Journal Article
Geoscientific Model Development, vol. 15, iss. 7, pp. 2881-2916, 2022
Authors
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, L. Ruby Leung
Abstract
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.
