"Evaluation 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" by Jinwon Kim

Article

Evaluation 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

Journal of Climate (2013)

Jinwon Kim, University of California, Los Angeles
Duane Waliser, University of California, Los Angeles
Chris A. Mattmann, California Institute of Technology
Linda O. Mearns, National Center for Atmospheric Research
Cameron E. Goodale, Jet Propulsion Laboratory, California Institute of Technology
Andrew F. Hart, Jet Propulsion Laboratory, California Institute of Technology
Dan J. Crichton, Jet Propulsion Laboratory, California Institute of Technology
Seth McGinnis, National Center for Atmospheric Research
Huikyo Lee, Jet Propulsion Laboratory, California Institute of Technology
Paul C. Loikith, Portland State University
Maziyar Boustani, Jet Propulsion Laboratory, California Institute of Technology

Download Find in your library

Abstract
Surface 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. AllRCMs 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.

Keywords

Ocean temperature,
Climatic changes,
Atmospheric models,
Climatology

Disciplines

Publication Date

August, 2013

DOI

10.1175/JCLI-D-12-00452.1

Publisher Statement

At the time of publication Paul C. Loikith was affiliated with the NASA Jet Propulsion Laboratory.

To the best of our knowledge, one or more of the authors of this work were federal employees at the time of writing.

Citation Information

Kim, J., Waliser, D. E., Mattmann, C. A., Mearns, L. O., Goodale, C. E., Hart, A. F., ... & Boustani, M. (2013). Evaluation 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. Journal of Climate, 26(15), 5698-5715.