Abstract
AbstractThe human impacts of changes in heat events depend on changes in the joint behavior of temperature and humidity. Little is currently known about these complex joint changes, either in observations or projections from general circulation models (GCMs). Further, GCMs do not fully reproduce the observed joint distribution, implying a need for simulation methods that combine information from GCMs with observations for use in impact studies. We present an observation-based, conditional quantile mapping approach for the simulation of future temperature and humidity. A temperature simulation is first produced by transforming historical temperature observations to include projected changes in the mean and temporal covariance structure from a GCM. Next, a humidity simulation is produced by transforming humidity observations to account for projected changes in the conditional humidity distribution given temperature, using a quantile regression model. We use the Community Earth System Model Large Ensemble (CESM1-LE) to estimate future changes in summertime (June–August) temperature and humidity over the continental United States (CONUS), and then use the proposed method to create future simulations of temperature and humidity at stations in the Global Summary of the Day dataset. We find that CESM1-LE projects decreases in summertime humidity across CONUS for a given deviation in temperature from the forced trend, but increases in the risk of high dewpoint on historically hot days. In comparison with raw CESM1-LE output, our observation-based simulation largely projects smaller changes in the future risk of either high or low humidity on days with historically warm temperatures.
Highlights
Assessing the potential societal impacts of changes in future heat events requires an understanding of projected changes in both temperature and humidity
It is well understood that raw general circulation models (GCMs) output is insufficient for these purposes, because GCM output forced with historical forcings does not fully reproduce observed climate variable distributions; see John and Soden (2007), Brands et al (2013), Tian et al (2013), and Zhao et al (2015) for examples evaluating GCM simulations of temperature and humidity or heat stress (see IPCC (2013), Chapter 9)
While no simulation method resolves all potential defects of future simulations (Dixon et al, 2016; Lanzante et al, 2018), observation-based simulations have the attractive property that they preserve most of the higher-order behavior of observational distributions and generally require more statistical modeling of distributions in GCM output than in observations
Summary
Assessing the potential societal impacts of changes in future heat events requires an understanding of projected changes in both temperature and humidity. It is well understood that raw GCM output is insufficient for these purposes, because GCM output forced with historical forcings does not fully reproduce observed climate variable distributions; see John and Soden (2007), Brands et al (2013), Tian et al (2013), and Zhao et al (2015) for examples evaluating GCM simulations of temperature and humidity or heat stress (see IPCC (2013), Chapter 9) This fact is not specific to temperature or humidity simulations, and a number of methods have been proposed to combine observations with model output to produce better calibrated future simulations, typically labeled “bias correction” methods (see, e.g., Ho et al (2012); Hawkins et al (2013); Cannon et al (2020) for reviews of the main types of methods). While our simulations and results are presented in terms of dew point, the dry bulb temperature and dew point determine the relative humidity value, so a relative humidity simulation is implicit in the proposed procedure
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