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Abstract:
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The impacts of heat events depend on both temperature and humidity. GCMs do not fully reproduce the observed joint distribution of these variables, implying a need for future simulation methods that combine GCM output with observations for use in impact studies. We present an observation-based, conditional quantile mapping approach to this problem. A temperature simulation is produced by transforming observations to include projected changes in mean and temporal covariance structure from a GCM. Next, a humidity simulation is produced by transforming observations to account for projected changes in the conditional humidity distribution given temperature. We use the CESM1 Large Ensemble (CESM1-LE) to estimate projected changes in summertime temperature and humidity over the Continental United States, and create future simulations using station observations from the Global Summary of the Day. We find e.g. that CESM1-LE projects increases in the risk of high dew point on historically hot days and, in comparison with raw CESM1-LE output, our observation-based simulation largely projects smaller changes in the future risk of high humidity on days with historically warm temperatures.
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