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Journal Articles Geoscientific Model Development Year : 2022

Better calibration of cloud parameterizations and subgrid effects increases the fidelity of the E3SM Atmosphere Model version 1

Po-Lun Ma
  • Function : Author
Bryce E. Harrop
  • Function : Author
Vincent E. Larson
  • Function : Author
Richard B. Neale
  • Function : Author
Andrew Gettelman
  • Function : Author
Hugh Morrison
  • Function : Author
Hailong Wang
  • Function : Author
Kai Zhang
  • Function : Author
Stephen A. Klein
  • Function : Author
Mark D. Zelinka
  • Function : Author
Yuying Zhang
  • Function : Author
Yun Qian
  • Function : Author
Jin-Ho Yoon
  • Function : Author
Christopher R. Jones
  • Function : Author
Meng Huang
  • Function : Author
Sheng-Lun Tai
  • Function : Author
Balwinder Singh
  • Function : Author
Peter A. Bogenschutz
  • Function : Author
Xue Zheng
  • Function : Author
Wuyin Lin
  • Function : Author
Johannes Quaas
  • Function : Author
Michael A. Brunke
  • Function : Author
Xubin Zeng
  • Function : Author
Johannes Mülmenstädt
  • Function : Author
Samson Hagos
  • Function : Author
Zhibo Zhang
  • Function : Author
Hua Song
  • Function : Author
Xiaohong Liu
  • Function : Author
Michael S. Pritchard
  • Function : Author
Hui Wan
  • Function : Author
Jingyu Wang
  • Function : Author
Qi Tang
  • Function : Author
Peter M. Caldwell
  • Function : Author
Jiwen Fan
  • Function : Author
Larry K. Berg
  • Function : Author
Jerome D. Fast
  • Function : Author
Mark A. Taylor
  • Function : Author
Jean-Christophe Golaz
  • Function : Author
Shaocheng Xie
  • Function : Author
Philip J. Rasch
  • Function : Author
L. Ruby Leung
  • Function : Author

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 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.
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insu-03726910 , version 1 (22-07-2022)

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Po-Lun Ma, Bryce E. Harrop, Vincent E. Larson, Richard B. Neale, Andrew Gettelman, et al.. Better calibration of cloud parameterizations and subgrid effects increases the fidelity of the E3SM Atmosphere Model version 1. Geoscientific Model Development, 2022, 15, pp.2881-2916. ⟨10.5194/gmd-15-2881-2022⟩. ⟨insu-03726910⟩
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