An Assessment of High-Resolution Gridded Temperature Datasets over California

Journal Article · Mon Apr 16 00:00:00 EDT 2018 · Journal of Climate

DOI:https://doi.org/10.1175/JCLI-D-17-0410.1· OSTI ID:1433291

Walton, Daniel ^[1]; Hall, Alex ^[2]

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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

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 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.

• Research Organization: Univ. of California, Los Angeles, CA (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Biological and Environmental Research (BER)
• Grant/Contract Number: SC0014061
• OSTI ID: 1433291
• Alternate ID(s): OSTI ID: 1541843
• Journal Information: Journal of Climate, Journal Name: Journal of Climate Vol. 31 Journal Issue: 10; ISSN 0894-8755
• Publisher: American Meteorological Society
• Country of Publication: United States
• Language: English

Related Subjects

54 ENVIRONMENTAL SCIENCES
Meteorology & Atmospheric Sciences