气候变化领域集成服务门户(CCPortal): Diverse estimates of annual maxima daily precipitation in 22 state-of-the-art quasi-global land observation datasets

CCPortal

DOI	10.1088/1748-9326/ab6a22
	Diverse estimates of annual maxima daily precipitation in 22 state-of-the-art quasi-global land observation datasets
	Bador M.; Alexander L.V.; Contractor S.; Roca R.
发表日期	2020
ISSN	17489318
卷号	15 期号:3
英文摘要	Observational evidence of precipitation extremes is vital to better understand how these events might change in a future warmer climate. Over the terrestrial regions of a quasi-global domain, we assess the representation of annual maxima of daily precipitation (Rx1day) in 22 observational products gridded at 1 1 resolution and clustered into four categories: station-based in situ, satellite observations with or without a correction to rain gauges, and reanalyses (5, 8, 4 and 5 datasets, respectively). We also evaluate the interproduct spread across the ensemble and within the four clusters, as a measure of observational uncertainty. We find that reanalyses present a heterogeneous representation of Rx1day in particular over the tropics, and their interproduct spread is the highest compared to any other cluster. Extreme precipitation in satellite data broadly compares well with in situ-based data. We find a general better agreement with in situ-based observations and less interproduct spread for the satellite products with a correction to rain gauges compared to the uncorrected products. Given the level of uncertainties associated with the estimation of Rx1day in the observations, none of the datasets can be thought of as the best estimate. Our recommendation is to avoid using reanalyses as observational evidence and to consider in situ and satellite data (the corrected version preferably) in an ensemble of products for a better estimation of precipitation extremes and their observational uncertainties. Based on this we choose a subsample of 10 datasets to reduce the interproduct spread in both the representation of Rx1day and its timing throughout the year, compared to all 22 datasets. We emphasize that the recommendations and selection of datasets given here may not be relevant for different precipitation indices, and other grid resolutions and time scales. © 2020 The Author(s). Published by IOP Publishing Ltd.
英文关键词	annual extremes of daily precipitation; global distribution of precipitation extremes; in situ and satellite precipitation observations; intercomparison of observational products
语种	英语
scopus关键词	Climate change; Rain gages; Satellites; Uncertainty analysis; Daily precipitations; Extreme precipitation; Intercomparisons; Precipitation extremes; Precipitation indices; Satellite observations; Satellite precipitation; Situ and satellite datum; Rain; data set; estimation method; extreme event; precipitation (climatology); raingauge; satellite data
来源期刊	Environmental Research Letters
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/154210
作者单位	CLEX ARC Centre of Excellence for Climate Extremes, Sydney, Australia; Climate Change Research Centre, School of BEES, UNSW, Sydney, Australia; School of Mathematics and Statistics, Faculty of Science, UNSW, Sydney, Australia; Laboratoire d'Etudes Géophysiques et d'Océanographie Spatiales, Toulouse, France
推荐引用方式 GB/T 7714	Bador M.,Alexander L.V.,Contractor S.,et al. Diverse estimates of annual maxima daily precipitation in 22 state-of-the-art quasi-global land observation datasets[J],2020,15(3).
APA	Bador M.,Alexander L.V.,Contractor S.,&Roca R..(2020).Diverse estimates of annual maxima daily precipitation in 22 state-of-the-art quasi-global land observation datasets.Environmental Research Letters,15(3).
MLA	Bador M.,et al."Diverse estimates of annual maxima daily precipitation in 22 state-of-the-art quasi-global land observation datasets".Environmental Research Letters 15.3(2020).