Articles | Volume 14, issue 4
https://doi.org/10.5194/gmd-14-1921-2021
© Author(s) 2021. 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-14-1921-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
12 Apr 2021
Model evaluation paper |
| 12 Apr 2021
| 12 Apr 2021
Quantifying and attributing time step sensitivities in present-day climate simulations conducted with EAMv1
Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington, USA
Shixuan Zhang
Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington, USA
Philip J. Rasch
Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington, USA
Vincent E. Larson
Department of Mathematical Sciences, University of Wisconsin – Milwaukee, Milwaukee, Wisconsin, USA
Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington, USA
Xubin Zeng
Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, Arizona, USA
Huiping Yan
School of Atmospheric Science, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China
Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington, USA
Related authors
Hui Wan, Abhishek Yenpure, Berk Geveci, Richard C. Easter, Philip J. Rasch, Kai Zhang, and Xubin Zeng
EGUsphere, https://doi.org/10.5194/egusphere-2024-4020, https://doi.org/10.5194/egusphere-2024-4020, 2025
Short summary
Short summary
In E3SMv2 and many other global climate models, the simulated anthropogenic aerosol influence on the Earth's energy balance is sensitive to the presence of clouds with very low droplet number concentrations. Numerical experiments conducted in this study suggest that mid- and high-latitude low-level stratus occurring under weak turbulence is an important cloud regime for understanding the causes of very low cloud droplet number concentrations in global climate simulations.
Yu Yao, Po-Lun Ma, Yi Qin, Matthew W. Christensen, Hui Wan, Kai Zhang, Balwinder Singh, Meng Huang, and Mikhail Ovchinnikov
EGUsphere, https://doi.org/10.5194/egusphere-2024-523, https://doi.org/10.5194/egusphere-2024-523, 2024
Preprint withdrawn
Short summary
Short summary
Giant aerosols have substantial effects on warm rain formation. However, it remains challenging to quantify the impact of giant particles at global scale. In this work, we applied earth system model to investigate its impacts by implementing new giant aerosol treatments to consider its physical process. We found this approach substantially affect liquid cloud and improved model's precipitation response to aerosols. Our findings demonstrate the significant impact of giant aerosols on climate.
Hui Wan, Kai Zhang, Christopher J. Vogl, Carol S. Woodward, Richard C. Easter, Philip J. Rasch, Yan Feng, and Hailong Wang
Geosci. Model Dev., 17, 1387–1407, https://doi.org/10.5194/gmd-17-1387-2024, https://doi.org/10.5194/gmd-17-1387-2024, 2024
Short summary
Short summary
Sophisticated numerical models of the Earth's atmosphere include representations of many physical and chemical processes. In numerical simulations, these processes need to be calculated in a certain sequence. This study reveals the weaknesses of the sequence of calculations used for aerosol processes in a global atmosphere model. A revision of the sequence is proposed and its impacts on the simulated global aerosol climatology are evaluated.
Christopher J. Vogl, Hui Wan, Carol S. Woodward, and Quan M. Bui
Geosci. Model Dev., 17, 1409–1428, https://doi.org/10.5194/gmd-17-1409-2024, https://doi.org/10.5194/gmd-17-1409-2024, 2024
Short summary
Short summary
Generally speaking, accurate climate simulation requires an accurate evolution of the underlying mathematical equations on large computers. The equations are typically formulated and evolved in process groups. Process coupling refers to how the evolution of each group is combined with that of other groups to evolve the full set of equations for the whole atmosphere. This work presents a mathematical framework to evaluate methods without the need to first implement the methods.
Shixuan Zhang, Kai Zhang, Hui Wan, and Jian Sun
Geosci. Model Dev., 15, 6787–6816, https://doi.org/10.5194/gmd-15-6787-2022, https://doi.org/10.5194/gmd-15-6787-2022, 2022
Short summary
Short summary
This study investigates the nudging implementation in the EAMv1 model. We find that (1) revising the sequence of calculations and using higher-frequency constraining data to improve the performance of a simulation nudged to EAMv1’s own meteorology, (2) using the relocated nudging tendency and 3-hourly ERA5 reanalysis to obtain a better agreement between nudged simulations and observations, and (3) using wind-only nudging are recommended for the estimates of global mean aerosol effects.
Kai Zhang, Wentao Zhang, Hui Wan, Philip J. Rasch, Steven J. Ghan, Richard C. Easter, Xiangjun Shi, Yong Wang, Hailong Wang, Po-Lun Ma, Shixuan Zhang, Jian Sun, Susannah M. Burrows, Manish Shrivastava, Balwinder Singh, Yun Qian, Xiaohong Liu, Jean-Christophe Golaz, Qi Tang, Xue Zheng, Shaocheng Xie, Wuyin Lin, Yan Feng, Minghuai Wang, Jin-Ho Yoon, and L. Ruby Leung
Atmos. Chem. Phys., 22, 9129–9160, https://doi.org/10.5194/acp-22-9129-2022, https://doi.org/10.5194/acp-22-9129-2022, 2022
Short summary
Short summary
Here we analyze the effective aerosol forcing simulated by E3SM version 1 using both century-long free-running and short nudged simulations. The aerosol forcing in E3SMv1 is relatively large compared to other models, mainly due to the large indirect aerosol effect. Aerosol-induced changes in liquid and ice cloud properties in E3SMv1 have a strong correlation. The aerosol forcing estimates in E3SMv1 are sensitive to the parameterization changes in both liquid and ice cloud processes.
Hui Wan, Kai Zhang, Philip J. Rasch, Vincent E. Larson, Xubin Zeng, Shixuan Zhang, and Ross Dixon
Geosci. Model Dev., 15, 3205–3231, https://doi.org/10.5194/gmd-15-3205-2022, https://doi.org/10.5194/gmd-15-3205-2022, 2022
Short summary
Short summary
This paper describes a tool embedded in a global climate model for sampling atmospheric conditions and monitoring physical processes as a numerical simulation is being carried out. The tool facilitates process-level model evaluation by allowing the users to select a wide range of quantities and processes to monitor at run time without having to do tedious ad hoc coding.
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.
Jinbo Xie, Qi Tang, Michael Prather, Jadwiga Richter, and Shixuan Zhang
Atmos. Chem. Phys., 25, 9315–9333, https://doi.org/10.5194/acp-25-9315-2025, https://doi.org/10.5194/acp-25-9315-2025, 2025
Short summary
Short summary
Analysis of the interaction between the climate and ozone in the stratosphere is complicated by the inability of climate models to simulate the quasi-biennial oscillation (QBO) – an important climate mode in the stratosphere. We use a set of model simulations that realistically simulate QBO and a novel ozone diagnostic tool to separate temperature- and circulation-driven QBO impacts. These are important for diagnosing model–model differences in QBO–ozone responses for climate projections.
Vincent Larson, Zhun Guo, Benjamin Stephens, Colin Zarzycki, Gerhard Dikta, Yun Qian, and Shaocheng Xie
EGUsphere, https://doi.org/10.5194/egusphere-2025-1593, https://doi.org/10.5194/egusphere-2025-1593, 2025
Short summary
Short summary
Global models of the atmosphere contain errors that lead to inaccurate simulations. A software tool ("QuadTune") is presented that attempts to mitigate some of the inaccuracies. It also displays diagnostic plots that provide hints about where the errors might lie in the model.
Hui Wan, Abhishek Yenpure, Berk Geveci, Richard C. Easter, Philip J. Rasch, Kai Zhang, and Xubin Zeng
EGUsphere, https://doi.org/10.5194/egusphere-2024-4020, https://doi.org/10.5194/egusphere-2024-4020, 2025
Short summary
Short summary
In E3SMv2 and many other global climate models, the simulated anthropogenic aerosol influence on the Earth's energy balance is sensitive to the presence of clouds with very low droplet number concentrations. Numerical experiments conducted in this study suggest that mid- and high-latitude low-level stratus occurring under weak turbulence is an important cloud regime for understanding the causes of very low cloud droplet number concentrations in global climate simulations.
Philip J. Rasch, Haruki Hirasawa, Mingxuan Wu, Sarah J. Doherty, Robert Wood, Hailong Wang, Andy Jones, James Haywood, and Hansi Singh
Geosci. Model Dev., 17, 7963–7994, https://doi.org/10.5194/gmd-17-7963-2024, https://doi.org/10.5194/gmd-17-7963-2024, 2024
Short summary
Short summary
We introduce a protocol to compare computer climate simulations to better understand a proposed strategy intended to counter warming and climate impacts from greenhouse gas increases. This slightly changes clouds in six ocean regions to reflect more sunlight and cool the Earth. Example changes in clouds and climate are shown for three climate models. Cloud changes differ between the models, but precipitation and surface temperature changes are similar when their cooling effects are made similar.
Soodabeh Namdari, Sanja Dmitrovic, Gao Chen, Yonghoon Choi, Ewan Crosbie, Joshua P. DiGangi, Glenn S. Diskin, Richard A. Ferrare, Johnathan W. Hair, Simon Kirschler, John B. Nowak, Kenneth L. Thornhill, Christiane Voigt, Holger Vömel, Xubin Zeng, and Armin Sorooshian
EGUsphere, https://doi.org/10.5194/egusphere-2024-3024, https://doi.org/10.5194/egusphere-2024-3024, 2024
Short summary
Short summary
We conducted this study to assess the accuracy of airborne measurements of wind, temperature, and humidity, essential for understanding atmospheric processes. Using data from NASA's ACTIVATE campaign, we compared measurements from the TAMMS and DLH aboard a Falcon aircraft with dropsondes from a King Air, matching data points based on location and time using statistical methods. The study showed strong agreement, confirming the reliability of these methods for advancing climate models.
Shuaiqi Tang, Hailong Wang, Xiang-Yu Li, Jingyi Chen, Armin Sorooshian, Xubin Zeng, Ewan Crosbie, Kenneth L. Thornhill, Luke D. Ziemba, and Christiane Voigt
Atmos. Chem. Phys., 24, 10073–10092, https://doi.org/10.5194/acp-24-10073-2024, https://doi.org/10.5194/acp-24-10073-2024, 2024
Short summary
Short summary
We examined marine boundary layer clouds and their interactions with aerosols in the E3SM single-column model (SCM) for a case study. The SCM shows good agreement when simulating the clouds with high-resolution models. It reproduces the relationship between cloud droplet and aerosol particle number concentrations as produced in global models. However, the relationship between cloud liquid water and droplet number concentration is different, warranting further investigation.
Bo-Wen Shen, Roger Pielke Sr., and Xubin Zeng
EGUsphere, https://doi.org/10.13140/RG.2.2.32941.15849, https://doi.org/10.13140/RG.2.2.32941.15849, 2024
Preprint archived
Short summary
Short summary
Our reanalysis does not support the claim that Lorenz’s and Lilly’s formulas are mathematically or physically comparable. No common factor of 2^{−2/3} can be robustly determined from Lorenz's data, disproving the assumption that the saturation time difference and turnover time are linearly proportional over wave modes. These issues, along with the highly simplified features of the Lorenz 1969 model, indicate that an upper bound for the predictability limit has not been robustly determined.
Sanja Dmitrovic, Johnathan W. Hair, Brian L. Collister, Ewan Crosbie, Marta A. Fenn, Richard A. Ferrare, David B. Harper, Chris A. Hostetler, Yongxiang Hu, John A. Reagan, Claire E. Robinson, Shane T. Seaman, Taylor J. Shingler, Kenneth L. Thornhill, Holger Vömel, Xubin Zeng, and Armin Sorooshian
Atmos. Meas. Tech., 17, 3515–3532, https://doi.org/10.5194/amt-17-3515-2024, https://doi.org/10.5194/amt-17-3515-2024, 2024
Short summary
Short summary
This study introduces and evaluates a new ocean surface wind speed product from the NASA Langley Research Center (LARC) airborne High-Spectral-Resolution Lidar – Generation 2 (HSRL-2) during the NASA ACTIVATE mission. We show that HSRL-2 surface wind speed data are accurate when compared to ground-truth dropsonde measurements. Therefore, the HSRL-2 instrument is able obtain accurate, high-resolution surface wind speed data in airborne field campaigns.
Nenghan Wan, Xiaomao Lin, Roger A. Pielke Sr., Xubin Zeng, and Amanda M. Nelson
Hydrol. Earth Syst. Sci., 28, 2123–2137, https://doi.org/10.5194/hess-28-2123-2024, https://doi.org/10.5194/hess-28-2123-2024, 2024
Short summary
Short summary
Global warming occurs at a rate of 0.21 K per decade, resulting in about 9.5 % K−1 of water vapor response to temperature from 1993 to 2021. Terrestrial areas experienced greater warming than the ocean, with a ratio of 2 : 1. The total precipitable water change in response to surface temperature changes showed a variation around 6 % K−1–8 % K−1 in the 15–55° N latitude band. Further studies are needed to identify the mechanisms leading to different water vapor responses.
Leong Wai Siu, Joseph S. Schlosser, David Painemal, Brian Cairns, Marta A. Fenn, Richard A. Ferrare, Johnathan W. Hair, Chris A. Hostetler, Longlei Li, Mary M. Kleb, Amy Jo Scarino, Taylor J. Shingler, Armin Sorooshian, Snorre A. Stamnes, and Xubin Zeng
Atmos. Meas. Tech., 17, 2739–2759, https://doi.org/10.5194/amt-17-2739-2024, https://doi.org/10.5194/amt-17-2739-2024, 2024
Short summary
Short summary
An unprecedented 3-year aerosol dataset was collected from a recent NASA field campaign over the western North Atlantic Ocean, which offers a special opportunity to evaluate two state-of-the-art remote sensing instruments, one lidar and the other polarimeter, on the same aircraft. Special attention has been paid to validate aerosol optical depth data and their uncertainties when no reference dataset is available. Physical reasons for the disagreement between two instruments are discussed.
Taufiq Hassan, Kai Zhang, Jianfeng Li, Balwinder Singh, Shixuan Zhang, Hailong Wang, and Po-Lun Ma
Geosci. Model Dev., 17, 3507–3532, https://doi.org/10.5194/gmd-17-3507-2024, https://doi.org/10.5194/gmd-17-3507-2024, 2024
Short summary
Short summary
Anthropogenic aerosol emissions are an essential part of global aerosol models. Significant errors can exist from the loss of emission heterogeneity. We introduced an emission treatment that significantly improved aerosol emission heterogeneity in high-resolution model simulations, with improvements in simulated aerosol surface concentrations. The emission treatment will provide a more accurate representation of aerosol emissions and their effects on climate.
Pengfei Shi, L. Ruby Leung, Bin Wang, Kai Zhang, Samson M. Hagos, and Shixuan Zhang
Geosci. Model Dev., 17, 3025–3040, https://doi.org/10.5194/gmd-17-3025-2024, https://doi.org/10.5194/gmd-17-3025-2024, 2024
Short summary
Short summary
Improving climate predictions have profound socio-economic impacts. This study introduces a new weakly coupled land data assimilation (WCLDA) system for a coupled climate model. We demonstrate improved simulation of soil moisture and temperature in many global regions and throughout the soil layers. Furthermore, significant improvements are also found in reproducing the time evolution of the 2012 US Midwest drought. The WCLDA system provides the groundwork for future predictability studies.
Yawen Liu, Yun Qian, Philip J. Rasch, Kai Zhang, Lai-yung Ruby Leung, Yuhang Wang, Minghuai Wang, Hailong Wang, Xin Huang, and Xiu-Qun Yang
Atmos. Chem. Phys., 24, 3115–3128, https://doi.org/10.5194/acp-24-3115-2024, https://doi.org/10.5194/acp-24-3115-2024, 2024
Short summary
Short summary
Fire management has long been a challenge. Here we report that spring-peak fire activity over southern Mexico and Central America (SMCA) has a distinct quasi-biennial signal by measuring multiple fire metrics. This signal is initially driven by quasi-biennial variability in precipitation and is further amplified by positive feedback of fire–precipitation interaction at short timescales. This work highlights the importance of fire–climate interactions in shaping fires on an interannual scale.
Yu Yao, Po-Lun Ma, Yi Qin, Matthew W. Christensen, Hui Wan, Kai Zhang, Balwinder Singh, Meng Huang, and Mikhail Ovchinnikov
EGUsphere, https://doi.org/10.5194/egusphere-2024-523, https://doi.org/10.5194/egusphere-2024-523, 2024
Preprint withdrawn
Short summary
Short summary
Giant aerosols have substantial effects on warm rain formation. However, it remains challenging to quantify the impact of giant particles at global scale. In this work, we applied earth system model to investigate its impacts by implementing new giant aerosol treatments to consider its physical process. We found this approach substantially affect liquid cloud and improved model's precipitation response to aerosols. Our findings demonstrate the significant impact of giant aerosols on climate.
Hui Wan, Kai Zhang, Christopher J. Vogl, Carol S. Woodward, Richard C. Easter, Philip J. Rasch, Yan Feng, and Hailong Wang
Geosci. Model Dev., 17, 1387–1407, https://doi.org/10.5194/gmd-17-1387-2024, https://doi.org/10.5194/gmd-17-1387-2024, 2024
Short summary
Short summary
Sophisticated numerical models of the Earth's atmosphere include representations of many physical and chemical processes. In numerical simulations, these processes need to be calculated in a certain sequence. This study reveals the weaknesses of the sequence of calculations used for aerosol processes in a global atmosphere model. A revision of the sequence is proposed and its impacts on the simulated global aerosol climatology are evaluated.
Christopher J. Vogl, Hui Wan, Carol S. Woodward, and Quan M. Bui
Geosci. Model Dev., 17, 1409–1428, https://doi.org/10.5194/gmd-17-1409-2024, https://doi.org/10.5194/gmd-17-1409-2024, 2024
Short summary
Short summary
Generally speaking, accurate climate simulation requires an accurate evolution of the underlying mathematical equations on large computers. The equations are typically formulated and evolved in process groups. Process coupling refers to how the evolution of each group is combined with that of other groups to evolve the full set of equations for the whole atmosphere. This work presents a mathematical framework to evaluate methods without the need to first implement the methods.
Jianfeng Li, Kai Zhang, Taufiq Hassan, Shixuan Zhang, Po-Lun Ma, Balwinder Singh, Qiyang Yan, and Huilin Huang
Geosci. Model Dev., 17, 1327–1347, https://doi.org/10.5194/gmd-17-1327-2024, https://doi.org/10.5194/gmd-17-1327-2024, 2024
Short summary
Short summary
By comparing E3SM simulations with and without regional refinement, we find that model horizontal grid spacing considerably affects the simulated aerosol mass budget, aerosol–cloud interactions, and the effective radiative forcing of anthropogenic aerosols. The study identifies the critical physical processes strongly influenced by model resolution. It also highlights the benefit of applying regional refinement in future modeling studies at higher or even convection-permitting resolutions.
Jim M. Haywood, Andy Jones, Anthony C. Jones, Paul Halloran, and Philip J. Rasch
Atmos. Chem. Phys., 23, 15305–15324, https://doi.org/10.5194/acp-23-15305-2023, https://doi.org/10.5194/acp-23-15305-2023, 2023
Short summary
Short summary
The difficulties in ameliorating global warming and the associated climate change via conventional mitigation are well documented, with all climate model scenarios exceeding 1.5 °C above the preindustrial level in the near future. There is therefore a growing interest in geoengineering to reflect a greater proportion of sunlight back to space and offset some of the global warming. We use a state-of-the-art Earth-system model to investigate two of the most prominent geoengineering strategies.
Armin Sorooshian, Mikhail D. Alexandrov, Adam D. Bell, Ryan Bennett, Grace Betito, Sharon P. Burton, Megan E. Buzanowicz, Brian Cairns, Eduard V. Chemyakin, Gao Chen, Yonghoon Choi, Brian L. Collister, Anthony L. Cook, Andrea F. Corral, Ewan C. Crosbie, Bastiaan van Diedenhoven, Joshua P. DiGangi, Glenn S. Diskin, Sanja Dmitrovic, Eva-Lou Edwards, Marta A. Fenn, Richard A. Ferrare, David van Gilst, Johnathan W. Hair, David B. Harper, Miguel Ricardo A. Hilario, Chris A. Hostetler, Nathan Jester, Michael Jones, Simon Kirschler, Mary M. Kleb, John M. Kusterer, Sean Leavor, Joseph W. Lee, Hongyu Liu, Kayla McCauley, Richard H. Moore, Joseph Nied, Anthony Notari, John B. Nowak, David Painemal, Kasey E. Phillips, Claire E. Robinson, Amy Jo Scarino, Joseph S. Schlosser, Shane T. Seaman, Chellappan Seethala, Taylor J. Shingler, Michael A. Shook, Kenneth A. Sinclair, William L. Smith Jr., Douglas A. Spangenberg, Snorre A. Stamnes, Kenneth L. Thornhill, Christiane Voigt, Holger Vömel, Andrzej P. Wasilewski, Hailong Wang, Edward L. Winstead, Kira Zeider, Xubin Zeng, Bo Zhang, Luke D. Ziemba, and Paquita Zuidema
Earth Syst. Sci. Data, 15, 3419–3472, https://doi.org/10.5194/essd-15-3419-2023, https://doi.org/10.5194/essd-15-3419-2023, 2023
Short summary
Short summary
The NASA Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE) produced a unique dataset for research into aerosol–cloud–meteorology interactions. HU-25 Falcon and King Air aircraft conducted systematic and spatially coordinated flights over the northwest Atlantic Ocean. This paper describes the ACTIVATE flight strategy, instrument and complementary dataset products, data access and usage details, and data application notes.
Shixuan Zhang, Kai Zhang, Hui Wan, and Jian Sun
Geosci. Model Dev., 15, 6787–6816, https://doi.org/10.5194/gmd-15-6787-2022, https://doi.org/10.5194/gmd-15-6787-2022, 2022
Short summary
Short summary
This study investigates the nudging implementation in the EAMv1 model. We find that (1) revising the sequence of calculations and using higher-frequency constraining data to improve the performance of a simulation nudged to EAMv1’s own meteorology, (2) using the relocated nudging tendency and 3-hourly ERA5 reanalysis to obtain a better agreement between nudged simulations and observations, and (3) using wind-only nudging are recommended for the estimates of global mean aerosol effects.
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.
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.
Susannah M. Burrows, Richard C. Easter, Xiaohong Liu, Po-Lun Ma, Hailong Wang, Scott M. Elliott, Balwinder Singh, Kai Zhang, and Philip J. Rasch
Atmos. Chem. Phys., 22, 5223–5251, https://doi.org/10.5194/acp-22-5223-2022, https://doi.org/10.5194/acp-22-5223-2022, 2022
Short summary
Short summary
Sea spray particles are composed of a mixture of salts and organic substances from oceanic microorganisms. In prior work, our team developed an approach connecting sea spray chemistry to ocean biology, called OCEANFILMS. Here we describe its implementation within an Earth system model, E3SM. We show that simulated sea spray chemistry is consistent with observed seasonal cycles and that sunlight reflected by simulated Southern Ocean clouds increases, consistent with analysis of satellite data.
Hui Wan, Kai Zhang, Philip J. Rasch, Vincent E. Larson, Xubin Zeng, Shixuan Zhang, and Ross Dixon
Geosci. Model Dev., 15, 3205–3231, https://doi.org/10.5194/gmd-15-3205-2022, https://doi.org/10.5194/gmd-15-3205-2022, 2022
Short summary
Short summary
This paper describes a tool embedded in a global climate model for sampling atmospheric conditions and monitoring physical processes as a numerical simulation is being carried out. The tool facilitates process-level model evaluation by allowing the users to select a wide range of quantities and processes to monitor at run time without having to do tedious ad hoc coding.
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.
Hossein Dadashazar, David Painemal, Majid Alipanah, Michael Brunke, Seethala Chellappan, Andrea F. Corral, Ewan Crosbie, Simon Kirschler, Hongyu Liu, Richard H. Moore, Claire Robinson, Amy Jo Scarino, Michael Shook, Kenneth Sinclair, K. Lee Thornhill, Christiane Voigt, Hailong Wang, Edward Winstead, Xubin Zeng, Luke Ziemba, Paquita Zuidema, and Armin Sorooshian
Atmos. Chem. Phys., 21, 10499–10526, https://doi.org/10.5194/acp-21-10499-2021, https://doi.org/10.5194/acp-21-10499-2021, 2021
Short summary
Short summary
This study investigates the seasonal cycle of cloud drop number concentration (Nd) over the western North Atlantic Ocean (WNAO) using multiple datasets. Reasons for the puzzling discrepancy between the seasonal cycles of Nd and aerosol concentration were identified. Results indicate that Nd is highest in winter (when aerosol proxy values are often lowest) due to conditions both linked to cold-air outbreaks and that promote greater droplet activation.
Cited articles
Barrett, A. I., Wellmann, C., Seifert, A., Hoose, C., Vogel, B., and Kunz, M.:
One Step at a Time: How Model Time Step Significantly Affects
Convection-Permitting Simulations, J. Adv. Model. Earth Sy., 11, 641–658,
https://doi.org/10.1029/2018MS001418,
2019. a
Beljaars, A., Bechtold, P., Köhler, M., Morcrette, J. J., A.Tompkins,
Viterbo, P., and Wedi, N.: The numerics of physicalparameterization, in:
Seminar on Recent Developments in Numerical Methods for Atmospheric and Ocean
Modelling, European Centre For Medium-Range Weather Forecasts, Shinfield
Park, Reading, United Kingdom, ECMWF, 2004. a, b
Beljaars, A., Dutra, E., Balsamo, G., and Lemarié, F.: On the numerical stability of surface–atmosphere coupling in weather and climate models, Geosci. Model Dev., 10, 977–989, https://doi.org/10.5194/gmd-10-977-2017, 2017. a
Beljaars, A., Balsamo, G., Bechtold, P., Bozzo, A., Forbes, R., Hogan, R. J.,
Köhler, M., Morcrette, J.-J., Tompkins, A. M., Viterbo, P., and Wedi, N.:
The Numerics of Physical Parametrization in the ECMWF Model, Front.
Earth Sci., 6, 137, https://doi.org/10.3389/feart.2018.00137,
2018. a, b
Beljaars, A. C. M.: Numerical schemes for parametrizations, in: Seminar on
Numerical Methods in Atmospheric Models, European Centre For
Medium-Range Weather Forecasts, Shinfield Park, Reading, 1–42,
available at: https://www.ecmwf.int/node/8028 (last access: 6 April 2021), 1991. a
Bogenschutz, P. A., Gettelman, A., Morrison, H., Larson, V. E., Craig, C., and
Schanen, D. P.: Higher-order turbulence closure and its impact on climate
simulations in the Community Atmosphere Model, J. Climate, 26, 9655–9676,
https://doi.org/10.1175/JCLI-D-13-00075.1, 2013. a
Bony, S., Dufresne, J.-L., Treut, H. L., Morcrette, J.-J., and Senior, C.: On
dynamic and thermodynamic components of cloud changes, Clim. Dynam., 22,
71–86, https://doi.org/10.1007/s00382-003-0369-6, 2004. a, b, c, d
Dennis, J. M., Edwards, J., Evans, K. J., Guba, O., Lauritzen, P. H., Mirin,
A. A., St-Cyr, A., Taylor, M. A., and Worley, P. H.: CAM-SE: A scalable
spectral element dynamical core for the Community Atmosphere Model, Int. J.
High Perform., 26, 74–89, 2012. a
E3SM developers, Zhang, S., and Wan, H.: EAM source codes and scripts for time step sensitivity experiments (Version 1), Zenodo, https://doi.org/10.5281/zenodo.4118705, 2020. a
Gettelman, A. and Morrison, H.: Advanced two-moment bulk microphysics for
global models, Part I: Off-line tests and comparison with other schemes,
J. Climate, 28, 1268–1287, https://doi.org/10.1175/JCLI-D-14-00102.1, 2015. a
Gettelman, A., Morrison, H., Santos, S., Bogenschutz, P., and Caldwell, P. M.:
Advanced Two-Moment Bulk Microphysics for Global Models. Part II: Global
Model Solutions and Aerosol–Cloud Interactions, J. Climate, 28,
1288–1307, https://doi.org/10.1175/JCLI-D-14-00103.1,
2015. a, b, c
Golaz, J.-C., Larson, V., and Cotton, W.: A PDF-Based Model for Boundary Layer
Clouds. Part I: Method and Model Description, J. Atmos. Sci., 59, 3540–3551,
https://doi.org/10.1175/1520-0469(2002)059<3540:APBMFB>2.0.CO;2, 2002. a, b
Guba, O., Taylor, M., and St-Cyr, A.: Optimization-based limiters for the
spectral element method, J. Comput. Phys., 267, 176–195,
https://doi.org/10.1016/j.jcp.2014.02.029, 2014. a
Guerra, J. E. and Ullrich, P. A.: A high-order staggered finite-element vertical discretization for non-hydrostatic atmospheric models, Geosci. Model Dev., 9, 2007–2029, https://doi.org/10.5194/gmd-9-2007-2016, 2016. a
Hardiman, S. C., Boutle, I. A., Bushell, A. C., Butchart, N., Cullen, M. J. P.,
Field, P. R., Furtado, K., Manners, J. C., Milton, S. F., Morcrette, C.,
O?Connor, F. M., Shipway, B. J., Smith, C., Walters, D. N., Willett, M. R.,
Williams, K. D., Wood, N., Abraham, N. L., Keeble, J., Maycock, A. C.,
Thuburn, J., and Woodhouse, M. T.: Processes Controlling Tropical Tropopause
Temperature and Stratospheric Water Vapor in Climate Models, J.
Climate, 28, 6516–6535, https://doi.org/10.1175/JCLI-D-15-0075.1,
2015. a
Iacono, M. J., Delamere, J. S., Mlawer, E. J., Shephard, M. W., Clough, S. A.,
and Collins, W. D.: Radiative forcing by long–lived greenhouse gases:
Calculations with the AER radiative transfer models, J. Geophys. Res., 113,
D13103, https://doi.org/10.1029/2008JD009944, 2008. a
Kinnmark, I. P. and Gray, W. G.: One step integration methods of third-fourth
order accuracy with large hyperbolic stability limits, Math.
Comput. Simulat., 26, 181–188,
https://doi.org/10.1016/0378-4754(84)90056-9,
1984. a
Kooperman, G. J., Pritchard, M. S., Ghan, S. J., Wang, M., Somerville, R.
C. J., and Russell, L. M.: Constraining the influence of natural variability
to improve estimates of global aerosol indirect effects in a nudged version
of the Community Atmosphere Model 5, J. Geophys. Res.-Atmos., 117, D23204, https://doi.org/10.1029/2012JD018588,
2012. a
Larson, V. E. and Golaz, J.-C.: Using Probability Density Functions to Derive
Consistent Closure Relationships among Higher-Order Moments, Mon. Weather
Rev., 133, 1023–1042, https://doi.org/10.1175/MWR2902.1,
2005. a, b
Larson, V. E., Golaz, J.-C., and Cotton, W. R.: Small-Scale and Mesoscale
Variability in Cloudy Boundary Layers: Joint Probability Density Functions,
J. Atmos. Sci., 59, 3519–3539,
https://doi.org/10.1175/1520-0469(2002)059<3519:SSAMVI>2.0.CO;2,
2002. a, b
Lauritzen, P. H. and Williamson, D. L.: A Total Energy Error Analysis of
Dynamical Cores and Physics-Dynamics Coupling in the Community Atmosphere
Model (CAM), J. Adv. Model. Earth Sy., 11, 1309–1328,
https://doi.org/10.1029/2018MS001549,
2019. a
Lauritzen, P. H., Nair, R. D., Herrington, A. R., Callaghan, P., Goldhaber, S.,
Dennis, J. M., Bacmeister, J. T., Eaton, B. E., Zarzycki, C. M., Taylor,
M. A., Ullrich, P. A., Dubos, T., Gettelman, A., Neale, R. B., Dobbins, B.,
Reed, K. A., Hannay, C., Medeiros, B., Benedict, J. J., and Tribbia, J. J.:
NCAR Release of CAM-SE in CESM2.0: A Reformulation of the Spectral
Element Dynamical Core in Dry-Mass Vertical Coordinates With Comprehensive
Treatment of Condensates and Energy, J. Adv. Model. Earth Sy., 10,
1537–1570, https://doi.org/10.1029/2017MS001257,
2018. a, b, c
Lin, G., Wan, H., Zhang, K., Qian, Y., and Ghan, S. J.: Can nudging be used to
quantify model sensitivities in precipitation and cloud forcing?, J.
Adva. Model. Earth Sy., 8, 1073–1091,
https://doi.org/10.1002/2016MS000659,
2016. a
Lin, S.-J.: A “Vertically Lagrangian” Finite-Volume Dynamical Core for Global
Models, Mon. Weather Rev., 132, 2293–2307,
https://doi.org/10.1175/1520-0493(2004)132<2293:AVLFDC>2.0.CO;2, 2004. a
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
Ma, H.-Y., Xie, S., Boyle, J. S., Klein, S. A., and Zhang, Y.: Metrics and
Diagnostics for Precipitation-Related Processes in Climate Model Short-Range
Hindcasts, J. Climate, 26, 1516–1534, https://doi.org/10.1175/JCLI-D-12-00235.1,
2013. a
Mishra, S., Srinivasan, J., and Nanjundiah, R.: The Impact of the Time Step on
the Intensity of ITCZ in an Aquaplanet GCM, Mon. Weather Rev., 136, 4077–4091,
https://doi.org/10.1175/2008MWR2478.1, 2008. a
Mishra, S. K. and Sahany, S.: Effects of time step size on the simulation of
tropical climate in NCAR–CAM 3, Clim. Dynam., 37, 689–704,
https://doi.org/10.1007/s00382-011-0994-4, 2011. a
Mishra, S. K. and Srinivasan, J.: Sensitivity of the simulated precipitation to changes in convective relaxation time scale, Ann. Geophys., 28, 1827–1846, https://doi.org/10.5194/angeo-28-1827-2010, 2010. 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., 102, 16663–16682,
https://doi.org/10.1029/97JD00237, 1997. a
Morrison, H. and Gettelman, A.: A New Two-Moment Bulk Stratiform Cloud
Microphysics Scheme in the Community Atmosphere Model, Version 3 (CAM3).
Part I: Description and Numerical Tests, J. Climate, 21, 3642–3659,
https://doi.org/10.1175/2008JCLI2105.1, 2008a. a
Morrison, H. and Gettelman, A.: A new two-moment bulk stratiform cloud
microphysics scheme in the NCAR Community Atmosphere Model (CAM3), Part
I: Description and numerical tests, J. Climate, 21, 3642–3659,
https://doi.org/10.1175/2008JCLI2105.1, 2008b. a
Neale, R. B., Richter, J. H., and Jochum, M.: The impact of convection on
ENSO: From a delayed oscillator to a series of events, J. Climate, 21,
5904–5924, 2008. a
Oleson, K., Lawrence, D., Gordon, B. B., Drewniak, B., Huang, M., Koven, C. D.,
Levis, S., Li, F., Riley, W. J., Subin, Z. M., Swenson, S. C., Thornton,
P. E., Bozbiyik, A., Fisher, R., Heald, C. L., Kluzek, E., Lamarque, J.,
Lawrence, P. J., R., L. L., Sacks, W., Sun, Y., Tang, J., and Yang, Z.:
Technical description of version 4.5 of the Community Land Model (CLM),
NCAR technical note ncar/tn-503+str, NCAR, https://doi.org/10.5065/D6RR1W7M, 2013. a
Park, S. and Bretherton, C. S.: The University of Washington shallow convection
and moist turbulence schemes and their impact on climate simulations with the
Community Atmosphere Model, J. Climate, 22, 3449–3469,
https://doi.org/10.1175/2008JCLI2557.1, 2009. a
Park, S., Bretherton, C. S., and Rasch, P. J.: Integrating Cloud Processes in
the Community Atmosphere Model, Version 5., J. Climate, 27, 6821–6855,
https://doi.org/10.1175/JCLI-D-14-00087.1, 2014. a
Qian, Y., Yan, H., Hou, Z., Johannesson, G., Klein, S., Lucas, D., Neale, R.,
Rasch, P., Swiler, L., Tannahill, J., Wang, H., Wang, M., and Zhao, C.:
Parametric sensitivity analysis of precipitation at global and local scales
in the Community Atmosphere Model CAM5, J. Adv. Model. Earth
Sy., 7, 382–411, https://doi.org/10.1002/2014MS000354,
2015. a
Qian, Y., Wan, H., Yang, B., Golaz, J.-C., Harrop, B., Hou, Z., Larson, V. E.,
Leung, L. R., Lin, G., Lin, W., Ma, P.-L., Ma, H.-Y., Rasch, P., Singh, B.,
Wang, H., Xie, S., and Zhang, K.: Parametric Sensitivity and Uncertainty
Quantification in the Version 1 of E3SM Atmosphere Model Based on Short
Perturbed Parameter Ensemble Simulations, J. Geophys. Res.-Atmos., 123, 13046–13073, https://doi.org/10.1029/2018JD028927,
2018. a
Rasch, P. J., Xie, S., Ma, P.-L., Lin, W., Wang, H., Tang, Q., Burrows, S. M.,
Caldwell, P., Zhang, K., Easter, R. C., Cameron-Smith, P., Singh, B., Wan,
H., Golaz, J.-C., Harrop, B. E., Roesler, E., Bacmeister, J., Larson, V. E.,
Evans, K. J., Qian, Y., Taylor, M., Leung, L. R., Zhang, Y., Brent, L.,
Branstetter, M., Hannay, C., Mahajan, S., Mametjanov, A., Neale, R., Richter,
J. H., Yoon, J.-H., Zender, C. S., Bader, D., Flanner, M., Foucar, J. G.,
Jacob, R., Keen, N., Klein, S. A., Liu, X., Salinger, A., Shrivastava, M.,
and Yang, Y.: An Overview of the Atmospheric Component of the Energy Exascale
Earth System Model, J. Adv. Model. Earth Sy., 11,
2377–2411, https://doi.org/10.1029/2019MS001629,
2019. a, b, c, d, e
Richter, J. H. and Rasch, P. J.: Effects of Convective Momentum Transport on
the Atmospheric Circulation in the Community Atmosphere Model, Version 3,
J. Climate, 21, 1487–1499, https://doi.org/10.5194/gmd-10-2221-2017, 2008. a
Santos, S. P., Bretherton, C., and Caldwell, P.: Cloud Process Coupling and
Time Integration in the E3SM Atmosphere Model, Earth and Space Science Open
Archive, p. 23, https://doi.org/10.1002/essoar.10504538.1,
2020a. a, b
Santos, S. P., Caldwell, P. M., and Bretherton, C. S.: Numerically Relevant Timescales in the MG2 Microphysics Model, J. Adv. Model. Earth Sy., 12, e2019MS001972, https://doi.org/10.1029/2019MS001972, 2020b. a
Spiteri, R. J. and Ruuth, S. J.: A New Class of Optimal High-Order
Strong-Stability-Preserving Time Discretization Methods, SIAM J. Numer.
Anal., 40, 469–491, https://doi.org/10.1137/S0036142901389025, 2002. a
Sportisse, B.: An Analysis of Operator Splitting Techniques in the Stiff Case,
J. Comput. Phys., 161, 140–168,
https://doi.org/10.1006/jcph.2000.6495,
2000. a
Sun, J., Zhang, K., Wan, H., Ma, P.-L., Tang, Q., and Zhang, S.: Impact of
Nudging Strategy on the Climate Representativeness and Hindcast Skill of
Constrained EAMv1 Simulations, J. Adv. Model. Earth Sy., 11, 3911–3933,
https://doi.org/10.1029/2019MS001831, 2019. a
Taylor, M. A., Cyr, A. S., and Fournier, A.: A Non-oscillatory Advection
Operator for the Compatible Spectral Element Method,, International
Conference on Computational Science, Springer, Berlin, Heidelberg,
https://doi.org/10.1007/978-3-642-01973-9_31, 2009. a
Wan, H., Rasch, P. J., Zhang, K., Kazil, J., and Leung, L. R.: Numerical issues associated with compensating and competing processes in climate models: an example from ECHAM-HAM, Geosci. Model Dev., 6, 861–874, https://doi.org/10.5194/gmd-6-861-2013, 2013. a
Wan, H., Rasch, P. J., Zhang, K., Qian, Y., Yan, H., and Zhao, C.: Short ensembles: an efficient method for discerning climate-relevant sensitivities in atmospheric general circulation models, Geosci. Model Dev., 7, 1961–1977, https://doi.org/10.5194/gmd-7-1961-2014, 2014. 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, R. L., 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, Y., Liu, X., Hoose, C., and Wang, B.: Different contact angle distributions for heterogeneous ice nucleation in the Community Atmospheric Model version 5, Atmos. Chem. Phys., 14, 10411–10430, https://doi.org/10.5194/acp-14-10411-2014, 2014. a
Williamson, D. L.: Time-Split versus Process-Split Coupling of
Parameterizations and Dynamical Core, Mon. Weather Rev., 130,
2024–2041,
https://doi.org/10.1175/1520-0493(2002)130<2024:TSVPSC>2.0.CO;2,
2002. a
Xie, S., Ma, H.-Y., Boyle, J. S., Klein, S. A., and Zhang, Y.: On the
Correspondence between Short- and Long-Time-Scale Systematic Errors in
CAM4/CAM5 for the Year of Tropical Convection, J. Climate, 25,
7937–7955, https://doi.org/10.1175/JCLI-D-12-00134.1,
2012. a
Xie, S., Lin, W., Rasch, P. J., Ma, P., Neale, R., Larson, V. E., Qian, Y.,
Bogenschutz, P. A., Caldwell, P., Cameron-Smith, P., Golaz, J., Mahajan, S.,
Singh, B., Tang, Q., Wang, H., Yoon, J., Zhang, K., and Zhang, Y.:
Understanding cloud and convective characteristics in version 1 of the E3SM
atmosphere model, J. Adv. Model. Earth Sy., 10, 2618–2644,
https://doi.org/10.1029/2018MS001350, 2018. a, b, c
Yang, B., Qian, Y., Lin, G., Leung, L. R., Rasch, P. J., Zhang, G. J.,
McFarlane, S. A., Zhao, C., Zhang, Y., Wang, H., Wang, M., and Liu, X.:
Uncertainty quantification and parameter tuning in the CAM5 Zhang-McFarlane
convection scheme and impact of improved convection on the global circulation
and climate, J. Geophys. Res.-Atmos., 118, 395–415,
https://doi.org/10.1029/2012JD018213,
2013. a
Yu, S. and Pritchard, M. S.: The effect of large-scale model time step and
multiscale coupling frequency on cloud climatology, vertical structure, and
rainfall extremes in a superparameterized GCM, J. Adv. Model. Earth Sy.,
7, 1977–1996, https://doi.org/10.1002/2015MS000493, 2015. a
Zhang, G. J. and McFarlane, N. A.: Sensitivity of climate simulations to the
parameterization of cumulus convection in the Canadian Climate Centre general
circulation model, Atmos. Ocean, 33, 407–446,
https://doi.org/10.1080/07055900.1995.9649539, 1995. a
Zhang, K., Wan, H., Liu, X., Ghan, S. J., Kooperman, G. J., Ma, P.-L., Rasch, P. J., Neubauer, D., and Lohmann, U.: Technical Note: On the use of nudging for aerosol–climate model intercomparison studies, Atmos. Chem. Phys., 14, 8631–8645, https://doi.org/10.5194/acp-14-8631-2014, 2014. a
Zhang, K., Rasch, P. J., Taylor, M. A., Wan, H., Leung, R., Ma, P.-L., Golaz, J.-C., Wolfe, J., Lin, W., Singh, B., Burrows, S., Yoon, J.-H., Wang, H., Qian, Y., Tang, Q., Caldwell, P., and Xie, S.: Impact of numerical choices on water conservation in the E3SM Atmosphere Model version 1 (EAMv1), Geosci. Model Dev., 11, 1971–1988, https://doi.org/10.5194/gmd-11-1971-2018, 2018. a, b, c
Zhang, S. and Wan, H.: EAM output from time step sensitivity experiments [Data set], Zenodo, https://doi.org/10.5281/zenodo.4668866, 2021. a
Zwiers, F. W. and von Storch, H.: Taking Serial Correlation into Account in
Tests of the Mean, J. Climate, 8, 336–351,
https://doi.org/10.1175/1520-0442(1995)008<0336:TSCIAI>2.0.CO;2,
1995. a
Short summary
Numerical models used in weather and climate research and prediction unavoidably contain numerical errors resulting from temporal discretization, and the impact of such errors can be substantial. Complex process interactions often make it difficult to pinpoint the exact sources of such errors. This study uses a series of sensitivity experiments to identify components in a global atmosphere model that are responsible for time step sensitivities in various cloud regimes.
Numerical models used in weather and climate research and prediction unavoidably contain...
