak</a>) and unemployment was going to plunge below 5%.</p>
<p>The forecasts released on Friday come in three sets – “baseline”, an earlier economic recovery, and a later recovery.</p>
<p>“<a href="https://www.rba.gov.au/publications/smp/2020/may/pdf/forecast-table-2020-05.pdf">Baseline</a>”, the central set with which we will concern ourselves here, is both shocking, and disconcertingly encouraging.</p>
<hr>
<figure class="align-center zoomable">
<a href="https://images.theconversation.com/files/333769/original/file-20200508-49542-qm31pw.JPG?ixlib=rb-1.1.0&q=45&auto=format&w=1000&fit=clip"><img alt="" src="https://images.theconversation.com/files/333769/original/file-20200508-49542-qm31pw.JPG?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/333769/original/file-20200508-49542-qm31pw.JPG?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=467&fit=crop&dpr=1 600w, https://images.theconversation.com/files/333769/original/file-20200508-49542-qm31pw.JPG?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=467&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/333769/original/file-20200508-49542-qm31pw.JPG?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=467&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/333769/original/file-20200508-49542-qm31pw.JPG?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=587&fit=crop&dpr=1 754w, https://images.theconversation.com/files/333769/original/file-20200508-49542-qm31pw.JPG?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=587&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/333769/original/file-20200508-49542-qm31pw.JPG?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=587&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px"></a>
<figcaption>
<span class="caption"></span>
<span class="attribution"><a class="source" href="https://www.rba.gov.au/publications/smp/2020/may/pdf/forecast-table-2020-05.pdf">Reserve Bank Statement on Monetary Policy, May 2020</a></span>
</figcaption>
</figure>
<hr>
<p>On employment, it predicts a drop of more than 7% in the first half of this year, most of it in the “June quarter”, the three months of April, May and June that we are in the middle of.</p>
<p>Thirteen million of us were employed in March, making a drop of 7%, a drop of 900,000. Put differently, one in every 13 of us will lose their jobs.</p>
<p>Harder to believe is that by December next year 6% of the workforce will have got them back.</p>
<p>It sounds like what the prime minister referred to earlier in the crisis as a “snapback”, the economy snapping back to where it was.</p>
<p>Except that it’s not. </p>
<figure class="align-center zoomable">
<a href="https://images.theconversation.com/files/333770/original/file-20200508-49550-aupyxy.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=1000&fit=clip"><img alt="" src="https://images.theconversation.com/files/333770/original/file-20200508-49550-aupyxy.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/333770/original/file-20200508-49550-aupyxy.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=397&fit=crop&dpr=1 600w, https://images.theconversation.com/files/333770/original/file-20200508-49550-aupyxy.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=397&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/333770/original/file-20200508-49550-aupyxy.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=397&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/333770/original/file-20200508-49550-aupyxy.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=499&fit=crop&dpr=1 754w, https://images.theconversation.com/files/333770/original/file-20200508-49550-aupyxy.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=499&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/333770/original/file-20200508-49550-aupyxy.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=499&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px"></a>
<figcaption>
<span class="caption"></span>
<span class="attribution"><a class="source" href="https://www.rba.gov.au/publications/smp/2020/may/">Reserve Bank Statement on Monetary Policy, May 2020</a></span>
</figcaption>
</figure>
<p>Six per cent of a small number is a lot less than 7% of a big number. </p>
<p>The bank’s forecasts have far fewer people in work all the way out to mid 2022 (the limit of the published forecasts) and doubless well beyond.</p>
<p>The unemployment rate would shoot up to 10% by June and take a long while to fall.</p>
<figure class="align-center ">
<img alt="" src="https://images.theconversation.com/files/333771/original/file-20200508-49579-11yunh9.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/333771/original/file-20200508-49579-11yunh9.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=397&fit=crop&dpr=1 600w, https://images.theconversation.com/files/333771/original/file-20200508-49579-11yunh9.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=397&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/333771/original/file-20200508-49579-11yunh9.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=397&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/333771/original/file-20200508-49579-11yunh9.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=499&fit=crop&dpr=1 754w, https://images.theconversation.com/files/333771/original/file-20200508-49579-11yunh9.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=499&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/333771/original/file-20200508-49579-11yunh9.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=499&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px">
<figcaption>
<span class="caption"></span>
<span class="attribution"><a class="source" href="https://www.rba.gov.au/publications/smp/2020/may/">Reserve Bank Statement on Monetary Policy, May 2020</a></span>
</figcaption>
</figure>
<p>The baseline economic growth forecast is also drawn as a V. </p>
<p>After economic activity shrinks more than 8% in the June quarter, we are asked to believe it will bound back 7% in the year that follows. </p>
<p>But that will still leave us with much lower living standards than we would have had, missing the usual 2-3% per year increase.</p>
<figure class="align-center zoomable">
<a href="https://images.theconversation.com/files/333772/original/file-20200509-49589-1bv3imh.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=1000&fit=clip"><img alt="" src="https://images.theconversation.com/files/333772/original/file-20200509-49589-1bv3imh.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/333772/original/file-20200509-49589-1bv3imh.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=397&fit=crop&dpr=1 600w, https://images.theconversation.com/files/333772/original/file-20200509-49589-1bv3imh.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=397&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/333772/original/file-20200509-49589-1bv3imh.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=397&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/333772/original/file-20200509-49589-1bv3imh.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=499&fit=crop&dpr=1 754w, https://images.theconversation.com/files/333772/original/file-20200509-49589-1bv3imh.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=499&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/333772/original/file-20200509-49589-1bv3imh.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=499&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px"></a>
<figcaption>
<span class="caption"></span>
<span class="attribution"><a class="source" href="https://www.rba.gov.au/publications/smp/2020/may/">Reserve Bank Statement on Monetary Policy, May 2020</a></span>
</figcaption>
</figure>
<p>The reason I fear the baseline forecasts aren’t frightening enough is that they are partly built on a return to form for household spending, which accounts for 65% of gross domestic product. </p>
<p>After diving 15% mainly in this quarter we are asked to believe it will climb back 13% in the year that follows.</p>
<p>Maybe. But here’s another theory. While we’ve been restricted in movement or without jobs we’ve become used to spending less (and used to flying less, and used to hanging onto our cars for longer and hanging on to the money we’ve got).</p>
<hr>
<p>
<em>
<strong>
Read more:
<a href="https://theconversation.com/how-will-the-coronavirus-recession-compare-with-the-worst-in-australias-history-136379">How will the coronavirus recession compare with the worst in Australia's history?</a>
</strong>
</em>
</p>
<hr>
<p>My suspicion is that these behaviours can be learned, and we’ve been doing them long enough to learn them. </p>
<p>During the global financial crisis we tightened our belts and then kept them tight for years, saving far more than the offical forecasts expected, in part because we had been shocked and felt certain about the future.</p>
<p>A recovery that had been forecast to be V-shaped looked more like a flat-bottomed boat when graphed. It’s a picture I find more believable than a snapback. </p>
<p>We are unlikley to get back where we would have been for a very long time.</p><img src="https://counter.theconversation.com/content/138213/count.gif" alt="The Conversation" width="1" height="1" />
<p class="fine-print"><em><span>Peter Martin does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.</span></em></p>The global financial crisis taught us recoveries needn’t be V-shaped.Peter Martin, Visiting Fellow, Crawford School of Public Policy, Australian National UniversityLicensed as Creative Commons – attribution, no derivatives.tag:theconversation.com,2011:article/1339792020-03-25T16:01:26Z2020-03-25T16:01:26ZFour graphs that show how the coronavirus pandemic could now unfold<figure><img src="https://images.theconversation.com/files/322522/original/file-20200324-155631-1iobxmd.jpg?ixlib=rb-1.1.0&rect=0%2C2%2C1000%2C663&q=45&auto=format&w=496&fit=clip" /><figcaption><span class="caption">
</span> <span class="attribution"><a class="source" href="https://www.shutterstock.com/image-photo/high-angle-view-multiethnic-people-clapping-150364787">sirtravelalot/Shutterstock</a></span></figcaption></figure><p>The <a href="https://experience.arcgis.com/experience/685d0ace521648f8a5beeeee1b9125cd">COVID-19 pandemic</a> has already caused several thousand deaths, widespread health problems, massive anxiety and economic losses. Most people are concerned with what happens day by day as we wait for <a href="https://doi.org/10.1016/S0140-6736(20)30567-5">control measures</a> <a href="https://www.imperial.ac.uk/mrc-global-infectious-disease-analysis/news--wuhan-coronavirus/">to work</a>. </p>
<p>But we should also be concerned about whether or not we will be living with the virus for a long time. Will we be able to eradicate COVID-19, as we did with <a href="https://www.who.int/ith/diseases/sars/en/">Sars</a>? Or will we need to learn to live with it like we do with the <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3215607/">common cold</a>? We have been experiencing epidemics and pandemics for centuries, so there are lessons we can draw from these examples.</p>
<p>To understand what happens to the virus in the long term, we need to look at how large epidemics work, starting with “patient zero”. If there is significant human-to-human transmission, the virus begins to spread, causing a fast increase in the number of cases (illustrated in the figure below). At the same time, those who overcome the disease and develop resistance are henceforth immune, at least for a while. The people who are newly infected will increasingly have contact with these immune people, rather than with those who have not yet had the disease. This process effectively protects the susceptible population and causes the initial fast growth to slow and eventually stop.</p>
<figure class="align-center ">
<img alt="" src="https://images.theconversation.com/files/322291/original/file-20200323-112688-1vsktee.png?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/322291/original/file-20200323-112688-1vsktee.png?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=600&fit=crop&dpr=1 600w, https://images.theconversation.com/files/322291/original/file-20200323-112688-1vsktee.png?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=600&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/322291/original/file-20200323-112688-1vsktee.png?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=600&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/322291/original/file-20200323-112688-1vsktee.png?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=754&fit=crop&dpr=1 754w, https://images.theconversation.com/files/322291/original/file-20200323-112688-1vsktee.png?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=754&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/322291/original/file-20200323-112688-1vsktee.png?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=754&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px">
<figcaption>
<span class="caption">An example of a disease progress curve for a long-term scenario following the initial outbreak: quick eradication. The number of cases and duration of the epidemic for illustrative purpose only.</span>
</figcaption>
</figure>
<p>The level of <a href="https://doi.org/10.1093/cid/cir007">herd immunity</a> needed to stop the spread depends on both the number of contacts an average person has and how infectious the disease is. If highly contagious, this can be <a href="https://www.scielosp.org/article/bwho/2000.v78n8/949-949/en/">as much as 95%</a>. This protection can be achieved by a combination of reducing infectiousness through immunity, either natural or acquired, or vaccination, or by reducing transmission. Quarantine and mass restrictions on travel have proved effective, as shown in China, where the number of COVID-19 infections outside of Hubei province, where it started, have been few.</p>
<p>What happens next depends on the disease characteristics and human actions. The <a href="https://doi.org/10.1086/511989">1918 flu virus</a> did not persist <a href="https://www.vox.com/2020/3/9/21164957/covid-19-spanish-flu-mortality-rate-death-rate">after the early 1920s</a> probably because <a href="https://doi.org/10.1016/S0264-410X(02)00254-2">enough people became immune</a> to it. However, many pathogens are difficult to eradicate globally, although local success is possible. For example,<a href="https://doi.org/10.1051/vetres:2001106">foot-and-mouth disease</a>, which affects sheep and livestock, <a href="https://doi.org/10.1016/j.prevetmed.2013.07.013">survives in many countries</a>. The outbreak in the UK in 2001 was reduced to local islands of infection by an animal movement ban and then eradicated by massive culling. But it took a <a href="https://doi.org/10.1038/35097116">long time and high costs to finally bring it to an end</a> (figure below). Like many countries, the UK now has <a href="https://www.gov.uk/guidance/get-a-cattle-passport">strict rules of animal imports</a>, aimed at stopping the disease from arriving again. </p>
<figure class="align-center ">
<img alt="" src="https://images.theconversation.com/files/322290/original/file-20200323-112688-qgwvry.png?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/322290/original/file-20200323-112688-qgwvry.png?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=600&fit=crop&dpr=1 600w, https://images.theconversation.com/files/322290/original/file-20200323-112688-qgwvry.png?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=600&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/322290/original/file-20200323-112688-qgwvry.png?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=600&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/322290/original/file-20200323-112688-qgwvry.png?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=754&fit=crop&dpr=1 754w, https://images.theconversation.com/files/322290/original/file-20200323-112688-qgwvry.png?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=754&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/322290/original/file-20200323-112688-qgwvry.png?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=754&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px">
<figcaption>
<span class="caption">An example of a disease progress curve for a long-term scenario following the initial outbreak: slow eradication. The number of cases and duration of the epidemic for illustrative purpose only.</span>
</figcaption>
</figure>
<p>It is possible that we will eradicate COVID-19 in selected countries or regions, but not necessarily worldwide. Although there are hopes that a <a href="https://dx.doi.org/10.12932/AP-200220-0772">vaccine</a> will be successful within the next year, this is not certain. If it happens, very stringent travel checks may need to be imposed for at least a substantial time – such a restriction, added on to concerns of the impact of air travel on climate change, may mean that the tourism industry may never recover.</p>
<p>Some diseases prove impossible to eradicate even in the long term and will persist following the first outbreak (figure below). Diseases originating in Europe and Africa <a href="http://dx.doi.org/10.1257/jep.24.2.163">were brought to North America</a> for the first time in the late 1400s and early 1500s. Encountering a population with little immunity, <a href="http://dx.doi.org/10.1098/rstb.2018.0255">smallpox</a> and other diseases spread rapidly causing the collapse of indigenous communities. Subsequent outbreaks were lower, but smallpox and measles persisted until the 20th century.</p>
<figure class="align-center ">
<img alt="" src="https://images.theconversation.com/files/322289/original/file-20200323-112666-152rnfk.png?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/322289/original/file-20200323-112666-152rnfk.png?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=600&fit=crop&dpr=1 600w, https://images.theconversation.com/files/322289/original/file-20200323-112666-152rnfk.png?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=600&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/322289/original/file-20200323-112666-152rnfk.png?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=600&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/322289/original/file-20200323-112666-152rnfk.png?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=754&fit=crop&dpr=1 754w, https://images.theconversation.com/files/322289/original/file-20200323-112666-152rnfk.png?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=754&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/322289/original/file-20200323-112666-152rnfk.png?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=754&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px">
<figcaption>
<span class="caption">An example of a disease progress curve for a long-term scenario following the initial outbreak: persistence at low levels. The number of cases and duration of the epidemic for illustrative purpose only.</span>
</figcaption>
</figure>
<p>In temperate climates, seasonal influenza spreads rapidly through winter but mostly dies out in summer, only to come back the following year. In between outbreaks, the flu virus <a href="https://science.sciencemag.org/content/320/5874/340">survives in Asia</a> from where it emerges every year. Large measles epidemics, before vaccines were available, <a href="http://doi.org/10.1098/rspb.1993.0011">occurred every two or three years</a>, interspersed with small outbreaks (figure below). The recurring pattern was caused by people being born all the time without vaccine protection. Next winter, when children went back to school, there were enough susceptible ones to create a large outbreak. With mass vaccination of children, this influx was slowed down enough to create herd immunity and almost eradicate the disease.
However, measles is returning because <a href="http://doi.org/10.1056/NEJMp1905099">vaccination levels are falling below the herd-immunity threshold</a>.</p>
<figure class="align-center ">
<img alt="" src="https://images.theconversation.com/files/322288/original/file-20200323-112712-wpve8b.png?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/322288/original/file-20200323-112712-wpve8b.png?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=600&fit=crop&dpr=1 600w, https://images.theconversation.com/files/322288/original/file-20200323-112712-wpve8b.png?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=600&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/322288/original/file-20200323-112712-wpve8b.png?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=600&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/322288/original/file-20200323-112712-wpve8b.png?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=754&fit=crop&dpr=1 754w, https://images.theconversation.com/files/322288/original/file-20200323-112712-wpve8b.png?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=754&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/322288/original/file-20200323-112712-wpve8b.png?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=754&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px">
<figcaption>
<span class="caption">An example of a disease progress curve for a long-term scenario following the initial outbreak: recurring epidemics. The number of cases and duration of the epidemic for illustrative purpose only.</span>
</figcaption>
</figure>
<p>So what is the future of COVID-2019? While we cannot be sure, <a href="http://dx.doi.org/10.1098/rstb.2018.0255">mathematical models</a> help us explore scenarios and identify potential outcomes, building on our experience of past outbreaks. The governments are hoping that a <a href="https://www.imperial.ac.uk/mrc-global-infectious-disease-analysis/news--wuhan-coronavirus/">combination</a> of social distancing, border closures, isolation of cases, testing and increasing immunity in the population will slow down the spread of the coronavirus and will hopefully open up successful eradication strategies. Yet past experiences suggest that we may need to learn to live with the coronavirus for years to come.</p><img src="https://counter.theconversation.com/content/133979/count.gif" alt="The Conversation" width="1" height="1" />
<p class="fine-print"><em><span>Adam Kleczkowski has received funding from BBSRC, NSF, Academy of Medical Sciences, and the Scottish Government. </span></em></p><p class="fine-print"><em><span>Rowland Raymond Kao receives funding from Defra, the Wellcome Trust and the BBSRC. He is affiliated with the Department of the Environment, Food and Rural Affairs as a member of its Science Advisory Council </span></em></p>Learning from past epidemics about the long-term future of COVID-2019.Adam Kleczkowski, Professor of Mathematics and Statistics, University of Strathclyde Rowland Raymond Kao, Sir Timothy O'Shea Professor of Veterinary Epidemiology and Data Science, The University of EdinburghLicensed as Creative Commons – attribution, no derivatives.tag:theconversation.com,2011:article/1298742020-01-14T21:43:03Z2020-01-14T21:43:03ZEarthquake forecast for Puerto Rico: Dozens more large aftershocks are likely<figure><img src="https://images.theconversation.com/files/310035/original/file-20200114-151829-1q58f2i.jpg?ixlib=rb-1.1.0&rect=0%2C0%2C6000%2C4005&q=45&auto=format&w=496&fit=clip" /><figcaption><span class="caption">The Immaculate Conception Catholic Church lies in ruins after a magnitude 6.4 earthquake in Guayanilla, Puerto Rico, Jan. 7, 2020. </span> <span class="attribution"><a class="source" href="http://www.apimages.com/metadata/Index/Puerto-Rico-Earthquake/b8ba115143d140a393a5e19c164eb057/102/0">AP Photo/Carlos Giusti</a></span></figcaption></figure><p>Multiple strong and damaging <a href="https://earthquake.usgs.gov/earthquakes/eventpage/us70006vll/executive">earthquakes in southern Puerto Rico</a> starting around Dec. 28, 2019 have killed at least one person, caused many serious injuries and collapsed numerous buildings, including a multistory school in the town of Guánica that luckily was empty at the time. These quakes are the most damaging to strike Puerto Rico since 1918, and the island has been under a state of emergency since Jan. 6, 2020. </p>
<p>This flurry of quakes includes onshore and offshore events near the town of Indios and along Puerto Rico’s southwestern coast. So far it has included 11 <a href="https://www.usgs.gov/faqs/foreshocks-aftershocks-whats-difference?qt-news_science_products=0#qt-news_science_products">foreshocks</a> – smaller earthquakes that preceded the largest event, or mainshock – with magnitudes of 4 and greater. Major quakes occurred on Jan. 6 (magnitude 5.8) and Jan. 7 (magnitude 6.4 mainshock), followed by numerous large aftershocks. </p>
<p><a href="https://scholar.google.com/citations?user=khkdN24AAAAJ&hl=en">Seismologists like me</a> are constantly working to better understand earthquakes, including advancing ways to help vulnerable communities before, during and after damaging events. The physics of earthquakes are astoundingly complex, but our abilities to forecast future earthquakes during a strong sequence of events in real time is improving.</p>
<p>Forecasting earthquakes is not a strict prediction – it’s more like a weather forecast, in which scientists estimate the likelihood of future earthquake activity based on quakes that have already occurred, using established statistical laws that govern earthquake behavior.</p>
<h2>An undersea fault zone</h2>
<p>Puerto Rico spans a complex boundary between the Caribbean and North American <a href="https://pubs.usgs.gov/gip/dynamic/tectonic.html">tectonic plates</a>, which are sliding past each other in this region at a relative speed of about 2 centimeters per year. Over geologic time, this motion has created the Muertos Trough, a <a href="https://seismo.berkeley.edu/blog/2020/01/07/deadly-earthquakes-in-the-muertos-trough.html">15,000-foot depression in the sea floor</a> south of the island. </p>
<p>This plate boundary is riddled with interconnected fault structures. The present activity is occurring on and near at least three interrelated large faults. </p>
<figure class="align-center zoomable">
<a href="https://images.theconversation.com/files/309798/original/file-20200113-103966-1lb90ju.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=1000&fit=clip"><img alt="" src="https://images.theconversation.com/files/309798/original/file-20200113-103966-1lb90ju.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/309798/original/file-20200113-103966-1lb90ju.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=284&fit=crop&dpr=1 600w, https://images.theconversation.com/files/309798/original/file-20200113-103966-1lb90ju.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=284&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/309798/original/file-20200113-103966-1lb90ju.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=284&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/309798/original/file-20200113-103966-1lb90ju.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=356&fit=crop&dpr=1 754w, https://images.theconversation.com/files/309798/original/file-20200113-103966-1lb90ju.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=356&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/309798/original/file-20200113-103966-1lb90ju.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=356&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px"></a>
<figcaption>
<span class="caption">Multiple faults crisscross the eastern Caribbean. Those outlined in red have a potential to generate a large earthquake. The arrow at top right shows the direction of the North American plate’s motion relative to the Caribbean plate. Red stars denote intensity centers for past earthquakes.</span>
<span class="attribution"><a class="source" href="https://www.usgs.gov/media/images/topography-and-bathymetry-map-northeastern-caribbean">USGS</a></span>
</figcaption>
</figure>
<p>Faults are pre-existing weak zones between stronger rocks. In response to surprisingly small force (stress) changes, they rapidly slip to produce earthquakes. The “hair-trigger” nature of fault slip means that predicting the precise timing, location, and size of individual quakes is extremely challenging, if not impossible. </p>
<p>During an earthquake sequence, changing stresses act on nearby fault systems as stress is gradually redistributed within the Earth. This process generates thousands of protracted aftershocks.</p>
<p>Many earthquake sequences simply start with the mainshock. But it is not especially rare for scientists to recognize after the fact that foreshocks were occurring before the main event. Improvements in earthquake instrumentation and analysis are helping scientists detect foreshocks more often, although we have not yet figured out how to recognize them in real time. </p>
<h2>Will one shock lead to another?</h2>
<p>Researchers have known for over a century that the rate of earthquakes following a mainshock declines in a way that we can characterize statistically. There is also a well-established relationship between the magnitude of earthquakes and their relative number during an earthquake sequence. In most seismically active regions, for a decrease of one magnitude unit – say, from 4.0 to 3.0 – people can expect to experience about 10 times as many 3s compared to 4s in a given time period. </p>
<p>Using such statistical relationships allows us to forecast the probability and sizes of future earthquakes while an earthquake sequence is underway. Put another way, if we are experiencing an aftershock sequence, we can project the future rate of earthquakes and what magnitudes we expect those quakes to have. </p>
<p><div data-react-class="Tweet" data-react-props="{"tweetId":"1215468971060842497"}"></div></p>
<p>For example, as of Jan. 14, the <a href="https://earthquake.usgs.gov/earthquakes/eventpage/us70006vll/oaf/commentary">U.S. Geological Survey forecast</a> estimated a 3% chance of one or more quakes larger than magnitude 6.4 in Puerto Rico over the next seven days. It also noted that the region should expect between 40 and 210 smaller quakes, with magnitude 3 or larger – sizes that are likely to be felt – during that time. </p>
<p>With extended statistical modeling of earthquake sequences that include foreshock and aftershock probabilities, seismologists can forecast the likelihood of key earthquake scenarios to inform public safety efforts while earthquakes are occurring. For example, the USGS <a href="https://www.usgs.gov/news/magnitude-64-earthquake-puerto-rico">also estimated</a> as of Jan. 13 that there was an 81% chance that the largest shock had already occurred – namely, the magnitude 6.4 quake on Jan. 7. The agency calculated a 17% chance that a closely sized “doublet” 6.4 earthquake could yet occur. </p>
<p>Recognizing in real time when a set of earthquakes is likely to be a foreshock sequence is a challenging and active area of <a href="https://doi.org/10.1038/s41586-019-1606-4">earthquake forecasting research</a>. Progress in the effective forecasting and <a href="https://doi.org/10.1038/s43017-019-0007-4">communication of ongoing earthquake hazards</a> could mean the difference between life and death for people in the eastern Caribbean and other seismically active areas on an increasingly urbanized planet.</p>
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<p class="fine-print"><em><span>Richard Aster has received funding for earthquake research from the National Science Foundation, Los Alamos National Laboratory, Sandia National Laboratories and the U.S. Geological Survey. He is a past president of the Seismological Society of America (SSA) (2009-2011) and current chair of the board of directors of Incorporated Research Institutions for Seismology.
Aster also chairs the U.S. Geological Survey's Advanced National Seismic System Advisory Committee, and is a member of the Southern California Earthquake Center Advisory Council.
</span></em></p>Puerto Rico’s January earthquakes came after many foreshocks and have been followed by numerous aftershocks. Scientists are studying these sequences to improve earthquake forecasting.Richard Aster, Professor of Geophysics and Department Head, Colorado State UniversityLicensed as Creative Commons – attribution, no derivatives.