DOI: 10.5281/zenodo.7463485
title: Past and future weather extremes across Europe

description: <pre><strong>Past and future weather extremes across Europe </strong> This repository contains the annual exceedance index data for past and future weather extremes across Europe on NUTS1 scale. The code and an accompanying paper analyzing the impact of this weather extremes on the European agricultural sector on subnational scale will be published during 2023. We use a percentile-based approach to assess the annual exceedance index of the four weather extremes heat waves, cold waves, fire-risk and droughts for the past (1981–2020) and future (2006–2100) [<em><strong>Zhang</strong></em> et al., 2005]. For the past, we used daily weather records on a grid level (around 11 km at the equator) from the ERA5-Land reanalysis dataset, and for future projections, we use modelled daily weather records from EURO-CORDEX [<em><strong>Christensen</strong></em> et al., 2020, <em><strong>Muñoz</strong></em>, 2019]. For past and future fire-risk we use precalculated fire weathernindex data from ERA5 and EURO-CORDEX, respectively [<em><strong>Giannakopoulos</strong></em> et al., 2020]. We used the model average of the following driving GCMs and RCMs for future projections: ICHECs Earth System Model (EC-Earth), MPI-Ms Earth System Model (MPI-ESM-LR), SMHIs Regional Climate Model (RCA4). The baseline period for the historical scenario is 1981–2010, and for future projections 1981–2005. Daily thresholds for heat waves, cold waves, and flash droughts are estimated from the 90th percentile of the daily minimum and maximum temperature, 10th percentile of the daily minimum and maximum temperature, and 30th percentile of the soil volumetric water content (0–28cm), respectively [**Sutanto** et al., 2020]. We use a five days centre data window for all three extreme events to estimate the thresholds from the previously listed baseline periods. The annual exceedance index for heat waves is calculated as the sum of days, at least for three consecutive days; the daily temperature values exceed the thresholds for June, July, and August. For cold waves, the annual exceedance index is the sum of days, at least for three consecutive days; the daily temperature values are below the thresholds for January, February, October, November, and December. In-base, exceedance is calculated using bootstrapping (1000x repetitions) for both extreme events. Heat and cold wave exceedance indices are rescaled to NUTS1 regions using a maximum resampling. We use sequent peak analysis to detect annual flash droughts, remove minor droughts, and pool interdependent droughts for the season from June to October [**Biggs** et al., 2004]. The annual exceedance index of droughts is rescaled to NUTS1 regions by using a mean resampling. Parameters for fire-risk are listed in the table below while. </pre> Parameters of the analysis of the percentile-based extreme. Type Variable Percentile Window Min duration Rescaling Months Bootstrapping Heat wave tmin and tmax 90 5 3 max 6, 7, 8 yes Cold wave tmin and tmax 10 5 3 max 1, 2, 10, 11, 12 yes Flash drought swvl 0-28cm 30 5 5 mean 6, 7, 8, 9, 10 no Fire risk FWI 90 5 1 mean 3, 4, 5, 6, 7, 8, 9 yes <pre>Xuebin <em><strong>Zhang</strong></em>, Gabriele Hegerl, Francis W. Zwiers, and Jesse Kenyon. Avoiding inhomogeneity in percentile-based indices of temperature extremes. Journal of Climate, 18 (11):1641–1651, 2005. ISSN 08948755. doi: 10.1175/JCLI3366.1. Samuel Jonson <em><strong>Sutanto</strong></em>, Claudia Vitolo, Claudia Di Napoli, Mirko D’Andrea, and Henny A.J. Van Lanen. Heatwaves, droughts, and fires: Exploring compound and cascading dry hazards at the pan-European scale. Environment International, 134 (March 2019):105276, jan 2020. ISSN 01604120. doi: 10.1016/j.envint.2019.105276. J. Sabater <em><strong>Muñoz</strong></em>. ERA5-Land hourly data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), 2019. O. B. <em><strong>Christensen</strong></em>, W. J. Gutowski, G. Nikulin, and S. Legutke. CORDEX Archive Design, 2020. URL https://is-enes-data.github.io/cordex_archive_specifications.pdf Barry J. F. <em><strong>Biggs</strong></em>, Bente Clausen, Siegfried Demuth, Miriam Fendeková, Lars Gottschalk, Alan Gustard, Hege Hisdal, Matthew G. R. Holmes, Ian G. Jowett, Ladislav Kašpárek, Artur Kasprzyk, Elzbieta Kupczyk, Henny A.J. Van Lanen, Henrik Madsen, Terry J. Marsh, Bjarne Moeslund, Oldřich Novický, Elisabeth Peters, Wojciech Pokojski, Erik P. Querner, Gwyn Rees, Lars Roald, Kerstin Stahl, Lena M. Tallaksen, and Andrew R. Young. Hydrological Drought: Processes and Estimation Methods for Stream- flow and Groundwater. Elsevier, 1 edition, 2004. ISBN 0444517677. <em><strong>Giannakopoulos</strong></em>, C., Karali, A., Cauchy, A. (2020): Fire danger indicators for Europe from 1970 to 2098 derived from climate projections, version 1.0, Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.ca755de7 <strong>Funding</strong> Tobias Seydewitz acknowledges funding from the German Federal Ministry of Education and Research for the [BIOCLIMAPATHS](https://www.pik-potsdam.de/en/output/projects/all/647) project (grant agreement No 01LS1906A) under the Axis-ERANET call. The funders had no role in study design, data collection, analysis, decision to publish, or manuscript preparation. </pre>