Abstract
The most comprehensive continuous-coverage modern climatic data sets, known as reanalyses, come from combining state-of-the-art numerical weather prediction (NWP) models with diverse available observations. These reanalysis products estimate the path of climate evolution that actually happened, and their use in a probabilistic context—for example, to document trends in extreme events in response to climate change—is, therefore, limited. Free runs of NWP models without data assimilation can in principle be used for the latter purpose, but such simulations are computationally expensive and are prone to systematic biases. Here we produce a high-resolution, 100-member ensemble simulation of surface atmospheric temperature over North America for the 1979–2015 period using a comprehensive spatially extended non-stationary statistical model derived from the data based on the North American Regional Reanalysis. The surrogate climate realizations generated by this model are independent from, yet nearly statistically congruent with reality. This data set provides unique opportunities for the analysis of weather-related risk, with applications in agriculture, energy development, and protection of human life.
Highlights
Background & SummaryState-of-the-art numerical weather prediction models are expensive to run and are subject to biases due to imperfect physical parameterizations of unresolved processes[1]
Modeling and predicting temperature extremes is a task of utmost societal and economic importance, especially in the context of potential changes in the extreme weather associated with global warming[5,6]
The stochastically generated skewed (SGS) distributions are fit to the entire available time series—rather than to extreme values only,which allows one to reduce sampling uncertainties associated with a limited climate record
Summary
Background & SummaryState-of-the-art numerical weather prediction models are expensive to run and are subject to biases due to imperfect physical parameterizations of unresolved processes[1]. Our updated empirical SAT modeling framework uses the input 2-m air-temperature data from the North American Regional Reanalysis[25] over the 1979–2015 period to fit a multi-scale parametric statistical model for the one-way coupled sub-modules operating at different time resolutions and incorporating non-stationary dependencies on seasonal and external predictors reflecting the large-scale climate signals[12] (Fig. 1; see Methods for details).
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