Better calibration of cloud parameterizations and subgrid effects increases the fidelity of E3SM Atmosphere Model version 1
- 1Pacific Northwest National Laboratory, Richland, Washington, USA
- 2Department of Mathematical Sciences, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin, USA
- 3National Center for Atmospheric Research, Boulder, Colorado, USA
- 4Lawrence Livermore National Laboratory, Livermore, California, USA
- 5School of Earth Sciences and Environmental Engineering, Gwangju Institute of Science and Technology, Gwangju, South Korea
- 6Brookhaven National Laboratory, Upton, New York, USA
- 7Institute for Meteorology, Universität Leipzig, Leipzig, Germany
- 8LMD/IPSL, Sorbonne Université, Ecole Polytechnique, CNRS, Paris, France
- 9Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, AZ, USA
- 10Department of Physics, University of Maryland, Baltimore County, Baltimore, MD, USA
- 11Science Systems and Applications, Inc., Lanham, Maryland, USA
- 12Department of Atmospheric Sciences, Texas A&M University, College Station, TX, USA
- 13Sandia National Laboratory, Albuquerque, NM, USA
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 as well as incorrect attribution of the physical mechanisms governing the 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 longstanding 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 running 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.
Po-Lun Ma et al.
Status: final response (author comments only)
Po-Lun Ma et al.
Po-Lun Ma et al.
Viewed (geographical distribution)