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Effect of coupled global climate models sea surface temperature biases on simulated climate of the western United States
Author(s) -
Mejia John F.,
Koračin Darko,
Wilcox Eric M.
Publication year - 2018
Publication title -
international journal of climatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.58
H-Index - 166
eISSN - 1097-0088
pISSN - 0899-8418
DOI - 10.1002/joc.5817
Subject(s) - climatology , environmental science , downscaling , climate model , precipitation , gcm transcription factors , coupled model intercomparison project , ensemble average , sea surface temperature , climate change , general circulation model , atmospheric sciences , meteorology , geography , geology , oceanography
To diagnose the influence of sea surface temperature (SST) biases on temperature and precipitation patterns in the western United States, we analysed atmospheric and coupled global climate model (GCM) simulated output from the Coupled and Atmospheric Model Intercomparison Project versions 3 and 5 (CMIP3, AMIP3, CMIP5, and AMIP5). We further analyse the impact of SST biases in regional climate modelling simulations. CMIP3 and CMIP5 multi‐model ensembles show systematic warm SST biases offshore of California and the Baja California Peninsula (Baja) region, with ensemble mean SST biases of the order of ~3–5 °C. Throughout the western United States, 75% of all models in CMIP3 and CMIP5 exhibit wet precipitation biases and corresponding cold biases in surface temperature. The CMIP5 ensemble shows on average a stronger and more consistent relationship between Baja SST biases and precipitation over the west compared to the CMIP3 ensemble. We attempted to isolate the atmospheric response to regional SST biases using a regional climate model (RCM) based on the Weather Research and Forecasting model with a 36 km grid size. The RCM was driven with the CMIP3‐CCSM3 as boundary conditions with and without corrections of simulated SSTs. Results from RCM simulations further confirm that SST biases impact climate regionally and propagate over the western United States and can explain up to 80% of wet precipitation biases. Our regional GCM comparison and RCM experiment assess the robustness of model estimates of climate mean states and constitute an often neglected prerequisite for characterizing how errors transfer from GCM to regional downscaling modelling frameworks and how they could potentially affect downscaling application and impact studies.