Biological and Environmental Research - Earth and Environmental System Sciences
Earth and Environmental System Modeling
21 January 2021

A Better Way to Gain Insights into Climate Model Moist Process Errors

Diurnal cycle of cloud fraction composites (May-August 1997-2007)
Science

A multi-year short-range hindcast experiment and its experiment design are presented for better evaluation of atmospheric moist processes in climate models from diurnal to interannual time scales to facilitate model development.

Impact

This multi-year hindcast experiment provides a new opportunity to address several modeling issues associated with moist processes because the phenomena of interests are either interannual climate variability or only happen a few times in a given year, and thus multi-years hindcasts can be used to robustly quantify the errors associated with these phenomena rather than just mean state evaluation.

Summary

Three processes – the diurnal cycle of clouds during different cloud regimes over the Central U.S., precipitation and diabatic heating associated with the Madden-Julian Oscillation (MJO), and the response of precipitation, surface radiative and heat fluxes, as well as zonal wind stress to sea surface temperature anomalies associated with the El Niño-Southern Oscillation – are evaluated as examples to demonstrate how one can better utilize simulations from the multi-year hindcast experiment to gain insights into model errors and their connection to physical parameterizations or large-scale state. This is achieved by comparing the hindcasts with corresponding long-term observations for periods based on different phenomena. These analyses can only be done through this multi-year hindcast approach to establish robust statistics of the processes under a well-controlled large-scale environment because these phenomena are either interannual climate variability or only happen a few times in a given year (e.g. MJO, or cloud regime types). Furthermore, comparison of hindcasts to the typical simulations in climate mode with the same model allows one to infer what portion of a model’s climate error directly comes from fast errors in the parameterizations of moist processes.

Contact
Hsi-Yen Ma
Lawence Livermore National Laboratory
Funding
Publications
Ma, H, C Zhou, Y Zhang, SA Klein, MD Zelinka, X Zheng, S Xie, W Chen, and C Wu.  2021.  "A multi-year short-range hindcast experiment with CESM1 for evaluating climate model moist processes from diurnal to interannual timescales."  Geoscientific Model Development 14(1): 73-90.  https://doi.org/10.5194/gmd-14-73-2021.