855 The 1983-2016 Climate Hazards Infrared, MERRA High Resolution Tmax with Stations (CHIRTSmax) Climate Data Record

Wednesday, 9 January 2019
Hall 4 (Phoenix Convention Center - West and North Buildings)
Pete Peterson, Climate Hazards Group, Univ. of California, Santa Barbara, Santa Barbara, CA; and C. C. Funk, S. Peterson, S. Shukla, F. Davenport, J. Michaelsen, M. Landsfeld, G. Husak, L. S. Harrison, J. Rowland, M. Budde, K. R. Knapp, S. E. Nicholson, and T. Dinku

In this presentation we describe the new Climate Hazards group Infrared/MERRA2 with Stations (CHIRTSmax) monthly Tmax data set. The CHIRTSmax data set is a high resolution (0.05° latitude/longitude grid), quasi-global (60°S-70°N), 1983 to 2016 Climate Data Record (CDR) describing monthly 2m maximum air temperatures. This CDR is based on a high resolution climatology – the Climate Hazards group’s Tmax climatology (CHTclim) combined with 1) time-varying monthly Tmax estimates derived from the Modern Era Retrospective Reanalysis for Research and Applications, version 2 (MERRA2), 2) cloud-screened thermal infrared (TIR) brightness temperatures obtained from the Gridded Satellite (GridSat) geostationary satellite archive and 3) in situ monthly 2m Tmax air temperature observations obtained from the Global Historical Climatology Network (GHCN) v3.3 and the Climatic Research Unit (CRU).

Motivation for the CHIRTSmax data set comes from the need to monitor temperature trends and extremes in the many areas of the terrestrial surface of our globe that have low densities of station observations. While there might be additional observations available at the national level, many countries will have only a few, or no, station observations in publically available data sets like the GHCN and CRU. While geostationary Thermal Infrared (TIR) archives and reanalysis products have long been used in poorly gauged regions to provide estimates of precipitation, to date there has been little use of TIR data to estimate air temperatures. Here we describe a process for doing this, and provide some initial validation results and temperature trend analyses.

In many places, we find very reasonable levels of agreement between satellite-only and station-based CRU estimates, with monthly correlations greater than 0.8. Even higher levels of correlation are found with the MERRA2 data set. Combining these information sources with a high resolution climatology results in an accurate and useful data source suitable for use in areas lacking dense in situ air temperature observations. CHIRTSmax estimates indicate slightly faster warming than the CRU for South America and Africa, possibly indicating that limitations in the number of observations in these regions is causing an underestimate of the rate of global warming.

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