About

The faculty, staff and students in the Department of Atmospheric and Climate Science at the University of Washington are engaged in the study of a broad range of atmospheric phenomena and processes, using methods ranging from mathematical analysis to field experimentation. Research projects range in size from small studies involving individual scientists to large national and international programs involving teams of scientists. Research groups in the department are concerned with Atmospheric Chemistry, Atmospheric Dynamics, Boundary Layer Processes, Cloud and Aerosol Research, Glaciology and Planetary Atmospheres, Cloud Dynamics, Precipitation Processes, Storms, Weather Analysis and Forecasting, Climate, Global change, Airflow over mountains, and other topics. Some groups maintain special research facilities for the use of their students. In some of these activities, there is close cooperation with the University of Alaska Fairbanks, Oregon State University and the National Oceanic and Atmospheric Administration (NOAA) Regional Center through the Cooperative Institute for Climate, Ocean and Ecosystem Studies. Faculty members often have interests in more than one area, and research projects frequently involve questions of broad scope which do not fall neatly into a single category. This is particularly true of research projects directed toward understanding the chemical and physical modification of the environment by human activities.

Research topics

• Geology
• Climatology
• Environmental science
• Oceanography
• Geography
• Meteorology
• Computer Science
• Atmospheric sciences
• Machine Learning
• Statistics
• Paleontology
• Physical geography
• Mathematics

Selected publications

• Paleoclimate pattern effects help constrain climate sensitivity and 21st-century warming

  2025-10-07 · 1 citations

  articleOpen access

  Abstract: Paleoclimates provide examples of past climate change that inform estimates of modern warming from greenhouse-gas emissions, known as Earth's climate sensitivity. However, differences between past and present climate change must be accounted for when inferring climate sensitivity from paleoclimate evidence. The closest paleoclimate analog to near-term warming from greenhouse-gas emissions is the Pliocene (5.3-2.6 Ma), a warm epoch with atmospheric CO2 concentrations similar to today. Recent reconstructions indicate the Pliocene was 1°C warmer than previously thought, implying higher climate sensitivity, which is also supported by recent reconstructions showing more cooling with reduced CO2 at the Last Glacial Maximum (LGM; 19-23 thousand years ago). However, large-scale patterns of paleoclimate temperature change differ strongly from modern projections. Climate feedbacks and sensitivity depend on temperature patterns, and such "pattern effects" must be accounted for when using paleoclimates to constrain modern climate sensitivity. Here we combine data-assimilation reconstructions with atmospheric general circulation models to show Earth's climate is more sensitive to Pliocene forcing than modern CO2 forcing. Pliocene ice sheets, topography, and vegetation alter patterns of ocean warming and excite destabilizing cloud feedbacks, and LGM feedbacks are similarly amplified by the North American ice sheets. Accounting for paleoclimate pattern effects produces a best estimate (median) for modern climate sensitivity of 2.8°C and 66% confidence interval of 2.4-3.4°C (90% CI: 2.1-4.0°C), substantially reducing uncertainty in projections of 21st-century warming. Significance statement: Climate sensitivity's uncertain upper bound determines the worst-case projections of global warming. Recent paleoclimate reconstructions suggest high sensitivity of 5°C per CO2 doubling. However, by analyzing spatial patterns of Pliocene warming—the closest analog to near-term warming—we show that ice sheets and topography amplified past warming through regional impacts on oceans and clouds. Similarly, the Last Glacial Maximum's cooling was amplified by ocean and cloud responses to massive ice sheets. Because these amplifying feedbacks are associated with non-CO2 forcings unique to paleoclimates, the upper bound on modern warming from doubling CO2 is reduced by 1°C, constraining climate sensitivity to 2.1–4.0°C (90% confidence). Thus paleoclimate evidence revises climate sensitivity's upper bound and 21st-century warming projections.

  PublisherOA PDFDOI

• Performance of the Pangu‐Weather Deep Learning Model in Forecasting Tornadic Environments

  Geophysical Research Letters · 2025-04-09 · 3 citations

  articleOpen access

  Abstract The development of deep learning (DL) weather forecasting models has made rapid progress and achieved comparable or better skill than traditional Numerical Weather prediction (NWP) models, which are generally computationally intensive. However, applications of these DL models have yet to be fully explored, including for severe convective events. We evaluate the DL model Pangu‐Weather in forecasting tornadic environments with one‐day lead times using convective available potential energy (CAPE), 0–6 bulk wind difference (BWD6), and 0–3 km storm‐relative helicity (SRH3). We also compare its performance to the National Centers for Environmental Prediction (NCEP)'s Global Forecast System (GFS), a traditional NWP model. Pangu‐Weather generally outperforms GFS in predicting BWD6 and SRH3 at the closest grid point and hour of the storm report. However, Pangu‐Weather tends to underpredict the maximum values of all convective parameters in the 1–2 hr before the storm across the surrounding grid points compared to the GFS.

  PublisherOA PDFDOI

• Emulating chemistry-climate dynamics with a linear inverse model

  2025-07-17

  articleOpen access

  Abstract. Coupled chemistry–climate models (CCMs) are powerful tools for investigating chemical variability in the climate system, but high computational cost limits their use for hypothesis testing and adequately sampling variability on long timescales. Here, we present the first application of a linear inverse model (LIM) to emulate a CCM. A LIM is a lowdimensional empirical model that reproduces the CCM’s statistics and dynamics at low computational cost. By linearizing the CCM’s dynamics, the LIM captures coherent modes of variability, such as the El Niño Southern Oscillation (ENSO), that describe the coupled evolution of physical and chemical fields. Deterministic seasonal forecasts of the LIM result in skillful predictions of physical and chemical variables at lead times up to a year, outperforming damped persistence models. We show that the LIM’s skill in chemical fields depends on its coupled chemistry–climate modes: forecasts without the ENSO dynamical mode show a substantial loss of skill, suggesting the importance of ENSO in driving predictable chemical variability. These results demonstrate that the LIM can efficiently emulate CCM dynamics. It offers a practical tool for testing hypotheses about the drivers of chemistry-climate interactions and may enable efficient chemical data assimilation in the future.

  PublisherDOI

• Coupled Seasonal Data Assimilation of Sea Ice, Ocean, and Atmospheric Dynamics over the Last Millennium

  arXiv (Cornell University) · 2025-01-23

  preprintOpen access

  ``Online" data assimilation (DA) is used to generate a new seasonal-resolution reanalysis dataset over the last millennium by combining forecasts from an ocean--atmosphere--sea-ice coupled linear inverse model with climate proxy records. Instrumental verification reveals that this reconstruction achieves the highest correlation skill, while using fewer proxies, in surface temperature reconstructions compared to other paleo-DA products, particularly during boreal winter when proxy data are scarce. Reconstructed ocean and sea-ice variables also have high correlation with instrumental and satellite datasets. Verification against independent proxy records shows that reconstruction skill is robust throughout the last millennium. Analysis of the results reveals that the method effectively captures the seasonal evolution and amplitude of El Niño events. Reconstructed seasonal temperature variations are consistent with trends in orbital forcing over the last millennium.

  PublisherOA PDFDOI

• Supplementary material to "Emulating chemistry-climate dynamics with a linear inverse model"

  2025-07-17

  article
  PublisherDOI

• Testing the Limit of Atmospheric Predictability with a Machine Learning Weather Model

  ArXiv.org · 2025-04-28

  preprintOpen accessSenior author

  Atmospheric predictability research has long held that the limit of skillful deterministic weather forecasts is about 14 days. We challenge this limit using GraphCast, a machine-learning weather model, by optimizing forecast initial conditions using gradient-based techniques for twice-daily forecasts spanning 2020. This approach yields an average error reduction of 86% at 10 days, with skill lasting beyond 30 days. Mean optimal initial-condition perturbations reveal large-scale, spatially coherent corrections to ERA5, primarily reflecting an intensification of the Hadley circulation. Forecasts using GraphCast-optimal initial conditions in the Pangu-Weather model achieve a 21% error reduction, peaking at 4 days, indicating that analysis corrections reflect a combination of both model bias and a reduction in analysis error. These results demonstrate that, given accurate initial conditions, skillful deterministic forecasts are consistently achievable far beyond two weeks, challenging long-standing assumptions about the limits of atmospheric predictability.

  PublisherOA PDFDOI

• Coupled Seasonal Data Assimilation of Sea Ice, Ocean, and Atmospheric Dynamics over the Last Millennium

  Journal of Climate · 2025-09-11 · 5 citations

  article

  Abstract “Online” data assimilation (DA) is used to generate a seasonal-resolution reanalysis dataset over the last millennium by combining forecasts from an ocean–atmosphere–sea ice coupled linear inverse model with climate proxy records. Instrumental verification reveals that this reconstruction achieves the highest correlation skill while using fewer proxies in surface temperature reconstructions compared to other paleo-DA products, particularly during boreal winter when proxy data are scarce. Reconstructed ocean and sea ice variables also have high correlation with instrumental and satellite datasets. Verification against independent proxy records shows that reconstruction skill is robust throughout the last millennium. Analysis of the results reveals that the method effectively captures the seasonal evolution and amplitude of El Niño events, seasonal temperature trends that are consistent with orbital forcing over the last millennium, and polar-amplified cooling in the transition from the medieval climate anomaly to the little ice age. Significance Statement This paper introduces a new seasonal-resolution reanalysis of the last millennium, based on an “online” data assimilation method using a linear inverse model to assimilate paleoclimate proxies. We find good agreement when verifying the reconstruction against modern instrumental reanalyses and out-of-sample proxies. Results show that seasonal temperature trends are similar to predictions from orbital-insolation trends, and seasonal variability of modern El Niño events is similar to instrumental reanalyses. This framework offers a dynamically consistent, seasonally resolved view of past climate variability that supports broader applications in paleoclimate research.

  PublisherDOI

• Roles of MJO and Tropical–Extratropical Interactions in Subseasonal Conditions Related to Landfalling Atmospheric Rivers

  Journal of Climate · 2025-12-11

  article

  Abstract This study investigates the role of the Madden–Julian oscillation (MJO) and other tropical–extratropical interactions in generating atmospheric rivers (ARs) using a linear inverse model (LIM) framework. We examine subseasonal conditions that preferentially lead to landfalling ARs over Alaska, the Pacific Northwest, and California during boreal winters. We identify LIM dynamical modes that strongly project onto tropical sea surface temperature, such as El Niño–Southern Oscillation (ENSO); modes coupled with tropical heating, such as the MJO; and modes that are weakly coupled between the tropics and extratropics. The composite analysis of prolonged AR active conditions (14-day window) reveals that they are driven primarily by weakly coupled modes, with small contributions from tropically driven modes. The only significant signal from tropical heating modes is the subtropical vapor transport associated with MJO phases 6–7 for Alaska ARs. We also examine the role of the MJO in the optimal growth of initial conditions into the AR patterns. For all regions, the evolving optimals show nearly stationary phase patterns but propagating wave activity that shifts the location of maximum amplitude in time. The contributions from three mode groups are consistent across the regions, with the MJO partially contributing to the linear predictable growth, while weakly coupled modes remain the main drivers. The MJO and weakly coupled modes constructively interfere in subtropical vapor transport and destructively interfere in tropical convective activity. These findings underscore the importance of accurately resolving weakly coupled tropical–extratropical interactions to improve subseasonal AR predictions. Significance Statement Although the Madden–Julian oscillation (MJO) is often considered important for predicting atmospheric rivers on subseasonal time scales (10–30 days), our findings show that its contribution is relatively minor. Instead, other tropical–extratropical interactions that are only weakly connected to tropical heating play a larger role in driving atmospheric river activity. These weakly coupled processes tend to decay faster than MJO-related signals but are crucial for understanding and forecasting atmospheric river variability along the West Coast.

  PublisherDOI

• Top-of-atmosphere radiation over the last millennium reconstructed from proxies

  ArXiv.org · 2025-10-10

  preprintOpen accessSenior author

  Earth's energy imbalance at the top of the atmosphere is a key climate system metric, but its natural variability is poorly constrained by the short observational record and large uncertainty in coupled climate models. While existing ocean heat content reconstructions offer a longer perspective, they cannot separate the contributions of shortwave and longwave radiation, obscuring the underlying processes. We extend the energy budget record into the pre-industrial period by reconstructing the top-of-atmosphere radiation and related surface variables over the last millennium (850-2000 CE) by using data assimilation to combine proxy data and dynamics from a coupled climate emulator. Validation reveals skill in the reconstructed radiation fields, especially in the tropics. Results show a familiar last-millennium cooling trend, which coincides with persistent heat loss and a reduction in upper-ocean heat content. The cooling trend differs by season and latitude, and is associated with radiative anomalies suggestive of an eastward shift in Indo-Pacific convection. Following large volcanic eruptions, ocean heat content anomalies persist for 10-20 years on average, supporting previous evidence that the cooling trend was forced by decadally-paced eruptions. The reconstruction also reveals that the current rate of energy gain is unprecedented relative to the period before 1850.

  PublisherOA PDFDOI

• Deep Learning Atmospheric Models Reliably Simulate Out-of-Sample Land Heat and Cold Wave Frequencies

  ArXiv.org · 2025-07-03 · 1 citations

  preprintOpen access

  Deep learning (DL)-based general circulation models (GCMs) are emerging as fast simulators, yet their ability to replicate extreme events outside their training range remains unknown. Here, we evaluate two such models -- the hybrid Neural General Circulation Model (NGCM) and purely data-driven Deep Learning Earth System Model (DL\textit{ESy}M) -- against a conventional high-resolution land-atmosphere model (HiRAM) in simulating land heatwaves and coldwaves. All models are forced with observed sea surface temperatures and sea ice over 1900-2020, focusing on the out-of-sample early-20th-century period (1900-1960). Both DL models generalize successfully to unseen climate conditions, broadly reproducing the frequency and spatial patterns of heatwave and cold wave events during 1900-1960 with skill comparable to HiRAM. An exception is over portions of North Asia and North America, where all models perform poorly during 1940-1960. Due to excessive temperature autocorrelation, DL\textit{ESy}M tends to overestimate heatwave and cold wave frequencies, whereas the physics-DL hybrid NGCM exhibits persistence more similar to HiRAM.

  PublisherOA PDFDOI

Recent grants

• Collaborative Research: Developing a Next-Generation Approach to Regional Climate Prediction at High Resolution

  NSF · $381k · 2011–2016

• Ensemble-Based Hurricane State Estimation, Intensity Prediction, and Targeting

  NSF · $395k · 2009–2013

• Collaborative Research: P2C2--Assimilation of Cool and Warm Season Moisture Reconstructions and Atmospheric Conditions Over North America for the Past Millennium

  NSF · $145k · 2018–2021

• Collaborative Research: Optimized Deployment of Antarctic Surface Weather Observations

  NSF · $386k · 2016–2021

• P2C2: Paleoclimate Data Assimilation

  NSF · $423k · 2013–2017

Frequent coauthors

• Julien Emile‐Geay

  University of Southern California

  104 shared

• Eric J. Steig

  University of Washington

  66 shared

• Feng Zhu

  Climate and Global Dynamics Laboratory

  46 shared

• Robert Tardif

  University of Washington

  43 shared

• Daniel E. Amrhein

  NSF National Center for Atmospheric Research

  29 shared

• Michael P. Erb

  Northern Arizona University

  29 shared

• W. A. Perkins

  Allen Institute for Artificial Intelligence

  27 shared

• Kevin J. Anchukaitis

  University of Arizona

  24 shared

Education

• Ph.D., Atmospheric Sciences

  University of Washington

  1995

• M.S., Atmospheric Sciences

  University of Washington

  1992

• B.S., Atmospheric Sciences

  University of Washington

  1989

Awards & honors

• Annual Teaching Award, Department of Atmospheric Sciences, U…
• The Father James B. Macelwane Annual Awards in Meteorology,…