Articles | Volume 16, issue 9
https://doi.org/10.5194/gmd-16-2355-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-2355-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
05 May 2023
Development and technical paper |
| 05 May 2023
| 05 May 2023
Emulating aerosol optics with randomly generated neural networks
Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, WA, USA
Po-Lun Ma
Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, WA, USA
Balwinder Singh
Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, WA, USA
Joseph C. Hardin
Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, WA, USA
independent researcher
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Naser Mahfouz, Hassan Beydoun, Johannes Mülmenstädt, Noel Keen, Adam C. Varble, Luca Bertagna, Peter Bogenschutz, Andrew Bradley, Matthew W. Christensen, T. Conrad Clevenger, Aaron Donahue, Jerome Fast, James Foucar, Jean-Christophe Golaz, Oksana Guba, Walter Hannah, Benjamin Hillman, Robert Jacob, Wuyin Lin, Po-Lun Ma, Yun Qian, Balwinder Singh, Christopher Terai, Hailong Wang, Mingxuan Wu, Kai Zhang, Andrew Gettelman, Mark Taylor, L. Ruby Leung, Peter Caldwell, and Susannah Burrows
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Katherine M. Smith, Alice M. Barthel, LeAnn M. Conlon, Luke P. Van Roekel, Anthony Bartoletti, Jean-Christophe Golaz, Chengzhu Zhang, Carolyn Branecky Begeman, James J. Benedict, Gautam Bisht, Yan Feng, Walter Hannah, Bryce E. Harrop, Nicole Jeffery, Wuyin Lin, Po-Lun Ma, Mathew E. Maltrud, Mark R. Petersen, Balwinder Singh, Qi Tang, Teklu Tesfa, Jonathan D. Wolfe, Shaocheng Xie, Xue Zheng, Karthik Balaguru, Oluwayemi Garuba, Peter Gleckler, Aixue Hu, Jiwoo Lee, Ben Moore-Maley, and Ana C. Ordoñez
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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
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Yu Yao, Po-Lun Ma, Yi Qin, Matthew W. Christensen, Hui Wan, Kai Zhang, Balwinder Singh, Meng Huang, and Mikhail Ovchinnikov
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Kirsten L. Findell, Zun Yin, Eunkyo Seo, Paul A. Dirmeyer, Nathan P. Arnold, Nathaniel Chaney, Megan D. Fowler, Meng Huang, David M. Lawrence, Po-Lun Ma, and Joseph A. Santanello Jr.
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Jianfeng Li, Kai Zhang, Taufiq Hassan, Shixuan Zhang, Po-Lun Ma, Balwinder Singh, Qiyang Yan, and Huilin Huang
Geosci. Model Dev., 17, 1327–1347, https://doi.org/10.5194/gmd-17-1327-2024, https://doi.org/10.5194/gmd-17-1327-2024, 2024
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By comparing E3SM simulations with and without regional refinement, we find that model horizontal grid spacing considerably affects the simulated aerosol mass budget, aerosol–cloud interactions, and the effective radiative forcing of anthropogenic aerosols. The study identifies the critical physical processes strongly influenced by model resolution. It also highlights the benefit of applying regional refinement in future modeling studies at higher or even convection-permitting resolutions.
Yuying Zhang, Shaocheng Xie, Yi Qin, Wuyin Lin, Jean-Christophe Golaz, Xue Zheng, Po-Lun Ma, Yun Qian, Qi Tang, Christopher R. Terai, and Meng Zhang
Geosci. Model Dev., 17, 169–189, https://doi.org/10.5194/gmd-17-169-2024, https://doi.org/10.5194/gmd-17-169-2024, 2024
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We performed systematic evaluation of clouds simulated in the Energy
Exascale Earth System Model (E3SMv2) to document model performance and understand what updates in E3SMv2 have caused changes in clouds from E3SMv1 to E3SMv2. We find that stratocumulus clouds along the subtropical west coast of continents are dramatically improved, primarily due to the retuning done in CLUBB. This study offers additional insights into clouds simulated in E3SMv2 and will benefit future E3SM developments.
Exascale Earth System Model (E3SMv2) to document model performance and understand what updates in E3SMv2 have caused changes in clouds from E3SMv1 to E3SMv2. We find that stratocumulus clouds along the subtropical west coast of continents are dramatically improved, primarily due to the retuning done in CLUBB. This study offers additional insights into clouds simulated in E3SMv2 and will benefit future E3SM developments.
Shuaiqi Tang, Adam C. Varble, Jerome D. Fast, Kai Zhang, Peng Wu, Xiquan Dong, Fan Mei, Mikhail Pekour, Joseph C. Hardin, and Po-Lun Ma
Geosci. Model Dev., 16, 6355–6376, https://doi.org/10.5194/gmd-16-6355-2023, https://doi.org/10.5194/gmd-16-6355-2023, 2023
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Adam C. Varble, Po-Lun Ma, Matthew W. Christensen, Johannes Mülmenstädt, Shuaiqi Tang, and Jerome Fast
Atmos. Chem. Phys., 23, 13523–13553, https://doi.org/10.5194/acp-23-13523-2023, https://doi.org/10.5194/acp-23-13523-2023, 2023
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We evaluate how clouds change in response to changing atmospheric particle (aerosol) concentrations in a climate model and find that the model-predicted cloud brightness increases too much as aerosols increase because the cloud drop number increases too much. Excessive drizzle in the model mutes this difference. Many differences between observational and model estimates are explained by varying assumptions of how much liquid has been lost in clouds, which impacts the estimated cloud drop number.
Fabien Margairaz, Balwinder Singh, Jeremy A. Gibbs, Loren Atwood, Eric R. Pardyjak, and Rob Stoll
Geosci. Model Dev., 16, 5729–5754, https://doi.org/10.5194/gmd-16-5729-2023, https://doi.org/10.5194/gmd-16-5729-2023, 2023
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The Quick Environmental Simulation (QES) tool is a low-computational-cost fast-response framework. It provides high-resolution wind and concentration information to study complex problems, such as spore or smoke transport, urban pollution, and air quality. This paper presents the particle dispersion model and its validation against analytical solutions and wind-tunnel data for a mock-urban setting. In all cases, the model provides accurate results with competitive computational performance.
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
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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.
Aishwarya Raman, Thomas Hill, Paul J. DeMott, Balwinder Singh, Kai Zhang, Po-Lun Ma, Mingxuan Wu, Hailong Wang, Simon P. Alexander, and Susannah M. Burrows
Atmos. Chem. Phys., 23, 5735–5762, https://doi.org/10.5194/acp-23-5735-2023, https://doi.org/10.5194/acp-23-5735-2023, 2023
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Ice-nucleating particles (INPs) play an important role in cloud processes and associated precipitation. Yet, INPs are not accurately represented in climate models. This study attempts to uncover these gaps by comparing model-simulated INP concentrations against field campaign measurements in the SO for an entire year, 2017–2018. Differences in INP concentrations and variability between the model and observations have major implications for modeling cloud properties in high latitudes.
Zhe Feng, Joseph Hardin, Hannah C. Barnes, Jianfeng Li, L. Ruby Leung, Adam Varble, and Zhixiao Zhang
Geosci. Model Dev., 16, 2753–2776, https://doi.org/10.5194/gmd-16-2753-2023, https://doi.org/10.5194/gmd-16-2753-2023, 2023
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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.
Matthew W. Christensen, Po-Lun Ma, Peng Wu, Adam C. Varble, Johannes Mülmenstädt, and Jerome D. Fast
Atmos. Chem. Phys., 23, 2789–2812, https://doi.org/10.5194/acp-23-2789-2023, https://doi.org/10.5194/acp-23-2789-2023, 2023
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An increase in aerosol concentration (tiny airborne particles) is shown to suppress rainfall and increase the abundance of droplets in clouds passing over Graciosa Island in the Azores. Cloud drops remain affected by aerosol for several days across thousands of kilometers in satellite data. Simulations from an Earth system model show good agreement, but differences in the amount of cloud water and its extent remain despite modifications to model parameters that control the warm-rain process.
Marine Bonazzola, Hélène Chepfer, Po-Lun Ma, Johannes Quaas, David M. Winker, Artem Feofilov, and Nick Schutgens
Geosci. Model Dev., 16, 1359–1377, https://doi.org/10.5194/gmd-16-1359-2023, https://doi.org/10.5194/gmd-16-1359-2023, 2023
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Aerosol has a large impact on climate. Using a lidar aerosol simulator ensures consistent comparisons between modeled and observed aerosol. We present a lidar aerosol simulator that applies a cloud masking and an aerosol detection threshold. We estimate the lidar signals that would be observed at 532 nm by the Cloud-Aerosol Lidar with Orthogonal Polarization overflying the atmosphere predicted by a climate model. Our comparison at the seasonal timescale shows a discrepancy in the Southern Ocean.
Andrew Geiss, Sam J. Silva, and Joseph C. Hardin
Geosci. Model Dev., 15, 6677–6694, https://doi.org/10.5194/gmd-15-6677-2022, https://doi.org/10.5194/gmd-15-6677-2022, 2022
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This work demonstrates the use of modern machine learning techniques to enhance the resolution of atmospheric chemistry simulations. We evaluate the schemes for an 8 x 10 increase in resolution and find that they perform substantially better than conventional methods. Methods are introduced to target machine learning methods towards this type of problem, most notably by ensuring they do not break known physical constraints.
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
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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.
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
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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.
Shuaiqi Tang, Jerome D. Fast, Kai Zhang, Joseph C. Hardin, Adam C. Varble, John E. Shilling, Fan Mei, Maria A. Zawadowicz, and Po-Lun Ma
Geosci. Model Dev., 15, 4055–4076, https://doi.org/10.5194/gmd-15-4055-2022, https://doi.org/10.5194/gmd-15-4055-2022, 2022
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We developed an Earth system model (ESM) diagnostics package to compare various types of aerosol properties simulated in ESMs with aircraft, ship, and surface measurements from six field campaigns across spatial scales. The diagnostics package is coded and organized to be flexible and modular for future extension to other field campaign datasets and adapted to higher-resolution model simulations. Future releases will include comprehensive cloud and aerosol–cloud interaction diagnostics.
Susannah M. Burrows, Richard C. Easter, Xiaohong Liu, Po-Lun Ma, Hailong Wang, Scott M. Elliott, Balwinder Singh, Kai Zhang, and Philip J. Rasch
Atmos. Chem. Phys., 22, 5223–5251, https://doi.org/10.5194/acp-22-5223-2022, https://doi.org/10.5194/acp-22-5223-2022, 2022
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Sea spray particles are composed of a mixture of salts and organic substances from oceanic microorganisms. In prior work, our team developed an approach connecting sea spray chemistry to ocean biology, called OCEANFILMS. Here we describe its implementation within an Earth system model, E3SM. We show that simulated sea spray chemistry is consistent with observed seasonal cycles and that sunlight reflected by simulated Southern Ocean clouds increases, consistent with analysis of satellite data.
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
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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.
Artem G. Feofilov, Hélène Chepfer, Vincent Noël, Rodrigo Guzman, Cyprien Gindre, Po-Lun Ma, and Marjolaine Chiriaco
Atmos. Meas. Tech., 15, 1055–1074, https://doi.org/10.5194/amt-15-1055-2022, https://doi.org/10.5194/amt-15-1055-2022, 2022
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Space-borne lidars have been providing invaluable information of atmospheric optical properties since 2006, and new lidar missions are on the way to ensure continuous observations. In this work, we compare the clouds estimated from space-borne ALADIN and CALIOP lidar observations. The analysis of collocated data shows that the agreement between the retrieved clouds is good up to 3 km height. Above that, ALADIN detects 40 % less clouds than CALIOP, except for polar stratospheric clouds (PSCs).
Matthew W. Christensen, Andrew Gettelman, Jan Cermak, Guy Dagan, Michael Diamond, Alyson Douglas, Graham Feingold, Franziska Glassmeier, Tom Goren, Daniel P. Grosvenor, Edward Gryspeerdt, Ralph Kahn, Zhanqing Li, Po-Lun Ma, Florent Malavelle, Isabel L. McCoy, Daniel T. McCoy, Greg McFarquhar, Johannes Mülmenstädt, Sandip Pal, Anna Possner, Adam Povey, Johannes Quaas, Daniel Rosenfeld, Anja Schmidt, Roland Schrödner, Armin Sorooshian, Philip Stier, Velle Toll, Duncan Watson-Parris, Robert Wood, Mingxi Yang, and Tianle Yuan
Atmos. Chem. Phys., 22, 641–674, https://doi.org/10.5194/acp-22-641-2022, https://doi.org/10.5194/acp-22-641-2022, 2022
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Trace gases and aerosols (tiny airborne particles) are released from a variety of point sources around the globe. Examples include volcanoes, industrial chimneys, forest fires, and ship stacks. These sources provide opportunistic experiments with which to quantify the role of aerosols in modifying cloud properties. We review the current state of understanding on the influence of aerosol on climate built from the wide range of natural and anthropogenic laboratories investigated in recent decades.
Andrew Geiss and Joseph C. Hardin
Atmos. Meas. Tech., 14, 7729–7747, https://doi.org/10.5194/amt-14-7729-2021, https://doi.org/10.5194/amt-14-7729-2021, 2021
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Radars can suffer from missing or poor-quality data regions for several reasons: beam blockage, instrument failure, and near-ground blind zones, etc. Here, we demonstrate how deep convolutional neural networks can be used for filling in radar-missing data regions and that they can significantly outperform conventional approaches in terms of realism and accuracy.
Zhonghua Zheng, Matthew West, Lei Zhao, Po-Lun Ma, Xiaohong Liu, and Nicole Riemer
Atmos. Chem. Phys., 21, 17727–17741, https://doi.org/10.5194/acp-21-17727-2021, https://doi.org/10.5194/acp-21-17727-2021, 2021
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Aerosol mixing state is an important emergent property that affects aerosol radiative forcing and aerosol–cloud interactions, but it has not been easy to constrain this property globally. We present a framework for evaluating the error in aerosol mixing state induced by aerosol representation assumptions, which is one of the important contributors to structural uncertainty in aerosol models. Our study provides insights into potential improvements to model process representation for aerosols.
Christopher R. Williams, Karen L. Johnson, Scott E. Giangrande, Joseph C. Hardin, Ruşen Öktem, and David M. Romps
Atmos. Meas. Tech., 14, 4425–4444, https://doi.org/10.5194/amt-14-4425-2021, https://doi.org/10.5194/amt-14-4425-2021, 2021
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In addition to detecting clouds, vertically pointing cloud radars detect individual insects passing over head. If these insects are not identified and removed from raw observations, then radar-derived cloud properties will be contaminated. This work identifies clouds in radar observations due to their continuous and smooth structure in time, height, and velocity. Cloud masks are produced that identify cloud vertical structure that are free of insect contamination.
Raghavendra Krishnamurthy, Rob K. Newsom, Larry K. Berg, Heng Xiao, Po-Lun Ma, and David D. Turner
Atmos. Meas. Tech., 14, 4403–4424, https://doi.org/10.5194/amt-14-4403-2021, https://doi.org/10.5194/amt-14-4403-2021, 2021
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Planetary boundary layer (PBL) height is a critical parameter in atmospheric models. Continuous PBL height measurements from remote sensing measurements are important to understand various boundary layer mechanisms, especially during daytime and evening transition periods. Due to several limitations in existing methodologies to detect PBL height from a Doppler lidar, in this study, a machine learning (ML) approach is tested. The ML model is observed to improve the accuracy by over 50 %.
Sam J. Silva, Po-Lun Ma, Joseph C. Hardin, and Daniel Rothenberg
Geosci. Model Dev., 14, 3067–3077, https://doi.org/10.5194/gmd-14-3067-2021, https://doi.org/10.5194/gmd-14-3067-2021, 2021
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The activation of aerosol into cloud droplets is an important but uncertain process in the Earth system. The physical and chemical interactions that govern this process are too computationally expensive to explicitly resolve in modern Earth system models. Here, we demonstrate how hybrid machine learning approaches can provide a potential path forward, enabling the representation of the more detailed physics and chemistry at a reduced computational cost while still retaining physical information.
Duseong S. Jo, Alma Hodzic, Louisa K. Emmons, Simone Tilmes, Rebecca H. Schwantes, Michael J. Mills, Pedro Campuzano-Jost, Weiwei Hu, Rahul A. Zaveri, Richard C. Easter, Balwinder Singh, Zheng Lu, Christiane Schulz, Johannes Schneider, John E. Shilling, Armin Wisthaler, and Jose L. Jimenez
Atmos. Chem. Phys., 21, 3395–3425, https://doi.org/10.5194/acp-21-3395-2021, https://doi.org/10.5194/acp-21-3395-2021, 2021
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Secondary organic aerosol (SOA) is a major component of submicron particulate matter, but there are a lot of uncertainties in the future prediction of SOA. We used CESM 2.1 to investigate future IEPOX SOA concentration changes. The explicit chemistry predicted substantial changes in IEPOX SOA depending on the future scenario, but the parameterization predicted weak changes due to simplified chemistry, which shows the importance of correct physicochemical dependencies in future SOA prediction.
Jingyu Wang, Jiwen Fan, Robert A. Houze Jr., Stella R. Brodzik, Kai Zhang, Guang J. Zhang, and Po-Lun Ma
Geosci. Model Dev., 14, 719–734, https://doi.org/10.5194/gmd-14-719-2021, https://doi.org/10.5194/gmd-14-719-2021, 2021
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This paper presents an evaluation of the E3SM model against NEXRAD radar observations for the warm seasons during 2014–2016. The COSP forward simulator package is implemented in the model to generate radar reflectivity, and the NEXRAD observations are coarsened to the model resolution for comparison. The model severely underestimates the reflectivity above 4 km. Sensitivity tests on the parameters from cumulus parameterization and cloud microphysics do not improve this model bias.
Johannes Quaas, Antti Arola, Brian Cairns, Matthew Christensen, Hartwig Deneke, Annica M. L. Ekman, Graham Feingold, Ann Fridlind, Edward Gryspeerdt, Otto Hasekamp, Zhanqing Li, Antti Lipponen, Po-Lun Ma, Johannes Mülmenstädt, Athanasios Nenes, Joyce E. Penner, Daniel Rosenfeld, Roland Schrödner, Kenneth Sinclair, Odran Sourdeval, Philip Stier, Matthias Tesche, Bastiaan van Diedenhoven, and Manfred Wendisch
Atmos. Chem. Phys., 20, 15079–15099, https://doi.org/10.5194/acp-20-15079-2020, https://doi.org/10.5194/acp-20-15079-2020, 2020
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Anthropogenic pollution particles – aerosols – serve as cloud condensation nuclei and thus increase cloud droplet concentration and the clouds' reflection of sunlight (a cooling effect on climate). This Twomey effect is poorly constrained by models and requires satellite data for better quantification. The review summarizes the challenges in properly doing so and outlines avenues for progress towards a better use of aerosol retrievals and better retrievals of droplet concentrations.
Cited articles
Albrecht, B. A.: Aerosols, cloud microphysics, and fractional cloudiness,
Science, 245, 1227–1230, 1989. a
Angeline, P., Saunders, G., and Pollack, J.: An evolutionary algorithm that
constructs recurrent neural networks, IEEE T. Neural Networ.,
5, 54–65, https://doi.org/10.1109/72.265960, 1994. a
Baker, B., Gupta, O., Naik, N., and Raskar, R.: Designing Neural Network
Architectures using Reinforcement Learning, ArXiv [preprint],
https://doi.org/10.48550/arXiv.1611.02167, 2017. a
Bellouin, N., Quaas, J., Gryspeerdt, E., Kinne, S., Stier, P., Watson-Parris, D., Boucher, O., Carslaw, K. S., Christensen, M., Daniau, A.-L., Dufresne, J.-L., Feingold, G., Fiedler, S., Forster, P., Gettelman, A., Haywood, J. M., Lohmann, U., Malavelle, F., Mauritsen, T., McCoy, D. T., Myhre, G., Mülmenstädt, J., Neubauer, D., Possner, A., Rugenstein, M., Sato, Y., Schulz, M., Schwartz, S. E., Sourdeval, O., Storelvmo, T., Toll, V., Winker, D., and Stevens, B.:
Bounding global aerosol radiative forcing of climate change, Rev.
Geophys., 58, e2019RG000660, https://doi.org/10.1029/2019RG000660, 2020. a, b
Bergstra, J., Yamins, D., and Cox, D.: Making a Science of Model Search:
Hyperparameter Optimization in Hundreds of Dimensions for Vision
Architectures, in: Proceedings of the 30th International Conference on
Machine Learning, edited by: Dasgupta, S. and McAllester, D.,
Proceedings of Machine Learning Research, Atlanta, Georgia,
USA, Vol. 28, 115–123, https://doi.org/10.5555/3042817.3042832, 2013. a
Boucher, O., Randall, D., Artaxo, P., Bretherton, C., Feingold, G., Forster,
P., Kerminen, V.-M., Kondo, Y., Liao, H., Lohmann, U., Rasch, P., Satheesh,
S., Sherwood, S., Stevens, B., and Zhang, X.: Clouds and Aerosols. In:
Climate Change 2013: The Physical Science Basis, Contribution of Working
Group I to the Fifth Assessment Report of the Intergovernmental Panel on
Climate Change, Cambridge University Press, Cambridge, United Kingdom and New
York, NY, USA, https://doi.org/10.1017/CBO9781107415324.016, 2013. a
Boukabara, S.-A., Krasnopolsky, V., Penny, S. G., Stewart, J. Q., McGovern, A., Hall, D., Ten Hoeve, J. E., Hickey, J., Huang, H.-L. A., Williams, J. K., Ide, K., Tissot, P., Haupt, S. E., Casey, K. S., Oza, N., Geer, A. J., Maddy, E. S., and Hoffman, R. N.: Outlook for exploiting artificial intelligence in the earth and
environmental sciences, B. Am. Meteorol. Soc., 102,
E1016–E1032, 2021. a
Brenowitz, N. D. and Bretherton, C. S.: Prognostic validation of a neural
network unified physics parameterization, Geophys. Res. Lett., 45,
6289–6298, 2018. a
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 Sy.,
14, e2021MS002794, https://doi.org/10.1029/2021MS002794, 2022. a
Clevert, D.-A., Unterthiner, T., and Hochreiter, S.: Fast and accurate deep
network learning by exponential linear units (elus), arXiv [preprint],
https://doi.org/10.48550/arXiv.1511.07289, 2015. 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 Sy., 12, e2019MS001916, https://doi.org/10.1029/2019MS001916, 2020. a
Fan, J., Wang, Y., Rosenfeld, D., and Liu, X.: Review of aerosol–cloud
interactions: Mechanisms, significance, and challenges, J.
Atmos. Sci., 73, 4221–4252, 2016. a
Friedlander, S. K.: Smoke, Dust, and Haze Fundamentals of Aerosol Dynamics,
Topics in chemical engineering, Oxford University Press, New York, NY, ISBN 0-19-512999-7, 2000. a
Geiss, A.: avgeiss/aerosol_optics_ml: GMD Supplementary Code (Version v1), Zenodo [code], https://doi.org/10.5281/zenodo.6767169, 2022a. a
Geiss, A.: Aerosol Optics ML Datasets, Zenodo [data set], https://doi.org/10.5281/zenodo.6762700, 2022b. a
Geiss, A., Silva, S. J., and Hardin, J. C.: Downscaling atmospheric chemistry simulations with physically consistent deep learning, Geosci. Model Dev., 15, 6677–6694, https://doi.org/10.5194/gmd-15-6677-2022, 2022. a
Gelbard, F. and Seinfeld, J. H.: The general dynamic equation for aerosols.
Theory and application to aerosol formation and growth, Journal of Colloid
and Interface Science, 68, 363–382,
https://doi.org/10.1016/0021-9797(79)90289-3, 1979. a
Gelbard, F., Tambour, Y., and Seinfeld, J. H.: Sectional representations for
simulating aerosol dynamics, J. Colloid Interf. Sci., 76,
541–556, https://doi.org/10.1016/0021-9797(80)90394-X, 1980. a
Gettelman, A., Gagne, D. J., Chen, C.-C., Christensen, M., Lebo, Z., Morrison,
H., and Gantos, G.: Machine learning the warm rain process, J.
Adv. Model. Earth Sy., 13, e2020MS002268, https://doi.org/10.1029/2020MS002268, 2021. a, b
Ghan, S., Laulainen, N., Easter, R., Wagener, R., Nemesure, S., Chapman, E.,
Zhang, Y., and Leung, R.: Evaluation of aerosol direct radiative forcing in
MIRAGE, J. Geophys. Res.-Atmos., 106, 5295–5316, 2001. a
Glorot, X., Bordes, A., and Bengio, Y.: Deep sparse rectifier neural networks,
in: Proceedings of the fourteenth international conference on artificial
intelligence and statistics, JMLR Workshop and Conference
Proceedings, https://proceedings.mlr.press/v15/glorot11a.html
(last access: 10 February 2023), 315–323, 2011. a
Golaz, J.-C., Caldwell, P. M., Van Roekel, L. P., Petersen, M. R., Tang, Q., Wolfe, J. D., Abeshu, G., Anantharaj, V., Asay-Davis, X. S., Bader, D. C., Baldwin, S. A., Bisht, G., Bogenschutz, P. A., Branstetter, M., Brunke, M. A., Brus, S. R., Burrows, S. M., Cameron-Smith, P. J., Donahue, A. S., Deakin, M., Easter, R. C., Evans, K. J., Feng, Y., Flanner, M., Foucar, J. G., Fyke, J. G., Griffin, B. M., Hannay, C., Harrop, B. E., Hoffman, M. J., Hunke, E. C., Jacob, R. L., Jacobsen, D. W., Jeffery, N., Jones, P. W., Keen, N. D., Klein, S. A., Larson, V. E., Leung, L. R., Li, H.-Y., Lin, W., Lipscomb, W. H., Ma, P.-L., Mahajan, S., Maltrud, M. E., Mametjanov, A., McClean, J. L., McCoy, R. B., Neale, R. B., Price, S. F., Qian, Y., Rasch, P. J., Reeves Eyre, J. E. J., Riley, W. J., Ringler, T. D., Roberts, A. F., Roesler, E. L., Salinger, A. G., Shaheen, Z., Shi, X., Singh, B., Tang, J., Taylor, M. A., Thornton, P. E., Turner, A. K., Veneziani, M., Wan, H., Wang, H., Wang, S., Williams, D. N., Wolfram, P. J., Worley, P. H., Xie, S., Yang, Y., Yoon, J.-H., Zelinka, M. D., Zender, C. S., Zeng, X., Zhang, C.,Zhang, K., Zhang, Y., Zheng, X., Zhou, T., and Zhu, Q.: The DOE E3SM coupled model version 1: Overview and evaluation at
standard resolution, J. Adv. Model. Earth Sy., 11,
2089–2129, 2019. a, b
Goodfellow, I., Bengio, Y., and Courville, A.: Deep Learning, MIT Press,
http://www.deeplearningbook.org (last access: 23 June 2022), 2016. a
Hansen, J., Sato, M., Ruedy, R., Nazarenko, L., Lacis, A., Schmidt, G. A., Russell, G., Aleinov, I., Bauer, M., Bauer, S., Bell, N., Cairns, B., Canuto, V., Chandler, M., Cheng, Y., Del Genio, A., Faluvegi, G., Fleming, E., Friend, A., Hall, T., Jackman, C., Kelley, M., Kiang, N., Koch, D., Lean, J., Lerner, J., Lo, K., Menon, S., Miller, R., Minnis, P., Novakov, T., Oinas, V., Perlwitz, Ja., Perlwitz, Ju., Rind, D., Romanou, A., Shindell, D., Stone, P., Sun, S., Tausnev, N., Thresher, D., Wielicki, B., Wong, T., Yao, M., and Zhang, S.: Efficacy of climate
forcings, J. Geophys. Res.-Atmos., 110, https://doi.org/10.1029/2005JD005776, 2005. a
Hansen, J. E. and Travis, L. D.: Light scattering in planetary atmospheres,
Space Sci. Rev., 16, 527–610, https://doi.org/10.1007/BF00168069, 1974. a
He, K., Zhang, X., Ren, S., and Sun, J.: Delving deep into rectifiers:
Surpassing human-level performance on imagenet classification, in:
Proceedings of the IEEE international conference on computer vision,
1026–1034, https://doi.org/10.1109/ICCV.2015.123, 2015. a
He, K., Zhang, X., Ren, S., and Sun, J.: Deep Residual Learning for Image
Recognition, in: 2016 IEEE Conference on Computer Vision and Pattern
Recognition (CVPR), 770–778, https://doi.org/10.1109/CVPR.2016.90, 2016. a, b
Hornik, K., Stinchcombe, M., and White, H.: Multilayer feedforward networks are
universal approximators, Neural Networks, 2, 359–366,
https://doi.org/10.1016/0893-6080(89)90020-8, 1989. a
Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K. Q.: Densely
connected convolutional networks, in: Proceedings of the IEEE conference on
computer vision and pattern recognition, 4700–4708, https://doi.org/10.1109/CVPR.2017.243, 2017. a
Feurer, M. and Hutter, F.: Hyperparameter Optimization, in: Automated Machine Learning. The Springer Series on Challenges in Machine Learning, edited by: Hutter, F., Kotthoff, L., and Vanschoren, J., Springer, Cham, https://doi.org/10.1007/978-3-030-05318-5_1, 2019. a
Iacono, M. J., Delamere, J. S., Mlawer, E. J., Shephard, M. W., Clough, S. A.,
and Collins, W. D.: Radiative forcing by long-lived greenhouse gases:
Calculations with the AER radiative transfer models, J. Geophys.
Res.-Atmos., 113, https://doi.org/10.1029/2008JD009944, 2008. a, b
Ioffe, S. and Szegedy, C.: Batch Normalization: Accelerating Deep Network
Training by Reducing Internal Covariate Shift, Proceedings of
Machine Learning Research, PMLR, Lille, France, Vol. 37, https://proceedings.mlr.press/v37/ioffe15.html (last access: 23 June 2022), 2015. a
Johnson, J. S., Regayre, L. A., Yoshioka, M., Pringle, K. J., Lee, L. A., Sexton, D. M. H., Rostron, J. W., Booth, B. B. B., and Carslaw, K. S.: The importance of comprehensive parameter sampling and multiple observations for robust constraint of aerosol radiative forcing, Atmos. Chem. Phys., 18, 13031–13053, https://doi.org/10.5194/acp-18-13031-2018, 2018. a
Krasnopolsky, V., Belochitski, A. A., Hou, Y., Lord, S. J., and Yang, F.:
Accurate and fast neural network emulations of long and short wave radiation
for the NCEP global forecast system model, NCEP Office Note, https://repository.library.noaa.gov/view/noaa/6951 (last access: 1 May 2023), 2012. a
Krasnopolsky, V. M., Fox-Rabinovitz, M. S., and Belochitski, A. A.: Using
ensemble of neural networks to learn stochastic convection parameterizations
for climate and numerical weather prediction models from data simulated by a
cloud resolving model, Advances in Artificial Neural Systems, 2013, 485913, https://doi.org/10.1155/2013/485913, 2013. a
Lamarque, J.-F., Emmons, L. K., Hess, P. G., Kinnison, D. E., Tilmes, S., Vitt, F., Heald, C. L., Holland, E. A., Lauritzen, P. H., Neu, J., Orlando, J. J., Rasch, P. J., and Tyndall, G. K.: CAM-chem: description and evaluation of interactive atmospheric chemistry in the Community Earth System Model, Geosci. Model Dev., 5, 369–411, https://doi.org/10.5194/gmd-5-369-2012, 2012. a
Lang, K. and Witbrock, M.: Learning to Tell Two Spirals Apart, in: Proceedings
of the 1988 Connectionist Models Summer School, Morgan Kaufmann Publishers, San Mateo CA, https://doi.org/10.13140/2.1.3459.2329,
1988. a, b, c
Liu, X., Easter, R. C., Ghan, S. J., Zaveri, R., Rasch, P., Shi, X., Lamarque, J.-F., Gettelman, A., Morrison, H., Vitt, F., Conley, A., Park, S., Neale, R., Hannay, C., Ekman, A. M. L., Hess, P., Mahowald, N., Collins, W., Iacono, M. J., Bretherton, C. S., Flanner, M. G., and Mitchell, D.: Toward a minimal representation of aerosols in climate models: description and evaluation in the Community Atmosphere Model CAM5, Geosci. Model Dev., 5, 709–739, https://doi.org/10.5194/gmd-5-709-2012, 2012. a, b, c, d
Liu, X., Ma, P.-L., Wang, H., Tilmes, S., Singh, B., Easter, R. C., Ghan, S. J., and Rasch, P. J.: Description and evaluation of a new four-mode version of the Modal Aerosol Module (MAM4) within version 5.3 of the Community Atmosphere Model, Geosci. Model Dev., 9, 505–522, https://doi.org/10.5194/gmd-9-505-2016, 2016. a, b
McGraw, R.: Description of Aerosol Dynamics by the Quadrature Method of
Moments, Aerosol Sci. Tech., 27, 255–265,
https://doi.org/10.1080/02786829708965471, 1997. a
Mlawer, E. and Clough, S.: On the extension of rapid radiative transfer model
to the shortwave region, in: Proceedings of the 6th Atmospheric Radiation
Measurement (ARM) Science Team Meeting, US Department of Energy,
CONF-9603149, 1997. a
Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J., and Clough, S. A.:
Radiative transfer for inhomogeneous atmospheres: RRTM, a validated
correlated-k model for the longwave, J. Geophys. Res.-Atmos., 102, 16663–16682, 1997. a
NCAR: CESM Documentation,
https://escomp.github.io/CESM/versions/master/html/index.html (last access: 15 February 2023),
2020. a
Neale, R. B., Chen, C.-C., Gettelman, A., Lauritzen, P. H., Park, S.,
Williamson, D. L., Conley, A. J., Garcia, R., Kinnison, D., Lamarque, J.-F.,
Marsh, D., Mills, M., Smith, A. K., Tilmes, S., Vitt, F., Morrison, H.,
Cameron-Smith, P., Collins, W. D., Iacono, M. J., Easter, R. C., Ghan, S. J.,
Liu, X., Rasch, P. J., and Taylor, M. A.: Description of the NCAR Community
Atmosphere Model (CAM 5.0), NCAR Technical Note,
https://www.cesm.ucar.edu/models/cesm1.0/cam/docs/description/cam5_desc.pdf (last access: 15 February 2023),
2012. a
Rasch, P. J., Xie, S., Ma, P.-L., Lin, W., Wang, H., Tang, Q., Burrows, S. M., Caldwell, P., Zhang, K., Easter, R. C., Cameron-Smith, P., Singh, B., Wan, H., Golaz, J.-C., Harrop, B. E., Roesler, E., Bacmeister, J., Larson, V. E., Evans, K. J., Qian, Y., Taylor, M., Leung, L. R., Zhang, Y., Brent, L., Branstetter, M., Hannay, C., Mahajan, S., Mametjanov, A., Neale, R., Richter, J. H., Yoon, J.-H., Zender, C. S., Bader, D., Flanner, M., Foucar, J. G., Jacob, R., Keen, N., Klein, S. A., Liu, X., Salinger, A. G., Shrivastava, M., and Yang, Y.: An overview of the atmospheric
component of the Energy Exascale Earth System Model, J. Adv.
Model. Earth Sy., 11, 2377–2411, 2019. 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, 2018. a
Scher, S.: Toward data-driven weather and climate forecasting: Approximating a
simple general circulation model with deep learning, Geophys. Res.
Lett., 45, 12616–12622, 2018. a
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov,
R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting,
J. Mach. Learn. Res., 15, 1929–1958,
http://jmlr.org/papers/v15/srivastava14a.html (last access: 1 May 2023), 2014. a
Sumlin, B. J., Heinson, W. R., and Chakrabarty, R. K.: Retrieving the aerosol
complex refractive index using PyMieScatt: A Mie computational package with
visualization capabilities, J. Quant. Spectrosc.
Ra., 205, 127–134,
https://doi.org/10.1016/j.jqsrt.2017.10.012, 2018. a, b
Twomey, S.: The influence of pollution on the shortwave albedo of clouds, J.
Atmos. Sci., 34, 1149–1152, 1977. a
van de Hulst, H. C.: Light scattering by small particles, John Wiley and Sons, New York, ISBN 9780486139753, 1957. a
Vetterling, W. T., Flannery, B. P., Press, W. H., and Teukolsky, S. A.: Numerical recipes in
C: the art of scientific computing, Cambridge University Press, ISBN 0-521-43108-5, 1988. a
Wang, H., Easter, R. C., Zhang, R., Ma, P.-L., Singh, B., Zhang, K.,Ganguly, D., Rasch, P. J., Burrows, S. M., Ghan, S. J., Lou, S., Qian, Y., Yang, Y., Feng, Y., Flanner, M., Leung, L. R., Liu, X., Shrivastava, M., Sun, J.,Tang, Q., Xie, S., and Yoon, J.-H.: Aerosols in the E3SM
Version 1: New developments and their impacts on radiative forcing, J.
Adv. Model. Earth Sy., 12, e2019MS001851, https://doi.org/10.1029/2019MS001851, 2020. a
Wang, J., Liu, Z., Foster, I., Chang, W., Kettimuthu, R., and Kotamarthi, V. R.: Fast and accurate learned multiresolution dynamical downscaling for precipitation, Geosci. Model Dev., 14, 6355–6372, https://doi.org/10.5194/gmd-14-6355-2021, 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
Weyn, J. A., Durran, D. R., and Caruana, R.: Improving data-driven global
weather prediction using deep convolutional neural networks on a cubed
sphere, J. Adv. Model. Earth Sy., 12, e2020MS002109, https://doi.org/10.1029/2020MS002109,
2020. a
Wiscombe, W. J.: Mie Scattering Calculations: Advances in Technique and Fast,
Vector-speed Computer Codes, University Corporation for Atmospheric Research
Technical Note, No. NCAR/TN-140+STR), https://doi.org/10.5065/D6ZP4414, 1979. a, b, c, d
Wiscombe, W. J.: Improved Mie scattering algorithms, Appl. Optics, 19,
1505–1509, https://doi.org/10.1364/AO.19.001505, 1980a. a, b
Wiscombe, W.: MIEV0, GitHub [code], https://github.com/avgeiss/aerosol_optics_ml/blob/main/mie_codes/miev0.F (last access: 5 October 2022), 1980b.
a
Xie, S., Kirillov, A., Girshick, R., and He, K.: Exploring Randomly Wired
Neural Networks for Image Recognition, 2019 IEEE/CVF International Conference
on Computer Vision (ICCV), 1284–1293, https://doi.org/10.1109/ICCV.2019.00137,
2019. a, b, c
Yao, X.: Evolving artificial neural networks, P. IEEE, 87,
1423–1447, https://doi.org/10.1109/5.784219, 1999. a
Zhang, H., Sharma, G., Dhawan, S., Dhanraj, D., Li, Z., and Biswas, P.:
Comparison of discrete, discrete-sectional, modal and moment models for
aerosol dynamics simulations, Aerosol Sci. Tech., 54, 739–760,
https://doi.org/10.1080/02786826.2020.1723787, 2020. a
Zoph, B., Vasudevan, V., Shlens, J., and Le, Q. V.: Learning Transferable
Architectures for Scalable Image Recognition, 2018 IEEE/CVF Conference on
Computer Vision and Pattern Recognition, 8697–8710, https://doi.org/10.1109/CVPR.2018.00907, 2018. a
Short summary
Atmospheric aerosols play a critical role in Earth's climate, but it is too computationally expensive to directly model their interaction with radiation in climate simulations. This work develops a new neural-network-based parameterization of aerosol optical properties for use in the Energy Exascale Earth System Model that is much more accurate than the current one; it also introduces a unique model optimization method that involves randomly generating neural network architectures.
Atmospheric aerosols play a critical role in Earth's climate, but it is too computationally...
