Eastern equatorial Pacific sea surface temperature annual cycle in the Kiel Climate Model: simulation benefits from enhancing atmospheric resolution
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A long-standing difficulty of climate models is to capture the annual cycle (AC) of eastern equatorial Pacific (EEP) sea surface temperature (SST). In this study, we first examine the EEP SST AC in a set of integrations of the coupled Kiel Climate Model, in which only atmosphere model resolution differs. When employing coarse horizontal and vertical atmospheric resolution, significant biases in the EEP SST AC are observed. These are reflected in an erroneous timing of the cold tongue’s onset and termination as well as in an underestimation of the boreal spring warming amplitude. A large portion of these biases are linked to a wrong simulation of zonal surface winds, which can be traced back to precipitation biases on both sides of the equator and an erroneous low-level atmospheric circulation over land. Part of the SST biases also is related to shortwave radiation biases related to cloud cover biases. Both wind and cloud cover biases are inherent to the atmospheric component, as shown by companion uncoupled atmosphere model integrations forced by observed SSTs. Enhancing atmosphere model resolution, horizontal and vertical, markedly reduces zonal wind and cloud cover biases in coupled as well as uncoupled mode and generally improves simulation of the EEP SST AC. Enhanced atmospheric resolution reduces convection biases and improves simulation of surface winds over land. Analysis of a subset of models from the Coupled Model Intercomparison Project phase 5 (CMIP5) reveals that in these models, very similar mechanisms are at work in driving EEP SST AC biases.
KeywordsAnnual cycle SST Equatorial Pacific Kiel Climate Model
This work was supported by the German Ministry of Education and Research (BMBF) grant SACUS (03G0837A) and EU FP7/2007–2013 under Grant agreement no. 603521, project PREFACE, and SFB 754 “Climate-Biochemistry Interactions in the tropical Ocean”. We thank two anonymous reviewers for helpful comments and feedbacks on this work. The climate model integrations were performed at the Computing Centre of Kiel University. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups (listed in Table 2 of this paper) for producing and making available their model output. For CMIP the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals.
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