Podrobno

Developing Guidelines for working with Multi-Model Ensembles in CMIP
ID Katzenberger, Anja (Avtor), ID Perez-Carrasquilla, Jhayron S. (Avtor), ID Gemmell, Keighan (Avtor), ID Galytska, Evgenia (Avtor), ID Leclerc, Christine (Avtor), ID Puthukulangara, Punya (Avtor), ID Roy, Indrani (Avtor), ID Varuolo-Clarke, Arianna (Avtor), ID Tošić, Milica (Avtor), ID Črnivec, Nina (Avtor)

URLURL - Izvorni URL, za dostop obiščite https://esd.copernicus.org/articles/17/495/2026/ Povezava se odpre v novem oknu
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Izvleček
Earth System Models (ESMs) are a key tool for studying the climate under changing conditions. Over recent decades, it has been established to not only rely on projections of a single model but to combine various ESMs in multi-model ensembles (MMEs) to improve robustness and quantify the uncertainty of the projections. The data access for MME studies has been fundamentally facilitated by the World Climate Research Programme's Coupled Model Intercomparison Project (CMIP) – a collaborative effort bringing together ESMs from modelling communities all over the world. Despite the CMIP standardization processes, addressing specific research questions using MMEs requires unique ensemble design, analysis, and interpretation choices. Based on the collective expertise within the Fresh Eyes on CMIP initiative, mainly composed of early-career researchers engaged in CMIP, we have identified common issues and questions encountered while working with climate MMEs. Here, we provide a comprehensive literature review addressing these questions. We provide statistics tracing the development of the climate MMEs analysis field throughout the last decades, and, synthesizing existing studies, we outline guidelines regarding model evaluation, model dependence, weighting methods, and uncertainty treatment. We summarize a collection of useful resources for MME studies, we review common questions and strategies, and finally, we outline emerging scientific trends, such as the integration of machine learning (ML) techniques, single model initial-condition large ensembles (SMILEs), and computational resource considerations. We thereby aim to support researchers working with climate MMEs, particularly in the upcoming 7th phase of CMIP.

Jezik:Angleški jezik
Ključne besede:climatology, climate models, CMIP project, multi-model ensembles
Vrsta gradiva:Članek v reviji
Tipologija:1.02 - Pregledni znanstveni članek
Organizacija:FMF - Fakulteta za matematiko in fiziko
Status publikacije:Objavljeno
Različica publikacije:Objavljena publikacija
Leto izida:2026
Št. strani:Str. 495–532
Številčenje:Vol. 17, iss. 3
PID:20.500.12556/RUL-182478 Povezava se odpre v novem oknu
UDK:551.58
ISSN pri članku:2190-4987
DOI:10.5194/esd-17-495-2026 Povezava se odpre v novem oknu
COBISS.SI-ID:277848579 Povezava se odpre v novem oknu
Datum objave v RUL:13.05.2026
Število ogledov:23
Število prenosov:3
Metapodatki:XML DC-XML DC-RDF
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Gradivo je del revije

Naslov:Earth system dynamics
Založnik:Copernicus Publ.
ISSN:2190-4987
COBISS.SI-ID:522761753 Povezava se odpre v novem oknu

Licence

Licenca:CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
Povezava:http://creativecommons.org/licenses/by/4.0/deed.sl
Opis:To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.

Sekundarni jezik

Jezik:Slovenski jezik
Ključne besede:klimatologija, klimatski modeli, projekt CMIP, večmodelni pristop

Projekti

Financer:ARIS - Javna agencija za znanstvenoraziskovalno in inovacijsko dejavnost Republike Slovenije
Številka projekta:P1-0188
Naslov:Astrofizika in fizika atmosfere

Financer:Drugi - Drug financer ali več financerjev
Program financ.:Univerza v Ljubljani
Številka projekta:SN-ZRD/22-27/510
Naslov:Napredne podnebno odporne rešitve za trajnostno biogospodarstvo in družbeno-ekonomski razvoj
Akronim:A-RISE

Financer:NOAA - National Oceanic and Atmospheric Administration
Številka projekta:NA20OAR4310390

Financer:NASA - National Aeronautics and Space Administration
Program financ.:Modeling, Analysis, and Prediction Program
Številka projekta:80NSSC21K1134

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