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  Climate model downscaling in central Asia: a dynamical and a neural network approach

Fallah, B. H., Rostami, M., Russo, E., Harder, P., Menz, C., Hoffmann, P., Didovets, I., Hattermann, F. F. (2025): Climate model downscaling in central Asia: a dynamical and a neural network approach. - Geoscientific Model Development, 18, 1, 161-180.
https://doi.org/10.5194/gmd-18-161-2025

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???ViewItemFull_lblCiteItemAs???: https://publications.pik-potsdam.de/pubman/item/item_30596 ???ViewItemFull_lblCiteItemVersionAs???: https://publications.pik-potsdam.de/pubman/item/item_30596_9
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https://doi.org/10.5194/gmd-2023-227 (???ENUM_CONTENTCATEGORY_pre-print???)
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 ???ViewItemFull_lblCreators???:
Fallah, Bijan H.1, 2, ???ENUM_CREATORROLE_AUTHOR???                 
Rostami, Masoud1, ???ENUM_CREATORROLE_AUTHOR???                 
Russo, Emmanuele3, ???ENUM_CREATORROLE_AUTHOR???
Harder, Paula3, ???ENUM_CREATORROLE_AUTHOR???
Menz, Christoph1, ???ENUM_CREATORROLE_AUTHOR???                 
Hoffmann, Peter1, ???ENUM_CREATORROLE_AUTHOR???                 
Didovets, Iulii1, ???ENUM_CREATORROLE_AUTHOR???                 
Hattermann, Fred Fokko1, ???ENUM_CREATORROLE_AUTHOR???                 
???ViewItemFull_lblAffiliations???:
1Potsdam Institute for Climate Impact Research, ou_persistent13              
2Submitting Corresponding Author, Potsdam Institute for Climate Impact Research, ou_29970              
3External Organizations, ou_persistent22              

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 ???ViewItemFull_lblAbstract???: To estimate future climate change impacts, usually high-resolution climate projections are necessary. Statistical and
dynamical downscaling or a hybrid of both methods are mostly used to produce input datasets for impact modelers. In this
study, we use the regional climate model (RCM) COSMO-CLM (CCLM) version 6.0 to identify the added value of dynam-
ically downscaling a general circulation model (GCM) from the sixth phase of the Coupled Model Inter-comparison Project
(CMIP6) and its climate change projections’ signal over Central Asia (CA). We use the MPI-ESM1-2-HR (at 1° spatial reso-5
lution) to drive the CCLM (at 0.22° horizontal resolution) for the historical period of 1985-2014 and the projection period of
2019-2100 under three different shared socioeconomic pathways (SSPs): SSP1-2.6, SSP3-7.0 and SSP5-8.5 scenarios. Using
the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) gridded observation dataset, we evaluate the
CCLM performance over the historical period using a simulation driven by ERAInterim reanalysis. CCLM’s added value, com-
pared to its driving GCM, is significant over CA mountainous areas, which are at higher risk of extreme precipitation events.10
Furthermore, we downscale the CCLM for future climate projections. We present high-resolution maps of heavy precipitation
changes based on CCLM and compare them with CMIP6 GCMs ensemble. Our analysis shows a significant increase in heavy
precipitation intensity and frequency over CA areas that are already at risk of extreme climatic events in the present day. Fi-
nally, applying our single model high-resolution dynamical downscaling, we train a convolutional neural network (CNN) to
map the low-resolution GCM simulations to the dynamically downscaled CCLM ones. We show that applied CNN could em-15
ulate the GCM-CCLM model chain over large CA areas. However, this specific emulator has shortcomings when applied to a
new GCM-CCLM model chain. Our downscaling data and the pre-trained CNN model could be used by scientific communities
interested in downscaling CMIP6 models and searching for a trade-off between the dynamical and statistical methods.

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???ViewItemFull_lblLanguages???: eng - English
 ???ViewItemFull_lblDates???: 2023-12-052024-11-192025-01-152025-01-15
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 ???ViewItemFull_lblPages???: 20
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 ???ViewItemFull_lblIdentifiers???: ???ENUM_IDENTIFIERTYPE_ORGANISATIONALK???: RD2 - Climate Resilience
???ENUM_IDENTIFIERTYPE_PIKDOMAIN???: RD2 - Climate Resilience
???ENUM_IDENTIFIERTYPE_PIKDOMAIN???: RD1 - Earth System Analysis
???ENUM_IDENTIFIERTYPE_ORGANISATIONALK???: RD1 - Earth System Analysis
???ENUM_IDENTIFIERTYPE_WORKINGGROUP???: Hydroclimatic Risks
???ENUM_IDENTIFIERTYPE_MDB_ID???: No data to archive
???ENUM_IDENTIFIERTYPE_REGIONALK???: Asia
???ENUM_IDENTIFIERTYPE_MODELMETHOD???: Machine Learning
???ENUM_IDENTIFIERTYPE_MODELMETHOD???: Nonlinear Data Analysis
???ENUM_IDENTIFIERTYPE_RESEARCHTK???: Atmosphere
???ENUM_IDENTIFIERTYPE_RESEARCHTK???: Weather
???ENUM_IDENTIFIERTYPE_OATYPE???: Gold Open Access
???ENUM_IDENTIFIERTYPE_DOI???: 10.5194/gmd-18-161-2025
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???ViewItemFull_lblSourceTitle???: Geoscientific Model Development
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???ENUM_IDENTIFIERTYPE_PUBLISHER???: Copernicus