Published August 8, 2023 | Version v1
Journal article Open

Spatiotemporal trend analysis of climate indices for the European continent

  • 1. ROR icon Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung
  • 2. ROR icon University of Bremen

Description

The objective of this study is to analyze and visualize the spatial distribution of trends for 74 climate indices on a monthly time-scale in direction, magnitude, and significance level at a resolution of 0.1° during the period of 1950–2021 over the European region. The Mann–Kendall and Sen's slope estimators reveal that growing degree days with mean air temperature >4 °C (gd4) and heating degree days with mean air temperature <17 °C (hd17) show the largest increase (0.93 °C August) and decrease (1.03 °C July), respectively. The universal thermal climate index (utci), relative humidity (rh), wind chill index (wci), global radiation (bio20), and potential evapotranspiration (pet) are of significant importance due to higher correlation and magnitude of change. Country-specific zoning shows the highest warmer days during August experienced by Bosnia and Herzegovina (southeastern Europe) and lower colder days during January by Belarus (eastern Europe). High wind and high utci experienced by Liechtenstein (southeastern Europe) region during July. The highest wci was experienced by San Marino (southern Europe) in June and Portugal (southern Europe) in March. Bio20 and rh decline were experienced by Russia (eastern Europe) and Moldova (southeastern Europe) in May and September, respectively. Results are useful to mitigate the risk associated with each of the climate indices for specific European regions.

To get an output for the study region other than Europe follow the methodology of the manuscript: https://doi.org/10.2166/wcc.2023.183. The stored output folder contains 74 climate indices, correlation values using Pearson, Kendall, and Spearman, and spatiotemporal trend analysis (nature and magnitude of change) values using Mann-Kendall and Sen's slope. Follow the steps mentioned in the Readme file to apply a similar approach for other regions. 

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