Challenges and Prospects for Reducing Coupled Climate Model SST Biases in the Tropics and Pacific

  • 20 February 2017
  • Number of views: 4867

CLIVAR is a national research program that focuses on understanding and predicting climate variability and change on intraseasonal-to-centennial timescales. (Credit: U.S. CLIVAR Program).A new report synthesizes results from a vast amount of research in modeling sea surface temperature, identifying gaps in knowledge and recommending future research avenues.

The U.S. Climate Variability and Predictability Program (CLIVAR) Eastern Tropical Ocean Synthesis Working Group analyzed known problems in climate models of the eastern Atlantic and Pacific, which show warmer sea surface temperatures and more precipitation than what is observed in nature.

The group focused on surface balance components, and regional errors due to clouds, deep convection, winds, and ocean eddies. Based on their assessment, the group proposed recommendations for model improvement. 

This study was supported by the CPO Climate Variability and Predictability program. 

Read the report

Abstract

Well-known problems trouble coupled general circulation models of the eastern Atlantic and Pacific Ocean basins. Model climates are significantly more symmetric about the equator than is observed. Model sea surface temperatures are biased warm south and southeast of the equator, and the atmosphere is too rainy within a band south of the equator. Near-coastal eastern equatorial SSTs are too warm, producing a zonal SST gradient in the Atlantic opposite in sign to that observed. The U.S. Climate Variability and Predictability Program (CLIVAR) Eastern Tropical Ocean Synthesis Working Group (WG) has pursued an updated assessment of coupled model SST biases, focusing on the surface energy balance components, on regional error sources from clouds, deep convection, winds, and ocean eddies; on the sensitivity to model resolution; and on remote impacts. Motivated by the assessment, the WG makes the following recommendations: 1) encourage identification of the specific parameterizations contributing to the biases in individual models, as these can be model dependent; 2) restrict multimodel intercomparisons to specific processes; 3) encourage development of high-resolution coupled models with a concurrent emphasis on parameterization development of finer-scale ocean and atmosphere features, including low clouds; 4) encourage further availability of all surface flux components from buoys, for longer continuous time periods, in persistently cloudy regions; and 5) focus on the eastern basin coastal oceanic upwelling regions, where further opportunities for observational–modeling synergism exist.

 

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