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  Constraining the Pattern and Magnitude of Projected Extreme Precipitation Change in a Multimodel Ensemble

Kotz, M., Lange, S., Wenz, L., Levermann, A. (2024): Constraining the Pattern and Magnitude of Projected Extreme Precipitation Change in a Multimodel Ensemble. - Journal of Climate, 37, 1, 97-111.
https://doi.org/10.1175/JCLI-D-23-0492.1

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Item Permalink: https://publications.pik-potsdam.de/pubman/item/item_29133 Version Permalink: https://publications.pik-potsdam.de/pubman/item/item_29133_4
Genre: Journal Article

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 Creators:
Kotz, Maximilian1, Author              
Lange, Stefan1, Author              
Wenz, Leonie1, Author              
Levermann, Anders1, Author              
Affiliations:
1Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: Projections of precipitation extremes over land are crucial for socioeconomic risk assessments, yet model discrepancies limit their application. Here we use a pattern-filtering technique to identify low-frequency changes in individual members of a multimodel ensemble to assess discrepancies across models in the projected pattern and magnitude of change. Specifically, we apply low-frequency component analysis (LFCA) to the intensity and frequency of daily precipitation extremes over land in 21 CMIP-6 models. LFCA brings modest but statistically significant improvements in the agreement between models in the spatial pattern of projected change, particularly in scenarios with weak greenhouse forcing. Moreover, we show that LFCA facilitates a robust identification of the rates at which increasing precipitation extremes scale with global temperature change within individual ensemble members. While these rates approximately match expectations from the Clausius-Clapeyron relation on average across models, individual models exhibit considerable and significant differences. Monte Carlo simulations indicate that these differences contribute to uncertainty in the magnitude of projected change at least as much as differences in the climate sensitivity. Last, we compare these scaling rates with those identified from observational products, demonstrating that virtually all climate models significantly underestimate the rates at which increases in precipitation extremes have scaled with global temperatures historically. Constraining projections with observations therefore amplifies the projected intensification of precipitation extremes as well as reducing the relative error of their distribution.

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Language(s): eng - English
 Dates: 2023-10-022023-12-112024-01-01
 Publication Status: Finally published
 Pages: 15
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: Organisational keyword: RD4 - Complexity Science
Organisational keyword: RD3 - Transformation Pathways
PIKDOMAIN: RD4 - Complexity Science
PIKDOMAIN: RD3 - Transformation Pathways
Research topic keyword: Atmosphere
Research topic keyword: Extremes
Research topic keyword: Weather
Regional keyword: Global
Model / method: Nonlinear Data Analysis
DOI: 10.1175/JCLI-D-23-0492.1
Working Group: Data-based analysis of climate decisions
Working Group: Data-Centric Modeling of Cross-Sectoral Impacts
MDB-ID: yes - 3519
OATYPE: Green Open Access
 Degree: -

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Project name : Impact of intensified weather extremes on Europe's economy (ImpactEE)
Grant ID : 93350
Funding program : Europe and Global Challenges
Funding organization : VolkswagenStiftung
Project name : Provision of climate and bio-physical forcing data for health impact projections
Grant ID : 409670289
Funding program : FOR 2936: Climate Change and Health in Sub-Saharan Africa
Funding organization : Deutsche Forschungsgemeinschaft (DFG)
Project name : Quantifying direct and indirect costs of climate-related hazards (QUIDIC)
Grant ID : 01LP1907A
Funding program : Ökonomie des Klimawandels
Funding organization : BMBF
Project name : RECEIPT
Grant ID : 820712
Funding program : Horizon 2020 (Horizon 2020) (H2020-LC-CLA-2018-2)
Funding organization : European Commission (EC)

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Title: Journal of Climate
Source Genre: Journal, SCI, Scopus, p3
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Publ. Info: -
Pages: - Volume / Issue: 37 (1) Sequence Number: - Start / End Page: 97 - 111 Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/journals254
Publisher: American Meteorological Society (AMS)