A Stepwise‐Clustered Precipitation Downscaling Method for Ensemble Climatic Projections in the Mediterranean Region

ABSTRACT Precipitation changes dynamically in the Mediterranean region. Therefore, the projection of future precipitation and its historical distribution mechanism is essential for climate mitigation and adaptation. In this study, a stepwise clustered precipitation downscaling method (SCPD) was developed and adopted in the Mediterranean region to reveal the inherent variation rules and trends over the future 100 years under two SSP scenarios. A cutting and merging multivariate process is introduced to build a cluster tree for supporting further downscaling and projecting steps. The ensemble average from the global climate model (GCM) dataset is used for precipitation projections. The precipitation performance of SCPD, evaluated by R 2 , is fairly decent. The precipitation projections vary with the original rainfall patterns over the gauge stations. Dry places tend to become comparably drier in the future. Precipitation in the northern Mediterranean region shows a drier winter–spring and wetter summer–autumn. Opposite trends emerged in the southern part, with increasing winter precipitation and decreasing summer rainfall. The rising carbon dioxide concentration will further intensify the decrease in rainfall. However, the centres of these two EOFs are not identical. The contributions of NAO (positive) and Niño 3.4 (negative) to PC1 are relatively high. Accordingly, the strongest positive correlation with PC2 is SCAND, as well as negative correlations with AO, NAO and EAWR. Positive anomaly precipitation is attributed to PC1, whereas PC2 is responsible for most of the negative variance precipitation.

Authors

Institutions

Publication Details

Journal
International Journal of Climatology
Published
2024-11-07
DOI
https://doi.org/10.1002/joc.8651
Citations
1
Primary Topic
Climate variability and models
Type
article
Field-Weighted Citation Impact
0.28

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Stepwise‐Clustered Precipitation Downscaling Method for Ensemble Climatic Projections in the Mediterranean Region

Lü Chen, Siyu Wang, Chong Zhang, Guohe Huang
1 citations
International Journal of Climatology
Climate variability and models
0.28
article

A Stepwise‐Clustered Precipitation Downscaling Method for Ensemble Climatic Projections in the Mediterranean Region

Lü Chen, Siyu Wang, Chong Zhang, Guohe Huang
article en
1 citations

Abstract

ABSTRACT Precipitation changes dynamically in the Mediterranean region. Therefore, the projection of future precipitation and its historical distribution mechanism is essential for climate mitigation and adaptation. In this study, a stepwise clustered precipitation downscaling method (SCPD) was developed and adopted in the Mediterranean region to reveal the inherent variation rules and trends over the future 100 years under two SSP scenarios. A cutting and merging multivariate process is introduced to build a cluster tree for supporting further downscaling and projecting steps. The ensemble average from the global climate model (GCM) dataset is used for precipitation projections. The precipitation performance of SCPD, evaluated by R 2 , is fairly decent. The precipitation projections vary with the original rainfall patterns over the gauge stations. Dry places tend to become comparably drier in the future. Precipitation in the northern Mediterranean region shows a drier winter–spring and wetter summer–autumn. Opposite trends emerged in the southern part, with increasing winter precipitation and decreasing summer rainfall. The rising carbon dioxide concentration will further intensify the decrease in rainfall. However, the centres of these two EOFs are not identical. The contributions of NAO (positive) and Niño 3.4 (negative) to PC1 are relatively high. Accordingly, the strongest positive correlation with PC2 is SCAND, as well as negative correlations with AO, NAO and EAWR. Positive anomaly precipitation is attributed to PC1, whereas PC2 is responsible for most of the negative variance precipitation.

International Journal of ClimatologyVol. 44(15)
University of Regina (CA)
Canada Research Chairs, Mitacs
Climate action
Openalex Percentile: Top 41%
Climate variability and models
0.28
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

A Stepwise‐Clustered Precipitation Downscaling Method for Ensemble Climatic Projections in the Mediterranean Region — Lü Chen, Siyu Wang, et al. · International Journal of Climatology (2024) | TGRS Research Map | TGRS