Data Assimilation for Climate Research: Model Parameter Estimation of Large‐Scale Condensation Scheme. Issue 1 (6th January 2020)

• Record Type: Journal Article
• Authors: Kotsuki, Shunji; Sato, Yousuke; Miyoshi, Takemasa
• Is Part Of: Journal of geophysical research. Volume 125:Issue 1(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-01-06
• Subjects: data assimilation -- parameter estimation -- global climate model -- large‐scale condensation -- radiation -- liquid water path; Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1029/2019JD031304
• Languages: English
• ISSNs: 2169-897X

Abstract

Abstract: This study proposes using data assimilation (DA) for climate research as a tool for optimizing model parameters objectively. Mitigating radiation bias is very important for climate change assessments with general circulation models. With the Nonhydrostatic ICosahedral Atmospheric Model (NICAM), this study estimated an autoconversion parameter in a large‐scale condensation scheme. We investigated two approaches to reducing radiation bias: examining useful satellite observations for parameter estimation and exploring the advantages of estimating spatially varying parameters. The parameter estimation accelerated autoconversion speed when we used liquid water path, outgoing longwave radiation, or outgoing shortwave radiation (OSR). Accelerated autoconversion reduced clouds and mitigated overestimated OSR bias of the NICAM. An ensemble‐based DA with horizontal localization can estimate spatially varying parameters. When liquid water path was used, the local parameter estimation resulted in better cloud representations and improved OSR bias in regions where shallow clouds are dominant. Key Points: This study proposes using data assimilation for climate research as a tool for optimizing model parameters objectively When liquid water path or outgoing radiation was used, parameter estimation reduced clouds and mitigated radiation biases of a GCM Estimating spatially varying parameters was beneficial for improving cloud representations in regions where shallow clouds are dominant