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Abstract. Comparisons of observed and modeled climate behavior often focus on central tendencies, which overlook other important distributional characteristics related to quantiles and variability. We propose two permutation procedures, standard and stratified, for assessing the accuracy of climate models. Both procedures eliminate the need to model cross-correlations in the data, encouraging their application in a variety of contexts. By making only slightly stronger assumptions, the stratified procedure dramatically strengthens the ability to detect a difference in the distribution of observed and climate model data. The proposed procedures allow researchers to identify potential model deficiencies over space and time for a variety of distributional characteristics, providing a more comprehensive assessment of climate model accuracy, which will hopefully lead to further model refinements. The proposed statistical methodology is applied to temperature data generated by the state-of-the-art North American Coordinated Regional Climate Downscaling Experiment (NA-CORDEX). 

BackgroundWildfire simultaneity affects the availability and distribution of resources for fire management: multiple small fires require more resources to fight than one large fire does. AimsThe aim of this study was to project the effects of climate change on simultaneous large wildfires in the Western USA, regionalised by administrative divisions used for wildfire management. MethodsWe modelled historical wildfire simultaneity as a function of selected fire indexes using generalised linear models trained on observed climate and fire data from 1984 to 2016. We then applied these models to regional climate model simulations of the 21st century from the NA-CORDEX data archive. Key resultsThe results project increases in the number of simultaneous 1000+ acre (4+ km2) fires in every part of the Western USA at multiple return periods. These increases are more pronounced at higher levels of simultaneity, especially in the Northern Rockies region, which shows dramatic increases in the recurrence of high return levels. ConclusionsIn all regions, the models project a longer season of high simultaneity, with a slightly earlier start and notably later end. These changes would negatively impact the effectiveness of fire response. ImplicationsBecause firefighting decisions about resource distribution, pre-positioning, and suppression strategies consider simultaneity as a factor, these results underscore the importance of potential changes in simultaneity for fire management decision-making. 

Abstract Modern science’s ability to produce, store, and analyze big datasets is changing the way that scientific research is practiced. Philosophers have only begun to comprehend the changed nature of scientific reasoning in this age of “big data.” We analyze data-focused practices in biology and climate modeling, identifying distinct species of data-centric science: phenomena-laden in biology and phenomena-agnostic in climate modeling, each better suited for its own domain of application, though each entail trade-offs. We argue that data-centric practices in science are not monolithic because the opportunities and challenges presented by big data vary across scientific domains.