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Article
Surface Temperature Probability Distributions in the NARCCAP Hindcast Experiment: Evaluation Methodology, Metrics, and Results
Journal of Climate
  • Paul C. Loikith, Portland State University
  • Duane E. Waliser, NASA Jet Propulsion Laboratory
  • Huikyo Lee, NASA Jet Propulsion Laboratory
  • Jinwon Kim, University of California, Los Angeles
  • J. David Neelin, University of California, Los Angeles
  • Benjamin R. Lintner, Rutgers University
  • Seth McGinnis, National Center for Atmospheric Research
  • Chris A. Mattmann, NASA Jet Propulsion Laboratory
  • Linda O. Mears, National Center for Atmospheric Research
Document Type
Article
Publication Date
2-4-2015
Subjects
  • Surface temperature,
  • Earth temperature,
  • Atmospheric models,
  • Climatic changes -- Mathematical models
Abstract

Methodology is developed and applied to evaluate the characteristics of daily surface temperature distributions in a six-member regional climate model (RCM) hindcast experiment conducted as part of the North American Regional Climate Change Assessment Program (NARCCAP). A surface temperature dataset combining gridded station observations and reanalysis is employed as the primary reference. Temperature biases are documented across the distribution, focusing on the median and tails. Temperature variance is generally higher in the RCMs than reference, while skewness is reasonably simulated in winter over the entire domain and over the western United States and Canada in summer. Substantial differences in skewness exist over the southern and eastern portions of the domain in summer. Four examples with observed long-tailed probability distribution functions (PDFs) are selected for model comparison. Long cold tails in the winter are simulated with high fidelity for Seattle, Washington, and Chicago, Illinois. In summer, theRCMs are unable to capture the distribution width and long warm tails for the coastal location of Los Angeles, California, while long cold tails are poorly realized for Houston, Texas. The evaluation results are repeated using two additional reanalysis products adjusted by station observations and two standard reanalysis products to assess the impact of observational uncertainty. Results are robust when compared with those obtained using the adjusted reanalysis products as reference, while larger uncertainties are introduced when standard reanalysis is employed as reference. Model biases identified in this work will allow for further investigation into associated mechanisms and implications for future simulations of temperature extremes.

Description

At the time of publication Paul C. Loikith was affiliated with the NASA Jet Propulsion Laboratory. This work was authored as part of the Contributor's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.

DOI
10.1175/JCLI-D-13-00457.1
Persistent Identifier
http://archives.pdx.edu/ds/psu/16990
Citation Information
Loikith, P. C., Waliser, D. E., Lee, H., Kim, J., Neelin, J. D., Lintner, B. R., ... & Mearns, L. O. (2015). Surface Temperature Probability Distributions in the NARCCAP Hindcast Experiment: Evaluation Methodology, Metrics, and Results. Journal of Climate, 28(3), 978-997.