Surface Temperature Probability Distributions in the NARCCAP Hindcast Experiment: Evaluation Methodology, Metrics, and Results

Author:

Loikith Paul C.1,Waliser Duane E.2,Lee Huikyo1,Kim Jinwon3,Neelin J. David4,Lintner Benjamin R.5,McGinnis Seth6,Mattmann Chris A.2,Mearns Linda O.6

Affiliation:

1. Jet Propulsion Laboratory/California Institute of Technology, Pasadena, California

2. Jet Propulsion Laboratory/California Institute of Technology, Pasadena, and Joint Institute for Regional Earth System Science and Engineering, University of Los Angeles, Los Angeles, California

3. Joint Institute for Regional Earth System Science and Engineering, University of Los Angeles, and Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, Los Angeles, California

4. Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, California

5. Department of Environmental Sciences, Rutgers, The State University of New Jersey, New Brunswick, New Jersey

6. Institute for Mathematical Applications to the Geosciences, National Center for Atmospheric Research, Boulder, Colorado

Abstract

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, the RCMs 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.

Publisher

American Meteorological Society

Subject

Atmospheric Science

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3