Better calibration of cloud parameterizations and subgrid effects increases the fidelity of the E3SM Atmosphere Model version 1
Abstract
Realistic simulation of the Earth's mean-state climate remains a major challenge, and yet it is crucial for predicting the climate system in transition. Deficiencies in models' process representations, propagation of errors from one process to another, and associated compensating errors can often confound the interpretation and improvement of model simulations. These errors and biases can also lead to unrealistic climate projections and incorrect attribution of the physical mechanisms governing past and future climate change. Here we show that a significantly improved global atmospheric simulation can be achieved by focusing on the realism of process assumptions in cloud calibration and subgrid effects using the Energy Exascale Earth System Model (E3SM) Atmosphere Model version 1 (EAMv1). The calibration of clouds and subgrid effects informed by our understanding of physical mechanisms leads to significant improvements in clouds and precipitation climatology, reducing common and long-standing biases across cloud regimes in the model. The improved cloud fidelity in turn reduces biases in other aspects of the system. Furthermore, even though the recalibration does not change the global mean aerosol and total anthropogenic effective radiative forcings (ERFs), the sensitivity of clouds, precipitation, and surface temperature to aerosol perturbations is significantly reduced. This suggests that it ismore »
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- Publication Date:
- Research Org.:
- Brookhaven National Laboratory (BNL), Upton, NY (United States); Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Atmospheric Radiation Measurement (ARM) Data Center; Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
- Sponsoring Org.:
- USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC), Biological and Environmental Research (BER)
- Contributing Org.:
- Pacific Northwest National Laboratory (PNNL); Brookhaven National Laboratory (BNL); Argonne National Laboratory (ANL); Oak Ridge National Laboratory (ORNL)
- OSTI Identifier:
- 1861923
- Alternate Identifier(s):
- OSTI ID: 1846019; OSTI ID: 1861964; OSTI ID: 1864504; OSTI ID: 1864730
- Report Number(s):
- BNL-222782-2022-JAAM; LLNL-JRNL-826259; PNNL-SA-166161
Journal ID: ISSN 1991-9603
- Grant/Contract Number:
- 65814; 74358; 66187; SCW1453; 57131; AC52-07NA27344.; NA0003525; AC52-07NA27344; AC05-76RL01830; SC0012704; AC02-05CH11231
- Resource Type:
- Published Article
- Journal Name:
- Geoscientific Model Development (Online)
- Additional Journal Information:
- Journal Name: Geoscientific Model Development (Online) Journal Volume: 15 Journal Issue: 7; Journal ID: ISSN 1991-9603
- Publisher:
- Copernicus GmbH
- Country of Publication:
- Germany
- Language:
- English
- Subject:
- 58 GEOSCIENCES; 54 ENVIRONMENTAL SCIENCES
Citation Formats
Ma, Po-Lun, Harrop, Bryce E., Larson, Vincent E., Neale, Richard B., Gettelman, Andrew, Morrison, Hugh, Wang, Hailong, Zhang, Kai, Klein, Stephen A., Zelinka, Mark D., Zhang, Yuying, Qian, Yun, Yoon, Jin-Ho, Jones, Christopher R., Huang, Meng, Tai, Sheng-Lun, Singh, Balwinder, Bogenschutz, Peter A., Zheng, Xue, Lin, Wuyin, Quaas, Johannes, Chepfer, Hélène, Brunke, Michael A., Zeng, Xubin, Mülmenstädt, Johannes, Hagos, Samson, Zhang, Zhibo, Song, Hua, Liu, Xiaohong, Pritchard, Michael S., Wan, Hui, Wang, Jingyu, Tang, Qi, Caldwell, Peter M., Fan, Jiwen, Berg, Larry K., Fast, Jerome D., Taylor, Mark A., Golaz, Jean-Christophe, Xie, Shaocheng, Rasch, Philip J., and Leung, L. Ruby. Better calibration of cloud parameterizations and subgrid effects increases the fidelity of the E3SM Atmosphere Model version 1. Germany: N. p., 2022.
Web. doi:10.5194/gmd-15-2881-2022.
Ma, Po-Lun, Harrop, Bryce E., Larson, Vincent E., Neale, Richard B., Gettelman, Andrew, Morrison, Hugh, Wang, Hailong, Zhang, Kai, Klein, Stephen A., Zelinka, Mark D., Zhang, Yuying, Qian, Yun, Yoon, Jin-Ho, Jones, Christopher R., Huang, Meng, Tai, Sheng-Lun, Singh, Balwinder, Bogenschutz, Peter A., Zheng, Xue, Lin, Wuyin, Quaas, Johannes, Chepfer, Hélène, Brunke, Michael A., Zeng, Xubin, Mülmenstädt, Johannes, Hagos, Samson, Zhang, Zhibo, Song, Hua, Liu, Xiaohong, Pritchard, Michael S., Wan, Hui, Wang, Jingyu, Tang, Qi, Caldwell, Peter M., Fan, Jiwen, Berg, Larry K., Fast, Jerome D., Taylor, Mark A., Golaz, Jean-Christophe, Xie, Shaocheng, Rasch, Philip J., & Leung, L. Ruby. Better calibration of cloud parameterizations and subgrid effects increases the fidelity of the E3SM Atmosphere Model version 1. Germany. https://doi.org/10.5194/gmd-15-2881-2022
Ma, Po-Lun, Harrop, Bryce E., Larson, Vincent E., Neale, Richard B., Gettelman, Andrew, Morrison, Hugh, Wang, Hailong, Zhang, Kai, Klein, Stephen A., Zelinka, Mark D., Zhang, Yuying, Qian, Yun, Yoon, Jin-Ho, Jones, Christopher R., Huang, Meng, Tai, Sheng-Lun, Singh, Balwinder, Bogenschutz, Peter A., Zheng, Xue, Lin, Wuyin, Quaas, Johannes, Chepfer, Hélène, Brunke, Michael A., Zeng, Xubin, Mülmenstädt, Johannes, Hagos, Samson, Zhang, Zhibo, Song, Hua, Liu, Xiaohong, Pritchard, Michael S., Wan, Hui, Wang, Jingyu, Tang, Qi, Caldwell, Peter M., Fan, Jiwen, Berg, Larry K., Fast, Jerome D., Taylor, Mark A., Golaz, Jean-Christophe, Xie, Shaocheng, Rasch, Philip J., and Leung, L. Ruby. Thu .
"Better calibration of cloud parameterizations and subgrid effects increases the fidelity of the E3SM Atmosphere Model version 1". Germany. https://doi.org/10.5194/gmd-15-2881-2022.
@article{osti_1861923,
title = {Better calibration of cloud parameterizations and subgrid effects increases the fidelity of the E3SM Atmosphere Model version 1},
author = {Ma, Po-Lun and Harrop, Bryce E. and Larson, Vincent E. and Neale, Richard B. and Gettelman, Andrew and Morrison, Hugh and Wang, Hailong and Zhang, Kai and Klein, Stephen A. and Zelinka, Mark D. and Zhang, Yuying and Qian, Yun and Yoon, Jin-Ho and Jones, Christopher R. and Huang, Meng and Tai, Sheng-Lun and Singh, Balwinder and Bogenschutz, Peter A. and Zheng, Xue and Lin, Wuyin and Quaas, Johannes and Chepfer, Hélène and Brunke, Michael A. and Zeng, Xubin and Mülmenstädt, Johannes and Hagos, Samson and Zhang, Zhibo and Song, Hua and Liu, Xiaohong and Pritchard, Michael S. and Wan, Hui and Wang, Jingyu and Tang, Qi and Caldwell, Peter M. and Fan, Jiwen and Berg, Larry K. and Fast, Jerome D. and Taylor, Mark A. and Golaz, Jean-Christophe and Xie, Shaocheng and Rasch, Philip J. and Leung, L. Ruby},
abstractNote = {Realistic simulation of the Earth's mean-state climate remains a major challenge, and yet it is crucial for predicting the climate system in transition. Deficiencies in models' process representations, propagation of errors from one process to another, and associated compensating errors can often confound the interpretation and improvement of model simulations. These errors and biases can also lead to unrealistic climate projections and incorrect attribution of the physical mechanisms governing past and future climate change. Here we show that a significantly improved global atmospheric simulation can be achieved by focusing on the realism of process assumptions in cloud calibration and subgrid effects using the Energy Exascale Earth System Model (E3SM) Atmosphere Model version 1 (EAMv1). The calibration of clouds and subgrid effects informed by our understanding of physical mechanisms leads to significant improvements in clouds and precipitation climatology, reducing common and long-standing biases across cloud regimes in the model. The improved cloud fidelity in turn reduces biases in other aspects of the system. Furthermore, even though the recalibration does not change the global mean aerosol and total anthropogenic effective radiative forcings (ERFs), the sensitivity of clouds, precipitation, and surface temperature to aerosol perturbations is significantly reduced. This suggests that it is possible to achieve improvements to the historical evolution of surface temperature over EAMv1 and that precise knowledge of global mean ERFs is not enough to constrain historical or future climate change. Cloud feedbacks are also significantly reduced in the recalibrated model, suggesting that there would be a lower climate sensitivity when it is run as part of the fully coupled E3SM. This study also compares results from incremental changes to cloud microphysics, turbulent mixing, deep convection, and subgrid effects to understand how assumptions in the representation of these processes affect different aspects of the simulated atmosphere as well as its response to forcings. We conclude that the spectral composition and geographical distribution of the ERFs and cloud feedback, as well as the fidelity of the simulated base climate state, are important for constraining the climate in the past and future.},
doi = {10.5194/gmd-15-2881-2022},
journal = {Geoscientific Model Development (Online)},
number = 7,
volume = 15,
place = {Germany},
year = {Thu Apr 07 00:00:00 EDT 2022},
month = {Thu Apr 07 00:00:00 EDT 2022}
}
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