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contributor authorKim, Jinwon
contributor authorWaliser, Duane E.
contributor authorMattmann, Chris A.
contributor authorMearns, Linda O.
contributor authorGoodale, Cameron E.
contributor authorHart, Andrew F.
contributor authorCrichton, Dan J.
contributor authorMcGinnis, Seth
contributor authorLee, Huikyo
contributor authorLoikith, Paul C.
contributor authorBoustani, Maziyar
date accessioned2017-06-09T17:07:02Z
date available2017-06-09T17:07:02Z
date copyright2013/08/01
date issued2013
identifier issn0894-8755
identifier otherams-79635.pdf
identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4222437
description abstracturface air temperature, precipitation, and insolation over the conterminous United States region from the North American Regional Climate Change Assessment Program (NARCCAP) regional climate model (RCM) hindcast study are evaluated using the Jet Propulsion Laboratory (JPL) Regional Climate Model Evaluation System (RCMES). All RCMs reasonably simulate the observed climatology of these variables. RCM skill varies more widely for the magnitude of spatial variability than the pattern. The multimodel ensemble is among the best performers for all these variables. Systematic biases occur across these RCMs for the annual means, with warm biases over the Great Plains (GP) and cold biases in the Atlantic and the Gulf of Mexico (GM) coastal regions. Wet biases in the Pacific Northwest and dry biases in the GM/southern Great Plains also occur in most RCMs. All RCMs suffer problems in simulating summer rainfall in the Arizona?New Mexico region. RCMs generally overestimate surface insolation, especially in the eastern United States. Negative correlation between the biases in insolation and precipitation suggest that these two fields are related, likely via clouds. Systematic variations in biases for regions, seasons, variables, and metrics suggest that the bias correction in applying climate model data to assess the climate impact on various sectors must be performed accordingly. Precipitation evaluation with multiple observations reveals that observational data can be an important source of uncertainties in model evaluation; thus, cross examination of observational data is important for model evaluation.
publisherAmerican Meteorological Society
titleEvaluation of the Surface Climatology over the Conterminous United States in the North American Regional Climate Change Assessment Program Hindcast Experiment Using a Regional Climate Model Evaluation System
typeJournal Paper
journal volume26
journal issue15
journal titleJournal of Climate
identifier doi10.1175/JCLI-D-12-00452.1
journal fristpage5698
journal lastpage5715
treeJournal of Climate:;2013:;volume( 026 ):;issue: 015
contenttypeFulltext


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