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S. C. Pryor and J. T. Schoof


Climate science is increasingly using (i) ensembles of climate projections from multiple models derived using different assumptions and/or scenarios and (ii) process-oriented diagnostics of model fidelity. Efforts to assign differential credibility to projections and/or models are also rapidly advancing. A framework to quantify and depict the credibility of statistically downscaled model output is presented and demonstrated. The approach employs transfer functions in the form of robust and resilient generalized linear models applied to downscale daily minimum and maximum temperature anomalies at 10 locations using predictors drawn from ERA-Interim reanalysis and two global climate models (GCM; GFDL-ESM2M and MPI-ESM-LR). The downscaled time series are used to derive several impact-relevant Climate Extreme (CLIMDEX) temperature indices that are assigned credibility based on 1) the reproduction of relevant large-scale predictors by the GCMs (i.e., fraction of regression beta weights derived from predictors that are well reproduced) and 2) the degree of variance in the observations reproduced in the downscaled series following application of a new variance inflation technique. Credibility of the downscaled predictands varies across locations and between the two GCM and is generally higher for minimum temperature than for maximum temperature. The differential credibility assessment framework demonstrated here is easy to use and flexible. It can be applied as is to inform decision-makers about projection confidence and/or can be extended to include other components of the transfer functions, and/or used to weight members of a statistically downscaled ensemble.

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J. T. Schoof and S. C. Pryor


Markov chains are widely used tools for modeling daily precipitation occurrence. Given the assumption that the Markov chain model is the right model for daily precipitation occurrence, the choice of Markov model order was examined on a monthly basis for 831 stations in the contiguous United States using long-term data. The model order was first identified using the Bayesian information criteria (BIC). The maximum-likelihood estimates of the Markov transition probabilities were computed from 100 bootstrapped samples and were then used to generate 50-yr precipitation occurrence series. The distributions of dry- and wet-spell lengths in the resulting series were then compared with observations using a two-sample Kolmogorov–Smirnov (K-S) test. The results suggest that the most parsimonious model, as identified by the BIC, usually (in approximately 68% of the cases) reproduced the wet- and dry-spell length distributions. However, the K-S test often indicated a second-order model when the BIC indicated a first-order model. In a smaller number of cases, the BIC indicated a higher-order model than the K-S test. In both cases, the differences were found to be due to the distribution of wet spells rather than dry spells. It is concluded that models chosen on the basis of the BIC may not adequately reproduce the distributions of wet and dry spells for some locations and times of year.

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J. T. Schoof, T. W. Ford, and S. C. Pryor


Humidity is a key determinant of heat wave impacts, but studies investigating changes in extreme heat events have not differentiated between events characterized by high temperatures and those characterized by simultaneously elevated temperature and humidity. The authors present a framework, using air temperature (T) and equivalent temperature (T E; a measure combining temperature and specific humidity), to examine changes in local percentile-based extreme heat events characterized by high temperature (T only) and those with high temperature and humidity (T-and-T E events). Application to one observational dataset (PRISM), four reanalysis products (1981–2015), and seven U.S. regions reveals widespread changes in heat wave characteristics over the 35-yr period. Agreement among the datasets employed on several heat wave metrics suggests that many of the findings are robust. With the exception of the northern plains region, all regions experienced increases in both T-only and T-and-T E heat wave day (HWD) frequency in each of the reanalyses. In the northern plains, all datasets have negative trends in T-only HWD frequency and positive trends in T-and-T E HWD frequency. Trends in HWD frequency were generally accompanied by changes in the spatial footprint in heat wave conditions. Temperature has increased significantly during T-only HWDs in the western regions, while increases in T E during T-and-T E HWDs have occurred in the central United States and Northeast region. These findings suggest that equivalent temperature provides an alternative perspective on the evolution of regional heat wave climatology. Studies considering changes in regional heat wave impacts should carefully consider the role of atmospheric moisture.

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