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Regional assessment of the parameter‐dependent performance of CAM4 in simulating tropical clouds
Author(s) -
Zhang Yuying,
Xie Shaocheng,
Covey Curt,
Lucas Donald D.,
Gleckler Peter,
Klein Stephen A.,
Tannahill John,
Doutriaux Charles,
Klein Richard
Publication year - 2012
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/2012gl052184
Subject(s) - climate model , environmental science , latin hypercube sampling , climatology , parametrization (atmospheric modeling) , atmospheric sciences , meteorology , gcm transcription factors , atmosphere (unit) , representation (politics) , climate change , mathematics , statistics , general circulation model , physics , radiative transfer , geology , monte carlo method , oceanography , quantum mechanics , politics , political science , law
Representation of clouds remains among the largest uncertainties in climate models and thus climate projections. Clouds vary significantly over different climate regimes and are controlled by different dynamics and physics. Using the cloud simulator output from perturbed‐parameter ensemble climate runs with prescribed monthly sea surface temperature, this study examines the performance of the Community Atmosphere Model version 4 (CAM4) in simulating clouds over different tropical regions. Perturbing 28 selected parameters shows that model performance is quite sensitive to parameter values in different cloud regimes. Carefully adjusting these parameters could lead to a better simulation of clouds over many regions compared with the default model. Latin hypercube runs that pseudo‐randomly sample the 28 parameters simultaneously have much wider spread and more spatial variations than the runs with parameters varied One‐At‐a‐Time (OAT), suggesting the importance of non‐linearities and interactions among parameters associated with different physical processes. The perturbed parameters have a relatively large impact on the mean bias compared to the pattern error.