Future Changes in Precipitation Over Northern Europe Based on a Multi-model Ensemble from CMIP6: Focus on Tana River Basin

Accurate climate projections help policymakers mitigate the negative effects of climatic changes and prioritize environmental issues based on scientific evidences. These projections rely heavily on the outputs of GCMs (General Circulation Models), but the large number of GCMs and their different out...

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Veröffentlicht in:Water resources management 2023-05, Vol.37 (6-7), p.2447-2463
Hauptverfasser: Moradian, Sogol, Torabi Haghighi, Ali, Asadi, Maryam, Mirbagheri, Seyed Ahmad
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Sprache:eng
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Zusammenfassung:Accurate climate projections help policymakers mitigate the negative effects of climatic changes and prioritize environmental issues based on scientific evidences. These projections rely heavily on the outputs of GCMs (General Circulation Models), but the large number of GCMs and their different outputs in each region confuses researchers in their selection. In this paper, we analyzed the performance of a CMIP6 (Climate Model Intercomparison Project Phase 6) multi-model ensemble for Pr (precipitation) data over NE (Northern Europe). First of all, we evaluated the overall performance of 12 CMIP6 models from GCMs in 30 years of 1985–2014. Furthermore, future projections were analyzed between 2071 and 2100 using SSP1-2.6 and SSP5-8.5 (Shared Socioeconomic Pathways). Then, simulations were statistically improved using an ensemble method to correct the systematic error of the CMIP6 models and then the capacity of postprocessed data to reproduce historical trends of climate events was investigated. Finally, the possible spatio-temporal changes of future Pr data were explored in Tana River Basin. The results of this study show that different CMIP6 models do not have the same accuracy in estimating Pr in the study area. However, the ensemble method can be effective in increasing the accuracy of the projections. The results of this study projected a change in the monthly Pr data over Tana River Basin by 2.46% and 2.06% from 2071 to 2100 compared to the historical period, based on SSP1-2.6 and SSP5-8.5, respectively.
ISSN:0920-4741
1573-1650
DOI:10.1007/s11269-022-03272-4