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Title: Evaluation of CMIP5 Model Precipitation Using PERSIANN-CDR

Abstract

The purpose of this study is to use the PERSIANN–Climate Data Record (PERSIANN-CDR) dataset to evaluate the ability of 32 CMIP5 models in capturing the behavior of daily extreme precipitation estimates globally. The daily long-term historical global PERSIANN-CDR allows for a global investigation of eight precipitation indices that is unattainable with other datasets. Quantitative comparisons against CPC daily gauge; GPCP One-Degree Daily (GPCP1DD); and TRMM 3B42, version 7 (3B42V7), datasets show the credibility of PERSIANN-CDR to be used as the reference data for global evaluation of CMIP5 models. This work uniquely defines different study regions by partitioning global land areas into 25 groups based on continent and climate zone type. Results show that model performance in warm temperate and equatorial regions in capturing daily extreme precipitation behavior is largely mixed in terms of index RMSE and correlation, suggesting that these regions may benefit from weighted model averaging schemes or model selection as opposed to simple model averaging. The three driest climate regions (snow, polar, and arid) exhibit high correlations and low RMSE values when compared against PERSIANN-CDR estimates, with the exceptions of the cold regions showing an inability to capture the 95th and 99th percentile annual total precipitation characteristics. Amore » comprehensive assessment of each model’s performance in each continent–climate zone defined group is provided as a guide for both model developers to target regions and processes that are not yet fully captured in certain climate types, and for climate model output users to be able to select the models and/or the study areas that may best fit their applications of interest.« less

Authors:
 [1];  [2];  [2];  [3];  [2];  [2];  [4];  [2];  [2]
  1. Center for Hydrometeorology and Remote Sensing, Department of Civil and Environmental Engineering, University of California, Irvine, Irvine, California, Nong Lam University, Ho Chi Minh City, Vietnam
  2. Center for Hydrometeorology and Remote Sensing, Department of Civil and Environmental Engineering, University of California, Irvine, Irvine, California
  3. Center for Hydrometeorology and Remote Sensing, Department of Civil and Environmental Engineering, University of California, Irvine, Irvine, California, Department of Hydraulic Engineering, Civil Engineering College, Zhejiang University, Hangzhou, China
  4. State Key Laboratory of Earth Surface Processes and Resource Ecology, College of Global Change and Earth System Science, Beijing Normal University, and Joint Center for Global Change Studies, Beijing, China
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1375072
Grant/Contract Number:  
IA0000018
Resource Type:
Published Article
Journal Name:
Journal of Hydrometeorology
Additional Journal Information:
Journal Name: Journal of Hydrometeorology Journal Volume: 18 Journal Issue: 9; Journal ID: ISSN 1525-755X
Publisher:
American Meteorological Society
Country of Publication:
United States
Language:
English

Citation Formats

Nguyen, Phu, Thorstensen, Andrea, Sorooshian, Soroosh, Zhu, Qian, Tran, Hoang, Ashouri, Hamed, Miao, Chiyuan, Hsu, KuoLin, and Gao, Xiaogang. Evaluation of CMIP5 Model Precipitation Using PERSIANN-CDR. United States: N. p., 2017. Web. doi:10.1175/JHM-D-16-0201.1.
Nguyen, Phu, Thorstensen, Andrea, Sorooshian, Soroosh, Zhu, Qian, Tran, Hoang, Ashouri, Hamed, Miao, Chiyuan, Hsu, KuoLin, & Gao, Xiaogang. Evaluation of CMIP5 Model Precipitation Using PERSIANN-CDR. United States. doi:10.1175/JHM-D-16-0201.1.
Nguyen, Phu, Thorstensen, Andrea, Sorooshian, Soroosh, Zhu, Qian, Tran, Hoang, Ashouri, Hamed, Miao, Chiyuan, Hsu, KuoLin, and Gao, Xiaogang. Fri . "Evaluation of CMIP5 Model Precipitation Using PERSIANN-CDR". United States. doi:10.1175/JHM-D-16-0201.1.
@article{osti_1375072,
title = {Evaluation of CMIP5 Model Precipitation Using PERSIANN-CDR},
author = {Nguyen, Phu and Thorstensen, Andrea and Sorooshian, Soroosh and Zhu, Qian and Tran, Hoang and Ashouri, Hamed and Miao, Chiyuan and Hsu, KuoLin and Gao, Xiaogang},
abstractNote = {The purpose of this study is to use the PERSIANN–Climate Data Record (PERSIANN-CDR) dataset to evaluate the ability of 32 CMIP5 models in capturing the behavior of daily extreme precipitation estimates globally. The daily long-term historical global PERSIANN-CDR allows for a global investigation of eight precipitation indices that is unattainable with other datasets. Quantitative comparisons against CPC daily gauge; GPCP One-Degree Daily (GPCP1DD); and TRMM 3B42, version 7 (3B42V7), datasets show the credibility of PERSIANN-CDR to be used as the reference data for global evaluation of CMIP5 models. This work uniquely defines different study regions by partitioning global land areas into 25 groups based on continent and climate zone type. Results show that model performance in warm temperate and equatorial regions in capturing daily extreme precipitation behavior is largely mixed in terms of index RMSE and correlation, suggesting that these regions may benefit from weighted model averaging schemes or model selection as opposed to simple model averaging. The three driest climate regions (snow, polar, and arid) exhibit high correlations and low RMSE values when compared against PERSIANN-CDR estimates, with the exceptions of the cold regions showing an inability to capture the 95th and 99th percentile annual total precipitation characteristics. A comprehensive assessment of each model’s performance in each continent–climate zone defined group is provided as a guide for both model developers to target regions and processes that are not yet fully captured in certain climate types, and for climate model output users to be able to select the models and/or the study areas that may best fit their applications of interest.},
doi = {10.1175/JHM-D-16-0201.1},
journal = {Journal of Hydrometeorology},
number = 9,
volume = 18,
place = {United States},
year = {2017},
month = {9}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
DOI: 10.1175/JHM-D-16-0201.1

Citation Metrics:
Cited by: 4 works
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