An improved hindcast approach for evaluation and diagnosis of physical processes in global climate models

• Published in: Journal of Advances in Modeling Earth Systems
• Main Authors: Ma, H. -Y., Chuang, C. C., Klein, S. A., Lo, M. -H., Zhang, Y., Xie, S., Zheng, X., Ma, P. -L., Phillips, T. J.
• Language: unknown
• Published: 2022
• Subjects: 54 ENVIRONMENTAL SCIENCES; Sea ice
• Online Access: http://www.osti.gov/servlets/purl/1239491; https://www.osti.gov/biblio/1239491; https://doi.org/10.1002/2015MS000490

Here, we present an improved procedure of generating initial conditions (ICs) for climate model hindcast experiments with specified sea surface temperature and sea ice. The motivation is to minimize errors in the ICs and lead to a better evaluation of atmospheric parameterizations' performance in the hindcast mode. We apply state variables (horizontal velocities, temperature and specific humidity) from the operational analysis/reanalysis for the atmospheric initial states. Without a data assimilation system, we apply a two-step process to obtain other necessary variables to initialize both the atmospheric (e.g., aerosols and clouds) and land models (e.g., soil moisture). First, we nudge only the model horizontal velocities towards operational analysis/reanalysis values, given a 6-hour relaxation time scale, to obtain all necessary variables. Compared to the original strategy in which horizontal velocities, temperature and specific humidity are nudged, the revised approach produces a better representation of initial aerosols and cloud fields which are more consistent and closer to observations and model's preferred climatology. Second, we obtain land ICs from an offline land model simulation forced with observed precipitation, winds, and surface fluxes. This approach produces more realistic soil moisture in the land ICs. With this refined procedure, the simulated precipitation, clouds, radiation, and surface air temperature over land are improved in the Day 2 mean hindcasts. Following this procedure, we propose a “Core” integration suite which provides an easily repeatable test allowing model developers to rapidly assess the impacts of various parameterization changes on the fidelity of modelled cloud-associated processes relative to observations.