Changes in mean and extreme temperature and precipitation events from different weighted multi-model ensembles over the northern half of Morocco


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Output type: Journal article

UM6P affiliated Publication?: Yes

Author list: Balhane S., Driouech F., Chafki O., Manzanas R., Chehbouni A., Moufouma-Okia W.

Publisher: Springer (part of Springer Nature): Springer Open Choice Hybrid Journals

Publication year: 2021

Journal: Climate Dynamics (0930-7575)

ISSN: 0930-7575

eISSN: 1432-0894

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113134822&doi=10.1007%2fs00382-021-05910-w&partnerID=40&md5=210349dd51f049ee902ef6be10c7b946

Languages: English (EN-GB)


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Abstract

Internal variability, multiple emission scenarios, and different model responses to anthropogenic forcing are ultimately behind a wide range of uncertainties that arise in climate change projections. Model weighting approaches are generally used to reduce the uncertainty related to the choice of the climate model. This study compares three multi-model combination approaches: a simple arithmetic mean and two recently developed weighting-based alternatives. One method takes into account models’ performance only and the other accounts for models’ performance and independence. The effect of these three multi-model approaches is assessed for projected changes of mean precipitation and temperature as well as four extreme indices over northern Morocco. We analyze different widely used high-resolution ensembles issued from statistical (NEXGDDP) and dynamical (Euro-CORDEX and bias-adjusted Euro-CORDEX) downscaling. For the latter, we also investigate the potential added value that bias adjustment may have over the raw dynamical simulations. Results show that model weighting can significantly reduce the spread of the future projections increasing their reliability. Nearly all model ensembles project a significant warming over the studied region (more intense inland than near the coasts), together with longer and more severe dry periods. In most cases, the different weighting methods lead to almost identical spatial patterns of climate change, indicating that the uncertainty due to the choice of multi-model combination strategy is nearly negligible. © 2021, The Author(s).


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Last updated on 2021-23-10 at 23:02