Articles | Volume 19, issue 5
https://doi.org/10.5194/gmd-19-1937-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-19-1937-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
09 Mar 2026
Development and technical paper |
| 09 Mar 2026
| 09 Mar 2026
Improving thermodynamic nudging in the E3SM Atmosphere Model version 2 (EAMv2): strategy and hindcast skills on weather systems
Atmospheric, Climate, and Earth Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
Atmospheric, Climate, and Earth Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
Bryce E. Harrop
Atmospheric, Climate, and Earth Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
Aniruddha Bora
Division of Applied Mathematics, Brown University, Providence, RI 02912, USA
George Karniadakis
Division of Applied Mathematics, Brown University, Providence, RI 02912, USA
Khemraj Shukla
Division of Applied Mathematics, Brown University, Providence, RI 02912, USA
Kai Zhang
Atmospheric, Climate, and Earth Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
Related authors
Jinbo Xie, Qi Tang, Michael Prather, Jadwiga Richter, and Shixuan Zhang
Atmos. Chem. Phys., 25, 9315–9333, https://doi.org/10.5194/acp-25-9315-2025, https://doi.org/10.5194/acp-25-9315-2025, 2025
Short summary
Short summary
Analysis of the interaction between the climate and ozone in the stratosphere is complicated by the inability of climate models to simulate the quasi-biennial oscillation (QBO) – an important climate mode in the stratosphere. We use a set of model simulations that realistically simulate QBO and a novel ozone diagnostic tool to separate temperature- and circulation-driven QBO impacts. These are important for diagnosing model–model differences in QBO–ozone responses for climate projections.
Taufiq Hassan, Kai Zhang, Jianfeng Li, Balwinder Singh, Shixuan Zhang, Hailong Wang, and Po-Lun Ma
Geosci. Model Dev., 17, 3507–3532, https://doi.org/10.5194/gmd-17-3507-2024, https://doi.org/10.5194/gmd-17-3507-2024, 2024
Short summary
Short summary
Anthropogenic aerosol emissions are an essential part of global aerosol models. Significant errors can exist from the loss of emission heterogeneity. We introduced an emission treatment that significantly improved aerosol emission heterogeneity in high-resolution model simulations, with improvements in simulated aerosol surface concentrations. The emission treatment will provide a more accurate representation of aerosol emissions and their effects on climate.
Pengfei Shi, L. Ruby Leung, Bin Wang, Kai Zhang, Samson M. Hagos, and Shixuan Zhang
Geosci. Model Dev., 17, 3025–3040, https://doi.org/10.5194/gmd-17-3025-2024, https://doi.org/10.5194/gmd-17-3025-2024, 2024
Short summary
Short summary
Improving climate predictions have profound socio-economic impacts. This study introduces a new weakly coupled land data assimilation (WCLDA) system for a coupled climate model. We demonstrate improved simulation of soil moisture and temperature in many global regions and throughout the soil layers. Furthermore, significant improvements are also found in reproducing the time evolution of the 2012 US Midwest drought. The WCLDA system provides the groundwork for future predictability studies.
Jianfeng Li, Kai Zhang, Taufiq Hassan, Shixuan Zhang, Po-Lun Ma, Balwinder Singh, Qiyang Yan, and Huilin Huang
Geosci. Model Dev., 17, 1327–1347, https://doi.org/10.5194/gmd-17-1327-2024, https://doi.org/10.5194/gmd-17-1327-2024, 2024
Short summary
Short summary
By comparing E3SM simulations with and without regional refinement, we find that model horizontal grid spacing considerably affects the simulated aerosol mass budget, aerosol–cloud interactions, and the effective radiative forcing of anthropogenic aerosols. The study identifies the critical physical processes strongly influenced by model resolution. It also highlights the benefit of applying regional refinement in future modeling studies at higher or even convection-permitting resolutions.
Shixuan Zhang, Kai Zhang, Hui Wan, and Jian Sun
Geosci. Model Dev., 15, 6787–6816, https://doi.org/10.5194/gmd-15-6787-2022, https://doi.org/10.5194/gmd-15-6787-2022, 2022
Short summary
Short summary
This study investigates the nudging implementation in the EAMv1 model. We find that (1) revising the sequence of calculations and using higher-frequency constraining data to improve the performance of a simulation nudged to EAMv1’s own meteorology, (2) using the relocated nudging tendency and 3-hourly ERA5 reanalysis to obtain a better agreement between nudged simulations and observations, and (3) using wind-only nudging are recommended for the estimates of global mean aerosol effects.
Kai Zhang, Wentao Zhang, Hui Wan, Philip J. Rasch, Steven J. Ghan, Richard C. Easter, Xiangjun Shi, Yong Wang, Hailong Wang, Po-Lun Ma, Shixuan Zhang, Jian Sun, Susannah M. Burrows, Manish Shrivastava, Balwinder Singh, Yun Qian, Xiaohong Liu, Jean-Christophe Golaz, Qi Tang, Xue Zheng, Shaocheng Xie, Wuyin Lin, Yan Feng, Minghuai Wang, Jin-Ho Yoon, and L. Ruby Leung
Atmos. Chem. Phys., 22, 9129–9160, https://doi.org/10.5194/acp-22-9129-2022, https://doi.org/10.5194/acp-22-9129-2022, 2022
Short summary
Short summary
Here we analyze the effective aerosol forcing simulated by E3SM version 1 using both century-long free-running and short nudged simulations. The aerosol forcing in E3SMv1 is relatively large compared to other models, mainly due to the large indirect aerosol effect. Aerosol-induced changes in liquid and ice cloud properties in E3SMv1 have a strong correlation. The aerosol forcing estimates in E3SMv1 are sensitive to the parameterization changes in both liquid and ice cloud processes.
Hui Wan, Kai Zhang, Philip J. Rasch, Vincent E. Larson, Xubin Zeng, Shixuan Zhang, and Ross Dixon
Geosci. Model Dev., 15, 3205–3231, https://doi.org/10.5194/gmd-15-3205-2022, https://doi.org/10.5194/gmd-15-3205-2022, 2022
Short summary
Short summary
This paper describes a tool embedded in a global climate model for sampling atmospheric conditions and monitoring physical processes as a numerical simulation is being carried out. The tool facilitates process-level model evaluation by allowing the users to select a wide range of quantities and processes to monitor at run time without having to do tedious ad hoc coding.
Hui Wan, Shixuan Zhang, Philip J. Rasch, Vincent E. Larson, Xubin Zeng, and Huiping Yan
Geosci. Model Dev., 14, 1921–1948, https://doi.org/10.5194/gmd-14-1921-2021, https://doi.org/10.5194/gmd-14-1921-2021, 2021
Short summary
Short summary
Numerical models used in weather and climate research and prediction unavoidably contain numerical errors resulting from temporal discretization, and the impact of such errors can be substantial. Complex process interactions often make it difficult to pinpoint the exact sources of such errors. This study uses a series of sensitivity experiments to identify components in a global atmosphere model that are responsible for time step sensitivities in various cloud regimes.
Fang Li, David Lawrence, Brendan Rogers, Chantelle Burton, Huilin Huang, Yiquan Jiang, Johannes Kaiser, Matthew Kasoar, Hanna Lee, Ruby Leung, Lars Nieradzik, Aihui Wang, Daniel Ward, Ligeer Ce, Yangchun Li, Zhongda Lin, Apostolos Voulgarakis, and Yongkang Xue
EGUsphere, https://doi.org/10.5194/egusphere-2025-6115, https://doi.org/10.5194/egusphere-2025-6115, 2025
Short summary
Short summary
Extreme fire events are increasing, and fires are projected to rise across most regions, posing growing risks to ecosystems and society. As a key Earth system process, fire is now modeled in most Earth System Models (ESMs). FireMIP within CMIP7 will evaluate fire simulations in state‑of‑the‑art ESMs, project future fire changes, and provide quantitative, process‑based understanding of fire's role in the Earth system.
Koichi Sakaguchi, Seth A. McGinnis, L. Ruby Leung, Melissa S. Bukovsky, Rachel R. McCrary, Ziming Chen, Chuan-Chieh Chang, and Yanjie Li
EGUsphere, https://doi.org/10.5194/egusphere-2025-5544, https://doi.org/10.5194/egusphere-2025-5544, 2025
Short summary
Short summary
We evaluated Rossby waves in dynamical downscaling simulations over North America, and their connections to surface air temperature variability and heatwaves. Simulated Rossby wave propagation is distorted by flow discontinuities at lateral boundaries and by biased mean wind patterns, thereby breaking the region-specific connections between Rossby waves and surface temperature. Adjusting simulated large-scale winds to match the forcing data can reduce these biases.
Pengfei Shi, L. Ruby Leung, Zhaoxia Pu, Samson Hagos, and Karthik Balaguru
EGUsphere, https://doi.org/10.5194/egusphere-2025-4910, https://doi.org/10.5194/egusphere-2025-4910, 2025
Short summary
Short summary
Reliable climate prediction requires accurate initialization of the ocean state. We developed a new data assimilation system that incorporates ocean temperature and salinity observations into a fully coupled climate model. This system improves simulations of Earth system variability from years to decades, and enhances skills in simulating winter temperature and precipitation variability over the United States. The results advance more reliable and skillful climate predictions.
Naser Mahfouz, Hassan Beydoun, Johannes Mülmenstädt, Noel Keen, Adam C. Varble, Luca Bertagna, Peter Bogenschutz, Andrew Bradley, Matthew W. Christensen, T. Conrad Clevenger, Aaron Donahue, Jerome Fast, James Foucar, Jean-Christophe Golaz, Oksana Guba, Walter Hannah, Benjamin Hillman, Robert Jacob, Wuyin Lin, Po-Lun Ma, Yun Qian, Balwinder Singh, Christopher Terai, Hailong Wang, Mingxuan Wu, Kai Zhang, Andrew Gettelman, Mark Taylor, L. Ruby Leung, Peter Caldwell, and Susannah Burrows
Atmos. Chem. Phys., 25, 15105–15120, https://doi.org/10.5194/acp-25-15105-2025, https://doi.org/10.5194/acp-25-15105-2025, 2025
Short summary
Short summary
Our study assesses the aerosol effective radiative forcing in a global cloud-resolving atmosphere model at ultra-high resolution. We demonstrate that global aerosol forcing signal can be robustly reproduced across resolutions when aerosol activation processes are carefully parameterized. Further, we argue that simplified prescribed aerosol schemes will open the door for further process/mechanism studies under controlled conditions.
Yuanqi Hong, Guta Abeshu, Hong-Yi Li, Dingbao Wang, Mengqi Zhao, Thomas Wild, Günter Blöschl, and Ruby Leung
EGUsphere, https://doi.org/10.5194/egusphere-2025-5039, https://doi.org/10.5194/egusphere-2025-5039, 2025
Short summary
Short summary
Rain runs off in two main ways: saturation excess when soil is full, and infiltration excess when rain exceeds infiltration. They often occur simultaneously in a watershed, but most models treat them separately. We introduce a new theory that captures both processes within a single framework, tracking their shift over space and time. Tested over 181 U.S. watersheds, the theory well predicts streamflow in most watersheds and improves understanding of runoff under diverse climates.
Dongyu Feng, Zeli Tan, Darren Engwirda, Jonathan D. Wolfe, Donghui Xu, Chang Liao, Gautam Bisht, James J. Benedict, Tian Zhou, Mithun Deb, Hong-Yi Li, and L. Ruby Leung
Nat. Hazards Earth Syst. Sci., 25, 3619–3639, https://doi.org/10.5194/nhess-25-3619-2025, https://doi.org/10.5194/nhess-25-3619-2025, 2025
Short summary
Short summary
Our study explores how riverine and coastal flooding during hurricanes is influenced by the interaction of atmosphere, land, river, and ocean conditions. Using an advanced Earth system model, we simulate Hurricane Irene to evaluate how meteorological and hydrological uncertainties affect flood modeling. Our findings reveal the importance of a multi-component modeling system, how hydrological conditions play critical roles in flood modeling, and greater flood risks if multiple factors are present.
Hui Wan, Abhishek Yenpure, Berk Geveci, Richard C. Easter, Philip J. Rasch, Kai Zhang, and Xubin Zeng
Geosci. Model Dev., 18, 5655–5680, https://doi.org/10.5194/gmd-18-5655-2025, https://doi.org/10.5194/gmd-18-5655-2025, 2025
Short summary
Short summary
In the Energy Exascale Earth System Model version 2 (E3SMv2) and many other global models, the simulated anthropogenic aerosol effective radiative forcing is sensitive to the presence of clouds with very low droplet number concentrations. Numerical experiments presented in this paper indicate that mid- and high-latitude low-level stratus occurring under weak turbulence is a key cloud regime for investigating the causes of these very low cloud droplet number concentrations in E3SMv2.
Jinbo Xie, Qi Tang, Michael Prather, Jadwiga Richter, and Shixuan Zhang
Atmos. Chem. Phys., 25, 9315–9333, https://doi.org/10.5194/acp-25-9315-2025, https://doi.org/10.5194/acp-25-9315-2025, 2025
Short summary
Short summary
Analysis of the interaction between the climate and ozone in the stratosphere is complicated by the inability of climate models to simulate the quasi-biennial oscillation (QBO) – an important climate mode in the stratosphere. We use a set of model simulations that realistically simulate QBO and a novel ozone diagnostic tool to separate temperature- and circulation-driven QBO impacts. These are important for diagnosing model–model differences in QBO–ozone responses for climate projections.
Zeli Tan, Donghui Xu, Sourav Taraphdar, Jiangqin Ma, Gautam Bisht, and L. Ruby Leung
Hydrol. Earth Syst. Sci., 29, 3833–3852, https://doi.org/10.5194/hess-29-3833-2025, https://doi.org/10.5194/hess-29-3833-2025, 2025
Short summary
Short summary
Flow depth and velocity determine various river functions, but their high-resolution simulations are expensive. Here, we developed a downscaling approach that can provide fast and accurate estimation of high-resolution river hydrodynamics. The 84-fold acceleration achieved by the method makes reliable flood risk analysis that needs hundreds or thousands of model runs feasible. More importantly, it provides an opportunity to couple large-scale hydrodynamics with local processes in river models.
Jianfeng Li, Andrew Geiss, Zhe Feng, L. Ruby Leung, Yun Qian, and Wenjun Cui
Earth Syst. Sci. Data, 17, 3721–3740, https://doi.org/10.5194/essd-17-3721-2025, https://doi.org/10.5194/essd-17-3721-2025, 2025
Short summary
Short summary
We developed a high-resolution (4 km and hourly) observational derecho dataset over the United States east of the Rocky Mountains from 2004 to 2021 by using a mesoscale convective system dataset, bow echoes detected by a machine learning method, hourly gust speeds, and physically based identification criteria.
Chang Liao, L. Ruby Leung, Yilin Fang, Teklu Tesfa, and Robinson Negron-Juarez
Geosci. Model Dev., 18, 4601–4624, https://doi.org/10.5194/gmd-18-4601-2025, https://doi.org/10.5194/gmd-18-4601-2025, 2025
Short summary
Short summary
Understanding horizontal groundwater flow is important for understanding how water moves through the ground. Current climate models often simplify this process because they do not have information about the land surface that is detailed enough. Our study developed a new model that divides the land surface into hillslopes to better represent how groundwater flows. This model can help improve predictions of water availability and how it affects ecosystems.
Lingbo Li, Hong-Yi Li, Guta Abeshu, Jinyun Tang, L. Ruby Leung, Chang Liao, Zeli Tan, Hanqin Tian, Peter Thornton, and Xiaojuan Yang
Earth Syst. Sci. Data, 17, 2713–2733, https://doi.org/10.5194/essd-17-2713-2025, https://doi.org/10.5194/essd-17-2713-2025, 2025
Short summary
Short summary
We have developed new maps that reveal how organic carbon from soil leaches into headwater streams over the contiguous United States. We use advanced artificial intelligence techniques and a massive amount of data, including observations at over 2500 gauges and a wealth of climate and environmental information. The maps are a critical step in understanding and predicting how carbon moves through our environment, hence making them a useful tool for tackling climate challenges.
Pengfei Shi, L. Ruby Leung, and Bin Wang
Geosci. Model Dev., 18, 2443–2460, https://doi.org/10.5194/gmd-18-2443-2025, https://doi.org/10.5194/gmd-18-2443-2025, 2025
Short summary
Short summary
Improving climate predictions has significant socio-economic impacts. In this study, we develop and apply a new weakly coupled ocean data assimilation (WCODA) system to a coupled climate model. The WCODA system improves simulations of ocean temperature and salinity across many global regions. This system is meant to advance our understanding of the ocean's role in climate predictability.
Guta Wakbulcho Abeshu, Hong-Yi Li, Mingjie Shi, and L. Ruby Leung
Hydrol. Earth Syst. Sci., 29, 1847–1864, https://doi.org/10.5194/hess-29-1847-2025, https://doi.org/10.5194/hess-29-1847-2025, 2025
Short summary
Short summary
This study examined how water availability, climate dryness, and plant productivity interact at the catchment scale. Using various indices and statistical methods, we found a 0–2-month lag in these interactions. Strong correlations during peak-productivity months were observed, with a notable hysteresis effect in vegetation response to changes in water availability and climate dryness. The findings help better understand catchment responses to climate variability.
Katherine M. Smith, Alice M. Barthel, LeAnn M. Conlon, Luke P. Van Roekel, Anthony Bartoletti, Jean-Christophe Golaz, Chengzhu Zhang, Carolyn Branecky Begeman, James J. Benedict, Gautam Bisht, Yan Feng, Walter Hannah, Bryce E. Harrop, Nicole Jeffery, Wuyin Lin, Po-Lun Ma, Mathew E. Maltrud, Mark R. Petersen, Balwinder Singh, Qi Tang, Teklu Tesfa, Jonathan D. Wolfe, Shaocheng Xie, Xue Zheng, Karthik Balaguru, Oluwayemi Garuba, Peter Gleckler, Aixue Hu, Jiwoo Lee, Ben Moore-Maley, and Ana C. Ordoñez
Geosci. Model Dev., 18, 1613–1633, https://doi.org/10.5194/gmd-18-1613-2025, https://doi.org/10.5194/gmd-18-1613-2025, 2025
Short summary
Short summary
Version 2.1 of the U.S. Department of Energy's Energy Exascale Earth System Model (E3SM) adds the Fox-Kemper et al. (2011) mixed-layer eddy parameterization, which restratifies the ocean surface layer through an overturning streamfunction. Results include surface layer bias reduction in temperature, salinity, and sea ice extent in the North Atlantic; a small strengthening of the Atlantic meridional overturning circulation; and improvements to many atmospheric climatological variables.
Malcolm J. Roberts, Kevin A. Reed, Qing Bao, Joseph J. Barsugli, Suzana J. Camargo, Louis-Philippe Caron, Ping Chang, Cheng-Ta Chen, Hannah M. Christensen, Gokhan Danabasoglu, Ivy Frenger, Neven S. Fučkar, Shabeh ul Hasson, Helene T. Hewitt, Huanping Huang, Daehyun Kim, Chihiro Kodama, Michael Lai, Lai-Yung Ruby Leung, Ryo Mizuta, Paulo Nobre, Pablo Ortega, Dominique Paquin, Christopher D. Roberts, Enrico Scoccimarro, Jon Seddon, Anne Marie Treguier, Chia-Ying Tu, Paul A. Ullrich, Pier Luigi Vidale, Michael F. Wehner, Colin M. Zarzycki, Bosong Zhang, Wei Zhang, and Ming Zhao
Geosci. Model Dev., 18, 1307–1332, https://doi.org/10.5194/gmd-18-1307-2025, https://doi.org/10.5194/gmd-18-1307-2025, 2025
Short summary
Short summary
HighResMIP2 is a model intercomparison project focusing on high-resolution global climate models, that is, those with grid spacings of 25 km or less in the atmosphere and ocean, using simulations of decades to a century in length. We are proposing an update of our simulation protocol to make the models more applicable to key questions for climate variability and hazard in present-day and future projections and to build links with other communities to provide more robust climate information.
Mariya Petrenko, Ralph Kahn, Mian Chin, Susanne E. Bauer, Tommi Bergman, Huisheng Bian, Gabriele Curci, Ben Johnson, Johannes W. Kaiser, Zak Kipling, Harri Kokkola, Xiaohong Liu, Keren Mezuman, Tero Mielonen, Gunnar Myhre, Xiaohua Pan, Anna Protonotariou, Samuel Remy, Ragnhild Bieltvedt Skeie, Philip Stier, Toshihiko Takemura, Kostas Tsigaridis, Hailong Wang, Duncan Watson-Parris, and Kai Zhang
Atmos. Chem. Phys., 25, 1545–1567, https://doi.org/10.5194/acp-25-1545-2025, https://doi.org/10.5194/acp-25-1545-2025, 2025
Short summary
Short summary
We compared smoke plume simulations from 11 global models to each other and to satellite smoke amount observations aimed at constraining smoke source strength. In regions where plumes are thick and background aerosol is low, models and satellites compare well. However, the input emission inventory tends to underestimate in many places, and particle property and loss rate assumptions vary enormously among models, causing uncertainties that require systematic in situ measurements to resolve.
Yilin Fang, Hoang Viet Tran, and L. Ruby Leung
Geosci. Model Dev., 18, 19–32, https://doi.org/10.5194/gmd-18-19-2025, https://doi.org/10.5194/gmd-18-19-2025, 2025
Short summary
Short summary
Hurricanes may worsen water quality in the lower Mississippi River basin (LMRB) by increasing nutrient runoff. We found that runoff parameterizations greatly affect nitrate–nitrogen runoff simulated using an Earth system land model. Our simulations predicted increased nitrogen runoff in the LMRB during Hurricane Ida in 2021, albeit less pronounced than the observations, indicating areas for model improvement to better understand and manage nutrient runoff loss during hurricanes in the region.
Johannes Mülmenstädt, Andrew S. Ackerman, Ann M. Fridlind, Meng Huang, Po-Lun Ma, Naser Mahfouz, Susanne E. Bauer, Susannah M. Burrows, Matthew W. Christensen, Sudhakar Dipu, Andrew Gettelman, L. Ruby Leung, Florian Tornow, Johannes Quaas, Adam C. Varble, Hailong Wang, Kai Zhang, and Youtong Zheng
Atmos. Chem. Phys., 24, 13633–13652, https://doi.org/10.5194/acp-24-13633-2024, https://doi.org/10.5194/acp-24-13633-2024, 2024
Short summary
Short summary
Stratocumulus clouds play a large role in Earth's climate by reflecting incoming solar energy back to space. Turbulence at stratocumulus cloud top mixes in dry, warm air, which can lead to cloud dissipation. This process is challenging for coarse-resolution global models to represent. We show that global models nevertheless agree well with our process understanding. Global models also think the process is less important for the climate than other lines of evidence have led us to conclude.
Fang Li, Xiang Song, Sandy P. Harrison, Jennifer R. Marlon, Zhongda Lin, L. Ruby Leung, Jörg Schwinger, Virginie Marécal, Shiyu Wang, Daniel S. Ward, Xiao Dong, Hanna Lee, Lars Nieradzik, Sam S. Rabin, and Roland Séférian
Geosci. Model Dev., 17, 8751–8771, https://doi.org/10.5194/gmd-17-8751-2024, https://doi.org/10.5194/gmd-17-8751-2024, 2024
Short summary
Short summary
This study provides the first comprehensive assessment of historical fire simulations from 19 Earth system models in phase 6 of the Coupled Model Intercomparison Project (CMIP6). Most models reproduce global totals, spatial patterns, seasonality, and regional historical changes well but fail to simulate the recent decline in global burned area and underestimate the fire response to climate variability. CMIP6 simulations address three critical issues of phase-5 models.
Fan Mei, Jennifer M. Comstock, Mikhail S. Pekour, Jerome D. Fast, Krista L. Gaustad, Beat Schmid, Shuaiqi Tang, Damao Zhang, John E. Shilling, Jason M. Tomlinson, Adam C. Varble, Jian Wang, L. Ruby Leung, Lawrence Kleinman, Scot Martin, Sebastien C. Biraud, Brian D. Ermold, and Kenneth W. Burk
Earth Syst. Sci. Data, 16, 5429–5448, https://doi.org/10.5194/essd-16-5429-2024, https://doi.org/10.5194/essd-16-5429-2024, 2024
Short summary
Short summary
Our study explores a comprehensive dataset from airborne field studies (2013–2018) conducted using the US Department of Energy's Gulfstream 1 (G-1). The 236 flights span diverse regions, including the Arctic, US Southern Great Plains, US West Coast, eastern North Atlantic, Amazon Basin in Brazil, and Sierras de Córdoba range in Argentina. This dataset provides unique insights into atmospheric dynamics, aerosols, and clouds and makes data available in a more accessible format.
Johannes Mülmenstädt, Edward Gryspeerdt, Sudhakar Dipu, Johannes Quaas, Andrew S. Ackerman, Ann M. Fridlind, Florian Tornow, Susanne E. Bauer, Andrew Gettelman, Yi Ming, Youtong Zheng, Po-Lun Ma, Hailong Wang, Kai Zhang, Matthew W. Christensen, Adam C. Varble, L. Ruby Leung, Xiaohong Liu, David Neubauer, Daniel G. Partridge, Philip Stier, and Toshihiko Takemura
Atmos. Chem. Phys., 24, 7331–7345, https://doi.org/10.5194/acp-24-7331-2024, https://doi.org/10.5194/acp-24-7331-2024, 2024
Short summary
Short summary
Human activities release copious amounts of small particles called aerosols into the atmosphere. These particles change how much sunlight clouds reflect to space, an important human perturbation of the climate, whose magnitude is highly uncertain. We found that the latest climate models show a negative correlation but a positive causal relationship between aerosols and cloud water. This means we need to be very careful when we interpret observational studies that can only see correlation.
Tapio Schneider, L. Ruby Leung, and Robert C. J. Wills
Atmos. Chem. Phys., 24, 7041–7062, https://doi.org/10.5194/acp-24-7041-2024, https://doi.org/10.5194/acp-24-7041-2024, 2024
Short summary
Short summary
Climate models are crucial for predicting climate change in detail. This paper proposes a balanced approach to improving their accuracy by combining traditional process-based methods with modern artificial intelligence (AI) techniques while maximizing the resolution to allow for ensemble simulations. The authors propose using AI to learn from both observational and simulated data while incorporating existing physical knowledge to reduce data demands and improve climate prediction reliability.
Taufiq Hassan, Kai Zhang, Jianfeng Li, Balwinder Singh, Shixuan Zhang, Hailong Wang, and Po-Lun Ma
Geosci. Model Dev., 17, 3507–3532, https://doi.org/10.5194/gmd-17-3507-2024, https://doi.org/10.5194/gmd-17-3507-2024, 2024
Short summary
Short summary
Anthropogenic aerosol emissions are an essential part of global aerosol models. Significant errors can exist from the loss of emission heterogeneity. We introduced an emission treatment that significantly improved aerosol emission heterogeneity in high-resolution model simulations, with improvements in simulated aerosol surface concentrations. The emission treatment will provide a more accurate representation of aerosol emissions and their effects on climate.
Lingcheng Li, Gautam Bisht, Dalei Hao, and L. Ruby Leung
Earth Syst. Sci. Data, 16, 2007–2032, https://doi.org/10.5194/essd-16-2007-2024, https://doi.org/10.5194/essd-16-2007-2024, 2024
Short summary
Short summary
This study fills a gap to meet the emerging needs of kilometer-scale Earth system modeling by developing global 1 km land surface parameters for land use, vegetation, soil, and topography. Our demonstration simulations highlight the substantial impacts of these parameters on spatial variability and information loss in water and energy simulations. Using advanced explainable machine learning methods, we identified influential factors driving spatial variability and information loss.
Bryce E. Harrop, Jian Lu, L. Ruby Leung, William K. M. Lau, Kyu-Myong Kim, Brian Medeiros, Brian J. Soden, Gabriel A. Vecchi, Bosong Zhang, and Balwinder Singh
Geosci. Model Dev., 17, 3111–3135, https://doi.org/10.5194/gmd-17-3111-2024, https://doi.org/10.5194/gmd-17-3111-2024, 2024
Short summary
Short summary
Seven new experimental setups designed to interfere with cloud radiative heating have been added to the Energy Exascale Earth System Model (E3SM). These experiments include both those that test the mean impact of cloud radiative heating and those examining its covariance with circulations. This paper documents the code changes and steps needed to run these experiments. Results corroborate prior findings for how cloud radiative heating impacts circulations and rainfall patterns.
Pengfei Shi, L. Ruby Leung, Bin Wang, Kai Zhang, Samson M. Hagos, and Shixuan Zhang
Geosci. Model Dev., 17, 3025–3040, https://doi.org/10.5194/gmd-17-3025-2024, https://doi.org/10.5194/gmd-17-3025-2024, 2024
Short summary
Short summary
Improving climate predictions have profound socio-economic impacts. This study introduces a new weakly coupled land data assimilation (WCLDA) system for a coupled climate model. We demonstrate improved simulation of soil moisture and temperature in many global regions and throughout the soil layers. Furthermore, significant improvements are also found in reproducing the time evolution of the 2012 US Midwest drought. The WCLDA system provides the groundwork for future predictability studies.
Yawen Liu, Yun Qian, Philip J. Rasch, Kai Zhang, Lai-yung Ruby Leung, Yuhang Wang, Minghuai Wang, Hailong Wang, Xin Huang, and Xiu-Qun Yang
Atmos. Chem. Phys., 24, 3115–3128, https://doi.org/10.5194/acp-24-3115-2024, https://doi.org/10.5194/acp-24-3115-2024, 2024
Short summary
Short summary
Fire management has long been a challenge. Here we report that spring-peak fire activity over southern Mexico and Central America (SMCA) has a distinct quasi-biennial signal by measuring multiple fire metrics. This signal is initially driven by quasi-biennial variability in precipitation and is further amplified by positive feedback of fire–precipitation interaction at short timescales. This work highlights the importance of fire–climate interactions in shaping fires on an interannual scale.
Yu Yao, Po-Lun Ma, Yi Qin, Matthew W. Christensen, Hui Wan, Kai Zhang, Balwinder Singh, Meng Huang, and Mikhail Ovchinnikov
EGUsphere, https://doi.org/10.5194/egusphere-2024-523, https://doi.org/10.5194/egusphere-2024-523, 2024
Preprint withdrawn
Short summary
Short summary
Giant aerosols have substantial effects on warm rain formation. However, it remains challenging to quantify the impact of giant particles at global scale. In this work, we applied earth system model to investigate its impacts by implementing new giant aerosol treatments to consider its physical process. We found this approach substantially affect liquid cloud and improved model's precipitation response to aerosols. Our findings demonstrate the significant impact of giant aerosols on climate.
Hui Wan, Kai Zhang, Christopher J. Vogl, Carol S. Woodward, Richard C. Easter, Philip J. Rasch, Yan Feng, and Hailong Wang
Geosci. Model Dev., 17, 1387–1407, https://doi.org/10.5194/gmd-17-1387-2024, https://doi.org/10.5194/gmd-17-1387-2024, 2024
Short summary
Short summary
Sophisticated numerical models of the Earth's atmosphere include representations of many physical and chemical processes. In numerical simulations, these processes need to be calculated in a certain sequence. This study reveals the weaknesses of the sequence of calculations used for aerosol processes in a global atmosphere model. A revision of the sequence is proposed and its impacts on the simulated global aerosol climatology are evaluated.
Jianfeng Li, Kai Zhang, Taufiq Hassan, Shixuan Zhang, Po-Lun Ma, Balwinder Singh, Qiyang Yan, and Huilin Huang
Geosci. Model Dev., 17, 1327–1347, https://doi.org/10.5194/gmd-17-1327-2024, https://doi.org/10.5194/gmd-17-1327-2024, 2024
Short summary
Short summary
By comparing E3SM simulations with and without regional refinement, we find that model horizontal grid spacing considerably affects the simulated aerosol mass budget, aerosol–cloud interactions, and the effective radiative forcing of anthropogenic aerosols. The study identifies the critical physical processes strongly influenced by model resolution. It also highlights the benefit of applying regional refinement in future modeling studies at higher or even convection-permitting resolutions.
Donghui Xu, Gautam Bisht, Zeli Tan, Chang Liao, Tian Zhou, Hong-Yi Li, and L. Ruby Leung
Geosci. Model Dev., 17, 1197–1215, https://doi.org/10.5194/gmd-17-1197-2024, https://doi.org/10.5194/gmd-17-1197-2024, 2024
Short summary
Short summary
We aim to disentangle the hydrological and hydraulic controls on streamflow variability in a fully coupled earth system model. We found that calibrating only one process (i.e., traditional calibration procedure) will result in unrealistic parameter values and poor performance of the water cycle, while the simulated streamflow is improved. To address this issue, we further proposed a two-step calibration procedure to reconcile the impacts from hydrological and hydraulic processes on streamflow.
Huisheng Bian, Mian Chin, Peter R. Colarco, Eric C. Apel, Donald R. Blake, Karl Froyd, Rebecca S. Hornbrook, Jose Jimenez, Pedro Campuzano Jost, Michael Lawler, Mingxu Liu, Marianne Tronstad Lund, Hitoshi Matsui, Benjamin A. Nault, Joyce E. Penner, Andrew W. Rollins, Gregory Schill, Ragnhild B. Skeie, Hailong Wang, Lu Xu, Kai Zhang, and Jialei Zhu
Atmos. Chem. Phys., 24, 1717–1741, https://doi.org/10.5194/acp-24-1717-2024, https://doi.org/10.5194/acp-24-1717-2024, 2024
Short summary
Short summary
This work studies sulfur in the remote troposphere at global and seasonal scales using aircraft measurements and multi-model simulations. The goal is to understand the sulfur cycle over remote oceans, spread of model simulations, and observation–model discrepancies. Such an understanding and comparison with real observations are crucial to narrow down the uncertainties in model sulfur simulations and improve understanding of the sulfur cycle in atmospheric air quality, climate, and ecosystems.
Shuaiqi Tang, Adam C. Varble, Jerome D. Fast, Kai Zhang, Peng Wu, Xiquan Dong, Fan Mei, Mikhail Pekour, Joseph C. Hardin, and Po-Lun Ma
Geosci. Model Dev., 16, 6355–6376, https://doi.org/10.5194/gmd-16-6355-2023, https://doi.org/10.5194/gmd-16-6355-2023, 2023
Short summary
Short summary
To assess the ability of Earth system model (ESM) predictions, we developed a tool called ESMAC Diags to understand how aerosols, clouds, and aerosol–cloud interactions are represented in ESMs. This paper describes its version 2 functionality. We compared the model predictions with measurements taken by planes, ships, satellites, and ground instruments over four regions across the world. Results show that this new tool can help identify model problems and guide future development of ESMs.
Calvin Howes, Pablo E. Saide, Hugh Coe, Amie Dobracki, Steffen Freitag, Jim M. Haywood, Steven G. Howell, Siddhant Gupta, Janek Uin, Mary Kacarab, Chongai Kuang, L. Ruby Leung, Athanasios Nenes, Greg M. McFarquhar, James Podolske, Jens Redemann, Arthur J. Sedlacek, Kenneth L. Thornhill, Jenny P. S. Wong, Robert Wood, Huihui Wu, Yang Zhang, Jianhao Zhang, and Paquita Zuidema
Atmos. Chem. Phys., 23, 13911–13940, https://doi.org/10.5194/acp-23-13911-2023, https://doi.org/10.5194/acp-23-13911-2023, 2023
Short summary
Short summary
To better understand smoke properties and its interactions with clouds, we compare the WRF-CAM5 model with observations from ORACLES, CLARIFY, and LASIC field campaigns in the southeastern Atlantic in August 2017. The model transports and mixes smoke well but does not fully capture some important processes. These include smoke chemical and physical aging over 4–12 days, smoke removal by rain, sulfate particle formation, aerosol activation into cloud droplets, and boundary layer turbulence.
Dongyu Feng, Zeli Tan, Donghui Xu, and L. Ruby Leung
Hydrol. Earth Syst. Sci., 27, 3911–3934, https://doi.org/10.5194/hess-27-3911-2023, https://doi.org/10.5194/hess-27-3911-2023, 2023
Short summary
Short summary
This study assesses the flood risks concurrently induced by river flooding and coastal storm surge along the coast of the contiguous United States using statistical and numerical models. We reveal a few hotspots of such risks, the critical spatial variabilities within a river basin and over the whole US coast, and the uncertainties of the risk assessment. We highlight the importance of weighing different risk measures to avoid underestimating or exaggerating the compound flood impacts.
Lingcheng Li, Yilin Fang, Zhonghua Zheng, Mingjie Shi, Marcos Longo, Charles D. Koven, Jennifer A. Holm, Rosie A. Fisher, Nate G. McDowell, Jeffrey Chambers, and L. Ruby Leung
Geosci. Model Dev., 16, 4017–4040, https://doi.org/10.5194/gmd-16-4017-2023, https://doi.org/10.5194/gmd-16-4017-2023, 2023
Short summary
Short summary
Accurately modeling plant coexistence in vegetation demographic models like ELM-FATES is challenging. This study proposes a repeatable method that uses machine-learning-based surrogate models to optimize plant trait parameters in ELM-FATES. Our approach significantly improves plant coexistence modeling, thus reducing errors. It has important implications for modeling ecosystem dynamics in response to climate change.
Qi Tang, Jean-Christophe Golaz, Luke P. Van Roekel, Mark A. Taylor, Wuyin Lin, Benjamin R. Hillman, Paul A. Ullrich, Andrew M. Bradley, Oksana Guba, Jonathan D. Wolfe, Tian Zhou, Kai Zhang, Xue Zheng, Yunyan Zhang, Meng Zhang, Mingxuan Wu, Hailong Wang, Cheng Tao, Balwinder Singh, Alan M. Rhoades, Yi Qin, Hong-Yi Li, Yan Feng, Yuying Zhang, Chengzhu Zhang, Charles S. Zender, Shaocheng Xie, Erika L. Roesler, Andrew F. Roberts, Azamat Mametjanov, Mathew E. Maltrud, Noel D. Keen, Robert L. Jacob, Christiane Jablonowski, Owen K. Hughes, Ryan M. Forsyth, Alan V. Di Vittorio, Peter M. Caldwell, Gautam Bisht, Renata B. McCoy, L. Ruby Leung, and David C. Bader
Geosci. Model Dev., 16, 3953–3995, https://doi.org/10.5194/gmd-16-3953-2023, https://doi.org/10.5194/gmd-16-3953-2023, 2023
Short summary
Short summary
High-resolution simulations are superior to low-resolution ones in capturing regional climate changes and climate extremes. However, uniformly reducing the grid size of a global Earth system model is too computationally expensive. We provide an overview of the fully coupled regionally refined model (RRM) of E3SMv2 and document a first-of-its-kind set of climate production simulations using RRM at an economic cost. The key to this success is our innovative hybrid time step method.
Koichi Sakaguchi, L. Ruby Leung, Colin M. Zarzycki, Jihyeon Jang, Seth McGinnis, Bryce E. Harrop, William C. Skamarock, Andrew Gettelman, Chun Zhao, William J. Gutowski, Stephen Leak, and Linda Mearns
Geosci. Model Dev., 16, 3029–3081, https://doi.org/10.5194/gmd-16-3029-2023, https://doi.org/10.5194/gmd-16-3029-2023, 2023
Short summary
Short summary
We document details of the regional climate downscaling dataset produced by a global variable-resolution model. The experiment is unique in that it follows a standard protocol designed for coordinated experiments of regional models. We found negligible influence of post-processing on statistical analysis, importance of simulation quality outside of the target region, and computational challenges that our model code faced due to rapidly changing super computer systems.
Aishwarya Raman, Thomas Hill, Paul J. DeMott, Balwinder Singh, Kai Zhang, Po-Lun Ma, Mingxuan Wu, Hailong Wang, Simon P. Alexander, and Susannah M. Burrows
Atmos. Chem. Phys., 23, 5735–5762, https://doi.org/10.5194/acp-23-5735-2023, https://doi.org/10.5194/acp-23-5735-2023, 2023
Short summary
Short summary
Ice-nucleating particles (INPs) play an important role in cloud processes and associated precipitation. Yet, INPs are not accurately represented in climate models. This study attempts to uncover these gaps by comparing model-simulated INP concentrations against field campaign measurements in the SO for an entire year, 2017–2018. Differences in INP concentrations and variability between the model and observations have major implications for modeling cloud properties in high latitudes.
Zhe Feng, Joseph Hardin, Hannah C. Barnes, Jianfeng Li, L. Ruby Leung, Adam Varble, and Zhixiao Zhang
Geosci. Model Dev., 16, 2753–2776, https://doi.org/10.5194/gmd-16-2753-2023, https://doi.org/10.5194/gmd-16-2753-2023, 2023
Short summary
Short summary
PyFLEXTRKR is a flexible atmospheric feature tracking framework with specific capabilities to track convective clouds from a variety of observations and model simulations. The package has a collection of multi-object identification algorithms and has been optimized for large datasets. This paper describes the algorithms and demonstrates applications for tracking deep convective cells and mesoscale convective systems from observations and model simulations at a wide range of scales.
Zeyu Xue, Paul Ullrich, and Lai-Yung Ruby Leung
Hydrol. Earth Syst. Sci., 27, 1909–1927, https://doi.org/10.5194/hess-27-1909-2023, https://doi.org/10.5194/hess-27-1909-2023, 2023
Short summary
Short summary
We examine the sensitivity and robustness of conclusions drawn from the PGW method over the NEUS by conducting multiple PGW experiments and varying the perturbation spatial scales and choice of perturbed meteorological variables to provide a guideline for this increasingly popular regional modeling method. Overall, we recommend PGW experiments be performed with perturbations to temperature or the combination of temperature and wind at the gridpoint scale, depending on the research question.
Dalei Hao, Gautam Bisht, Karl Rittger, Timbo Stillinger, Edward Bair, Yu Gu, and L. Ruby Leung
The Cryosphere, 17, 673–697, https://doi.org/10.5194/tc-17-673-2023, https://doi.org/10.5194/tc-17-673-2023, 2023
Short summary
Short summary
We comprehensively evaluated the snow simulations in E3SM land model over the western United States in terms of spatial patterns, temporal correlations, interannual variabilities, elevation gradients, and change with forest cover of snow properties and snow phenology. Our study underscores the need for diagnosing model biases and improving the model representations of snow properties and snow phenology in mountainous areas for more credible simulation and future projection of mountain snowpack.
Chandan Sarangi, Yun Qian, L. Ruby Leung, Yang Zhang, Yufei Zou, and Yuhang Wang
Atmos. Chem. Phys., 23, 1769–1783, https://doi.org/10.5194/acp-23-1769-2023, https://doi.org/10.5194/acp-23-1769-2023, 2023
Short summary
Short summary
We show that for air quality, the densely populated eastern US may see even larger impacts of wildfires due to long-distance smoke transport and associated positive climatic impacts, partially compensating the improvements from regulations on anthropogenic emissions. This study highlights the tension between natural and anthropogenic contributions and the non-local nature of air pollution that complicate regulatory strategies for improving future regional air quality for human health.
Dalei Hao, Gautam Bisht, Karl Rittger, Edward Bair, Cenlin He, Huilin Huang, Cheng Dang, Timbo Stillinger, Yu Gu, Hailong Wang, Yun Qian, and L. Ruby Leung
Geosci. Model Dev., 16, 75–94, https://doi.org/10.5194/gmd-16-75-2023, https://doi.org/10.5194/gmd-16-75-2023, 2023
Short summary
Short summary
Snow with the highest albedo of land surface plays a vital role in Earth’s surface energy budget and water cycle. This study accounts for the impacts of snow grain shape and mixing state of light-absorbing particles with snow on snow albedo in the E3SM land model. The findings advance our understanding of the role of snow grain shape and mixing state of LAP–snow in land surface processes and offer guidance for improving snow simulations and radiative forcing estimates in Earth system models.
Dongyu Feng, Zeli Tan, Darren Engwirda, Chang Liao, Donghui Xu, Gautam Bisht, Tian Zhou, Hong-Yi Li, and L. Ruby Leung
Hydrol. Earth Syst. Sci., 26, 5473–5491, https://doi.org/10.5194/hess-26-5473-2022, https://doi.org/10.5194/hess-26-5473-2022, 2022
Short summary
Short summary
Sea level rise, storm surge and river discharge can cause coastal backwater effects in downstream sections of rivers, creating critical flood risks. This study simulates the backwater effects using a large-scale river model on a coastal-refined computational mesh. By decomposing the backwater drivers, we revealed their relative importance and long-term variations. Our analysis highlights the increasing strength of backwater effects due to sea level rise and more frequent storm surge.
Yilin Fang, L. Ruby Leung, Charles D. Koven, Gautam Bisht, Matteo Detto, Yanyan Cheng, Nate McDowell, Helene Muller-Landau, S. Joseph Wright, and Jeffrey Q. Chambers
Geosci. Model Dev., 15, 7879–7901, https://doi.org/10.5194/gmd-15-7879-2022, https://doi.org/10.5194/gmd-15-7879-2022, 2022
Short summary
Short summary
We develop a model that integrates an Earth system model with a three-dimensional hydrology model to explicitly resolve hillslope topography and water flow underneath the land surface to understand how local-scale hydrologic processes modulate vegetation along water availability gradients. Our coupled model can be used to improve the understanding of the diverse impact of local heterogeneity and water flux on nutrient availability and plant communities.
Shixuan Zhang, Kai Zhang, Hui Wan, and Jian Sun
Geosci. Model Dev., 15, 6787–6816, https://doi.org/10.5194/gmd-15-6787-2022, https://doi.org/10.5194/gmd-15-6787-2022, 2022
Short summary
Short summary
This study investigates the nudging implementation in the EAMv1 model. We find that (1) revising the sequence of calculations and using higher-frequency constraining data to improve the performance of a simulation nudged to EAMv1’s own meteorology, (2) using the relocated nudging tendency and 3-hourly ERA5 reanalysis to obtain a better agreement between nudged simulations and observations, and (3) using wind-only nudging are recommended for the estimates of global mean aerosol effects.
Qirui Zhong, Nick Schutgens, Guido van der Werf, Twan van Noije, Kostas Tsigaridis, Susanne E. Bauer, Tero Mielonen, Alf Kirkevåg, Øyvind Seland, Harri Kokkola, Ramiro Checa-Garcia, David Neubauer, Zak Kipling, Hitoshi Matsui, Paul Ginoux, Toshihiko Takemura, Philippe Le Sager, Samuel Rémy, Huisheng Bian, Mian Chin, Kai Zhang, Jialei Zhu, Svetlana G. Tsyro, Gabriele Curci, Anna Protonotariou, Ben Johnson, Joyce E. Penner, Nicolas Bellouin, Ragnhild B. Skeie, and Gunnar Myhre
Atmos. Chem. Phys., 22, 11009–11032, https://doi.org/10.5194/acp-22-11009-2022, https://doi.org/10.5194/acp-22-11009-2022, 2022
Short summary
Short summary
Aerosol optical depth (AOD) errors for biomass burning aerosol (BBA) are evaluated in 18 global models against satellite datasets. Notwithstanding biases in satellite products, they allow model evaluations. We observe large and diverse model biases due to errors in BBA. Further interpretations of AOD diversities suggest large biases exist in key processes for BBA which require better constraining. These results can contribute to further model improvement and development.
Meng Huang, Po-Lun Ma, Nathaniel W. Chaney, Dalei Hao, Gautam Bisht, Megan D. Fowler, Vincent E. Larson, and L. Ruby Leung
Geosci. Model Dev., 15, 6371–6384, https://doi.org/10.5194/gmd-15-6371-2022, https://doi.org/10.5194/gmd-15-6371-2022, 2022
Short summary
Short summary
The land surface in one grid cell may be diverse in character. This study uses an explicit way to account for that subgrid diversity in a state-of-the-art Earth system model (ESM) and explores its implications for the overlying atmosphere. We find that the shallow clouds are increased significantly with the land surface diversity. Our work highlights the importance of accurately representing the land surface and its interaction with the atmosphere in next-generation ESMs.
Yilin Fang, L. Ruby Leung, Ryan Knox, Charlie Koven, and Ben Bond-Lamberty
Geosci. Model Dev., 15, 6385–6398, https://doi.org/10.5194/gmd-15-6385-2022, https://doi.org/10.5194/gmd-15-6385-2022, 2022
Short summary
Short summary
Accounting for water movement in the soil and water transport within the plant is important for plant growth in Earth system modeling. We implemented different numerical approaches for a plant hydrodynamic model and compared their impacts on the simulated aboveground biomass (AGB) at single points and globally. We found care should be taken when discretizing the number of soil layers for numerical simulations as it can significantly affect AGB if accuracy and computational costs are of concern.
Sol Kim, L. Ruby Leung, Bin Guan, and John C. H. Chiang
Geosci. Model Dev., 15, 5461–5480, https://doi.org/10.5194/gmd-15-5461-2022, https://doi.org/10.5194/gmd-15-5461-2022, 2022
Short summary
Short summary
The Energy Exascale Earth System Model (E3SM) project is a state-of-the-science Earth system model developed by the US Department of Energy (DOE). Understanding how the water cycle behaves in this model is of particular importance to the DOE’s mission. Atmospheric rivers (ARs) – which are crucial to the global water cycle – move vast amounts of water vapor through the sky and produce rain and snow. We find that this model reliably represents atmospheric rivers around the world.
Lingcheng Li, Gautam Bisht, and L. Ruby Leung
Geosci. Model Dev., 15, 5489–5510, https://doi.org/10.5194/gmd-15-5489-2022, https://doi.org/10.5194/gmd-15-5489-2022, 2022
Short summary
Short summary
Land surface heterogeneity plays a critical role in the terrestrial water, energy, and biogeochemical cycles. Our study systematically quantified the effects of four dominant heterogeneity sources on water and energy partitioning via Sobol' indices. We found that atmospheric forcing and land use land cover are the most dominant heterogeneity sources in determining spatial variability of water and energy partitioning. Our findings can help prioritize the future development of land surface models.
Kai Zhang, Wentao Zhang, Hui Wan, Philip J. Rasch, Steven J. Ghan, Richard C. Easter, Xiangjun Shi, Yong Wang, Hailong Wang, Po-Lun Ma, Shixuan Zhang, Jian Sun, Susannah M. Burrows, Manish Shrivastava, Balwinder Singh, Yun Qian, Xiaohong Liu, Jean-Christophe Golaz, Qi Tang, Xue Zheng, Shaocheng Xie, Wuyin Lin, Yan Feng, Minghuai Wang, Jin-Ho Yoon, and L. Ruby Leung
Atmos. Chem. Phys., 22, 9129–9160, https://doi.org/10.5194/acp-22-9129-2022, https://doi.org/10.5194/acp-22-9129-2022, 2022
Short summary
Short summary
Here we analyze the effective aerosol forcing simulated by E3SM version 1 using both century-long free-running and short nudged simulations. The aerosol forcing in E3SMv1 is relatively large compared to other models, mainly due to the large indirect aerosol effect. Aerosol-induced changes in liquid and ice cloud properties in E3SMv1 have a strong correlation. The aerosol forcing estimates in E3SMv1 are sensitive to the parameterization changes in both liquid and ice cloud processes.
Donghui Xu, Gautam Bisht, Khachik Sargsyan, Chang Liao, and L. Ruby Leung
Geosci. Model Dev., 15, 5021–5043, https://doi.org/10.5194/gmd-15-5021-2022, https://doi.org/10.5194/gmd-15-5021-2022, 2022
Short summary
Short summary
The runoff outputs in Earth system model simulations involve high uncertainty, which needs to be constrained by parameter calibration. In this work, we used a surrogate-assisted Bayesian framework to efficiently calibrate the runoff-generation processes in the Energy Exascale Earth System Model v1 at a global scale. The model performance was improved compared to the default parameter after calibration, and the associated parametric uncertainty was significantly constrained.
Shuaiqi Tang, Jerome D. Fast, Kai Zhang, Joseph C. Hardin, Adam C. Varble, John E. Shilling, Fan Mei, Maria A. Zawadowicz, and Po-Lun Ma
Geosci. Model Dev., 15, 4055–4076, https://doi.org/10.5194/gmd-15-4055-2022, https://doi.org/10.5194/gmd-15-4055-2022, 2022
Short summary
Short summary
We developed an Earth system model (ESM) diagnostics package to compare various types of aerosol properties simulated in ESMs with aircraft, ship, and surface measurements from six field campaigns across spatial scales. The diagnostics package is coded and organized to be flexible and modular for future extension to other field campaign datasets and adapted to higher-resolution model simulations. Future releases will include comprehensive cloud and aerosol–cloud interaction diagnostics.
Yun Lin, Jiwen Fan, Pengfei Li, Lai-yung Ruby Leung, Paul J. DeMott, Lexie Goldberger, Jennifer Comstock, Ying Liu, Jong-Hoon Jeong, and Jason Tomlinson
Atmos. Chem. Phys., 22, 6749–6771, https://doi.org/10.5194/acp-22-6749-2022, https://doi.org/10.5194/acp-22-6749-2022, 2022
Short summary
Short summary
How sea spray aerosols may affect cloud and precipitation over the region by acting as ice-nucleating particles (INPs) is unknown. We explored the effects of INPs from marine aerosols on orographic cloud and precipitation for an atmospheric river event observed during the 2015 ACAPEX field campaign. The marine INPs enhance the formation of ice and snow, leading to less shallow warm clouds but more mixed-phase and deep clouds. This work suggests models need to consider the impacts of marine INPs.
Susannah M. Burrows, Richard C. Easter, Xiaohong Liu, Po-Lun Ma, Hailong Wang, Scott M. Elliott, Balwinder Singh, Kai Zhang, and Philip J. Rasch
Atmos. Chem. Phys., 22, 5223–5251, https://doi.org/10.5194/acp-22-5223-2022, https://doi.org/10.5194/acp-22-5223-2022, 2022
Short summary
Short summary
Sea spray particles are composed of a mixture of salts and organic substances from oceanic microorganisms. In prior work, our team developed an approach connecting sea spray chemistry to ocean biology, called OCEANFILMS. Here we describe its implementation within an Earth system model, E3SM. We show that simulated sea spray chemistry is consistent with observed seasonal cycles and that sunlight reflected by simulated Southern Ocean clouds increases, consistent with analysis of satellite data.
Hui Wan, Kai Zhang, Philip J. Rasch, Vincent E. Larson, Xubin Zeng, Shixuan Zhang, and Ross Dixon
Geosci. Model Dev., 15, 3205–3231, https://doi.org/10.5194/gmd-15-3205-2022, https://doi.org/10.5194/gmd-15-3205-2022, 2022
Short summary
Short summary
This paper describes a tool embedded in a global climate model for sampling atmospheric conditions and monitoring physical processes as a numerical simulation is being carried out. The tool facilitates process-level model evaluation by allowing the users to select a wide range of quantities and processes to monitor at run time without having to do tedious ad hoc coding.
Pinya Wang, Yang Yang, Huimin Li, Lei Chen, Ruijun Dang, Daokai Xue, Baojie Li, Jianping Tang, L. Ruby Leung, and Hong Liao
Atmos. Chem. Phys., 22, 4705–4719, https://doi.org/10.5194/acp-22-4705-2022, https://doi.org/10.5194/acp-22-4705-2022, 2022
Short summary
Short summary
China is now suffering from both severe ozone (O3) pollution and heat events. We highlight that North China Plain is the hot spot of the co-occurrences of extremes in O3 and high temperatures in China. Such coupled extremes exhibit an increasing trend during 2014–2019 and will continue to increase until the middle of this century. And the coupled extremes impose more severe health impacts to human than O3 pollution occurring alone because of elevated O3 levels and temperatures.
Po-Lun Ma, Bryce E. Harrop, Vincent E. Larson, Richard B. Neale, Andrew Gettelman, Hugh Morrison, Hailong Wang, Kai Zhang, Stephen A. Klein, Mark D. Zelinka, Yuying Zhang, Yun Qian, Jin-Ho Yoon, Christopher R. Jones, Meng Huang, Sheng-Lun Tai, Balwinder Singh, Peter A. Bogenschutz, Xue Zheng, Wuyin Lin, Johannes Quaas, Hélène Chepfer, Michael A. Brunke, Xubin Zeng, Johannes Mülmenstädt, Samson Hagos, Zhibo Zhang, Hua Song, Xiaohong Liu, Michael S. Pritchard, Hui Wan, Jingyu Wang, Qi Tang, Peter M. Caldwell, Jiwen Fan, Larry K. Berg, Jerome D. Fast, Mark A. Taylor, Jean-Christophe Golaz, Shaocheng Xie, Philip J. Rasch, and L. Ruby Leung
Geosci. Model Dev., 15, 2881–2916, https://doi.org/10.5194/gmd-15-2881-2022, https://doi.org/10.5194/gmd-15-2881-2022, 2022
Short summary
Short summary
An alternative set of parameters for E3SM Atmospheric Model version 1 has been developed based on a tuning strategy that focuses on clouds. When clouds in every regime are improved, other aspects of the model are also improved, even though they are not the direct targets for calibration. The recalibrated model shows a lower sensitivity to anthropogenic aerosols and surface warming, suggesting potential improvements to the simulated climate in the past and future.
Sally S.-C. Wang, Yun Qian, L. Ruby Leung, and Yang Zhang
Atmos. Chem. Phys., 22, 3445–3468, https://doi.org/10.5194/acp-22-3445-2022, https://doi.org/10.5194/acp-22-3445-2022, 2022
Short summary
Short summary
This study develops an interpretable machine learning (ML) model predicting monthly PM2.5 fire emission over the contiguous US at 0.25° resolution and compares the prediction skills of the ML and process-based models. The comparison facilitates attributions of model biases and better understanding of the strengths and uncertainties in the two types of models at regional scales, for informing future model development and their applications in fire emission projection.
Guta Wakbulcho Abeshu, Hong-Yi Li, Zhenduo Zhu, Zeli Tan, and L. Ruby Leung
Earth Syst. Sci. Data, 14, 929–942, https://doi.org/10.5194/essd-14-929-2022, https://doi.org/10.5194/essd-14-929-2022, 2022
Short summary
Short summary
Existing riverbed sediment particle size data are sparsely available at individual sites. We develop a continuous map of median riverbed sediment particle size over the contiguous US corresponding to millions of river segments based on the existing observations and machine learning methods. This map is useful for research in large-scale river sediment using model- and data-driven approaches, teaching environmental and earth system sciences, planning and managing floodplain zones, etc.
Hong-Yi Li, Zeli Tan, Hongbo Ma, Zhenduo Zhu, Guta Wakbulcho Abeshu, Senlin Zhu, Sagy Cohen, Tian Zhou, Donghui Xu, and L. Ruby Leung
Hydrol. Earth Syst. Sci., 26, 665–688, https://doi.org/10.5194/hess-26-665-2022, https://doi.org/10.5194/hess-26-665-2022, 2022
Short summary
Short summary
We introduce a new multi-process river sediment module for Earth system models. Application and validation over the contiguous US indicate a satisfactory model performance over large river systems, including those heavily regulated by reservoirs. This new sediment module enables future modeling of the transportation and transformation of carbon and nutrients carried by the fine sediment along the river–ocean continuum to close the global carbon and nutrient cycles.
Claudia Tebaldi, Kalyn Dorheim, Michael Wehner, and Ruby Leung
Earth Syst. Dynam., 12, 1427–1501, https://doi.org/10.5194/esd-12-1427-2021, https://doi.org/10.5194/esd-12-1427-2021, 2021
Short summary
Short summary
We address the question of how large an initial condition ensemble of climate model simulations should be if we are concerned with accurately projecting future changes in temperature and precipitation extremes. We find that for most cases (and both models considered), an ensemble of 20–25 members is sufficient for many extreme metrics, spatial scales and time horizons. This may leave computational resources to tackle other uncertainties in climate model simulations with our ensembles.
Dalei Hao, Gautam Bisht, Yu Gu, Wei-Liang Lee, Kuo-Nan Liou, and L. Ruby Leung
Geosci. Model Dev., 14, 6273–6289, https://doi.org/10.5194/gmd-14-6273-2021, https://doi.org/10.5194/gmd-14-6273-2021, 2021
Short summary
Short summary
Topography exerts significant influence on the incoming solar radiation at the land surface. This study incorporated a well-validated sub-grid topographic parameterization in E3SM land model (ELM) version 1.0. The results demonstrate that sub-grid topography has non-negligible effects on surface energy budget, snow cover, and surface temperature over the Tibetan Plateau and that the ELM simulations are sensitive to season, elevation, and spatial scale.
Yongkang Xue, Tandong Yao, Aaron A. Boone, Ismaila Diallo, Ye Liu, Xubin Zeng, William K. M. Lau, Shiori Sugimoto, Qi Tang, Xiaoduo Pan, Peter J. van Oevelen, Daniel Klocke, Myung-Seo Koo, Tomonori Sato, Zhaohui Lin, Yuhei Takaya, Constantin Ardilouze, Stefano Materia, Subodh K. Saha, Retish Senan, Tetsu Nakamura, Hailan Wang, Jing Yang, Hongliang Zhang, Mei Zhao, Xin-Zhong Liang, J. David Neelin, Frederic Vitart, Xin Li, Ping Zhao, Chunxiang Shi, Weidong Guo, Jianping Tang, Miao Yu, Yun Qian, Samuel S. P. Shen, Yang Zhang, Kun Yang, Ruby Leung, Yuan Qiu, Daniele Peano, Xin Qi, Yanling Zhan, Michael A. Brunke, Sin Chan Chou, Michael Ek, Tianyi Fan, Hong Guan, Hai Lin, Shunlin Liang, Helin Wei, Shaocheng Xie, Haoran Xu, Weiping Li, Xueli Shi, Paulo Nobre, Yan Pan, Yi Qin, Jeff Dozier, Craig R. Ferguson, Gianpaolo Balsamo, Qing Bao, Jinming Feng, Jinkyu Hong, Songyou Hong, Huilin Huang, Duoying Ji, Zhenming Ji, Shichang Kang, Yanluan Lin, Weiguang Liu, Ryan Muncaster, Patricia de Rosnay, Hiroshi G. Takahashi, Guiling Wang, Shuyu Wang, Weicai Wang, Xu Zhou, and Yuejian Zhu
Geosci. Model Dev., 14, 4465–4494, https://doi.org/10.5194/gmd-14-4465-2021, https://doi.org/10.5194/gmd-14-4465-2021, 2021
Short summary
Short summary
The subseasonal prediction of extreme hydroclimate events such as droughts/floods has remained stubbornly low for years. This paper presents a new international initiative which, for the first time, introduces spring land surface temperature anomalies over high mountains to improve precipitation prediction through remote effects of land–atmosphere interactions. More than 40 institutions worldwide are participating in this effort. The experimental protocol and preliminary results are presented.
Hui Wan, Shixuan Zhang, Philip J. Rasch, Vincent E. Larson, Xubin Zeng, and Huiping Yan
Geosci. Model Dev., 14, 1921–1948, https://doi.org/10.5194/gmd-14-1921-2021, https://doi.org/10.5194/gmd-14-1921-2021, 2021
Short summary
Short summary
Numerical models used in weather and climate research and prediction unavoidably contain numerical errors resulting from temporal discretization, and the impact of such errors can be substantial. Complex process interactions often make it difficult to pinpoint the exact sources of such errors. This study uses a series of sensitivity experiments to identify components in a global atmosphere model that are responsible for time step sensitivities in various cloud regimes.
Cited articles
Adler, R. F., Huffman, G. J., Chang, A., Ferraro, R., Xie, P., Janowiak, J., Rudolf, B., Schneider, U., Curtis, S., Bolvin, D., Gruber, A., Susskind, J. and Arkin, P.: The Version-2 Global Precipitation Climatology Project (GPCP) Monthly Precipitation Analysis (1979–Present), J. Hydrometeorol., 4, 1147–1167, https://doi.org/10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2, 2003. a, b, c
Bretherton, C. S., Henn, B., Kwa, A., Brenowitz, N. D., Watt-Meyer, O., McGibbon, J., Perkins, W. A., Clark, S. K., and Harris, L.: Correcting coarse-grid weather and climate models by machine learning from global storm-resolving simulations, ESS Open Archive, p. 39, https://doi.org/10.1002/essoar.10507879.1, 2021. a
CESM2: Pre-Processing Reanalyses Data, Tech. Rep., NSF National Center for Atmospheric Research, https://ncar.github.io/CAM/doc/build/html/users_guide/physics-modifications-via-the-namelist.html#nudging (last access: 25 May 2025), 2025. a
Dennis, J. M., Edwards, J., Evans, K. J., Guba, O., Lauritzen, P. H., Mirin, A. A., St-Cyr, A., Taylor, M. A., and Worley, P. H.: CAM-SE: A scalable spectral element dynamical core for the Community Atmosphere Model, Int. J. High Perform. Comput. Appl., 26, 74–89, https://doi.org/10.1177/1094342011428142, 2012. a
Emanuel, K.: Tropical cyclone activity and global climate, Nature, 426, 644–648, https://doi.org/10.1038/nature02188, 2004. a, b
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016. a
Feng, L., Smith, S. J., Braun, C., Crippa, M., Gidden, M. J., Hoesly, R., Klimont, Z., van Marle, M., van den Berg, M., and van der Werf, G. R.: The generation of gridded emissions data for CMIP6, Geosci. Model Dev., 13, 461–482, https://doi.org/10.5194/gmd-13-461-2020, 2020. a
Gates, W. L., Boyle, J. S., Covey, C., Dease, C. G., Doutriaux, C. M., Drach, R. S., Fiorino, M., Gleckler, P. J., Hnilo, J. J., Marlais, S. M., Phillips, T. J., Potter, G. L., Santer, B. D., Sperber, K. R., Taylor, K. E., and Williams, D. N.: An Overview of the Results of the Atmospheric Model Intercomparison Project (AMIP I), B. Am. Meteorol. Soc., 80, 29–56, https://doi.org/10.1175/1520-0477(1999)080<0029:AOOTRO>2.0.CO;2, 1999. a
Gettelman, A. and Morrison, H.: Advanced two-moment bulk microphysics for global models, Part I: Off-line tests and comparison with other schemes, J. Climate, 28, 1268–1287, https://doi.org/10.1175/JCLI-D-14-00102.1, 2015. a
Golaz, J.-C., Larson, V., and Cotton, W.: A PDF-Based Model for Boundary Layer Clouds. Part I: Method and Model Description, J. Atmos. Sci., 59, 3540–3551, https://doi.org/10.1175/1520-0469(2002)059<3540:APBMFB>2.0.CO;2, 2002. a
Golaz, J.-C., Caldwell, P. M., Van Roekel, L. P., Petersen, M. R., Tang, Q., Wolfe, J. D., Abeshu, G., Anantharaj, V., Asay-Davis, X. S., Bader, D. C., Baldwin, S. A., Bisht, G., Bogenschutz, P. A., Branstetter, M., Brunke, M. A., Brus, S. R., Burrows, S. M., Cameron-Smith, P. J., Donahue, A. S., Deakin, M., Easter, R. C., Evans, K. J., Feng, Y., Flanner, M., Foucar, J. G., Fyke, J. G., Griffin, B. M., Hannay, C., Harrop, B. E., Hoffman, M. J., Hunke, E. C., Jacob, R. L., Jacobsen, D. W., Jeffery, N., Jones, P. W., Keen, N. D., Klein, S. A., Larson, V. E., Leung, L. R., Li, H.-Y., Lin, W., Lipscomb, W. H., Ma, P.-L., Mahajan, S., Maltrud, M. E., Mametjanov, A., McClean, J. L., McCoy, R. B., Neale, R. B., Price, S. F., Qian, Y., Rasch, P. J., Reeves Eyre, J. E. J., Riley, W. J., Ringler, T. D., Roberts, A. F., Roesler, E. L., Salinger, A. G., Shaheen, Z., Shi, X., Singh, B., Tang, J., Taylor, M. A., Thornton, P. E., Turner, A. K., Veneziani, M., Wan, H., Wang, H., Wang, S., Williams, D. N., Wolfram, P. J., Worley, P. H., Xie, S., Yang, Y., Yoon, J.-H., Zelinka, M. D., Zender, C. S., Zeng, X., Zhang, C., Zhang, K., Zhang, Y., Zheng, X., Zhou, T., and Zhu, Q.: The DOE E3SM Coupled Model Version 1: Overview and Evaluation at Standard Resolution, J. Adv. Model. Earth Syst., 11, 2089–2129, https://doi.org/10.1029/2018MS001603, 2019. a, b
Golaz, J.-C., Van Roekel, L. P., Zheng, X., Roberts, A. F., Wolfe, J. D., Lin, W., Bradley, A. M., Tang, Q., Maltrud, M. E., Forsyth, R. M., Zhang, C., Zhou, T., Zhang, K., Zender, C. S., Wu, M., Wang, H., Turner, A. K., Singh, B., Richter, J. H., Qin, Y., Petersen, M. R., Mametjanov, A., Ma, P.-L., Larson, V. E., Krishna, J., Keen, N. D., Jeffery, N., Hunke, E. C., Hannah, W. M., Guba, O., Griffin, B. M., Feng, Y., Engwirda, D., Di Vittorio, A. V., Dang, C., Conlon, L. M., Chen, C.-C.-J., Brunke, M. A., Bisht, G., Benedict, J. J., Asay-Davis, X. S., Zhang, Y., Zhang, M., Zeng, X., Xie, S., Wolfram, P. J., Vo, T., Veneziani, M., Tesfa, T. K., Sreepathi, S., Salinger, A. G., Reeves Eyre, J. E. J., Prather, M. J., Mahajan, S., Li, Q., Jones, P. W., Jacob, R. L., Huebler, G. W., Huang, X., Hillman, B. R., Harrop, B. E., Foucar, J. G., Fang, Y., Comeau, D. S., Caldwell, P. M., Bartoletti, T., Balaguru, K., Taylor, M. A., McCoy, R. B., Leung, L. R., and Bader, D. C.: The DOE E3SM Model Version 2: Overview of the Physical Model and Initial Model Evaluation, J. Adv. Model. Earth Syst., 14, e2022MS003156, https://doi.org/10.1029/2022MS003156, 2022. a, b, c, d
Hannah, W. M., Bradley, A. M., Guba, O., Tang, Q., Golaz, J.-C., and Wolfe, J.: Separating Physics and Dynamics Grids for Improved Computational Efficiency in Spectral Element Earth System Models, J. Adv. Model. Earth Syst., 13, e2020MS002419, https://doi.org/10.1029/2020MS002419, 2021. a
Herrington, A. R., Lauritzen, P. H., Reed, K. A., Goldhaber, S., and Eaton, B. E.: Exploring a Lower-Resolution Physics Grid in CAM-SE-CSLAM, J. Adv. Model. Earth Syst., 11, 1894–1916, https://doi.org/10.1029/2019MS001684, 2019. a
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on pressure levels from 1979 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.bd0915c6, 2018a. a
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on single levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.adbb2d47, 2018b. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteorol. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a, b, c
Hoesly, R. M., Smith, S. J., Feng, L., Klimont, Z., Janssens-Maenhout, G., Pitkanen, T., Seibert, J. J., Vu, L., Andres, R. J., Bolt, R. M., Bond, T. C., Dawidowski, L., Kholod, N., Kurokawa, J.-I., Li, M., Liu, L., Lu, Z., Moura, M. C. P., O'Rourke, P. R., and Zhang, Q.: Historical (1750–2014) anthropogenic emissions of reactive gases and aerosols from the Community Emissions Data System (CEDS), Geosci. Model Dev., 11, 369–408, https://doi.org/10.5194/gmd-11-369-2018, 2018. a
Huffman, G. J. and Bolvin, D. T.: TRMM and other data precipitation data set documentation, NASA Global Change Master Directory Doc., NASA, Greenbelt, USA, 44 pp., https://gpm.nasa.gov/sites/default/files/document_files/3B42_3B43_doc_V7.pdf (last access: 5 March 2026), 2015. a
Iacono, M. J., Delamere, J. S., Mlawer, E. J., Shephard, M. W., Clough, S. A., and Collins, W. D.: Radiative forcing by long–lived greenhouse gases: Calculations with the AER radiative transfer models, J. Geophys. Res.-Atmos., 113, D13103, https://doi.org/10.1029/2008JD009944, 2008. a
Jeuken, A. B. M., Siegmund, P. C., Heijboer, L. C., Feichter, J., and Bengtsson, L.: On the potential of assimilating meteorological analyses in a global climate model for the purpose of model validation, J. Geophys. Res.-Atmos., 101, 16939–16950, https://doi.org/10.1029/96JD01218, 1996. a, b, c
Kato, S., Rose, F. G., Rutan, D. A., Thorsen, T. J., Loeb, N. G., Doelling, D. R., Huang, X., Smith, W. L., Su, W., and Ham, S.-H.: Surface Irradiances of Edition 4.0 Clouds and the Earth's Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) Data Product, J. Climate, 31, 4501–4527, https://doi.org/10.1175/JCLI-D-17-0523.1, 2018. a, b, c
Knapp, K. R., Kruk, M. C., Levinson, D. H., Diamond, H. J., and Neumann, C. J.: The International Best Track Archive for Climate Stewardship (IBTrACS): Unifying Tropical Cyclone Data, B. Am. Meteorol. Soc., 91, 363–376, https://doi.org/10.1175/2009BAMS2755.1, 2010. a
Kooperman, G. J., Pritchard, M. S., Ghan, S. J., Wang, M., Somerville, R. C. J., and Russell, L. M.: Constraining the influence of natural variability to improve estimates of global aerosol indirect effects in a nudged version of the Community Atmosphere Model 5, J. Geophys. Res.-Atmos., 117, D23204, https://doi.org/10.1029/2012JD018588, 2012. a
Larson, V. E., Golaz, J.-C., and Cotton, W. R.: A PDF-Based Model for Boundary Layer Clouds. Part I: Method and Model Description, J. Atmos. Sci., 59, 3519–3539, https://doi.org/10.1175/1520-0469(2002)059<3519:SSAMVI>2.0.CO;2, 2002. a
Lauritzen, P. H., Nair, R. D., Herrington, A. R., Callaghan, P., Goldhaber, S., Dennis, J. M., Bacmeister, J. T., Eaton, B. E., Zarzycki, C. M., Taylor, M. A., Ullrich, P. A., Dubos, T., Gettelman, A., Neale, R. B., Dobbins, B., Reed, K. A., Hannay, C., Medeiros, B., Benedict, J. J., and Tribbia, J. J.: NCAR Release of CAM-SE in CESM2.0: A Reformulation of the Spectral Element Dynamical Core in Dry-Mass Vertical Coordinates With Comprehensive Treatment of Condensates and Energy, J. Adv. Model. Earth Syst., 10, 1537–1570, https://doi.org/10.1029/2017MS001257, 2018. a
Leung, L. R., Bader, D. C., Taylor, M. A., and McCoy, R. B.: An Introduction to the E3SM Special Collection: Goals, Science Drivers, Development, and Analysis, J. Adv. Model. Earth Syst., 12, e2019MS001821, https://doi.org/10.1029/2019MS001821, 2020. a
Lin, G., Wan, H., Zhang, K., Qian, Y., and Ghan, S. J.: Can nudging be used to quantify model sensitivities in precipitation and cloud forcing?, J. Adv. Model. Earth Syst., 8, 1073–1091, https://doi.org/10.1002/2016MS000659, 2016. a
Liu, X., Ma, P.-L., Wang, H., Tilmes, S., Singh, B., Easter, R. C., Ghan, S. J., and Rasch, P.: Description and evaluation of a new four–mode version of the Modal Aerosol Module (MAM4) within version 5.3 of the Community Atmosphere Model, Geosci. Model Dev., 9, 505–522, https://doi.org/10.5194/gmd-9-505-2016, 2016. a
Lo, J. C.-F., Yang, Z.-L., and Pielke Sr., R. A.: Assessment of three dynamical climate downscaling methods using the Weather Research and Forecasting (WRF) model, J. Geophys. Res.-Atmos., 113, https://doi.org/10.1029/2007JD009216, 2008. a, b
Lohmann, U. and Hoose, C.: Sensitivity studies of different aerosol indirect effects in mixed-phase clouds, Atmos. Chem. Phys., 9, 8917–8934, https://doi.org/10.5194/acp-9-8917-2009, 2009. a
Ma, H.-Y., Chuang, C. C., Klein, S. A., Lo, M.-H., Zhang, Y., Xie, S., Zheng, X., Ma, P.-L., Zhang, Y., and Phillips, T. J.: An improved hindcast approach for evaluation and diagnosis of physical processes in global climate models, J. Adv. Model. Earth Syst., 7, 1810–1827, https://doi.org/10.1002/2015MS000490, 2015. a, b, c
Ma, P.-L., Harrop, B. E., Larson, V. E., Neale, R. B., Gettelman, A., Morrison, H., Wang, H., Zhang, K., Klein, S. A., Zelinka, M. D., Zhang, Y., Qian, Y., Yoon, J.-H., Jones, C. R., Huang, M., Tai, S.-L., Singh, B., Bogenschutz, P. A., Zheng, X., Lin, W., Quaas, J., Chepfer, H., Brunke, M. A., Zeng, X., Mülmenstädt, J., Hagos, S., Zhang, Z., Song, H., Liu, X., Pritchard, M. S., Wan, H., Wang, J., Tang, Q., Caldwell, P. M., Fan, J., Berg, L. K., Fast, J. D., Taylor, M. A., Golaz, J.-C., Xie, S., Rasch, P. J., and Leung, L. R.: Better calibration of cloud parameterizations and subgrid effects increases the fidelity of the E3SM Atmosphere Model version 1, Geosci. Model Dev., 15, 2881–2916, https://doi.org/10.5194/gmd-15-2881-2022, 2022. a
Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J., and Clough, S. A.: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave, J. Geophys. Res.-Atmos., 102, 16663–16682, https://doi.org/10.1029/97JD00237, 1997. a
Morrison, H. and Gettelman, A.: A New Two-Moment Bulk Stratiform Cloud Microphysics Scheme in the Community Atmosphere Model, Version 3 (CAM3). Part I: Description and Numerical Tests, J. Climate, 21, 3642–3659, https://doi.org/10.1175/2008JCLI2105.1, 2008. a
NOAA GFDL: The GFDL Finite-Volume Cubed-Sphere Dynamical Core: Technical Description, Tech. rep., NOAA Geophysical Fluid Dynamics Laboratory, https://www.gfdl.noaa.gov/wp-content/uploads/2020/02/FV3-Technical-Description.pdf (last access: 5 March 2026), 2020. a
Oleson, K. W., Lawrence, M., Bonan, B., Drewniak, B. A., Huang, M., Koven, D., Levis, S., Li, F., Riley, J. P., Subin, M. C., Swenson, S. C., Thornton, E., Bozbiyik, A., Fisher, R. A., Heald, L., Kluzek, E., Lamarque, J., Lawrence, J., Leung, R., Lipscomb, W. H., Muszala, P., Ricciuto, M., Sacks, J., Sun, Y., Tang, J., and Yang, Z.: Technical description of version 4.5 of the Community Land Model (CLM), NCAR Technical Note NCAR/TN-503+STR, NCAR, USA, 420 pp., https://doi.org/10.5065/D6RR1W7M, 2013. a
Rasch, P. J., Xie, S., Ma, P.-L., Lin, W., Wang, H., Tang, Q., Burrows, S. M., Caldwell, P., Zhang, K., Easter, R. C., Cameron-Smith, P., Singh, B., Wan, H., Golaz, J.-C., Harrop, B. E., Roesler, E., Bacmeister, J., Larson, V. E., Evans, K. J., Qian, Y., Taylor, M., Leung, L. R., Zhang, Y., Brent, L., Branstetter, M., Hannay, C., Mahajan, S., Mametjanov, A., Neale, R., Richter, J. H., Yoon, J.-H., Zender, C. S., Bader, D., Flanner, M., Foucar, J. G., Jacob, R., Keen, N., Klein, S. A., Liu, X., Salinger, A., Shrivastava, M., and Yang, Y.: An Overview of the Atmospheric Component of the Energy Exascale Earth System Model, J. Adv. Model. Earth Syst., 11, 2377–2411, https://doi.org/10.1029/2019MS001629, 2019. a
Reynolds, R. W., Rayner, N. A., Smith, T. M., Stokes, D. C., and Wang, W.: An Improved In Situ and Satellite SST Analysis for Climate, J. Climate, 15, 1609–1625, https://doi.org/10.1175/1520-0442(2002)015<1609:AIISAS>2.0.CO;2, 2002. a
Rutz, J. J., Shields, C. A., Lora, J. M., Payne, A. E., Guan, B., Ullrich, P., O'Brien, T., Leung, L. R., Ralph, F. M., Wehner, M., Brands, S., Collow, A., Goldenson, N., Gorodetskaya, I., Griffith, H., Kashinath, K., Kawzenuk, B., Krishnan, H., Kurlin, V., Lavers, D., Magnusdottir, G., Mahoney, K., McClenny, E., Muszynski, G., Nguyen, P. D., Prabhat, Qian, Y., Ramos, A. M., Sarangi, C., Sellars, S., Shulgina, T., Tome, R., Waliser, D., Walton, D., Wick, G., Wilson, A. M., and Viale, M.: The Atmospheric River Tracking Method Intercomparison Project (ARTMIP): Quantifying Uncertainties in Atmospheric River Climatology, J. Geophys. Res.-Atmos., 124, 13777–13802, https://doi.org/10.1029/2019JD030936, 2019. a
Separovic, L., de Elía, R., and Laprise, R.: Impact of spectral nudging and domain size in studies of RCM response to parameter modification, Clim. Dynam., 38, 1325–1343, https://doi.org/10.1007/s00382-011-1072-7, 2012. a
Song, X., Zhang, G. J., Terai, C., and Xie, S.: Enhanced convective microphysics scheme and its impacts on mean climate in E3SM, J. Adv. Model. Earth Syst., 17, e2024MS004656, https://doi.org/10.1029/2024MS004656, 2025. a
Sun, J., Zhang, K., Wan, H., Ma, P.-L., Tang, Q., and Zhang, S.: Impact of Nudging Strategy on the Climate Representativeness and Hindcast Skill of Constrained EAMv1 Simulations, J. Adv. Model. Earth Syst., 11, 3911–3933, https://doi.org/10.1029/2019MS001831, 2019. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r
Taylor, M. A. and Fournier, A.: A compatible and conservative spectral element method on unstructured grids, J. Comput. Phys., 229, 5879–5895, https://doi.org/10.1016/j.jcp.2010.04.008, 2010. a
Ullrich, P. A.: TempestExtremes User Guide, https://climate.ucdavis.edu/tempestextremes.php (last access: 5 March 2026), 2020. a
Ullrich, P.: TempestExtremes v2.2.1, GitHub [code], https://github.com/ClimateGlobalChange/tempestextremes/releases/tag/v2.2.1 (last access: 5 March 2026), 2022. a
Ullrich, P. A., Zarzycki, C. M., McClenny, E. E., Pinheiro, M. C., Stansfield, A. M., and Reed, K. A.: TempestExtremes v2.1: a community framework for feature detection, tracking, and analysis in large datasets, Geosci. Model Dev., 14, 5023–5048, https://doi.org/10.5194/gmd-14-5023-2021, 2021. a, b, c, d
US Department of Energy: E3SM Project: Energy Exascale Earth System Model v2.0, GitHub [code], https://github.com/E3SM-Project/E3SM/releases/tag/v2.0.0 (last access: 5 March 2026), 2021. a
Vincent, C. L. and Hahmann, A. N.: The Impact of Grid and Spectral Nudging on the Variance of the Near-Surface Wind Speed, J. Appl. Meteorol. Clim., 54, 1021–1038, https://doi.org/10.1175/JAMC-D-14-0047.1, 2015. a, b
Wang, H., Easter, R. C., Zhang, R., Ma, P.-L., Singh, B., Zhang, K., Ganguly, D., Rasch, P. J., Burrows, S. M., Ghan, S. J., Lou, S., Qian, Y., Yang, Y., Feng, Y., Flanner, M., Leung, L. R., Liu, X., Shrivastava, M., Sun, J., Tang, Q., Xie, S., and Yoon, J.-H.: Aerosols in the E3SM Version 1: New Developments and Their Impacts on Radiative Forcing, J. Adv. Model. Earth Syst., 12, e2019MS001851, https://doi.org/10.1029/2019MS001851, 2020. a
Wang, Y., Liu, X., Hoose, C., and Wang, B.: Different contact angle distributions for heterogeneous ice nucleation in the Community Atmospheric Model version 5,, Atmos. Chem. Phys., 14, 10411–10430, https://doi.org/10.5194/acp-14-10411-2014, 2014. a
Watt-Meyer, O., Bretherton, C. S., Gentine, P., Harris, L. M., and Toms, B. A.: Correcting Weather and Climate Models by Machine Learning Nudged Historical Simulations, Geophys. Res. Lett., 48, e2021GL093814, https://doi.org/10.1029/2021GL093814, 2021. a, b, c
Wehrli, K., Guillod, B. P., Hauser, M., Leclair, M., and Seneviratne, S. I.: Assessing the Dynamic Versus Thermodynamic Origin of Climate Model Biases, Geophys. Res. Lett., 45, 8471–8479, https://doi.org/10.1029/2018GL079220, 2018. a
Wehrli, K., Guillod, B. P., Hauser, M., Leclair, M., and Seneviratne, S.: Identifying Key Driving Processes of Major Recent Heat Waves, J. Geophys. Res.-Atmos., 124, 11746–11765, https://doi.org/10.1029/2019JD030635, 2019. a
Wehrli, K., Luo, F., Hauser, M., Shiogama, H., Tokuda, D., Kim, H., Coumou, D., May, W., Le Sager, P., Selten, F., Martius, O., Vautard, R., and Seneviratne, S. I.: The ExtremeX global climate model experiment: investigating thermodynamic and dynamic processes contributing to weather and climate extremes, Earth Syst. Dynam., 13, 1167–1196, https://doi.org/10.5194/esd-13-1167-2022, 2022. a, b
WMO: Global Guide to Tropical Cyclone Forecasting, WMO-No. 1194, https://cyclone.wmo.int/pdf/Global-Guide-to-Tropical-Cyclone-Forecasting.pdf (last access: 5 March 2026), 2017. a
Xie, S., Lin, W., Rasch, P. J., Ma, P.-L., Neale, R., Larson, V. E., Qian, Y., Bogenschutz, P. A., Caldwell, P., Cameron-Smith, P., Golaz, J.-C., Mahajan, S., Singh, B., Tang, Q., Wang, H., Yoon, J.-H., Zhang, K., and Zhang, Y.: Understanding Cloud and Convective Characteristics in Version 1 of the E3SM Atmosphere Model, J. Adv. Model. Earth Syst., 10, 2618–2644, https://doi.org/10.1029/2018MS001350, 2018. a
Zhang, G. J. and McFarlane, N. A.: Sensitivity of climate simulations to the parameterization of cumulus convection in the Canadian Climate Centre general circulation model, Atmos. Ocean, 33, 407–446, https://doi.org/10.1080/07055900.1995.9649539, 1995. a
Zhang, K., O'Donnell, D., Kazil, J., Stier, P., Kinne, S., Lohmann, U., Ferrachat, S., Croft, B., Quaas, J., Wan, H., Rast, S., and Feichter, J.: The global aerosol-climate model ECHAM-HAM, version 2: sensitivity to improvements in process representations, Atmos. Chem. Phys., 12, 8911–8949, https://doi.org/10.5194/acp-12-8911-2012, 2012. a
Zhang, K., Wan, H., Liu, X., Ghan, S. J., Kooperman, G. J., Ma, P.-L., Rasch, P. J., Neubauer, D., and Lohmann, U.: Technical Note: On the use of nudging for aerosol-climate model intercomparison studies, Atmos. Chem. Phys., 14, 8631–8645, https://doi.org/10.5194/acp-14-8631-2014, 2014. a, b
Zhang, S.: E3SM Model Code for Zhang et. al. (2025), Zenodo [code], https://doi.org/10.5281/zenodo.16815018, 2025a. a
Zhang, S.: Analysis scripts and dataset for Zhang et. al. (2025), Zenodo [data set], https://doi.org/10.5281/zenodo.18226569, 2025b. a
Zhang, S., Zhang, K., Wan, H., and Sun, J.: Further improvement and evaluation of nudging in the E3SM Atmosphere Model version 1 (EAMv1): simulations of the mean climate, weather events, and anthropogenic aerosol effects, Geosci. Model Dev., 15, 6787–6816, https://doi.org/10.5194/gmd-15-6787-2022, 2022. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t, u
Zhang, S., Harrop, B., Leung, L. R., Charalampopoulos, A.-T., Barthel Sorensen, B., Xu, W., and Sapsis, T.: A Machine Learning Bias Correction on Large-Scale Environment of High-Impact Weather Systems in E3SM Atmosphere Model, J. Adv. Model. Earth Syst., 16, e2023MS004138, https://doi.org/10.1029/2023MS004138, 2024. a
Short summary
We developed a new method to guide the simulated atmosphere in an Earth system model so it better reflects real-world weather. By adjusting temperature and humidity, it reduces unwanted side effects and improves the realism of rainfall, energy flows, land–surface conditions, and extreme storms such as cyclones and atmospheric rivers. This makes the model more useful for testing its performance, understanding high-impact weather events, and creating reliable training data for machine learning.
We developed a new method to guide the simulated atmosphere in an Earth system model so it...
