Premium
A new compilation of globally gridded night‐time marine air temperatures: The UAHNMATv1 dataset
Author(s) -
Junod Robert A.,
Christy John R.
Publication year - 2020
Publication title -
international journal of climatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.58
H-Index - 166
eISSN - 1097-0088
pISSN - 0899-8418
DOI - 10.1002/joc.6354
Subject(s) - climatology , surface air temperature , environmental science , homogenization (climate) , sea surface temperature , air temperature , meteorology , current (fluid) , geology , geography , precipitation , oceanography , biodiversity , ecology , biology
Near‐surface air temperature over the oceans is a relatively unused parameter in understanding the current state of climate, but is useful as an independent temperature metric over the oceans and serves as a geographical and physical complement to near‐surface air temperature over land. Although one complete version of this dataset exists (HadNMAT2), it has been strongly recommended that various groups generate climate records independently, which is one motivation here. This University of Alabama in Huntsville (UAH) study began with the construction of monthly night‐time marine air temperature (UAHNMATv1) values from the early‐twentieth century through to the present era using air temperatures on ships. Data from the International Comprehensive Ocean–Atmosphere Data Set (ICOADS) Release 3.0 (R3.0) were used to compile a complete time series of gridded UAHNMATv1. The observations required detailed homogenization procedures since there are many biases to account for such as increasing ship height and changing observing practices. The UAHNMATv1 dataset, once homogenized, is gridded to 5.0° monthly anomalies from 1900 to 2018. This study will present results which quantify the variability and trends and compare to current trends of other related datasets that include HadNMAT2 and sea‐surface temperatures (HadISST & ERSSTv4). This new dataset has broad overall agreement both globally and regionally with HadNMAT2, HadISST, and ERSSTv4 datasets.