Year: 2024
Type: data-paper
Abstract: Global climate change is leading to an increase in compound hot-dry events, significantly impacting human habitats. Analysing the causes and effects of these events requires precise data, yet most meteorological data focus on variables rather than extremes, which hinders relevant research. A daily compound hot-dry events (CHDEs) dataset was developed from 1980 to 2100 under various socioeconomic scenarios, using the latest NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6) dataset to address this. The dataset has a spatial resolution of 0.25 degrees (approximately 30 kilometres), including three indicators, namely D (the yearly sum of hot-dry extreme days), prI (the intensity of daily precipitation), and tasI (the intensity of daily temperature). To validate the accuracy of the dataset, we compared observational data from China (National Meteorological Information Center, NMIC), Europe (ERA5), and North America (ERA5). Results show close alignment with estimated values from the observational daily dataset, both temporally and spatially. The predictive interval (PI) pass rates for the CHDEs dataset exhibit notably high values. For a 90% PI, D has a pass rate exceeding 85%, whilst prI and tasI respectively show a pass rate above 70% and 95%. These results underscore its suitability for conducting global and regional studies about compound hot-dry events.
Source: Scientific Data
Authors: Yizhuo Wen, Junhong Guo, Feng Wang, Zhenda Hao, Yifan Fei, Aili Yang, Yurui Fan, Faith Ka Shun Chan
Institutions: University of Nottingham Ningbo China, Xiamen University of Technology, North China Electric Power University, Beijing Normal University, State Key Laboratory of Earth Surface Processes and Resource Ecology, Central University of Finance and Economics, Brunel University of London, University of Leeds
Language: English
Subfield: Global and Planetary Change
Field: Environmental Science
Domain: Physical Sciences
SDG: Climate action
Open Access status: gold
Estimated APC paid: $2,290
Awards: G4381651219, G7696312491
OpenAlex ID: W4402901033