Amplified Mesoscale and Submesoscale Variability and Increased Concentration of Precipitation under Global Warming over Western North America

Cold-season precipitation statistics in simulations from the storm-resolving WRF Model at 6-km and 1-h resolution over western North America are analyzed. Pseudo–global warming future simulations for the 2041–80 period, constrained by GCMs under the RCP8.5 scenario, are compared to the 1981–2020 historical simulation. The analysis focuses on the dynamical properties of precipitation time series at subdaily scales and on the morphology of storms. The statistical distribution of precipitation intensities in each pixel of the simulation domain is characterized through nonparametric statistical indicators: frequency of wet hours, mean wet-hour precipitation intensity, and Gini coefficient as a measure of the temporal concentration of the precipitation volume. Additionally, the temporal and spatial Fourier power spectra of precipitation time series and precipitation fields are analyzed. The half-power period (HPP) and half-power wavelength (HPW) are defined as spectral measures of the characteristic scales of precipitation’s temporal and spatial patterns. The results show statistically significant increases in the mean wet-hour precipitation intensity and in the Gini coefficient in 99% of the pixels, indicating that the seasonal precipitation volume becomes more concentrated within a smaller number of hours with higher precipitation intensity. The statistics of change in the frequency of wet hours are more contrasted across the simulation domain. The changes are also reflected in the power spectra, which show the spatial and temporal variability increasing proportionally more with finer spatial and temporal scales and the HPW and HPP decreasing. In conclusion, these projected changes are expected to have consequences, not only in terms of hydrologic impacts but also in terms of the predictability of precipitation patterns.. Authors: Guilloteau, Clément [Univ. of California, Irvine, CA (United States)] (ORCID:0000000181427740); Chen, Xiaodong [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States); Univ. of Oklahoma, Norman, OK (United States)] (ORCID:0000000230892260); Leung, Lai-Yung Ruby [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)] (ORCID:0000000232219467); Foufoula-Georgiou, Efi [Univ. of California, Irvine, CA (United States)]. DOE Contract: AC05-76RL01830. Subjects: Climate change; Climate prediction; Deep convection; Model output statistics; Precipitation; Precipitation evaluation