Trends of extreme US weather events in the changing climate.
Autor: | Shenoy S; Physics Department, Stanford University, Stanford, CA 94305., Gorinevsky D; Department of Electrical Engineering, Stanford University, Stanford, CA 94305., Trenberth KE; Distinguished Scholar, NCAR and Honorary Academic, Department of Physics, Auckland University, 1010 Auckland, NZ., Chu S; Physics Department, Stanford University, Stanford, CA 94305.; Molecular and Cellular Physiology Department, Stanford University, Stanford, CA 94305.; Environmental Sciences and Engineering Department in the Doerr School of Sustainability, Stanford University, Stanford, CA 94305. |
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Jazyk: | angličtina |
Zdroj: | Proceedings of the National Academy of Sciences of the United States of America [Proc Natl Acad Sci U S A] 2022 Nov 22; Vol. 119 (47), pp. e2207536119. Date of Electronic Publication: 2022 Nov 14. |
DOI: | 10.1073/pnas.2207536119 |
Abstrakt: | Trends in extreme 100-y events of temperature and rainfall amounts in the continental United States are estimated, to see effects of climate change. This is a nontrivial statistical problem because climate change effects have to be extracted from "noisy" weather data within a limited time range. We use nonparametric Bayesian methods to estimate the trends of extreme events that have occurred between 1979 and 2019, based on data for temperature and rainfall. We focus on 100-y events for each month in [Formula: see text] geographical areas looking at hourly temperature and 5-d cumulative rainfall. Distribution tail models are constructed using extreme value theory (EVT) and data on 33-y events. This work shows it is possible to aggregate data from spatial points in diverse climate zones for a given month and fit an EVT model with the same parameters. This surprising result means there are enough extreme event data to see the trends in the 41-y record for each calendar month. The yearly trends of the risk of a 100-y high-temperature event show an average 2.1-fold increase over the last 41 y of data across all months, with a 2.6-fold increase for the months of July through October. The risk of high rainfall extremes increases in December and January 1.4-fold, but declines by 22% for the spring and summer months. |
Databáze: | MEDLINE |
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