Abstract
Warming is a major climate change concern, but the impact of high maximum temperatures depends upon the air’s moisture content. Trends in maximum summertime temperature, moisture, and heat index are tracked over three time periods: 1900–2011, 1950–2011, and 1979–2011; these trends differ notably from annual temperature trends. Trends are emphasized from two CRU datasets (CRUTS3.25 and CRUTS4.01) and two reanalyses (ERA-20C and 20CRv2). Maximum temperature trends tend towards warming that is stronger over the Great Lakes, the interior western and the northeastern contiguous United States. A warming hole in the Midwest generally decreases in size and magnitude when heat stress trends are calculated because the region has increasing moisture. CRU and nearly all reanalyses find cooling in the northern high plains that is not found in NOAA Climate Division trends. These NOAA trends are captured better by CRUTS401. Moistening in the northeast amplifies the heat stress there. Elsewhere the moisture trends are less clear. Drying over northern Texas (after 1996) in CRUTS401 translates into decreasing heat stress there (less so in CRUTS325). Though other reanalyses are not intended for long-term trends, MERRA-2 and ERA-Interim match observed trends better than other reanalyses.
Highlights
There can be indirect effects such as how direct input of precipitation (e.g. NARR, CFSR, MERRA2) can influence near-surface temperature. These and other details are beyond the scope of the intended comparisons and do not fit in the table
Details of the bias correction procedures (e.g. MERRA2 has a near-surface cold bias[28] that declines over the period) used for reanalyses are beyond the scope of this study
Regular Gridded Data, monolevel, ensemble mean rcm2rgrid used to interpolate to rectangular grid
Summary
Warming is a major climate change concern, but the impact of high maximum temperatures depends upon the air’s moisture content. Quality-controlled datasets[10,11,12] are used to calculate trends These datasets generally do not include moisture needed for THI and HI. A reanalysis like NNRA125 uses a consistent model but incorporates new data sources (e.g. satellites) as they become available, resulting in artificial jumps[25] in the time series of variables thereby casting doubt on a trend analysis[26]. These five datasets: CRU325, CRU401, NCD, 20CRv2, and ERA-20C are emphasized in this report. Other reanalyses: ERA-I27, MERRA228, CFSR29, NARR30, NNRA125, and NDRA231 are shown only for comparison as motivated above
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