The American Geophysical Union honored five 91探花 faculty and researchers from the Earth and space sciences and atmospheric and climate science departments this week at the annual meeting in New Orleans.

Each year, the meeting draws thousands of scientists, educators and policymakers to discover emerging research, discuss hurdles and network. Prior to the meeting, AGU announces awards for individuals who have made significant contributions to Earth and space science and presents them in person during the week.

The theme is, 鈥淲here Science Connects Us,鈥� and the 91探花awardees were recognized for research that advances understanding of natural hazards, the history of Earth, weather and climate change.

Here are the UW鈥檚 five recipients and their respective awards:

, a 91探花assistant professor of Earth and space sciences, studies how magmas form beneath volcanoes. She specializes in work that involves using samples from past volcanic eruptions to examine the behavior of volcanic gases like water, carbon, and sulfur, which can help researchers monitor active volcanoes. Muth received the for early career scientists who have made outstanding contributions to fields of volcanology, geochemistry, and petrology.

, a 91探花professor of atmospheric and climate science, studies predictability, mountain meteorology and numerical weather prediction. Durran鈥檚 recent research focuses on using deep learning to change our current paradigm for numerical weather prediction, seasonal forecasting and climate modeling. He holds a joint position with NVIDIA. Durran received the award for prominent scientists who have made exceptional contributions to the understanding of weather and climate.

, a 91探花postdoctoral researcher of atmospheric and climate science, studies the formation and evolution of aerosol particles in the atmosphere, which play a pivotal role in both air pollution and climate change. By identifying and characterizing the fundamental chemical processes governing aerosol behavior, his research supports efforts to predict current atmospheric conditions and the trajectory of air quality and climate moving forward. Kenseth received the recognizing outstanding science and accomplishments by researchers that are within three years of receiving their doctorate.

, a 91探花assistant professor of Earth and space sciences, uses simulations to study the interactions between planetary atmospheres, interiors and biospheres to better understand the long-term evolution of Earth, Venus and rocky exoplanets. By building a holistic understanding of planetary evolution, this work will help enable scientists to search for life on other planets. Krissansen-Totton received the recognizing significant contributions to planetary science by early career researchers

, a 91探花professor of Earth and space sciences, studies the ratio of elements and their isotopes in rocks and minerals to understand how planets form and evolve. His research introduced a new method for analysis involving isotopic 鈥渇ingerprints鈥� that allows scientists to learn about Earth鈥檚 crust, the composition of the mantle, the origins of magma and even the early solar system. Teng was inducted as a , a program that recognizes AGU members who have made exceptional contributions to Earth and space science through a breakthrough, discovery or innovation in their field.

So-called 鈥溾�� now seem almost commonplace as floods, storms and fires continue to set new standards for largest, strongest and most destructive. But to categorize weather as a true 100-year event, there must be just a 1% chance of it occurring in any given year. The trouble is that researchers don鈥檛 always know whether the weather aligns with the current climate or defies the odds.

Traditional weather forecasting models run on energy-hogging supercomputers that are typically housed at large research institutions. In the past five years, artificial intelligence has emerged as a powerful tool for cheaper, faster forecasting, but most AI-powered models can only accurately forecast 10 days into the future. 聽Still, longer-range forecasts are critical for climate science 鈥� and helping people prepare for seasons to come.

In a in AGU Advances, 91探花 researchers used AI to simulate the Earth鈥檚 current climate and interannual variability for up to 1,000 years. The model runs on a single processor and takes just 12 hours to generate a forecast. On a state-of-the-art supercomputer, the same simulation would take approximately 90 days.

鈥淲e are developing a tool that examines the variability in our current climate to help answer this lingering question: Is a given event the kind of thing that happens naturally, or not?鈥� said , a 91探花professor of atmospheric and climate science.

Durran was one of the first to introduce AI into weather forecasting more than five years ago when he and former 91探花graduate student partnered with Microsoft Research. Durran also holds a joint position as a researcher with California-based Nvidia.

鈥淭o train an AI model, you have to give it tons of data,鈥� Durran said. 鈥淏ut if you break up the available historical data by season, you don鈥檛 get very many chunks.鈥�

The most accurate global datasets for the daily weather go back to roughly 1979. Although there are plenty of days between then and now that can be used to train a daily weather forecast model, the same period contains fewer seasons.聽This lack of historical data was perceived as a barrier to using AI for seasonal forecasting.

Counterintuitively, the Durran group鈥檚 latest contribution to forecasting, Deep Learning Earth SYstem Model, or DLESyM聽, was trained for one-day forecasts, but still learned how to capture seasonal variability.

The model combines two neural networks: one representing the atmosphere and the other, the ocean. While traditional Earth-system models often join atmospheric and oceanic forecasts, researchers had yet to incorporate this approach into models powered by AI alone.

鈥淲e were the first to apply this framework to AI and we found out that it worked really well,鈥� said lead author , a 91探花graduate student in atmospheric and climate science. 鈥淲e鈥檙e presenting this as a model that defies a lot of the present assumptions surrounding AI in climate science.鈥�

Because the temperature of the sea surface changes slower than the air temperature, the oceanic model updates its predictions every four days, while the atmospheric model updates every 12 hours. Cresswell-Clay is currently working on adding a land-surface model to DLESyM.

鈥淥ur design opens the door for adding other components of the Earth system in the future,鈥� he said, especially components that have been difficult to model in the past, such as the relationship between soil, plants and the atmosphere. Instead of researchers coming up with an equation to represent this complex relationship, AI learns directly from the data.

The researchers showcased the model鈥檚 performance by comparing its forecasts of past events to those generated by the four leading models from the sixth phase of the Coupled Model Intercomparison Project, or CMIP6, all of which run on supercomputers. Climate predictions of future climate from these models were key resources used in the last report from the .

DLESyM simulated tropical cyclones and the seasonal cycle of the Indian summer monsoon better than the CMIP6 models. In mid-latitudes, DLESyM captured the month-to-month and interannual variability of weather patterns at least as well as the CMIP6 models.

For example, the model captured atmospheric 鈥渂locking鈥� events just as well as the leading physics-based models. Blocking refers to the formation of atmospheric ridges that keep regions hot and dry, and others cold or wet, by deflecting incoming weather systems. 鈥淎 lot of the existing climate models actually don鈥檛 do a very good job capturing this pattern,鈥� Cresswell-Clay said. 鈥淭he quality of our results validates our model and improves our trust in its future projections.鈥�

Neither the CMIP6 models nor DLESyM are 100% accurate, but the fact that the AI-based approach was competitive while using so much less power is significant.

鈥淣ot only does the model have a much lower carbon footprint, but anyone can download it from our website and run complex experiments, even if they don鈥檛 have supercomputer access,鈥� Durran said. 鈥淭his puts the technology within reach for many other researchers.鈥�

Other authors include , a visiting 91探花doctoral student in atmospheric and climate science; a 91探花doctoral student in atmospheric and climate science; , a 91探花doctoral student in atmospheric and climate science; Ra煤l A. Moreno, a doctoral student in atmospheric and climate science and , a postdoctoral researcher in neuro-cognitive modeling at the University of T眉bingen in Germany.

This work was funded by the U.S. Office of Naval Research, the U.S. Department of Defense, the University of Chinese Academy of Sciences, the National Science Foundation of China, Deutscher Akademischer Austauschdienst, International Max Planck Research School for Intelligent Systems, Deutsche Forschungsgemeinschaft, U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research and the NVIDIA Applied Research Accelerator Program.

For more information, contact Nathaniel Cresswell-Clay at nacc@atmos.washington.edu or Dale Durran at drdee@uw.edu

The future could take a radically different approach. A collaboration between the 91探花 and Microsoft Research shows how artificial intelligence can analyze past weather patterns to predict future events, much more efficiently and potentially someday more accurately than today鈥檚 technology.

The newly developed global weather model bases its predictions on the past 40 years of weather data, rather than on detailed physics calculations. The simple, data-based A.I. model can simulate a year鈥檚 weather around the globe much more quickly and almost as well as traditional weather models, by taking similar repeated steps from one forecast to the next, according to a published this summer in the Journal of Advances in Modeling Earth Systems.

鈥淢achine learning is essentially doing a glorified version of pattern recognition,鈥� said lead author , who did the research as part of his 91探花doctorate in atmospheric sciences. 鈥淚t sees a typical pattern, recognizes how it usually evolves and decides what to do based on the examples it has seen in the past 40 years of data.鈥�

On the left is the new paper鈥檚 鈥淒eep Learning Weather Prediction鈥� forecast. The middle is the actual weather for the 2017-18 year, and at right is the average weather for that day. Weyn et al./ Journal of Advances in Modeling Earth Systems

Although the new model is, unsurprisingly, less accurate than today鈥檚 top traditional forecasting models, the current A.I. design uses about 7,000 times less computing power to create forecasts for the same number of points on the globe. Less computational work means faster results.

That speedup would allow the forecasting centers to quickly run many models with slightly different starting conditions, a technique called 鈥溾�� that lets weather predictions cover the range of possible expected outcomes for a weather event 鈥� for instance, where a hurricane might strike.

鈥淭here’s so much more efficiency in this approach; that’s what’s so important about it,鈥� said author , a 91探花professor of atmospheric sciences. 鈥淭he promise is that it could allow us to deal with predictability issues by having a model that鈥檚 fast enough to run very large ensembles.鈥�

Co-author at Microsoft Research had initially approached the 91探花group to propose a project using artificial intelligence to make weather predictions based on historical data without relying on physical laws. Weyn was taking a 91探花computer science course in machine learning and decided to tackle the project.

鈥淎fter training on past weather data, the A.I. algorithm is capable of coming up with relationships between different variables that physics equations just can’t do,鈥� Weyn said. 鈥淲e can afford to use a lot fewer variables and therefore make a model that’s much faster.鈥�

To merge successful A.I. techniques with weather forecasting, the team mapped six faces of a cube onto planet Earth, then flattened out the cube鈥檚 six faces, like in an architectural paper model. The authors treated the polar faces differently because of their unique role in the weather as one way to improve the forecast鈥檚 accuracy.

The authors then tested their model by predicting the global height of the 500 hectopascal pressure, a standard variable in weather forecasting, every 12 hours for a full year. A recent , which included Weyn as a co-author, introduced WeatherBench as a benchmark test for data-driven weather forecasts. On that forecasting test, developed for three-day forecasts, this new model is one of the top performers.

The data-driven model would need more detail before it could begin to compete with existing operational forecasts, the authors say, but the idea shows promise as an alternative approach to generating weather forecasts, especially with a growing amount of previous forecasts and weather observations.

Weyn is now a data scientist with Microsoft鈥檚 weather and finance division. This research was funded by the U.S. Office of Naval Research and a Department of Defense graduate fellowship.

For more information, contact Durran at drdee@uw.edu or Weyn at jweyn@uw.edu.

In this year of extreme weather events 鈥� from devastating West Coast wildfires to tropical Atlantic storms that have exhausted the alphabet 鈥� scientists and members of the public are asking when these extreme events can be scientifically linked to climate change.

, a professor of atmospheric sciences at the 91探花, argues that climate science need to approach this question in a way similar to how weather forecasters issue warnings for hazardous weather.

In a new , published in the October issue of the Bulletin of the American Meteorological Society, he draws on the weather forecasting community鈥檚 experience in predicting extreme weather events such as tornadoes, flash floods, high winds and winter storms. If forecasters send out a mistaken alert too often, people will start to ignore them. If they don鈥檛 alert for severe events, people will get hurt. How can the atmospheric sciences community find the right balance?

Most current approaches to attributing extreme weather events to global warming, he says, such as the conditions leading to the ongoing Western wildfires, focus on the likelihood of raising a false alarm. Scientists do this by using statistics to estimate the increase in the probability of that event that is attributable to climate change. 聽Those statistical measures are closely related to the 鈥渇alse alarm ratio,鈥� an important metric used to assess the quality of hazardous weather warnings.

But there is a second key metric used to assess the performance of weather forecasters, he argues: The probably that the forecast will correctly warn of events that actually occur, known as 聽the 鈥減robability of detection.鈥� The ideal probability of detection score is 100%, while the ideal false-alarm rate would be zero.

Probability of detection has mostly been ignored when it comes to linking extreme events to climate change, he says. Yet both weather forecasting and climate change attribution face a tradeoff between the two. In both weather forecasting and climate-change attribution, calculations in the paper show that raising the thresholds to reduce false alarms produces a much greater drop in the probability of detection.

Drawing on a hypothetical example of a tornado forecaster whose false alarm ratio is zero, but is accompanied by a low probability of detection, he writes that such an 鈥渙verly cautious tornado forecasting strategy might be argued by some to be smart politics in the context of attributing extreme events to global warming, but it is inconsistent with the way meteorologists warn for a wide range of hazardous weather, and arguably with the way society expects to be warned about threats to property and human life.鈥�

Why does this matter? The paper concludes by noting: 鈥淚f a forecaster fails to warn for a tornado there may be serious consequences and loss of life, but missing the forecast does not make next year’s tornadoes more severe. On the other hand, every failure to alert the public about those extreme events actually influenced by global warming facilitates the illusion that mankind has time to delay the actions required to address the source of that warming. Because the residence time of CO₂ in the atmosphere is many hundreds to thousands of years the cumulative consequences of such errors can have a very long lifetime.鈥�

For more information, contact Durran at drdee@uw.edu.

During the decades since, and have sparked countless debates and pop culture references. But the question also holds practical importance: What do small, unpredictable events mean for the future of weather prediction?

A 91探花 study asks whether unobserved, minuscule disturbances 鈥� like those from butterfly wings 鈥� actually affect weather forecasts. Luckily for those who rely on the weather report, the answer is no.

“The butterfly effect is important, as an example of how errors might theoretically spread to larger scales, but actual butterflies don’t matter for forecasts,” said , a 91探花professor of atmospheric sciences.

He is lead author of “,” published in the February issue of the Bulletin of the American Meteorological Society.

What matters, he says, is getting the bigger picture right.

“The uncertainty in a meteorological forecast generated by ignoring the flapping of a butterfly’s wings 鈥� or even broader circulations 1 mile wide 鈥� is less than that produced by very-small-percentage errors in our observations of much larger-scale motions,” Durran said.

Thunderstorms can grow rapidly from a small cloud to a huge storm, and are notoriously difficult to forecast. The researchers used this as their test case.

“The evolution of thunderstorms is thought to be particularly sensitive to small-scale disturbances,” Durran said.

The study used computer simulations of squall lines, the row of thunderstorms that can form ahead of a cold air front. The authors looked at the effect of beginning the simulation with modest errors at different horizontal scales. Minor errors at large scales of about 80 miles (128 kilometers) mattered as much for the forecast as more significant errors at a smaller scale of about 5 miles (8 kilometers).

On the one hand, this is good news, since small-scale motions, which are almost impossible to observe routinely, don’t matter so much, confirming Durran’s on the meteorological irrelevance of butterflies. On the other hand, it’s bad news, because even little mistakes in the large-scale observations can throw off a forecast for a thunderstorm or a .

“Perhaps counterintuitively, you have to know the large scale with a great deal of precision to get the small scale right,” Durran said. “There’s a lot of energy in the larger scales, so if you make a small fraction of a percent error there, it might not seem like much at the start, but a couple hours into the forecast, it makes a difference.”

It’s not necessary to create a dense network of observing stations to measure the atmosphere at finer and finer scales, Durran said.

Instead of sweating the small stuff, he says, scientists need to improve the way they assimilate, or input, existing observations of the atmosphere on horizontal scales between 100 and 300 miles (160 to 480 km) in order to start local-area forecasts with the best possible description of the air circulating.

“It’s going to be difficult, but not impossible, to improve the larger scales,” Durran said聽聽聽聽 The other co-author is , a 91探花doctoral student in atmospheric sciences. The research was funded by the U.S. Office of Naval Research.

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For more information, contact Durran at drdee@uw.edu or 206-543-7440.

Grant number: N000141410287

It turns out that in sultry weather condensation on the outside of a canned beverage doesn’t just make it slippery: those drops can provide more heat than the surrounding air, meaning your drink would warm more than twice as much in humid weather compared to in dry heat. In typical summer weather in New Orleans, heat released by condensation warms the drink by 6 degrees Fahrenheit in five minutes.

“Probably the most important thing a does is not simply insulate the can, but keep condensation from forming on the outside of it,” said , a 91探花professor of atmospheric sciences.

He’s co-author of results published in the April issue of that give the exact warming for a range of plausible summer temperatures and humidity levels. For example, on the hottest, most humid day in Dhahran, Saudi Arabia, condensation alone would warm a can from near-freezing temperature to 48 degrees Fahrenheit in just five minutes.

The investigation began a couple of years ago when Durran was teaching 91探花Atmospheric Sciences 101 and trying to come up with a good example for the heat generated by condensation. Plenty of examples exist for evaporative cooling, but few for the reverse phenomenon. Durran thought droplets that form on a cold canned beverage might be just the example he was looking for.

A quick back-of-the-napkin calculation showed the heat released by water just four thousandths of an inch thick covering the can would heat its contents by 9 degrees Fahrenheit.

“I was surprised to think that such a tiny film of water could cause that much warming,” Durran said.

Though he’s normally more of a theoretician, Durran decided this result required experimental validation. He recruited co-author , a 91探花associate professor of atmospheric sciences, and they ran an initial test in Frierson’s little-used basement bathroom, using a space heater and hot shower to vary the temperature and humidity.

The findings corroborated the initial result, and they embarked on a larger-scale test.

“You can’t write an article for Physics Today where the data has come from a setup on the top of the toilet tank in one of the author’s bathrooms,” Durran said.

First they recruited colleagues in Frierson’s beachside hometown of Wilmington, North Carolina, to duplicate the experiment and compare results with those taken on a hot, dry Seattle day. But they decided they needed to test a wider range of conditions.

Finally, last summer undergraduates Stella Choi and Steven Brey joined the project to run a proper experiment in the 91探花Atmospheric Sciences building. They unearthed an experimental machine with styling that looks to be from the 1950s, last used decades ago to simulate cloud formation.

With funding for educational outreach from the National Science Foundation, the students first cooled a can in a bucket of ice water then dried it and placed it in the experimental chamber dialed up to the appropriate conditions. After five minutes they removed the can, weighed it to measure the amount of condensation, and recorded the final temperature of the water inside.

The phenomenon at work 鈥� latent heat of condensation 鈥� is central to Frierson’s research on water vapor, heat transfer and global climate change.

“We expect a much moister atmosphere with global warming because warmer air can hold a lot more water vapor,” Frierson said. Because heat is transferred when water evaporates or condenses, this change affects wind circulation, weather patterns and storm formation.

Durran’s research includes studies of thunderstorms, which are powered by heat released from condensation in rising moist air.

As for his demonstration of the heat released during this process, he and Frierson are now working with the National Center for Atmospheric Research to develop an educational tool that will let students around the world try the experiment and post their results online for comparison.

The example promises to become as classic as a cold drink on a hot summer day.

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For more information, contact Durran at 206-543-7440 or durrand@atmos.washington.edu and Frierson at 206-685-7364 or dargan@atmos.washington.edu.