Articles | Volume 24, issue 12
https://doi.org/10.5194/acp-24-7041-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/acp-24-7041-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Opinion
| Highlight paper
| 
19 Jun 2024
Opinion | Highlight paper |
| 19 Jun 2024
| 19 Jun 2024
Opinion: Optimizing climate models with process knowledge, resolution, and artificial intelligence
California Institute of Technology, Pasadena, CA, USA
Google Research, Mountain View, CA, USA
L. Ruby Leung
Pacific Northwest National Laboratory, Richland, WA, USA
Robert C. J. Wills
ETH Zurich, Zurich, Switzerland
Related authors
Mauricio Lima, Katherine Deck, Oliver R. A. Dunbar, and Tapio Schneider
EGUsphere, https://doi.org/10.48550/arXiv.2404.14212, https://doi.org/10.48550/arXiv.2404.14212, 2024
Short summary
Short summary
Machine learning is playing an increasingly important role in hydrological modeling. In this paper, we introduce an adaptation of existing machine learning models forecasting streamflow in river basins, redesigning them with the goal of integrating them into climate models. We demonstrate the effectiveness of our adapted model by showing that it outperforms a physics-based river model. These results motivate further studies of the use of machine learning based river models inside climate models.
Akshay Sridhar, Yassine Tissaoui, Simone Marras, Zhaoyi Shen, Charles Kawczynski, Simon Byrne, Kiran Pamnany, Maciej Waruszewski, Thomas H. Gibson, Jeremy E. Kozdon, Valentin Churavy, Lucas C. Wilcox, Francis X. Giraldo, and Tapio Schneider
Geosci. Model Dev., 15, 6259–6284, https://doi.org/10.5194/gmd-15-6259-2022, https://doi.org/10.5194/gmd-15-6259-2022, 2022
Short summary
Short summary
ClimateMachine is a new open-source Julia-language atmospheric modeling code. We describe its limited-area configuration and the model equations, and we demonstrate applicability through benchmark problems, including atmospheric flow in the shallow cumulus regime. We show that the discontinuous Galerkin numerics and model equations allow global conservation of key variables (up to sources and sinks). We assess CPU strong scaling and GPU weak scaling to show its suitability for large simulations.
Chang Liao, Ruby Leung, Yilin Fang, Teklu Tesfa, and Robinson Negron-Juarez
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-178, https://doi.org/10.5194/gmd-2024-178, 2024
Preprint under review for GMD
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 don't have detailed enough information about the land surface. 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.
Pengfei Shi, L. Ruby Leung, and Bin Wang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-183, https://doi.org/10.5194/gmd-2024-183, 2024
Preprint under review for GMD
Short summary
Short summary
Improving climate predictions has significant socio-economic impacts. In this study, we developed and applied a 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. It also enhances the simulation of interannual precipitation and temperature variability over the southern US. This system is to support future predictability studies.
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
EGUsphere, https://doi.org/10.5194/egusphere-2024-2785, https://doi.org/10.5194/egusphere-2024-2785, 2024
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.
Malcolm John 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
EGUsphere, https://doi.org/10.5194/egusphere-2024-2582, https://doi.org/10.5194/egusphere-2024-2582, 2024
Short summary
Short summary
HighResMIP2 is a model intercomparison project focussing on high resolution global climate models, that is those with grid spacings of 25 km or less in atmosphere and ocean, using simulations of decades to a century or so 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.
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.
Fan Mei, Jennifer M. Comstock, Mikhail S. Pekour, Jerome D. Fast, Beat Schmid, Krista L. Gaustad, Shuaiqi Tang, Damao Zhang, John E. Shilling, Jason 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 Discuss., https://doi.org/10.5194/essd-2024-97, https://doi.org/10.5194/essd-2024-97, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
Our study explores a rich dataset from the final decade of the U.S. DOE's Gulfstream-1 (G-1) aircraft operations (2013-2018). The 236 flights cover diverse regions, including the Arctic, U.S. Southern Great Plains, U.S. West Coast, Eastern North Atlantic, Amazon Basin in Brazil, and Sierras de Córdoba range in Argentina. This airborne dataset offers unprecedented insights into atmospheric dynamics, aerosols, and clouds with a more accessible data format.
Guta Wakbulcho Abeshu, Hong-Yi Li, Mingjie Shi, and Ruby Leung
EGUsphere, https://doi.org/10.5194/egusphere-2024-1748, https://doi.org/10.5194/egusphere-2024-1748, 2024
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, it 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.
Jianfeng Li, Andrew Geiss, Zhe Feng, L. Ruby Leung, Yun Qian, and Wenjun Cui
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-112, https://doi.org/10.5194/essd-2024-112, 2024
Preprint under review for ESSD
Short summary
Short summary
We develop 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 echo detection based on a machine learning method, hourly gust speed measurements, and physically based identification criteria.
Yilin Fang, Hoang Viet Tran, and L. Ruby Leung
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-70, https://doi.org/10.5194/gmd-2024-70, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Hurricanes may worsen the 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 LMRB during Hurricane Ida in 2021, but less pronounced than the observations, indicating areas for model improvement to better understand and manage nutrient runoff loss during hurricanes in the region.
Mauricio Lima, Katherine Deck, Oliver R. A. Dunbar, and Tapio Schneider
EGUsphere, https://doi.org/10.48550/arXiv.2404.14212, https://doi.org/10.48550/arXiv.2404.14212, 2024
Short summary
Short summary
Machine learning is playing an increasingly important role in hydrological modeling. In this paper, we introduce an adaptation of existing machine learning models forecasting streamflow in river basins, redesigning them with the goal of integrating them into climate models. We demonstrate the effectiveness of our adapted model by showing that it outperforms a physics-based river model. These results motivate further studies of the use of machine learning based river models inside climate models.
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. Discuss., https://doi.org/10.5194/gmd-2024-85, https://doi.org/10.5194/gmd-2024-85, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
This study provides the first comprehensive assessment of historical fire simulations from 19 CMIP6 ESMs. Most models reproduce global total, spatial pattern, seasonality, and regional historical changes well, but fail to simulate the recent decline in global burned area and underestimate the fire sensitivity to wet-dry conditions. They addressed three critical issues in CMIP5. We present targeted guidance for fire scheme development and methodologies to generate reliable fire projections.
David B. Bonan, Jakob Dörr, Robert C. J. Wills, Andrew F. Thompson, and Marius Årthun
The Cryosphere, 18, 2141–2159, https://doi.org/10.5194/tc-18-2141-2024, https://doi.org/10.5194/tc-18-2141-2024, 2024
Short summary
Short summary
Antarctic sea ice has exhibited variability over satellite records, including a period of gradual expansion and a period of sudden decline. We use a novel statistical method to identify sources of variability in observed Antarctic sea ice changes. We find that the gradual increase in sea ice is likely related to large-scale temperature trends, and periods of abrupt sea ice decline are related to specific flavors of equatorial tropical variability known as the El Niño–Southern Oscillation.
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.
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 Discuss., https://doi.org/10.5194/essd-2024-43, https://doi.org/10.5194/essd-2024-43, 2024
Preprint under review for ESSD
Short summary
Short summary
We have developed a new map that reveals 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 2,500 gauges and a wealth of climate and environmental information. The map is a critical step in understanding and predicting how carbon moves through our environment, hence a useful tool for tackling climate challenges.
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
EGUsphere, https://doi.org/10.5194/egusphere-2024-778, https://doi.org/10.5194/egusphere-2024-778, 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 drier, warmer air, which can lead to a reduction in cloud. 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 had led us to conclude.
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.
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.
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.
Jakob Simon Dörr, David B. Bonan, Marius Årthun, Lea Svendsen, and Robert C. J. Wills
The Cryosphere, 17, 4133–4153, https://doi.org/10.5194/tc-17-4133-2023, https://doi.org/10.5194/tc-17-4133-2023, 2023
Short summary
Short summary
The Arctic sea-ice cover is retreating due to climate change, but this retreat is influenced by natural (internal) variability in the climate system. We use a new statistical method to investigate how much internal variability has affected trends in the summer and winter Arctic sea-ice cover using observations since 1979. Our results suggest that the impact of internal variability on sea-ice retreat might be lower than what climate models have estimated.
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.
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.
Nicola Maher, Robert C. Jnglin Wills, Pedro DiNezio, Jeremy Klavans, Sebastian Milinski, Sara C. Sanchez, Samantha Stevenson, Malte F. Stuecker, and Xian Wu
Earth Syst. Dynam., 14, 413–431, https://doi.org/10.5194/esd-14-413-2023, https://doi.org/10.5194/esd-14-413-2023, 2023
Short summary
Short summary
Understanding whether the El Niño–Southern Oscillation (ENSO) is likely to change in the future is important due to its widespread impacts. By using large ensembles, we can robustly isolate the time-evolving response of ENSO variability in 14 climate models. We find that ENSO variability evolves in a nonlinear fashion in many models and that there are large differences between models. These nonlinear changes imply that ENSO impacts may vary dramatically throughout the 21st century.
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.
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.
Akshay Sridhar, Yassine Tissaoui, Simone Marras, Zhaoyi Shen, Charles Kawczynski, Simon Byrne, Kiran Pamnany, Maciej Waruszewski, Thomas H. Gibson, Jeremy E. Kozdon, Valentin Churavy, Lucas C. Wilcox, Francis X. Giraldo, and Tapio Schneider
Geosci. Model Dev., 15, 6259–6284, https://doi.org/10.5194/gmd-15-6259-2022, https://doi.org/10.5194/gmd-15-6259-2022, 2022
Short summary
Short summary
ClimateMachine is a new open-source Julia-language atmospheric modeling code. We describe its limited-area configuration and the model equations, and we demonstrate applicability through benchmark problems, including atmospheric flow in the shallow cumulus regime. We show that the discontinuous Galerkin numerics and model equations allow global conservation of key variables (up to sources and sinks). We assess CPU strong scaling and GPU weak scaling to show its suitability for large simulations.
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.
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.
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.
Jianfeng Li, Zhe Feng, Yun Qian, and L. Ruby Leung
Earth Syst. Sci. Data, 13, 827–856, https://doi.org/10.5194/essd-13-827-2021, https://doi.org/10.5194/essd-13-827-2021, 2021
Short summary
Short summary
Deep convection has different properties at different scales. We develop a 4 km h−1 observational data product of mesoscale convective systems and isolated deep convection in the United States from 2004–2017. We find that both types of convective systems contribute significantly to precipitation east of the Rocky Mountains but with distinct spatiotemporal characteristics. The data product will be useful for observational analyses and model evaluations of convection events at different scales.
Kurt C. Solander, Brent D. Newman, Alessandro Carioca de Araujo, Holly R. Barnard, Z. Carter Berry, Damien Bonal, Mario Bretfeld, Benoit Burban, Luiz Antonio Candido, Rolando Célleri, Jeffery Q. Chambers, Bradley O. Christoffersen, Matteo Detto, Wouter A. Dorigo, Brent E. Ewers, Savio José Filgueiras Ferreira, Alexander Knohl, L. Ruby Leung, Nate G. McDowell, Gretchen R. Miller, Maria Terezinha Ferreira Monteiro, Georgianne W. Moore, Robinson Negron-Juarez, Scott R. Saleska, Christian Stiegler, Javier Tomasella, and Chonggang Xu
Hydrol. Earth Syst. Sci., 24, 2303–2322, https://doi.org/10.5194/hess-24-2303-2020, https://doi.org/10.5194/hess-24-2303-2020, 2020
Short summary
Short summary
We evaluate the soil moisture response in the humid tropics to El Niño during the three most recent super El Niño events. Our estimates are compared to in situ soil moisture estimates that span five continents. We find the strongest and most consistent soil moisture decreases in the Amazon and maritime southeastern Asia, while the most consistent increases occur over eastern Africa. Our results can be used to improve estimates of soil moisture in tropical ecohydrology models at multiple scales.
Zhiyuan Hu, Jianping Huang, Chun Zhao, Yuanyuan Ma, Qinjian Jin, Yun Qian, L. Ruby Leung, Jianrong Bi, and Jianmin Ma
Atmos. Chem. Phys., 19, 12709–12730, https://doi.org/10.5194/acp-19-12709-2019, https://doi.org/10.5194/acp-19-12709-2019, 2019
Short summary
Short summary
This study investigates aerosol chemical compositions and relative contributions to total aerosols in the western US. The results show that trans-Pacific aerosols have a maximum concentration in the boreal spring, with the greatest contribution from dust. Over western North America, the trans-Pacific aerosols dominate the column-integrated aerosol mass and number concentration. However, near the surface, aerosols mainly originated from local emissions.
Mingchen Ma, Yang Gao, Yuhang Wang, Shaoqing Zhang, L. Ruby Leung, Cheng Liu, Shuxiao Wang, Bin Zhao, Xing Chang, Hang Su, Tianqi Zhang, Lifang Sheng, Xiaohong Yao, and Huiwang Gao
Atmos. Chem. Phys., 19, 12195–12207, https://doi.org/10.5194/acp-19-12195-2019, https://doi.org/10.5194/acp-19-12195-2019, 2019
Short summary
Short summary
Ozone pollution has become severe in China, and extremely high ozone episodes occurred in summer 2017 over the North China Plain. While meteorology impacts are clear, we find that enhanced biogenic emissions, previously ignored by the community, driven by high vapor pressure deficit, land cover change and urban landscape contribute substantially to ozone formation. This study has significant implications for ozone pollution control with more frequent heat waves and urbanization growth in future.
Chun Zhao, Mingyue Xu, Yu Wang, Meixin Zhang, Jianping Guo, Zhiyuan Hu, L. Ruby Leung, Michael Duda, and William Skamarock
Geosci. Model Dev., 12, 2707–2726, https://doi.org/10.5194/gmd-12-2707-2019, https://doi.org/10.5194/gmd-12-2707-2019, 2019
Short summary
Short summary
Simulations at global uniform and variable resolutions share similar characteristics of precipitation and wind in the refined region. The experiments reveal the significant impacts of resolution on simulating the distribution and intensity of precipitation and updrafts. This study provides evidence supporting the use of convection-permitting global variable-resolution simulations to study extreme precipitation.
Junxi Zhang, Yang Gao, L. Ruby Leung, Kun Luo, Huan Liu, Jean-Francois Lamarque, Jianren Fan, Xiaohong Yao, Huiwang Gao, and Tatsuya Nagashima
Atmos. Chem. Phys., 19, 887–900, https://doi.org/10.5194/acp-19-887-2019, https://doi.org/10.5194/acp-19-887-2019, 2019
Short summary
Short summary
ACCMIP simulations were used to study NOy deposition over East Asia in the future. Both dry and wet NOy deposition show significant decreases in the 2100s under RCP4.5 and RCP8.5 due to large anthropogenic emission reduction. The changes in climate only significantly affect the wet deposition primarily linked to changes in precipitation. Over the coastal seas of China, weaker transport of NOy from land due to emission reduction infers a larger impact from shipping and lightning emissions.
Ge Zhang, Yang Gao, Wenju Cai, L. Ruby Leung, Shuxiao Wang, Bin Zhao, Minghuai Wang, Huayao Shan, Xiaohong Yao, and Huiwang Gao
Atmos. Chem. Phys., 19, 565–576, https://doi.org/10.5194/acp-19-565-2019, https://doi.org/10.5194/acp-19-565-2019, 2019
Short summary
Short summary
Based on observed data, this study reveals a distinct seesaw feature of abnormally high and low PM2.5 concentrations in December 2015 and January 2016 over North China. The mechanism of the seesaw pattern was found to be linked to a super El Niño and the Arctic Oscillation (AO). During the mature phase of El Niño in December 2015, the weakened East Asian winter monsoon favors strong haze formation; however, the circulation pattern was reversed in the next month due to the phase change of the AO.
Junxi Zhang, Yang Gao, Kun Luo, L. Ruby Leung, Yang Zhang, Kai Wang, and Jianren Fan
Atmos. Chem. Phys., 18, 9861–9877, https://doi.org/10.5194/acp-18-9861-2018, https://doi.org/10.5194/acp-18-9861-2018, 2018
Short summary
Short summary
We used a regional model to investigate the impact of atmosphere with high temperature and low wind speed on ozone concentration. When these compound events (heat waves and stagnant weather) occur simultaneously, a striking ozone enhancement is revealed. This type of compound event is projected to increase more dominantly compared to single events in the future over the US, Europe, and China, implying the importance of reducing emissions in order to alleviate the impact from the compound events.
Christine A. Shields, Jonathan J. Rutz, Lai-Yung Leung, F. Martin Ralph, Michael Wehner, Brian Kawzenuk, Juan M. Lora, Elizabeth McClenny, Tashiana Osborne, Ashley E. Payne, Paul Ullrich, Alexander Gershunov, Naomi Goldenson, Bin Guan, Yun Qian, Alexandre M. Ramos, Chandan Sarangi, Scott Sellars, Irina Gorodetskaya, Karthik Kashinath, Vitaliy Kurlin, Kelly Mahoney, Grzegorz Muszynski, Roger Pierce, Aneesh C. Subramanian, Ricardo Tome, Duane Waliser, Daniel Walton, Gary Wick, Anna Wilson, David Lavers, Prabhat, Allison Collow, Harinarayan Krishnan, Gudrun Magnusdottir, and Phu Nguyen
Geosci. Model Dev., 11, 2455–2474, https://doi.org/10.5194/gmd-11-2455-2018, https://doi.org/10.5194/gmd-11-2455-2018, 2018
Short summary
Short summary
ARTMIP (Atmospheric River Tracking Method Intercomparison Project) is a community effort with the explicit goal of understanding the uncertainties, and the implications of those uncertainties, in atmospheric river science solely due to detection algorithm. ARTMIP strives to quantify these differences and provide guidance on appropriate algorithmic choices for the science question posed. Project goals, experimental design, and preliminary results are provided.
Kai Zhang, Philip J. Rasch, Mark A. Taylor, Hui Wan, Ruby Leung, Po-Lun Ma, Jean-Christophe Golaz, Jon Wolfe, Wuyin Lin, Balwinder Singh, Susannah Burrows, Jin-Ho Yoon, Hailong Wang, Yun Qian, Qi Tang, Peter Caldwell, and Shaocheng Xie
Geosci. Model Dev., 11, 1971–1988, https://doi.org/10.5194/gmd-11-1971-2018, https://doi.org/10.5194/gmd-11-1971-2018, 2018
Short summary
Short summary
The conservation of total water is an important numerical feature for global Earth system models. Even small conservation problems in the water budget can lead to systematic errors in century-long simulations for sea level rise projection. This study quantifies and reduces various sources of water conservation error in the atmosphere component of the Energy Exascale Earth System Model.
Randal D. Koster, Alan K. Betts, Paul A. Dirmeyer, Marc Bierkens, Katrina E. Bennett, Stephen J. Déry, Jason P. Evans, Rong Fu, Felipe Hernandez, L. Ruby Leung, Xu Liang, Muhammad Masood, Hubert Savenije, Guiling Wang, and Xing Yuan
Hydrol. Earth Syst. Sci., 21, 3777–3798, https://doi.org/10.5194/hess-21-3777-2017, https://doi.org/10.5194/hess-21-3777-2017, 2017
Short summary
Short summary
Large-scale hydrological variability can affect society in profound ways; floods and droughts, for example, often cause major damage and hardship. A recent gathering of hydrologists at a symposium to honor the career of Professor Eric Wood motivates the present survey of recent research on this variability. The surveyed literature and the illustrative examples provided in the paper show that research into hydrological variability continues to be strong, vibrant, and multifaceted.
Shi Zhong, Yun Qian, Chun Zhao, Ruby Leung, Hailong Wang, Ben Yang, Jiwen Fan, Huiping Yan, Xiu-Qun Yang, and Dongqing Liu
Atmos. Chem. Phys., 17, 5439–5457, https://doi.org/10.5194/acp-17-5439-2017, https://doi.org/10.5194/acp-17-5439-2017, 2017
Short summary
Short summary
An online climate–chemistry coupled model (WRF-Chem) is integrated for 5 years at cloud-permitting scale to quantify the impacts of urbanization-induced changes in land cover and pollutants emission on regional climate in the Yangtze River Delta region in eastern China. Urbanization over this region increases the frequency of extreme precipitation and heat wave in summer. The results could help China government in making policies in mitigating the environmental impact of urbanization.
Xiangyu Luo, Hong-Yi Li, L. Ruby Leung, Teklu K. Tesfa, Augusto Getirana, Fabrice Papa, and Laura L. Hess
Geosci. Model Dev., 10, 1233–1259, https://doi.org/10.5194/gmd-10-1233-2017, https://doi.org/10.5194/gmd-10-1233-2017, 2017
Short summary
Short summary
This study shows that alleviating vegetation-caused biases in DEM data, refining channel cross-sectional geometry and Manning roughness coefficients, as well as accounting for backwater effects can effectively improve the modeling of streamflow, river stages and flood extent in the Amazon Basin. The obtained understanding could be helpful to hydrological modeling in basins with evident inundation, which has important implications for improving land–atmosphere interactions in Earth system models.
Teklu K. Tesfa and Lai-Yung Ruby Leung
Geosci. Model Dev., 10, 873–888, https://doi.org/10.5194/gmd-10-873-2017, https://doi.org/10.5194/gmd-10-873-2017, 2017
Short summary
Short summary
Motivated by the significant topographic influence on land surface processes, this study explored two methods to discretize watersheds into two types of subgrid structures to capture spatial heterogeneity for land surface models. Adopting geomorphologic concepts in watershed discretization yields improved capability in capturing subgrid topographic heterogeneity, which also allowed climatic and land cover variability to be better represented with a nominal increase in computational requirements.
Jiwen Fan, L. Ruby Leung, Daniel Rosenfeld, and Paul J. DeMott
Atmos. Chem. Phys., 17, 1017–1035, https://doi.org/10.5194/acp-17-1017-2017, https://doi.org/10.5194/acp-17-1017-2017, 2017
Short summary
Short summary
How orographic mixed-phase clouds respond to changes in cloud condensation nuclei (CCN) and ice nucleating particles (INPs) is highly uncertain. We conducted this study to improve understanding of these processes. We found a new mechanism through which CCN can invigorate orographic mixed-phase clouds and drastically intensify snow precipitation when CCN concentrations are high. Our findings have very important implications for orographic precipitation in polluted regions.
Reindert J. Haarsma, Malcolm J. Roberts, Pier Luigi Vidale, Catherine A. Senior, Alessio Bellucci, Qing Bao, Ping Chang, Susanna Corti, Neven S. Fučkar, Virginie Guemas, Jost von Hardenberg, Wilco Hazeleger, Chihiro Kodama, Torben Koenigk, L. Ruby Leung, Jian Lu, Jing-Jia Luo, Jiafu Mao, Matthew S. Mizielinski, Ryo Mizuta, Paulo Nobre, Masaki Satoh, Enrico Scoccimarro, Tido Semmler, Justin Small, and Jin-Song von Storch
Geosci. Model Dev., 9, 4185–4208, https://doi.org/10.5194/gmd-9-4185-2016, https://doi.org/10.5194/gmd-9-4185-2016, 2016
Short summary
Short summary
Recent progress in computing power has enabled climate models to simulate more processes in detail and on a smaller scale. Here we present a common protocol for these high-resolution runs that will foster the analysis and understanding of the impact of model resolution on the simulated climate. These runs will also serve as a more reliable source for assessing climate risks that are associated with small-scale weather phenomena such as tropical cyclones.
Bin Zhao, Kuo-Nan Liou, Yu Gu, Cenlin He, Wee-Liang Lee, Xing Chang, Qinbin Li, Shuxiao Wang, Hsien-Liang R. Tseng, Lai-Yung R. Leung, and Jiming Hao
Atmos. Chem. Phys., 16, 5841–5852, https://doi.org/10.5194/acp-16-5841-2016, https://doi.org/10.5194/acp-16-5841-2016, 2016
Short summary
Short summary
We examine the impact of buildings on surface solar fluxes in Beijing by accounting for their 3-D structures. We find that inclusion of buildings changes surface solar fluxes by within ±1 W m−2, ±1–10 W m−2, and up to ±100 W m−2 at grid resolutions of 4 km, 800 m, and 90 m, respectively. We can resolve pairs of positive-negative flux deviations on different sides of buildings at ≤ 800 m resolutions. We should treat building-effect on solar fluxes differently in models with different resolutions.
Zhiyuan Hu, Chun Zhao, Jianping Huang, L. Ruby Leung, Yun Qian, Hongbin Yu, Lei Huang, and Olga V. Kalashnikova
Geosci. Model Dev., 9, 1725–1746, https://doi.org/10.5194/gmd-9-1725-2016, https://doi.org/10.5194/gmd-9-1725-2016, 2016
Short summary
Short summary
This study conducts the simulation of WRF-Chem with the quasi-global configuration for 2010–2014, and evaluates the simulation with multiple observation datasets for the first time. This study demonstrates that the WRF-Chem quasi-global simulation can be used for investigating trans-Pacific transport of aerosols and providing reasonable inflow chemical boundaries for the western USA to further understand the impact of transported pollutants on the regional air quality and climate.
C. He, K.-N. Liou, Y. Takano, R. Zhang, M. Levy Zamora, P. Yang, Q. Li, and L. R. Leung
Atmos. Chem. Phys., 15, 11967–11980, https://doi.org/10.5194/acp-15-11967-2015, https://doi.org/10.5194/acp-15-11967-2015, 2015
W.-L. Lee, Y. Gu, K. N. Liou, L. R. Leung, and H.-H. Hsu
Atmos. Chem. Phys., 15, 5405–5413, https://doi.org/10.5194/acp-15-5405-2015, https://doi.org/10.5194/acp-15-5405-2015, 2015
Short summary
Short summary
This paper investigates 3-D mountain effects on solar flux distributions and their impact on surface hydrology over the western United States, specifically the Rocky Mountains and the Sierra Nevada, using the global CCSM4 (CAM4/CLM4) with a 0.23°×0.31° resolution for simulations over 6 years. We show that deviations in the net surface fluxes are not only affected by 3-D mountains but also influenced by feedbacks of cloud and snow in association with the long-term simulations.
Y. Fang, C. Liu, and L. R. Leung
Geosci. Model Dev., 8, 781–789, https://doi.org/10.5194/gmd-8-781-2015, https://doi.org/10.5194/gmd-8-781-2015, 2015
Short summary
Short summary
1. A gradient projection method was used to reduce the computation time of carbon-nitrogen spin-up processes in CLM4.
2. Point-scale simulations showed that the cyclic stability of total carbon for some cases differs from that of the periodic atmospheric forcing, and some cases even showed instability.
3. The instability issue is resolved after the hydrology scheme in CLM4 is replaced with a flow model for variably saturated porous media.
C. Zhao, Z. Hu, Y. Qian, L. Ruby Leung, J. Huang, M. Huang, J. Jin, M. G. Flanner, R. Zhang, H. Wang, H. Yan, Z. Lu, and D. G. Streets
Atmos. Chem. Phys., 14, 11475–11491, https://doi.org/10.5194/acp-14-11475-2014, https://doi.org/10.5194/acp-14-11475-2014, 2014
T. K. Tesfa, H.-Y. Li, L. R. Leung, M. Huang, Y. Ke, Y. Sun, and Y. Liu
Geosci. Model Dev., 7, 947–963, https://doi.org/10.5194/gmd-7-947-2014, https://doi.org/10.5194/gmd-7-947-2014, 2014
J. Fan, L. R. Leung, P. J. DeMott, J. M. Comstock, B. Singh, D. Rosenfeld, J. M. Tomlinson, A. White, K. A. Prather, P. Minnis, J. K. Ayers, and Q. Min
Atmos. Chem. Phys., 14, 81–101, https://doi.org/10.5194/acp-14-81-2014, https://doi.org/10.5194/acp-14-81-2014, 2014
Y. Sun, Z. Hou, M. Huang, F. Tian, and L. Ruby Leung
Hydrol. Earth Syst. Sci., 17, 4995–5011, https://doi.org/10.5194/hess-17-4995-2013, https://doi.org/10.5194/hess-17-4995-2013, 2013
K. N. Liou, Y. Gu, L. R. Leung, W. L. Lee, and R. G. Fovell
Atmos. Chem. Phys., 13, 11709–11721, https://doi.org/10.5194/acp-13-11709-2013, https://doi.org/10.5194/acp-13-11709-2013, 2013
N. Voisin, L. Liu, M. Hejazi, T. Tesfa, H. Li, M. Huang, Y. Liu, and L. R. Leung
Hydrol. Earth Syst. Sci., 17, 4555–4575, https://doi.org/10.5194/hess-17-4555-2013, https://doi.org/10.5194/hess-17-4555-2013, 2013
Y. Fang, M. Huang, C. Liu, H. Li, and L. R. Leung
Geosci. Model Dev., 6, 1977–1988, https://doi.org/10.5194/gmd-6-1977-2013, https://doi.org/10.5194/gmd-6-1977-2013, 2013
C. Zhao, S. Chen, L. R. Leung, Y. Qian, J. F. Kok, R. A. Zaveri, and J. Huang
Atmos. Chem. Phys., 13, 10733–10753, https://doi.org/10.5194/acp-13-10733-2013, https://doi.org/10.5194/acp-13-10733-2013, 2013
N. Voisin, H. Li, D. Ward, M. Huang, M. Wigmosta, and L. R. Leung
Hydrol. Earth Syst. Sci., 17, 3605–3622, https://doi.org/10.5194/hess-17-3605-2013, https://doi.org/10.5194/hess-17-3605-2013, 2013
Y. Ke, L. R. Leung, M. Huang, and H. Li
Geosci. Model Dev., 6, 1609–1622, https://doi.org/10.5194/gmd-6-1609-2013, https://doi.org/10.5194/gmd-6-1609-2013, 2013
H. Wan, P. J. Rasch, K. Zhang, J. Kazil, and L. R. Leung
Geosci. Model Dev., 6, 861–874, https://doi.org/10.5194/gmd-6-861-2013, https://doi.org/10.5194/gmd-6-861-2013, 2013
Related subject area
Subject: Climate and Earth System | Research Activity: Atmospheric Modelling and Data Analysis | Altitude Range: Troposphere | Science Focus: Physics (physical properties and processes)
Investigation of the characteristics of low-level jets over North America in a convection-permitting Weather Research and Forecasting simulation
Impacts of tropical cyclone–heat wave compound events on surface ozone in eastern China: comparison between the Yangtze River and Pearl River deltas
The 2023 global warming spike was driven by the El Niño–Southern Oscillation
Present-day methane shortwave absorption mutes surface warming relative to preindustrial conditions
A novel method for detecting tropopause structures based on the bi-Gaussian function
Unraveling the discrepancies between Eulerian and Lagrangian moisture tracking models in monsoon- and westerly-dominated basins of the Tibetan Plateau
Increasing aerosol direct effect despite declining global emissions in MPI-ESM1.2
Multi-scale variability of southeastern Australian wind resources
Parameterizations for global thundercloud corona discharge distributions
The importance of an informed choice of CO2-equivalence metrics for contrail avoidance
Relative humidity over ice as a key variable for Northern Hemisphere midlatitude tropopause inversion layers
Technical note: Posterior uncertainty estimation via a Monte Carlo procedure specialized for 4D-Var data assimilation
Understanding the role of contrails and contrail cirrus in climate change: a global perspective
Interannual variations in Siberian carbon uptake and carbon release period
Using historical temperature to constrain the climate sensitivity, the transient climate response, and aerosol-induced cooling
Using a region-specific ice-nucleating particle parameterization improves the representation of Arctic clouds in a global climate model
Future reduction of cold extremes over East Asia due to thermodynamic and dynamic warming
General circulation models simulate negative liquid water path–droplet number correlations, but anthropogenic aerosols still increase simulated liquid water path
Global scenarios of anthropogenic mercury emissions
Impact of Asian aerosols on the summer monsoon strongly modulated by regional precipitation biases
Satellite quantification of methane emissions from South American countries: A high-resolution inversion of TROPOMI and GOSAT observations
Assessing methane emissions from collapsing Venezuelan oil production using TROPOMI
Constraining net long term climate feedback from satellite observed internal variability possible by mid 2030s
To what extent does CO2 diurnal cycle impact carbon flux estimates in CarboScope?
Climate variability can outweigh the influence of climate mean changes for extreme precipitation under global warming
Simulation of ozone–vegetation coupling and feedback in China using multiple ozone damage schemes
Can GCMs represent cloud adjustments to aerosol–cloud interactions?
Opinion: Can uncertainty in climate sensitivity be narrowed further?
Significant human health co-benefits of mitigating African emissions
Water vapour exchange between the atmospheric boundary layer and free troposphere over eastern China: seasonal characteristics and the El Niño–Southern Oscillation anomaly
Strong aerosol cooling alone does not explain cold-biased mid-century temperatures in CMIP6 models
Air pollution reductions caused by the COVID-19 lockdown open up a way to preserve the Himalayan glaciers
Modeling atmosphere–land interactions at a rainforest site – a case study using Amazon Tall Tower Observatory (ATTO) measurements and reanalysis data
Xiao Ma, Yanping Li, Zhenhua Li, and Fei Huo
Atmos. Chem. Phys., 24, 12013–12030, https://doi.org/10.5194/acp-24-12013-2024, https://doi.org/10.5194/acp-24-12013-2024, 2024
Short summary
Short summary
This study uses 4 km Weather Research and Forecasting simulations to investigate the features of low-level jets (LLJs) in North America. It identifies significant LLJ systems, such as the Great Plains LLJ. It also provides insight into LLJs poorly captured in coarser models, such as the northerly Quebec LLJ and the small-scale, low-level wind maxima around the Rocky Mountains. Furthermore, the study examines different physical mechanisms of forming three distinct types of LLJs.
Cuini Qi, Pinya Wang, Yang Yang, Huimin Li, Hui Zhang, Lili Ren, Xipeng Jin, Chenchao Zhan, Jianping Tang, and Hong Liao
Atmos. Chem. Phys., 24, 11775–11789, https://doi.org/10.5194/acp-24-11775-2024, https://doi.org/10.5194/acp-24-11775-2024, 2024
Short summary
Short summary
We investigate extremely hot weather impacts on surface ozone over the southeastern coast of China with and without tropical cyclones. Compared to hot days alone, ozone concentration decreased notably in the Yangtze River Delta (YRD) but increased in the Pearl River Delta (PRD) during tropical cyclones and hot days. The YRD benefited from strong and clean sea winds aiding ozone elimination. In contrast, the PRD experienced strong northeasterly winds that potentially transport ozone pollution.
Shiv Priyam Raghuraman, Brian Soden, Amy Clement, Gabriel Vecchi, Sofia Menemenlis, and Wenchang Yang
Atmos. Chem. Phys., 24, 11275–11283, https://doi.org/10.5194/acp-24-11275-2024, https://doi.org/10.5194/acp-24-11275-2024, 2024
Short summary
Short summary
The rapid global warming of 2023 has led to concerns that it could be externally driven. Here we show that climate models subject only to internal variability predict such warming spikes but rarely (p~1.6 %). However, when a prolonged La Niña immediately precedes an El Niño, as occurred leading up to 2023, such spikes are not uncommon (p~10.3 %). Virtually all of the spikes occur during an El Niño, strongly suggesting that internal variability drove the 2023 warming.
Robert J. Allen, Xueying Zhao, Cynthia A. Randles, Ryan J. Kramer, Bjørn H. Samset, and Christopher J. Smith
Atmos. Chem. Phys., 24, 11207–11226, https://doi.org/10.5194/acp-24-11207-2024, https://doi.org/10.5194/acp-24-11207-2024, 2024
Short summary
Short summary
Present-day methane shortwave absorption mutes 28% (7–55%) of the surface warming associated with its longwave absorption. The precipitation increase associated with the longwave radiative effects of the present-day methane perturbation is also muted by shortwave absorption but not significantly so. Methane shortwave absorption also impacts the magnitude of its climate feedback parameter, largely through the cloud feedback.
Kun Zhang, Tao Luo, Xuebin Li, Shengcheng Cui, Ningquan Weng, Yinbo Huang, and Yingjian Wang
Atmos. Chem. Phys., 24, 11157–11173, https://doi.org/10.5194/acp-24-11157-2024, https://doi.org/10.5194/acp-24-11157-2024, 2024
Short summary
Short summary
In order to deeply understand the formation mechanisms and evolution processes associated with vertical tropopause structures, this study proposes a new method for identifying the multiple characteristic parameters of vertical tropopause structures by fitting temperature profiles using the bi-Gaussian function. The identification results from the bi-Gaussian method are more reasonable and more consistent with the evolution process of atmospheric thermal stratifications.
Ying Li, Chenghao Wang, Qiuhong Tang, Shibo Yao, Bo Sun, Hui Peng, and Shangbin Xiao
Atmos. Chem. Phys., 24, 10741–10758, https://doi.org/10.5194/acp-24-10741-2024, https://doi.org/10.5194/acp-24-10741-2024, 2024
Short summary
Short summary
For moisture tracking over the Tibetan Plateau, we recommend using high-resolution forcing datasets, prioritizing temporal resolution over spatial resolution for WAM2layers, while for FLEXPART coupled with WaterSip, we suggest applying bias corrections to optimize the filtering of precipitation particles and adjust evaporation estimates.
Antoine Hermant, Linnea Huusko, and Thorsten Mauritsen
Atmos. Chem. Phys., 24, 10707–10715, https://doi.org/10.5194/acp-24-10707-2024, https://doi.org/10.5194/acp-24-10707-2024, 2024
Short summary
Short summary
Aerosol particles, from natural and human sources, have a cooling effect on the climate, partially offsetting global warming. They do this through direct (sunlight reflection) and indirect (cloud property alteration) mechanisms. Using a global climate model, we found that, despite declining emissions, the direct effect of human aerosols has increased while the indirect effect has decreased, which is attributed to the shift in emissions from North America and Europe to Southeast Asia.
Claire L. Vincent and Andrew J. Dowdy
Atmos. Chem. Phys., 24, 10209–10223, https://doi.org/10.5194/acp-24-10209-2024, https://doi.org/10.5194/acp-24-10209-2024, 2024
Short summary
Short summary
We investigate how wind speed at the height of a wind turbine changes during El Niño and La Niña years and with season and time of day in southeastern Australia. We found that El Niño and La Niña can cause average wind speed differences of around 1 m s-1 in some regions. The highest wind speeds occur in the afternoon or evening around mountains or the coast and during the night for inland areas. The results help show how placement of wind turbines can help balance electricity generation.
Sergio Soler, Francisco J. Gordillo-Vázquez, Francisco J. Pérez-Invernón, Patrick Jöckel, Torsten Neubert, Olivier Chanrion, Victor Reglero, and Nikolai Østgaard
Atmos. Chem. Phys., 24, 10225–10243, https://doi.org/10.5194/acp-24-10225-2024, https://doi.org/10.5194/acp-24-10225-2024, 2024
Short summary
Short summary
Sudden local ozone (O3) enhancements have been reported in different regions of the world since the 1970s. While the hot channel of lightning strokes directly produce significant amounts of nitrogen oxide, no direct emission of O3 is expected. Corona discharges in convective active regions could explain local O3 increases, which remains unexplained. We present the first mathematical functions that relate the global annual frequency of in-cloud coronas with four sets of meteorological variables.
Audran Borella, Olivier Boucher, Keith P. Shine, Marc Stettler, Katsumasa Tanaka, Roger Teoh, and Nicolas Bellouin
Atmos. Chem. Phys., 24, 9401–9417, https://doi.org/10.5194/acp-24-9401-2024, https://doi.org/10.5194/acp-24-9401-2024, 2024
Short summary
Short summary
This work studies how to compare the climate impact of the CO2 emitted and contrails formed by a flight. This is applied to contrail avoidance strategies that would decrease climate impact of flights by changing the trajectory of aircraft to avoid persistent contrail formation, at the risk of increasing CO2 emissions. We find that different comparison methods lead to different quantification of the total climate impact of a flight but lead to similar decisions of whether to reroute an aircraft.
Daniel Köhler, Philipp Reutter, and Peter Spichtinger
Atmos. Chem. Phys., 24, 10055–10072, https://doi.org/10.5194/acp-24-10055-2024, https://doi.org/10.5194/acp-24-10055-2024, 2024
Short summary
Short summary
In this work, the influence of humidity on the properties of the tropopause is studied. The tropopause is the interface between the troposphere and the stratosphere and represents a barrier for the transport of air masses between the troposphere and the stratosphere. We consider not only the tropopause itself, but also a layer around it called the tropopause inversion layer (TIL). It is shown that the moister the underlying atmosphere is, the more this layer acts as a barrier.
Michael Stanley, Mikael Kuusela, Brendan Byrne, and Junjie Liu
Atmos. Chem. Phys., 24, 9419–9433, https://doi.org/10.5194/acp-24-9419-2024, https://doi.org/10.5194/acp-24-9419-2024, 2024
Short summary
Short summary
To serve the uncertainty quantification (UQ) needs of 4D-Var data assimilation (DA) practitioners, we describe and justify a UQ algorithm from carbon flux inversion and incorporate its sampling uncertainty into the final reported UQ. The algorithm is mathematically proved, and its performance is shown for a carbon flux observing system simulation experiment. These results legitimize and generalize this algorithm's current use and make available this effective algorithm to new DA domains.
Dharmendra Kumar Singh, Swarnali Sanyal, and Donald J. Wuebbles
Atmos. Chem. Phys., 24, 9219–9262, https://doi.org/10.5194/acp-24-9219-2024, https://doi.org/10.5194/acp-24-9219-2024, 2024
Short summary
Short summary
Radiative forcing of contrails could triple by 2050 due to increased air traffic and potential changes in flight altitudes. Factors like air traffic patterns, fuel efficiency, alternative fuels, and climate change further influence this impact. By highlighting gaps in knowledge and uncertainties, this research helps set priorities for future studies and assess strategies to mitigate the environmental impact of aviation emissions.
Dieu Anh Tran, Christoph Gerbig, Christian Rödenbeck, and Sönke Zaehle
Atmos. Chem. Phys., 24, 8413–8440, https://doi.org/10.5194/acp-24-8413-2024, https://doi.org/10.5194/acp-24-8413-2024, 2024
Short summary
Short summary
The analysis of the atmospheric CO2 record from the Zotino Tall Tower Observatory (ZOTTO) in central Siberia shows significant increases in the length and amplitude of the CO2 uptake and release in the 2010–2021 period. The trend shows a stronger increase in carbon release amplitude compared to the uptake, suggesting that, despite enhanced growing season uptake, during this period climate warming did not elevate the annual net CO2 uptake as cold-season respirations also responded to the warming.
Olaf Morgenstern
Atmos. Chem. Phys., 24, 8105–8123, https://doi.org/10.5194/acp-24-8105-2024, https://doi.org/10.5194/acp-24-8105-2024, 2024
Short summary
Short summary
I use errors in climate model simulations to derive correction factors for the impacts of greenhouse gases and particles that bring these simulated temperature fields into agreement with an observational reconstruction of the Earth's temperature. On average across eight models, a reduction by about one-half of the particle-induced cooling would be required, causing only 0.24 K of cooling since 1850–1899. The greenhouse gas warming simulated by several highly sensitive models would also reduce.
Astrid Bragstad Gjelsvik, Robert Oscar David, Tim Carlsen, Franziska Hellmuth, Stefan Hofer, Zachary McGraw, Harald Sodemann, and Trude Storelvmo
EGUsphere, https://doi.org/10.5194/egusphere-2024-1879, https://doi.org/10.5194/egusphere-2024-1879, 2024
Short summary
Short summary
Ice formation in clouds has a substantial impact on radiation and precipitation, and must be realistically simulated in order to understand present and future Arctic climate. Rare aerosols known as ice-nucleating particles can play an important role for cloud ice formation, but their representation in global climate models is not well suited for the Arctic. In this study, the simulation of cloud phase is improved when the representation of these particles are constrained by Arctic observations.
Donghuan Li, Tianjun Zhou, Youcun Qi, Liwei Zou, Chao Li, Wenxia Zhang, and Xiaolong Chen
Atmos. Chem. Phys., 24, 7347–7358, https://doi.org/10.5194/acp-24-7347-2024, https://doi.org/10.5194/acp-24-7347-2024, 2024
Short summary
Short summary
Two sets of climate model simulations are used to investigate the dynamic and thermodynamic factors of future change in cold extremes in East Asia. Dynamic factor accounted for over 80 % of cold-month temperature anomalies in past 50 years. The intensity of cold extreme is expected to decrease by 5 ℃, with thermodynamic factor contributing ~ 75 % by the end of the 21st century. Changes in dynamic factor are driven by an upward trend of positive Arctic Oscillation-like sea level pressure pattern.
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.
Flora Maria Brocza, Peter Rafaj, Robert Sander, Fabian Wagner, and Jenny Marie Jones
Atmos. Chem. Phys., 24, 7385–7404, https://doi.org/10.5194/acp-24-7385-2024, https://doi.org/10.5194/acp-24-7385-2024, 2024
Short summary
Short summary
To understand how atmospheric mercury levels will change in the future, we model how anthropogenic Hg releases will change following developments in human energy use and mercury use and efforts to reduce pollution and battle climate change. Overall, the findings emphasize that it will be necessary to implement targeted Hg control measures in addition to stringent climate and clean air policies to achieve significant reductions in Hg emissions.
Zhen Liu, Massimo A. Bollasina, and Laura J. Wilcox
Atmos. Chem. Phys., 24, 7227–7252, https://doi.org/10.5194/acp-24-7227-2024, https://doi.org/10.5194/acp-24-7227-2024, 2024
Short summary
Short summary
The aerosol impact on monsoon precipitation and circulation is strongly influenced by a model-simulated spatio-temporal variability in the climatological monsoon precipitation across Asia, which critically modulates the efficacy of aerosol–cloud–precipitation interactions, the predominant driver of the total aerosol response. There is a strong interplay between South Asia and East Asia monsoon precipitation biases and their relative predominance in driving the overall monsoon response.
Sarah E. Hancock, Daniel Jacob, Zichong Chen, Hannah Nesser, Aaron Davitt, Daniel J. Varon, Melissa P. Sulprizio, Nicholas Balasus, Lucas A. Estrada, James D. East, Elise Penn, Cynthia A. Randles, John Worden, Ilse Aben, Robert J. Parker, and Joannes D. Maasakkers
EGUsphere, https://doi.org/10.5194/egusphere-2024-1763, https://doi.org/10.5194/egusphere-2024-1763, 2024
Short summary
Short summary
We quantify 2021 methane emissions in South America at up to 25 km × 25 km resolution using satellite methane observations. We find a 55 % upward correction to the national anthropogenic inventories reported to the United Nations Framework Convention on Climate Change (UNFCCC) under the Paris Agreement. Our estimates match inventories for Brazil, Bolivia, and Paraguay but are much higher for other countries. Livestock emissions (65 % of anthropogenic emissions) show the largest discrepancies.
Brian Nathan, Joannes D. Maasakkers, Stijn Naus, Ritesh Gautam, Mark Omara, Daniel J. Varon, Melissa P. Sulprizio, Lucas A. Estrada, Alba Lorente, Tobias Borsdorff, Robert J. Parker, and Ilse Aben
Atmos. Chem. Phys., 24, 6845–6863, https://doi.org/10.5194/acp-24-6845-2024, https://doi.org/10.5194/acp-24-6845-2024, 2024
Short summary
Short summary
Venezuela's Lake Maracaibo region is notoriously hard to observe from space and features intensive oil exploitation, although production has strongly decreased in recent years. We estimate methane emissions using 2018–2020 TROPOMI satellite observations with national and regional transport models. Despite the production decrease, we find relatively constant emissions from Lake Maracaibo between 2018 and 2020, indicating that there could be large emissions from abandoned infrastructure.
Alejandro Uribe, Frida Bender, and Thorsten Mauritsen
EGUsphere, https://doi.org/10.5194/egusphere-2024-1559, https://doi.org/10.5194/egusphere-2024-1559, 2024
Short summary
Short summary
Our study explores climate feedbacks, vital for understanding global warming. It links them to shifts in Earth's energy balance at the atmosphere's top due to natural temperature variations. It takes roughly 50-years to establish this connection. Combined satellite observations and reanalysis suggest that Earth cools more than expected under carbon dioxide influence. However, continuous satellite data until at least the mid-2030s are crucial for refining our understanding of climate feedbacks.
Saqr Munassar, Christian Roedenbeck, Michał Gałkowski, Frank-Thomas Koch, Kai U. Totsche, Santiago Botía, and Christoph Gerbig
EGUsphere, https://doi.org/10.5194/egusphere-2024-291, https://doi.org/10.5194/egusphere-2024-291, 2024
Short summary
Short summary
CO2 mole fractions simulated over a global stations showed an overestimation of CO2 if the diurnal cycle is missing NEE. This led to biases in the estimated fluxes derived from the inversions at continental and regional scales. IAVof estimated NEE was affected by the diurnal effect. The findings point to the importance of including the diurnal variations of CO2 in the biosphere priors used in inversions to better converge flux estimates among inversions, in particular those contributing to GCB.
Kalle Nordling, Nora Fahrenbach, and Bjørn Samset
EGUsphere, https://doi.org/10.5194/egusphere-2024-1068, https://doi.org/10.5194/egusphere-2024-1068, 2024
Short summary
Short summary
People experience daily weather, not changes in monthly averages. We investigate how the likelihood of events, which occurred once every ten years in the pre-industrial era. We analyze how summertime precipitation and daily maximum temperature events evolve. Our focus is on understanding the role of day-to-day variability in the change in the number of extreme weather days. We find that in most regions, a change in variability is the primary driver for change in summertime extreme precipitation.
Jiachen Cao, Xu Yue, and Mingrui Ma
Atmos. Chem. Phys., 24, 3973–3987, https://doi.org/10.5194/acp-24-3973-2024, https://doi.org/10.5194/acp-24-3973-2024, 2024
Short summary
Short summary
We implemented two widely used ozone damage schemes into a same regional model. Although the two schemes yielded distinct ozone vegetation damages, they predicted similar feedbacks to surface air temperature and ozone air quality in China. Our results highlighted the significance of ozone pollution control given its detrimental impacts on ecosystem functions, contributions to global warming, and amplifications of ozone pollution through ozone–vegetation coupling.
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
EGUsphere, https://doi.org/10.5194/egusphere-2024-778, https://doi.org/10.5194/egusphere-2024-778, 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 drier, warmer air, which can lead to a reduction in cloud. 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 had led us to conclude.
Steven C. Sherwood and Chris E. Forest
Atmos. Chem. Phys., 24, 2679–2686, https://doi.org/10.5194/acp-24-2679-2024, https://doi.org/10.5194/acp-24-2679-2024, 2024
Short summary
Short summary
The most fundamental parameter used to gauge the severity of future climate change is the so-called equilibrium climate sensitivity, which measures the warming that would ultimately occur due to a doubling of atmospheric carbon dioxide levels. Due to recent advances it is now thought to probably lie in the range 2.5–4 °C. We discuss this and the issues involved in evaluating and using the number, pointing to some pitfalls in current efforts but also possibilities for further progress.
Christopher D. Wells, Matthew Kasoar, Majid Ezzati, and Apostolos Voulgarakis
Atmos. Chem. Phys., 24, 1025–1039, https://doi.org/10.5194/acp-24-1025-2024, https://doi.org/10.5194/acp-24-1025-2024, 2024
Short summary
Short summary
Human-driven emissions of air pollutants, mostly caused by burning fossil fuels, impact both the climate and human health. Millions of deaths each year are caused by air pollution globally, and the future trends are uncertain. Here, we use a global climate model to study the effect of African pollutant emissions on surface level air pollution, and resultant impacts on human health, in several future emission scenarios. We find much lower health impacts under cleaner, lower-emission futures.
Xipeng Jin, Xuhui Cai, Xuesong Wang, Qianqian Huang, Yu Song, Ling Kang, Hongsheng Zhang, and Tong Zhu
Atmos. Chem. Phys., 24, 259–274, https://doi.org/10.5194/acp-24-259-2024, https://doi.org/10.5194/acp-24-259-2024, 2024
Short summary
Short summary
This work presents a climatology of water vapour exchange flux between the atmospheric boundary layer (ABL) and free troposphere (FT) over eastern China. The water vapour exchange maintains ABL humidity in cold months and moistens the FT in warm seasons, and its distribution has terrain-dependent features. The exchange flux is correlated with the El Niño–Southern Oscillation (ENSO) index and precipitation pattern. The study provides new insight into moisture transport and extreme weather.
Clare Marie Flynn, Linnea Huusko, Angshuman Modak, and Thorsten Mauritsen
Atmos. Chem. Phys., 23, 15121–15133, https://doi.org/10.5194/acp-23-15121-2023, https://doi.org/10.5194/acp-23-15121-2023, 2023
Short summary
Short summary
The latest-generation climate models show surprisingly cold mid-20th century global-mean temperatures, often despite exhibiting more realistic late 20th/early 21st century temperatures. A too-strong aerosol forcing in many models was thought to the be primary cause of these too-cold mid-century temperatures, but this was found to only be a partial explanation. This also partly undermines the hope to construct a strong relationship between the mid-century temperatures and aerosol forcing.
Suvarna Fadnavis, Bernd Heinold, T. P. Sabin, Anne Kubin, Katty Huang, Alexandru Rap, and Rolf Müller
Atmos. Chem. Phys., 23, 10439–10449, https://doi.org/10.5194/acp-23-10439-2023, https://doi.org/10.5194/acp-23-10439-2023, 2023
Short summary
Short summary
The influence of the COVID-19 lockdown on the Himalayas caused increases in snow cover and a decrease in runoff, ultimately leading to an enhanced snow water equivalent. Our findings highlight that, out of the two processes causing a retreat of Himalayan glaciers – (1) slow response to global climate change and (2) fast response to local air pollution – a policy action on the latter is more likely to be within the reach of possible policy action to help billions of people in southern Asia.
Amelie U. Schmitt, Felix Ament, Alessandro C. de Araújo, Marta Sá, and Paulo Teixeira
Atmos. Chem. Phys., 23, 9323–9346, https://doi.org/10.5194/acp-23-9323-2023, https://doi.org/10.5194/acp-23-9323-2023, 2023
Short summary
Short summary
Tall vegetation in forests affects the exchange of heat and moisture between the atmosphere and the land surface. We compared measurements from the Amazon Tall Tower Observatory to results from a land surface model to identify model shortcomings. Our results suggest that soil temperatures in the model could be improved by incorporating a separate canopy layer which represents the heat storage within the forest.
Cited articles
Adler, R. F., Sapiano, M. R. P., Huffman, G. J., Wang, J.-J., Gu, G., Bolvin, D., Chiu, L., Schneider, U., Becker, A., Nelkin, E., Xie, P., Ferraro, R., and Shin, D.-B.: The Global Precipitation Climatology Project (GPCP) monthly analysis (new version 2.3) and a review of 2017 global precipitation, Atmosphere, 9, 138, https://doi.org/10.3390/atmos9040138, 2018. a, b
Anber, U. M., Giangrande, S. E., Donner, L. J., and Jensen, M. P.: Updraft constraints on entrainment: insights from Amazonian deep convection, J. Atmos. Sci., 76, 2429–2442, https://doi.org/10.1175/JAS-D-18-0234.1, 2019. a
Arakawa, A. and Wu, C.-M.: A unified representation of deep moist convection in numerical modeling of the atmosphere: Part I, J. Atmos. Sci., 70, 1977–1992, https://doi.org/10.1175/JAS-D-12-0330.1, 2013. a
Arakawa, A., Jung, J.-H., and Wu, C.-M.: Toward unification of the multiscale modeling of the atmosphere, Atmos. Chem. Phys., 11, 3731–3742, https://doi.org/10.5194/acp-11-3731-2011, 2011. a
Azimi, S., Jaruga, A., de Jong, E. K., Arabas, S., and Schneider, T.: Training warm-rain bulk microphysics schemes using super-droplet simulations, J. Adv. Model. Earth Sys., in review, 2024. a
Balaji, V., Couvreux, F., Deshayes, J., Gautrais, J., Hourdin, F., and Rio, C.: Are general circulation models obsolete?, P. Natl. Acad. Sci. USA, 119, e2202075119, https://doi.org/10.1073/pnas.2202075119, 2022. a
Bauer, P., Stevens, B., and Hazeleger, W.: A digital twin of Earth for the green transition, Nat. Clim. Change, 11, 80–83, https://doi.org/10.1038/s41558-021-00986-y, 2021. a, b, c
Beucler, T., Pritchard, M., Rasp, S., Ott, J., Baldi, P., and Gentine, P.: Enforcing analytic constraints in neural networks emulating physical systems, Phys. Rev. Lett., 126, 098302, https://doi.org/10.1103/PhysRevLett.126.098302, 2021. a
Beucler, T., Pritchard, M., Yuval, J., Gupta, A., Peng, L., Rasp, S., Ahmed, F., O'Gorman, P. A., Neelin, J. D., Lutsko, N. J., and Pritchard, M.: Climate-invariant machine learning, Sci. Adv., 10, eadj7250, https://doi.org/10.1126/sciadv.adj7250, 2024. a
Bevacqua, E., Suarez-Gutierrez, L., Jézéquel, A., Lehner, F., Vrac, M., Yiou, P., and Zscheischler, J.: Advancing research on compound weather and climate events via large ensemble model simulations, Nat. Commun., 14, 2145, https://doi.org/10.1038/s41467-023-37847-5, 2023. a, b
Bock, L., Lauer, A., Schlund, M., Barreiro, M., Bellouin, N., Jones, C., Meehl, G., Predoi, V., Roberts, M., and Eyring, V.: Quantifying progress across different CMIP phases with the ESMValTool, J. Geophys. Res.-Atmos., 125, e2019JD032321, https://doi.org/10.1029/2019JD032321, 2020. a
Bolton, T. and Zanna, L.: Applications of deep learning to ocean data inference and subgrid parameterization, J. Adv. Model. Earth Sys., 11, 376–399, https://doi.org/10.1029/2018MS001472, 2019. a
Bony, S. and Dufresne, J. L.: Marine boundary layer clouds at the heart of tropical cloud feedback uncertainties in climate models, Geophys. Res. Lett., 32, L20806, https://doi.org/10.1029/2005GL023851, 2005. a, b
Brenowitz, N. D. and Bretherton, C. S.: Prognostic validation of a neural network unified physics parameterization, Geophys. Res. Lett., 45, 6289–6298, https://doi.org/10.1029/2018GL078510, 2018. a
Brenowitz, N. D., Beucler, T., Pritchard, M., and Bretherton, C. S.: Interpreting and stabilizing machine-learning parametrizations of convection, J. Atmos. Sci., 77, 4357–4375, https://doi.org/10.1175/JAS-D-20-0082.1, 2020. a
Bretherton, C. S.: Insights into low-latitude cloud feedbacks from high-resolution models, Philos. T. R. Soc. Lond. A, 373, 20140415, https://doi.org/10.1098/rsta.2014.0415, 2015. a, b
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, J. Adv. Model. Earth Sys., 14, e2021MS002794, https://doi.org/10.1029/2021MS002794, 2022. a, b, c
Brient, F. and Schneider, T.: Constraints on climate sensitivity from space-based measurements of low-cloud reflection, J. Clim., 29, 5821–5835, https://doi.org/10.1175/JCLI-D-15-0897.1, 2016. a
Businger, J. A., Wyngaard, J. C., Izumi, Y., and Bradley, E. F.: Flux-profile relationships in the atmospheric surface layer, J. Atmos. Sci., 28, 181–189, 1971. a
Caldwell, P. M., Bretherton, C. S., Zelinka, M. D., Klein, S. A., Santer, B. D., and Sanderson, B. M.: Statistical significance of climate sensitivity predictors obtained by data mining, Geophys. Res. Lett., 41, 1803–1808, https://doi.org/10.1002/2014GL059205, 2014. a
Caldwell, P. M., Zelinka, M. D., and Klein, S. A.: Evaluating emergent constraints on equilibrium climate sensitivity, J. Clim., 31, 3921–3942, https://doi.org/10.1175/JCLI-D-17-0631.1, 2018. a, b
Callies, J., Ferrari, R., and Bühler, O.: Transition from geostrophic turbulence to inertia–gravity waves in the atmospheric energy spectrum, P. Natl. Acad. Sci. USA, 111, 17033–17038, https://doi.org/10.1073/pnas.1410772111, 2014. a, b
Cess, R. D., Potter, G. L., Blanchet, J. P., Boer, G. J., Ghan, S. J., Kiehl, J. T., Le Treut, H., Li, Z.-X., Liang, X.-Z., Mitchell, J. F. B., Morcrette, J.-J., Randall, D. A., Riches, M. R., Roeckner, E., Schlese, U., Slingo, A., Taylor, K. E., Washington, W. M., Wetherald, R. T., and Yagai, I.: Interpretation of cloud-climate feedback as produced by 14 atmospheric general circulation models, Science, 245, 513–516, 1989. a
Chang, P., Zhang, S., Danabasoglu, G., Yeager, S. G., Fu, H.,Wang, H., Castruccio, F. S., Chen, Y., Edwards, J., Fu, D., Jia, Y., Laurindo, L. C., Liu, X., Rosenbloom, N., Small, R. J., Xu, G., Zeng, Y., Zhang, Q., Bacmeister, J., Bailey, D. A., Duan, X., DuVivier, A. K., Li, D., Li, Y., Neale, R., Stössel, A., Wang, L., Zhuang, Y., Baker, A., Bates, S., Dennis, J., Diao, X., Gan, B., Gopal, A., Jia, D., Jing, Z., Ma, X., Saravanan, R., Strand, W. G., Tao, J., Yang, H., Wang, X., Wei, Z., andWu, L.: An unprecedented set of high-resolution Earth system simulations for understanding multiscale interactions in climate variability and change, J. Adv. Model. Earth Sys., 12, e2020MS002298, https://doi.org/10.1029/2020MS002298, 2020. a
Chantry, M., Christensen, H., Dueben, P., and Palmer, T.: Opportunities and challenges for machine learning in weather and climate modelling: hard, medium and soft AI, Philos. T. R. Soc. A, 379, 20200083, https://doi.org/10.1098/rsta.2020.0083, 2021. a
Charbonneau, A., Deck, K., and Schneider, T.: A physics-constrained neural differential equation for data-driven seasonal snowpack forecasting, Artificial Intelligence for the Earth Systems, in review, 2023. a
Christopoulos, C. and Schneider, T.: Assessing biases and climate implications of the diurnal precipitation cycle in climate models, Geophys. Res. Lett., 48, e2021GL093017, https://doi.org/10.1029/2021GL093017, 2021. a
Clark, P., Roberts, N., Lean, H., Ballard, S. P., and Charlton-Perez, C.: Convection-permitting models: a step-change in rainfall forecasting, Meteor. Appl., 23, 165–181, https://doi.org/10.1002/met.1538, 2016. a
Cleary, E., Garbuno-Inigo, A., Lan, S., Schneider, T., and Stuart, A. M.: Calibrate, emulate, sample, J. Comp. Phys., 424, 109716, https://doi.org/10.1016/j.jcp.2020.109716, 2021. a
Cohen, Y., Lopez-Gomez, I., Jaruga, A., He, J., Kaul, C. M., and Schneider, T.: Unified entrainment and detrainment closures for extended eddy-diffusivity mass-flux schemes, J. Adv. Model. Earth Sys., 12, e2020MS002162, https://doi.org/10.1029/2020MS002162, 2020. a, b, c
Couvreux, F., Hourdin, F., Williamson, D., Roehrig, R., Volodina, V., Villefranque, N., Rio, C., Audouin, O., Salter, J., Bazile, E., Brient, F., Favot, F., Honnert, R., Lefebvre, M.-P., Madeleine, J.-B., Rodier, Q., and Xu, W.: Process-based climate model development harnessing machine learning: I. A calibration tool for parameterization improvement, J. Adv. Model. Earth Sys., 13, e2020MS002217, https://doi.org/10.1029/2020MS002217, 2021. a
Danabasoglu, G., Lamarque, J.-F., Bacmeister, J., Bailey, D. A., DuVivier, A. K., Edwards, J., Emmons, L. K., Fasullo, J., Garcia, R., Gettelman, A., Hannay, C., Holland, M. M., Large, W. G., Lauritzen, P. H., Lawrence, D. M., Lenaerts, J. T. M., Lindsay, K., Lipscomb, W. H., Mills, M. J., Neale, R., Oleson, K. W., Otto-Bliesner, B., Phillips, A. S., Sacks, W., Tilmes, S., van Kampenhout, L., Vertenstein, M., Bertini, A., Dennis, J., Deser, C., Fischer, C., Fox-Kemper, B., Kay, J. E., Kinnison, D., Kushner, P. J., Larson, V. E., Long, M. C., Mickelson, S., Moore, J. K., Nienhouse, E., Polvani, L., Rasch, P. J., and Strand, W. G.: The Community Earth System Model Version 2 (CESM2), J. Adv. Model. Earth Sys., 12, e2019MS001916, https://doi.org/10.1029/2019MS001916, 2020. a
de Rooy, W. C., Bechtold, P., Fröhlich, K., Hohenegger, C., Jonker, H., Mironov, D., Siebesma, A. P., Teixeira, J., and Yano, J.-I.: Entrainment and detrainment in cumulus convection: an overview, Q. J. Roy. Meteorol. Soc., 139, 1–19, https://doi.org/10.1002/qj.1959, 2013. a
Deser, C., Lehner, F., Rodgers, K. B., Ault, T., Delworth, T. L., DiNezio, P. N., Fiore, A., Frankignoul, C., Fyfe, J. C., Horton, D. E.,Kay, J. E., Knutti, R., Lovenduski, N. S., Marotzke, J., McKinnon, K. A., Minobe, S., Randerson, J., Screen, J. A., Simpson, I. R., and Ting, M.: Insights from Earth system model initial-condition large ensembles and future prospects, Nat. Clim. Change, 10, 277–286, 2020. a, b
Devine, G. M., Carslaw, K. S., Parker, D. J., and Petch, J. C.: The influence of subgrid surface-layer variability on vertical transport of a chemical species in a convective environment, Geophys. Res. Lett., 33, L15807, https://doi.org/10.1029/2006GL025986, 2006. a
Dewan, E. M.: Stratospheric wave spectra resembling turbulence, Science, 204, 832–835, 1979. a
Dong, L., Leung, L. R., Lu, J., and Song, F.: Double-ITCZ as an emergent constraint for future precipitation over Mediterranean climate regions in the North Hemisphere, Geophys. Res. Lett., 48, e2020GL091569, https://doi.org/10.1029/2020GL091569, 2021. a
Donoho, D.: Data science at the singularity, arXiv:2310.00865v1, https://arxiv.org/pdf/2310.00865.pdf (last access: 17 June 2023), 2023. a
Dunbar, O. R. A., Garbuno-Inigo, A., Schneider, T., and Stuart, A. M.: Calibration and uncertainty quantification of convective parameters in an idealized GCM, J. Adv. Model. Earth Sys., 13, e2020MS002454, https://doi.org/10.1029/2020MS002454, 2021. a, b
Edwards, P. N.: A Vast Machine: Computer Models, Climate Data, and the Politics of Global Warming, MIT Press, ISBN: 9780262518635, 2010. a
Fiedler, T., Pitman, A. J., Mackenzie, K., Wood, N., Jakob, C., and Perkins-Kirkpatrick, S. E.: Business risk and the emergence of climate analytics, Nat. Clim. Change, 11, 87–94, https://doi.org/10.1038/s41558-020-00984-6, 2021. a, b
Firl, G. J. and Randall, D. A.: Fitting and analyzing LES using multiple trivariate Gaussians, J. Atmos. Sci., 72, 1094–1116, https://doi.org/10.1175/JAS-D-14-0192.1, 2015. a
Foken, T.: 50 Years of the Monin-Obukhov similarity theory, Bound.-Lay. Meteorol., 119, 431–447, https://doi.org/10.1007/s10546-006-9048-6, 2006. a
Gentine, P., Pritchard, M., Rasp, S., Reinaudi, G., and Yacalis, G.: Could machine learning break the convection parameterization deadlock?, Geophys. Res. Lett., 45, 5742–5751, https://doi.org/10.1029/2018GL078202, 2018. a, b, c, d
Gettelman, A., Morrison, H., and Thompson, G.: Cloud microphysics across scales for weather and climate, in: Current Trends in the Representation of Physical Processes in Weather and Climate Models, edited by: Randall, D. A., Srinivasan, J., Nanjundiah, R. A., and Mukhopadhyay, P., 71–94, Springer, https://doi.org/10.1007/978-981-13-3396-5_4, 2019. a
Golaz, J.-C., Larson, V. E., and Cotton, W. R.: A PDF-based model for boundary layer clouds, Part I: Method and model description, J. Atmos. Sci., 59, 3540–3551, 2002. a
Gross, M.,Wan, H., Rasch, P. J., Caldwell, P. M.,Williamson, D. L., Klocke, D., Jablonowski, C., Thatcher, D. R., Wood, N., Cullen, M., Beare, B., and Willett, M., Lemarié, F., Blayo, E., Malardel, S., Termonia, P., Gassmann, A., Lauritzen, P. H., Johansen, H., Zarzycki, C. M., Sakaguchi, K., and Leung, R.: Physics–dynamics coupling in weather, climate, and Earth system models: Challenges and recent progress, Mon. Weather Rev., 146, 3505–3544, https://doi.org/10.1175/MWR-D-17-0345.1, 2018. a
Guo, H., Golaz, J.-C., Donner, L. J., Wyman, B., Zhao, M., and Ginoux, P.: CLUBB as a unified cloud parameterization: Opportunities and challenges, Geophys. Res. Lett., 42, 4540–4547, https://doi.org/10.1002/2015GL063672, 2015. a
Gutjahr, O., Putrasahan, D., Lohmann, K., Jungclaus, J. H., von Storch, J.-S., Brüggemann, N., Haak, H., and Stössel, A.: Max Planck Institute Earth System Model (MPI-ESM1.2) for the High-Resolution Model Intercomparison Project (HighResMIP), Geosci. Model Dev., 12, 3241–3281, https://doi.org/10.5194/gmd-12-3241-2019, 2019. a
Haarsma, R. J., Roberts, M. J., Vidale, P. L., Senior, C. A., Bellucci, A., Bao, Q., Chang, P., Corti, S., Fučkar, N. S., Guemas, V., von Hardenberg, J., Hazeleger, W., Kodama, C., Koenigk, T., Leung, L. R., Lu, J., Luo, J.-J., Mao, J., Mizielinski, M. S., Mizuta, R., Nobre, P., Satoh, M., Scoccimarro, E., Semmler, T., Small, J., and von Storch, J.-S.: High Resolution Model Intercomparison Project (HighResMIP v1.0) for CMIP6, Geosci. Model Dev., 9, 4185–4208, https://doi.org/10.5194/gmd-9-4185-2016, 2016. a, b
Hall, A., Cox, P., Huntingford, C., and Klein, S.: Progressing emergent constraints on future climate change, Nat. Clim. Change, 4, 269–278, 2019. a
Hansen, P. C.: Rank-Deficient and Discrete Ill-Posed Problems: Numerical Aspects of Linear Inversion, SIAM Monogr. on Mathematical Modeling and Computation, Society for Industrial and Applied Mathematics, Philadelphia, PA, ISBN: 0898714036, 1998. a
Harlaß, J., Latif, M., and Park, W.: Improving climate model simulation of tropical Atlantic sea surface temperature: The importance of enhanced vertical atmosphere model resolution, Geophys. Res. Lett., 42, 2401–2408, https://doi.org/10.1002/2015GL063310, 2015. a
Held, I. M.: The gap between simulation and understanding in climate modeling, Bull. Am. Meteorol. Soc., 86, 1609–1614, https://doi.org/10.1175/BAMS-86-11-1609, 2005. a
Hohenegger, C., Korn, P., Linardakis, L., Redler, R., Schnur, R., Adamidis, P., Bao, J., Bastin, S., Behravesh, M., Bergemann, M., Biercamp, J., Bockelmann, H., Brokopf, R., Brüggemann, N., Casaroli, L., Chegini, F., Datseris, G., Esch, M., George, G., Giorgetta, M., Gutjahr, O., Haak, H., Hanke, M., Ilyina, T., Jahns, T., Jungclaus, J., Kern, M., Klocke, D., Kluft, L., Kölling, T., Kornblueh, L., Kosukhin, S., Kroll, C., Lee, J., Mauritsen, T., Mehlmann, C., Mieslinger, T., Naumann, A. K., Paccini, L., Peinado, A., Praturi, D. S., Putrasahan, D., Rast, S., Riddick, T., Roeber, N., Schmidt, H., Schulzweida, U., Schütte, F., Segura, H., Shevchenko, R., Singh, V., Specht, M., Stephan, C. C., von Storch, J.-S., Vogel, R., Wengel, C., Winkler, M., Ziemen, F., Marotzke, J., and Stevens, B.: ICON-Sapphire: simulating the components of the Earth system and their interactions at kilometer and subkilometer scales, Geosci. Model Dev., 16, 779–811, https://doi.org/10.5194/gmd-16-779-2023, 2023. a, b
Hourdin, F., Williamson, D., Rio, C., Couvreux, F., Roehrig, R., Villefranque, N., Musat, I., Diallo, F. B., Fairhead, L., and Volodina, V.: Process-based climate model development harnessing machine learning: II. Model calibration from single column to global, J. Adv. Model. Earth Sys., 13, e2020MS002225, https://doi.org/10.1029/2020MS002225, 2021. a, b
Hourdin, F., Ferster, B., Deshayes, J., Mignot, J., Musat, I., and Williamson, D.: Toward machine-assisted tuning avoiding the underestimation of uncertainty in climate change projections, Sci. Adv., 9, eadf2758, https://doi.org/10.1126/sciadv.adf2758, 2023. a
Howland, M. F., Dunbar, O. R. A., and Schneider, T.: Parameter Uncertainty Quantification in an Idealized GCM With a Seasonal Cycle, J. Adv. Model. Earth Sys., 14, e2021MS002735, https://doi.org/10.1029/2021MS002735, 2022. a
Huang, D. Z., Schneider, T., and Stuart, A. M.: Iterated Kalman methodology for inverse problems, J. Comp. Phys., 463, 111262, https://doi.org/10.1016/j.jcp.2022.111262, 2022. a
Iglesias, M. A., Law, K. J. H., and Stuart, A. M.: Ensemble Kalman methods for inverse problems, Inverse Problems, 29, 045001, https://doi.org/10.1088/0266-5611/29/4/045001, 2013. a, b
Irrgang, C., Boers, N., Sonnewald, M., Barnes, E. A., Kadow, C., Staneva, J., and Saynisch-Wagner, J.: Towards neural Earth system modelling by integrating artificial intelligence in Earth system science, Nat. Mach. Intell., 3, 667–674, https://doi.org/10.1038/s42256-021-00374-3, 2022. a
Jeevanjee, N., Hassanzadeh, P., Hill, S., and Sheshadri, A.: A perspective on climate model hierarchies, J. Adv. Model. Earth Sys., 9, 1760–1771, https://doi.org/10.1002/2017MS001038, 2017. a
Kawai, H., Yukimoto, S., Koshiro, T., Oshima, N., Tanaka, T., Yoshimura, H., and Nagasawa, R.: Significant improvement of cloud representation in the global climate model MRI-ESM2, Geosci. Model Dev., 12, 2875–2897, https://doi.org/10.5194/gmd-12-2875-2019, 2019. a
Kennedy, M. C. and O'Hagan, A.: Bayesian calibration of computer models, J. Roy. Stat. Soc. B, 63, 425–464, https://doi.org/10.1111/1467-9868.00294, 2001. a, b
Klaver, R., Haarsma, R., Vidale, P. L., and Hazeleger, W.: Effective resolution in high resolution global atmospheric models for climate studies, Atmos. Sci. Lett., 21, e952, https://doi.org/10.1002/asl.952, 2020. a
Klein, S. A. and Hall, A.: Emergent constraints for cloud feedbacks, Curr. Clim. Change Rep., 1, 276–287, https://doi.org/10.1007/s40641-015-0027-1, 2015. a
Knight, C. G., Knight, S. H. E., Massey, N., and Allen, M. R.: Association of parameter, software, and hardware variation with large-scale behavior across 57,000 climate models, P. Natl. Acad. Sci. USA, 104, 12259–12264, https://doi.org/10.1073/pnas.0608144104, 2007. a
Koldunov, N., Kölling, T., Pedruzo-Bagazgoitia, X., Rackow, T., Redler, R., Sidorenko, D., Wieners, K.-H., and Ziemen, F. A.: nextGEMS: output of the model development cycle 3 simulations for ICON and IFS, World Data Center for Climate (WDCC) at DKRZ, https://doi.org/10.26050/WDCC/nextGEMS_cyc3, 2023. a
Kovachki, N., Li, Z., Liu, B., Azizzadenesheli, K., Bhattacharya, K., Stuart, A., and Anandkumar, A.: Neural operator: Learning maps between function spaces, J. Mach. Learn. Res., 24, 1–97, 2023. a
Kovachki, N. B. and Stuart, A. M.: Ensemble Kalman inversion: A derivative-free technique for machine learning tasks, Inverse Problems, 35, 095005, https://doi.org/10.48550/arXiv.1808.03620, 2019. a, b
Kratzert, F., Klotz, D., Brenner, C., Schulz, K., and Herrnegger, M.: Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks, Hydrol. Earth Syst. Sci., 22, 6005–6022, https://doi.org/10.5194/hess-22-6005-2018, 2018. a
Kratzert, F., Klotz, D., Shalev, G., Klambauer, G., Hochreiter, S., and Nearing, G.: Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets, Hydrol. Earth Syst. Sci., 23, 5089–5110, https://doi.org/10.5194/hess-23-5089-2019, 2019. a
Kratzert, F., Nearing, G., Addor, N., Erickson, T., Gauch, M., Gilon, O., Gudmundsson, L., Hassidim, A., Klotz, D., Nevo, S., Shalev, G., and Matias, Y.: Caravan – A global community dataset for large-sample hydrology, Nat. Sci. Data, 10, 61, https://doi.org/10.1038/s41597-023-01975-w, 2023. a
Lappen, C.-L. and Randall, D. A.: Toward a unified parameterization of the boundary layer and moist convection. Part I: A new type of mass-flux model, J. Atmos. Sci., 58, 2021–2036, 2001. a
Large, W. G., McWilliams, J. C., and Doney, S. C.: Oceanic vertical mixing: A review and a model with a nonlocal boundary layer parameterization, Rev. Geophys., 32, 363–403, https://doi.org/10.1029/94RG01872, 1994. a
Levine, M. E. and Stuart, A. M.: A framework for machine learning of model error in dynamical systems, Comm. Amer. Math. Soc., 2, 283–344, https://doi.org/10.1090/cams/10, 2022. a, b
Lindzen, R. S. and Fox-Rabinovitz, M.: Consistent vertical and horizontal resolution, Mon. Weather Rev., 117, 2575–2583, 1989. a
Lock, A. P., Brown, A. R., Bush, M. R., Martin, G. M., and Smith, R. N. B.: A new boundary layer mixing scheme. Part I: Scheme description and single-column model tests, Mon. Weather Rev., 128, 3187–3199, 2000. a
Loeb, N. G., Wielicki, B. A., Doelling, D. R., Smith, G. L., Keyes, D. F., Kato, S., Manalo-Smith, N., and Wong, T.: Toward optimal closure of the Earth's top-of-atmosphere radiation budget, J. Clim., 22, 748–766, https://doi.org/10.1175/2008JCLI2637.1, 2009. a
Lopez-Gomez, I., Cohen, Y., He, J., Jaruga, A., and Schneider, T.: A generalized mixing length closure for eddy-diffusivity mass-flux schemes of turbulence and convection, J. Adv. Model. Earth Sys., 12, e2020MS002161, https://doi.org/10.1029/2020MS002161, 2020. a, b
Lopez-Gomez, I., Christopoulos, C., Ervik, H. L., Dunbar, O. R. A., Cohen, Y., and Schneider, T.: Training physics-based machine-learning parameterizations with gradient-free ensemble Kalman methods, J. Adv. Model. Earth Sys., 14, e2022MS003105, https://doi.org/10.1029/2022MS003105, 2022. a, b, c, d, e
Lorenz, E. N.: Climatic predictability, in: The Physical Basis of Climate and Climate Modelling: Report of the International Study Conference in Stockholm, 29 July–10 August 1974, Vol. 16, GARP Publication Series, 132–136, World Meteorological Organization, 1975. a
Lucarini, V. and Chekroun, M. D.: Theoretical tools for understanding the climate crisis from Hasselmann's programme and beyond, Nat. Rev. Phys., 5, 744–765, https://doi.org/10.1038/s42254-023-00650-8, 2023. a, b
Lucarini, V., Blender, R., Herbert, C., Ragone, F., Pascale, S., and Wouters, J.: Mathematical and physical ideas for climate science, Rev. Geophys., 52, 809–859, https://doi.org/10.1002/2013RG000446, 2014. a
Luo, Y.: Terrestrial carbon-cycle feedback to climate warming, Annu. Rev. Ecol. Evol. Syst., 38, 683–712, 2007. a
Majda, A. J., Timofeyev, I., and Vanden-Eijnden, E.: Systematic strategies for stochastic mode reduction in climate, J. Atmos. Sci., 60, 1705–1722, 2003. a
Manabe, S. and Strickler, R. F.: Thermal equilibrium of the atmosphere with a convective adjustment, J. Atmos. Sci., 21, 361–385, 1964. a
Manabe, S. and Wetherald, R. T.: Thermal equilibrium of the atmosphere with a given distribution of relative humidity, J. Atmos. Sci., 24, 241–259, 1967. a
Mansfield, L. A., Gupta, A., Burnett, A. C., Green, B., Wilka, C., and Sheshadri, A.: Updates on model hierarchies for understanding and simulating the climate system: A focus on data-informed methods and climate change impacts, J. Adv. Model. Earth Sy., 15, e2023MS003715, https://doi.org/10.1029/2023MS003715, 2023. a
Mellor, G. L. and Yamada, T.: Development of a turbulence closure model for geophysical fluid problems, Rev. Geophys., 20, 851–875, 1982. a
Morrison, H., van Lier-Walqui, M., Kumjian, M. R., and Prat, O. P.: A Bayesian Approach for Statistical–Physical Bulk Parameterization of Rain Microphysics, Part I: Scheme Description, J. Atmos. Sci., 77, 1019–1041, 2020. a
Moshe, Z., Metzger, A., Elidan, G., Kratzert, F., Nevo, S., and El-Yaniv, R.: HydroNets: Leveraging river structure for hydrologic modeling, in: ICLR Workshop on AI for Earth Sciences, https://arxiv.org/abs/2007.00595, 2020. a
National Academies of Sciences, Engineering, and Medicine: Reproducibility and Replicability in Science, The National Academies Press, Washington, D.C., https://doi.org/10.17226/25303, 2019. a
Nearing, G., Cohen, D., Dube, V., Gauch, M., Gilon, O., Harrigan, S., Hassidim, A., Klotz, D., Kratzert, F., Metzger, A., Nevo, S., Pappenberger, F., Prudhomme, C., Shalev, G., Shenzis, S., Tekalign, T. Y., Weitzner, D., and Matias, Y.: Global prediction of extreme floods in ungauged watersheds, Nature, 627, 559–563, https://doi.org/10.1038/s41586-024-07145-1, 2024. a
Nearing, G. S., Kratzert, F., Sampson, A. K., Pelissier, C. S., Klotz, D., Frame, J. M., Prieto, C., and Gupta, H. V.: What role does hydrological science play in the age of machine learning?, Water Resour. Res., 57, e2020WR028091, https://doi.org/10.1029/2020WR028091, 2021. a
Nelsen, N. H. and Stuart, A. M.: The random feature model for input-output maps between Banach spaces, SIAM J. Sci. Comput., 43, A3212–A3243, https://doi.org/10.1137/20M133957X, 2021. a
O'Gorman, P. A. and Dwyer, J. G.: Using machine learning to parameterize moist convection: Potential for modeling of climate, climate change, and extreme events, J. Adv. Model. Earth Sy., 10, 2548–2563, https://doi.org/10.1029/2018MS001351, 2018. a, b, c, d
Palmer, T. N.: A personal perspective on modelling the climate system, Proc. R. Soc. Lond. A, 472, 20150772, https://doi.org/10.1098/rspa.2015.0772, 2016. a
President's Council of Advisors on Science and Technology: Extreme weather risk in a changing climate: Enhancing prediction and protecting communities, Tech. rep., Executive Office of the President of the United States, Washington, D.C., https://www.whitehouse.gov/wp-content/uploads/2023/04/PCAST_Extreme-Weather-Report_April2023.pdf (last access: 15 March 2024), 2023. a, b
Rackow, T., Pedruzo-Bagazgoitia, X., Becker, T., Milinski, S., Sandu, I., Aguridan, R., Bechtold, P., Beyer, S., Bidlot, J., Boussetta, S., Diamantakis, M., Dueben, P., Dutra, E., Forbes, R., Goessling, H. F., Hadade, I., Hegewald, J., Keeley, S., Kluft, L., Koldunov, N., Koldunov, A., Kölling, T., Kousal, J., Mogensen, K., Quintino, T., Polichtchouk, I., Sármány, D., Sidorenko, D., Streffing, J., Sützl, B., Takasuka, D., Tietsche, S., Valentini, M., Vannière, B., Wedi, N., Zampieri, L., and Ziemen, F.: Multi-year simulations at kilometre scale with the Integrated Forecasting System coupled to FESOM2.5/NEMOv3.4, EGUsphere, pp. 1–59, https://doi.org/10.5194/egusphere-2024-913, 2024. a
Randall, D. A.: Beyond deadlock, Geophys. Res. Lett., 40, 5970–5976, https://doi.org/10.1002/2013GL057998, 2013. a, b
Randall, D. A., Khairoutdinov, M., Arakawa, A., and Grabowski, W.: Breaking the cloud parameterization deadlock, Bull. Am. Meteorol. Soc., 84, 1547–1564, https://doi.org/10.1175/BAMS-84-11-1547, 2003. a
Rasp, S., Pritchard, M. S., and Gentine, P.: Deep learning to represent subgrid processes in climate models, P. Natl. Acad. Sci. USA, 115, 9684–9689, https://doi.org/10.1073/pnas.1810286115, 2018. a, b
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., and Prabhat: Deep learning and process understanding for data-driven Earth system science, Nature, 566, 195–204, https://doi.org/10.1038/s41586-019-0912-1, 2019. a
Roberts, M. J., Baker, A., Blockley, E. W., Calvert, D., Coward, A., Hewitt, H. T., Jackson, L. C., Kuhlbrodt, T., Mathiot, P., Roberts, C. D., Schiemann, R., Seddon, J., Vannière, B., and Vidale, P. L.: Description of the resolution hierarchy of the global coupled HadGEM3-GC3.1 model as used in CMIP6 HighResMIP experiments, Geosci. Model Dev., 12, 4999–5028, https://doi.org/10.5194/gmd-12-4999-2019, 2019. a
Romps, D. M.: A direct measure of entrainment, J. Atmos. Sci., 67, 1908–1927, https://doi.org/10.1175/2010JAS3371.1, 2010. a
Ruelle, D.: General linear response formula in statistical mechanics, and the fluctuation-dissipation theorem far from equilibrium, Phys. Lett. A, 245, 220–224, 1998. a
Sane, A., Reichl, B. G., Adcroft, A., and Zanna, L.: Parameterizing vertical mixing coefficients in the ocean surface boundary layer using neural networks, J. Adv. Model. Earth Sys., 15, e2023MS003890, https://doi.org/10.1029/2023MS003890, 2023. a
Savre, J. and Herzog, M.: A general description of entrainment in buoyant cloudy plumes including the effects of mixing induced evaporation, J. Atmos. Sci., 76, 479–496, https://doi.org/10.1175/JAS-D-17-0326.1, 2019. a
Schirber, S., Klocke, D., Pincus, R., Quaas, J., and Anderson, J. L.: Parameter estimation using data assimilation in an atmospheric general circulation model: From a perfect toward the real world, J. Adv. Model. Earth Sys., 5, 58–70, https://doi.org/10.1029/2012MS000167, 2013. a, b
Schneider, T.: Atmospheric spectra plots from “Opinion: Optimizing climate models with process-knowledge, resolution, and AI”, Atmos. Chem. Phys. 2024, CaltechDATA [data set], https://doi.org/10.22002/qemqk-rgq45, 2024. a
Schneider, T., Kaul, C. M., and Pressel, K. G.: Possible climate transitions from breakup of stratocumulus decks under greenhouse warming, Nat. Geosci., 12, 163–167, 2019. a
Schneider, T., Kaul, C. M., and Pressel, K. G.: Solar geoengineering may not prevent strong warming from direct effects of CO2 on stratocumulus cloud cover, P. Natl. Acad. Sci. USA, 117, 30179–30185, https://doi.org/10.1073/pnas.2003730117, 2020. a
Schneider, T., Jeevanjee, N., and Socolow, R.: Accelerating progress in climate science, Phys. Today, 74, 44–51, https://doi.org/10.1063/PT.3.4772, 2021a. a, b
Schneider, T., Stuart, A. M., and Wu, J.: Learning stochastic closures using ensemble Kalman inversion, Trans. Math. Appl., 5, 1–31, https://doi.org/10.1093/imatrm/tnab003, 2021b. a
Schneider, T., Behera, S., Boccaletti, G., Deser, C., Emanuel, K., Ferrari, R., Leung, L. R., Lin, N., Müller, T., Navarra, A., Ndiaye, O., Stuart, A., Tribbia, J., and Yamagata, T.: Harnessing AI and computing to advance climate modeling and prediction, Nat. Clim. Change, 13, 887–889, https://doi.org/10.1038/s41558-023-01769-3, 2023. a, b
Schulzweida, U.: CDO User Guide (2.3.0), https://doi.org/10.5281/zenodo.10020800, 2023. a
Schumann, U.: The horizontal spectrum of vertical velocities near the tropopause from global to gravity wave scales, J. Atmos. Sci., 76, 3847–3862, https://doi.org/10.1175/JAS-D-19-0160.1, 2019. a, b, c, d
Shankar, V., Chakraborty, D., Viswanathan, V., and Maulik, R.: Differentiable turbulence: Closure as a PDE-constrained optimization, arXiv:2307.03683v2, 2024. a
Shen, Z., Sridhar, A., Tan, Z., Jaruga, A., and Schneider, T.: A library of large-eddy simulations forced by global climate models, J. Adv. Model. Earth Sys., 14, e2021MS002631, https://doi.org/10.1029/2021MS002631, 2022. a, b
Shima, S., Kusano, K., Kawano, A., Sugiyama, T., and Kawahara, S.: The super-droplet method for the numerical simulation of clouds and precipitation: a particle-based and probabilistic microphysics model coupled with a non-hydrostatic model, Q. J. R. Meteorol. Soc., 135, 1307–1320, 2009. a
Siebesma, A. P., Soares, P. M. M., and Teixeira, J.: A combined eddy-diffusivity mass-flux approach for the convective boundary layer, J. Atmos. Sci., 64, 1230–1248, https://doi.org/10.1175/JAS3888.1, 2007. a
Silvestri, S., Wagner, G., Hill, C., Ardakani, M. R., Blaschke, J., Campin, J.-M., Churavy, V., Constantinou, N., Edelman, A., Marshall, J., Ramadhan, A., Souza, A., and Ferrari, R.: Oceananigans.jl: A model that achieves breakthrough resolution, memory and energy efficiency in global ocean simulations, submitted, https://arxiv.org/abs/2309.06662, 2024. a
Skamarock, W. C.: Evaluating mesoscale NWP models using kinetic energy spectra, Mon. Weather Rev., 132, 3019–3032, 2004. a
Skamarock, W. C., Park, S.-H., Klemp, J. B., and Snyder, C.: Atmospheric kinetic energy spectra from global high-resolution nonhydrostatic simulations, J. Atmos. Sci., 71, 4369–4381, https://doi.org/10.1175/JAS-D-14-0114.1, 2014. a
Slingo, J., Bates, P., Bauer, P., Belcher, S., Palmer, T., Stephens, G., Stevens, B., Stocker, T., and Teutsch, G.: Ambitious partnership needed for reliable climate prediction, Nat. Clim. Change, 12, 499–503, https://doi.org/10.1038/s41558-022-01384-8, 2022. a, b, c
Smalley, M. A., Lebsock, M. D., and Teixeira, J.: Quantifying the impact of vertical resolution on the representation of marine boundary layer physics for global-scale models, Mon. Weather Rev., 151, 2977–2992, https://doi.org/10.1175/MWR-D-23-0078.1, 2023. a
Soares, P. M. M., Miranda, P. M. A., Siebesma, A. P., and Teixeira, J.: An eddy-diffusivity/mass-flux parametrization for dry and shallow cumulus convection, Q. J. Roy. Meteorol. Soc., 130, 3365–3384, 2004. a
Stainforth, D. A., Aina, T., Christensen, C., Collins, M., Faull, N., Frame, D. J., Kettleborough, J. A., Knight, S., Martin, A., Murphy, J. M., Piani, C., Sexton, D., Smith, L. A., Spicer, R. A., Thorpe, A. J., and Allen, M. R.: Uncertainty in predictions of the climate response to rising levels of greenhouse gases, Nature, 433, 403–406, 2005. a
Stephens, G. L.: Cloud feedbacks in the climate system: A critical review, J. Clim., 18, 237–273, https://doi.org/10.1175/JCLI-3243.1, 2005. a, b
Stevens, B., Satoh, M., Auger, L., Biercamp, J., Bretherton, C. S., Chen, X., Düben, P., Judt, F., Khairoutdinov, M., Klocke, D., Kodama, C., Kornblueh, L., Lin, S.-J., Neumann, P., Putman, W. M., Röber, N., Shibuya, R., Vanniere, B., Vidale, P. L., Wedi, N., and Zhou, L.: DYAMOND: The DYnamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domains, Prog. Earth Planet. Sci., 6, 61, https://doi.org/10.1186/s40645-019-0304-z, 2019. a, b
Subramanian, A., Ummenhofer, C., Giannini, A., Holland, M., Legg, S., Mahadevan, A., Perovich, D., Small, J., Teixeira, J., and Thompson, L.: Translating process understanding to improve climate models. A US CLIVAR White Paper, Report 2016-3, US CLIVAR, https://doi.org/10.5065/D63X851Q, 2016. a
Tan, Z., Kaul, C. M., Pressel, K. G., Cohen, Y., Schneider, T., and Teixeira, J.: An extended eddy-diffusivity mass-flux scheme for unified representation of subgrid-scale turbulence and convection, J. Adv. Model. Earth Sys., 10, 770–800, https://doi.org/10.1002/2017MS001162, 2018. a
Tao, C., Xie, S., Ma, H.-Y., Bechtold, P., Cui, Z., Vaillancourt, P. A., Van Weverberg, K., Wang, Y.-C., Wong, M., Yang, J., Zhang, G. J., Choi, I.-J., Tang, S., Wei, J., Wu, W.-Y., Zhang, M., Neelin, J. D., and Zeng, X.: Diurnal cycle of precipitation over the tropics and central United States: intercomparison of general circulation models, Q. J. Roy. Meteorol. Soc., 150, 911–936, https://doi.org/10.1002/qj.4629, 2024. a
Tarantola, A.: Inverse Problem Theory: Methods for Data Fitting and Model Parameter Estimation, Elsevier, New York, ISBN: 0444427651, 1987. a
Thuburn, J., Weller, H., Vallis, G. K., Beare, R. J., and Whitall, M.: A framework for convection and boundary layer parameterization derived from conditional filtering, J. Atmos. Sci., 75, 965–981, https://doi.org/10.1175/JAS-D-17-0130.1, 2018. a
Tian, B. and Dong, X.: The double-ITCZ bias in CMIP3, CMIP5, and CMIP6 models based on annual mean precipitation, Geophys. Res. Lett., 47, e2020GL087232, https://doi.org/10.1029/2020GL087232, 2020. a
Vallis, G. K.: Atmospheric and Oceanic Fluid Dynamics: Fundamentals and Large-Scale Circulation, Cambridge University Press, Cambridge, UK, ISBN: 110706550X, 2006. a
VanZandt, T. E.: A universal spectrum of buoyancy waves in the atmosphere, Geophys. Res. Lett., 9, 575–578, 1982. a
Vial, J., Dufresne, J.-L., and Bony, S.: On the interpretation of inter-model spread in CMIP5 climate sensitivity estimates, Clim. Dynam., 41, 3339–3362, https://doi.org/10.1007/s00382-013-1725-9, 2013. a, b
Wagner, G. L., Hillier, A., Constantinou, N. C., Silvestri, S., Souza, A., Burns, K., Ramadhan, A., Hill, C., Campin, J.-M., Marshall, J., and Ferrari, R.: CATKE: a turbulent-kinetic-energy-based parameterization for ocean microturbulence with dynamic convective adjustment, arXiv:2306.13204, in review, 2024. a
Wang, P., Yuval, J., and O'Gorman, P. A.: Non-local parameterization of atmospheric subgrid processes with neural networks, J. Adv. Model. Earth Sys., 14, e2022MS002984, https://doi.org/10.1029/2022MS002984, 2022. a
Watson-Parris, D.: Machine learning for weather and climate are worlds apart, Philos. T. R. Soc. A, 379, 20200098, https://doi.org/10.1098/rsta.2020.0098, 2021. a
Watt-Meyer, O., Brenowitz, N. D., Clark, S. K., Henn, B., Kwa, A., McGibbon, J., Perkins, W. A., and Bretherton, C. S.: Correcting weather and climate models by machine learning nudged historical simulations, Geophys. Res. Lett., 48, e2021GL092555, https://doi.org/10.1029/2021GL092555, 2021. a, b
Wedi, N. P.: Increasing horizontal resolution in numerical weather prediction and climate simulations: illusion or panacea?, Philos. T. R. Soc. A, 372, 20130289, https://doi.org/10.1098/rsta.2013.0289, 2014. a
Wedi, N. P., Polichtchouk, I., Dueben, P., Anantharaj, V. G., Bauer, P., Boussetta, S., Browne, P., Deconinck, W., Gaudin, W., Hadade, I., Hatfield, S., Iffrig, O., Lopez, P., Maciel, P., Mueller, A., Saarinen, S., Sandu, I., Quintino, T., and Vitart, F.: A baseline for global weather and climate simulations at 1-km resolution, J. Adv. Model. Earth Sys., 12, e2020MS00219, https://doi.org/10.1029/2020MS002192, 2020. a, b
Wills, R. C. J. and Schneider, T.: Error analysis of climate models from CMIP3 through CMIP6, including AMIP and higher-resolution models, CaltechDATA [data set], https://doi.org/10.22002/z24s9-nqc90, 2024. a
Wills, R. C. J., Battisti, D. S., Armour, K. C., Schneider, T., and Deser, C.: Pattern recognition methods to separate forced responses from internal variability in climate model ensembles and observations, J. Clim., 33, 8693–8719, 2020. a
Witek, M. L., Teixeira, J., and Matheou, G.: An integrated TKE-based eddy-diffusivity/mass-flux boundary layer closure for the dry convective boundary layer, J. Atmos. Sci., 68, 1526–1540, 2011. a
Wouters, J. and Lucarini, V.: Multi-level dynamical systems: Connecting the Ruelle response theory and the Mori-Zwanzig approach, J. Stat. Phys., 151, 850–860, https://doi.org/10.1007/s10955-013-0726-8, 2013. a
Wouters, J., Dolaptchiev, S. I., Lucarini, V., and Achatz, U.: Parameterization of stochastic multiscale triads, Nonlin. Processes Geophys., 23, 435–445, https://doi.org/10.5194/npg-23-435-2016, 2016. a
Wu, J.-L., Levine, M. E., Schneider, T., and Stuart, A. M.: Learning about structural errors in models of complex dynamical systems, J. Comp. Phys., 513, 113157, https://doi.org/10.1016/j.jcp.2024.113157, 2024. a, b, c
Yeo, K. and Romps, D. M.: Measurement of convective entrainment using Lagrangian particles, J. Atmos. Sci., 70, 266–277, https://doi.org/10.1175/JAS-D-12-0144.1, 2013. a
Yu, S., Hannah, W. M., Peng, L., Bhouri, M. A., Gupta, R., Lin, J., Lütjens, B., Will, J. C., Beucler, T., Harrop, B. E., Hillman, B. R., Jenney, A., Ferretti, S., Liu, N., Anandkumar, A., Brenowitz, N. D., Eyring, V., Geneva, N., Gentine, P., Mandt, S., Pathak, J., Subramaniam, A., Vondrick, C., Yu, R., Zanna, L., Zheng, T., Abernathey, R., Ahmed, F., Bader, D. C., Baldi, P., Barnes, E., Bretherton, C., Caldwell, P., Chuang, W., Han, Y., Huang, Y., Iglesias-Suarez, F., Jantre, S., Kashinath, K., Khairoutdinov, M., Kurth, T., Lutsko, N., Ma, P.-L., Mooers, G., Neelin, J. D., Randall, D., Shamekh, S., Taylor, M. A., Urban, N., Yuval, J., Zhang, G., and Pritchard, M.: ClimSim: An open large-scale dataset for training high-resolution physics emulators in hybrid multi-scale climate simulators, arXiv:2306.08754, 2023. a, b
Yuval, J. and O'Gorman, P. A.: Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions, Nat. Commun., 11, 1–10, https://doi.org/10.1038/s41467-020-17142-3, 2020. a, b, c
Yuval, J., O'Gorman, P. A., and Hill, C. N.: Use of neural networks for stable, accurate and physically consistent parameterization of subgrid atmospheric processes with good performance at reduced precision, Geophys. Res. Lett., 48, e2020GL091363, https://doi.org/10.1029/2020GL091363, 2021. a, b, c
Zanna, L. and Bolton, T.: Data-driven equation discovery of ocean mesoscale closures, Geophys. Res. Lett., 47, e2020GL088376, https://doi.org/10.1029/2020GL088376, 2020. a
Zelinka, M. D., Myers, T. A., McCoy, D. T., Po-Chedley, S., Caldwell, P. M., Ceppi, P., Klein, S. A., and Taylor, K. E.: Causes of higher climate sensitivity in CMIP6 models, Geophys. Res. Lett., 47, e2019GL085782, https://doi.org/10.1029/2019GL085782, 2020. a, b
Zhang, T., Li, L., Lin, Y., Xue, W., Xie, F., Xu, H., and Huang, X.: An automatic and effective parameter optimization method for model tuning, Geosci. Model Dev., 8, 3579–3591, https://doi.org/10.5194/gmd-8-3579-2015, 2015. a
Zhou, W., Leung, L. R., and Lu, J.: Linking large-scale double-ITCZ bias to local-scale drizzling bias in climate models, J. Clim., 35, 7965–7979, 2022. a
Zhu, J., Otto-Bliesner, B. L., Brady, E. C., Gettelman, A., Bacmeister, J. T., Neale, R. B., Poulsen, C. J., Shaw, J. K., McGraw, Z. S., and Kay, J. E.: LGM paleoclimate constraints inform cloud parameterizations and equilibrium climate sensitivity in CESM2, J. Adv. Model. Earth Sys., 14, e2021MS002776, https://doi.org/10.1029/2021MS002776, 2022. a
Zwanzig, R.: Nonequilibrium Statistical Mechanics, Oxford University Press, ISBN: 0195140184, 2001. a
Executive editor
This article was solicited for the ACP 20th Anniversary collection. It received positive reviews that very nicely contributed to the ideas and to which the authors responded thoroughly. It is a stimulating read, combining 'big-picture' considerations with more detailed technical discussion of important and illuminating examples.
This article was solicited for the ACP 20th Anniversary collection. It received positive...
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.
Climate models are crucial for predicting climate change in detail. This paper proposes a...
Special issue
Altmetrics
Final-revised paper
Preprint