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  • A global dataset of daily maximum and minimum near-surface air temperature at 1 km resolution over land (2003–2020)
Title: A global dataset of daily maximum and minimum near-surface air temperature at 1 km resolution over land (2003–2020)
Abstract. Near-surface air temperature (Ta) is a key variable in global climatestudies. A global gridded dataset of daily maximum and minimum Ta (Tmax⁡ and Tmin⁡) is particularly valuable and critically needed inthe scientific and policy communities but is still not available. In this paper, we developed a global dataset of daily Tmax⁡ and Tmin⁡at 1 km resolution over land across 50∘ S–79∘ N from 2003 to 2020 through the combined use of ground-station-basedTa measurements and satellite observations (i.e., digital elevation model and land surface temperature) via a state-of-the-artstatistical method named Spatially Varying Coefficient Models with Sign Preservation (SVCM-SP). The root mean square errors in our estimates rangedfrom 1.20 to 2.44 ∘C for Tmax⁡ and 1.69 to 2.39 ∘C for Tmin⁡. We found that the accuracies were affectedprimarily by land cover types, elevation ranges, and climate backgrounds. Our dataset correctly represents a negative relationship betweenTa and elevation and a positive relationship between Ta and land surface temperature; it captured spatial and temporalpatterns of Ta realistically. This global 1 km gridded daily Tmax⁡ and Tmin⁡ dataset is the first of its kind, and weexpect it to be of great value to global studies such as the urban heat island phenomenon, hydrological modeling, and epidemic forecasting. The data havebeen published by Iowa State University at https://doi.org/10.25380/iastate.c.6005185 (Zhang and Zhou, 2022).  more » « less
Award ID(s):
2041859
PAR ID:
10399497
Author(s) / Creator(s):
; ; ; ; ; ;
Date Published:
Journal Name:
Earth System Science Data
Volume:
14
Issue:
12
ISSN:
1866-3516
Page Range / eLocation ID:
5637 to 5649
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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