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Title: An Assessment of High-Resolution Gridded Temperature Datasets over California

Abstract

High-resolution gridded datasets are in high demand because they are spatially complete and include important finescale details. Previous assessments have been limited to two to three gridded datasets or analyzed the datasets only at the station locations. Here, eight high-resolution gridded temperature datasets are assessed two ways: at the stations, by comparing with Global Historical Climatology Network–Daily data; and away from the stations, using physical principles. This assessment includes six station-based datasets, one interpolated reanalysis, and one dynamically downscaled reanalysis. California is used as a test domain because of its complex terrain and coastlines, features known to differentiate gridded datasets. As expected, climatologies of station-based datasets agree closely with station data. However, away from stations, spread in climatologies can exceed 6°C. Some station-based datasets are very likely biased near the coast and in complex terrain, due to inaccurate lapse rates. Many station-based datasets have large unphysical trends (>1°C decade −1 ) due to unhomogenized or missing station data—an issue that has been fixed in some datasets by using homogenization algorithms. Meanwhile, reanalysis-based gridded datasets have systematic biases relative to station data. Dynamically downscaled reanalysis has smaller biases than interpolated reanalysis, and has more realistic variability and trends. Dynamical downscaling alsomore » captures snow–albedo feedback, which station-based datasets miss. Overall, these results indicate that 1) gridded dataset choice can be a substantial source of uncertainty, and 2) some datasets are better suited for certain applications.« less

Authors:
 [1];  [2]
  1. Institute of the Environment and Sustainability, University of California, Los Angeles, Los Angeles, California
  2. Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, Los Angeles, California
Publication Date:
Research Org.:
Univ. of California, Los Angeles, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
OSTI Identifier:
1433291
Alternate Identifier(s):
OSTI ID: 1541843
Grant/Contract Number:  
SC0014061
Resource Type:
Published Article
Journal Name:
Journal of Climate
Additional Journal Information:
Journal Name: Journal of Climate Journal Volume: 31 Journal Issue: 10; Journal ID: ISSN 0894-8755
Publisher:
American Meteorological Society
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; Meteorology & Atmospheric Sciences

Citation Formats

Walton, Daniel, and Hall, Alex. An Assessment of High-Resolution Gridded Temperature Datasets over California. United States: N. p., 2018. Web. doi:10.1175/JCLI-D-17-0410.1.
Walton, Daniel, & Hall, Alex. An Assessment of High-Resolution Gridded Temperature Datasets over California. United States. doi:10.1175/JCLI-D-17-0410.1.
Walton, Daniel, and Hall, Alex. Tue . "An Assessment of High-Resolution Gridded Temperature Datasets over California". United States. doi:10.1175/JCLI-D-17-0410.1.
@article{osti_1433291,
title = {An Assessment of High-Resolution Gridded Temperature Datasets over California},
author = {Walton, Daniel and Hall, Alex},
abstractNote = {High-resolution gridded datasets are in high demand because they are spatially complete and include important finescale details. Previous assessments have been limited to two to three gridded datasets or analyzed the datasets only at the station locations. Here, eight high-resolution gridded temperature datasets are assessed two ways: at the stations, by comparing with Global Historical Climatology Network–Daily data; and away from the stations, using physical principles. This assessment includes six station-based datasets, one interpolated reanalysis, and one dynamically downscaled reanalysis. California is used as a test domain because of its complex terrain and coastlines, features known to differentiate gridded datasets. As expected, climatologies of station-based datasets agree closely with station data. However, away from stations, spread in climatologies can exceed 6°C. Some station-based datasets are very likely biased near the coast and in complex terrain, due to inaccurate lapse rates. Many station-based datasets have large unphysical trends (>1°C decade −1 ) due to unhomogenized or missing station data—an issue that has been fixed in some datasets by using homogenization algorithms. Meanwhile, reanalysis-based gridded datasets have systematic biases relative to station data. Dynamically downscaled reanalysis has smaller biases than interpolated reanalysis, and has more realistic variability and trends. Dynamical downscaling also captures snow–albedo feedback, which station-based datasets miss. Overall, these results indicate that 1) gridded dataset choice can be a substantial source of uncertainty, and 2) some datasets are better suited for certain applications.},
doi = {10.1175/JCLI-D-17-0410.1},
journal = {Journal of Climate},
number = 10,
volume = 31,
place = {United States},
year = {2018},
month = {5}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
DOI: 10.1175/JCLI-D-17-0410.1

Citation Metrics:
Cited by: 6 works
Citation information provided by
Web of Science

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