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A High-Resolution Global Gridded Historical Dataset of Climate Extreme Indices

1
Department of Economics, Ca’ Foscari University of Venice, 30121 Venice, Italy
2
Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC), 30175 Venice, Italy
Received: 18 February 2019 / Revised: 8 March 2019 / Accepted: 9 March 2019 / Published: 13 March 2019
(This article belongs to the Special Issue Overcoming Data Scarcity in Earth Science)
Climate extreme indices (CEIs) are important metrics that not only assist in the analysis of regional and global extremes in meteorological events, but also aid climate modellers and policymakers in the assessment of sectoral impacts. Global high-spatial-resolution CEI datasets derived from quality-controlled historical observations, or reanalysis data products are scarce. This study introduces a new high-resolution global gridded dataset of CEIs based on sub-daily temperature and precipitation data from the Global Land Data Assimilation System (GLDAS). The dataset called “CEI_0p25_1970_2016” includes 71 annual (and in some cases monthly) CEIs at 0.25 × 0.25 gridded resolution, covering 47 years over the period 1970–2016. The data of individual indices are publicly available for download in the commonly used Network Common Data Form 4 (NetCDF4) format. Potential applications of CEI_0p25_1970_2016 presented here include the assessment of sectoral impacts (e.g., Agriculture, Health, Energy, and Hydrology), as well as the identification of hot spots (clusters) showing similar historical spatial patterns of high/low temperature and precipitation extremes. CEI_0p25_1970_2016 fills gaps in existing CEI datasets by encompassing not only more indices, but also by being the only comprehensive global gridded CEI data available at high spatial resolution. View Full-Text
Keywords: climate extreme indices (CEIs); ClimPACT; GLDAS; Expert Team on Climate Change Detection and Indices (ETCCDI); Expert Team on Sector-specific Climate Indices (ET-SCI) climate extreme indices (CEIs); ClimPACT; GLDAS; Expert Team on Climate Change Detection and Indices (ETCCDI); Expert Team on Sector-specific Climate Indices (ET-SCI)
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Mistry, M.N. A High-Resolution Global Gridded Historical Dataset of Climate Extreme Indices. Data 2019, 4, 41.

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