Presentation and Evaluation of the IPSL-CM6A-LR Climate Model

Olivier Boucher*, Jérôme Servonnat, Anna Lea Albright, Olivier Aumont, Yves Balkanski, Vladislav Bastrikov, Slimane Bekki, Rémy Bonnet, Sandrine Bony, Laurent Bopp, Pascale Braconnot, Patrick Brockmann, Patricia Cadule, Arnaud Caubel, Frederique Cheruy, Francis Codron, Anne Cozic, David Cugnet, Fabio D'Andrea, Paolo DaviniCasimir de Lavergne, Sébastien Denvil, Julie Deshayes, Marion Devilliers, Agnes Ducharne, Jean Louis Dufresne, Eliott Dupont, Christian Éthé, Laurent Fairhead, Lola Falletti, Simona Flavoni, Marie Alice Foujols, Sébastien Gardoll, Guillaume Gastineau, Josefine Ghattas, Jean Yves Grandpeix, Bertrand Guenet, Lionel, E. Guez, Eric Guilyardi, Matthieu Guimberteau, Didier Hauglustaine, Frédéric Hourdin, Abderrahmane Idelkadi, Sylvie Joussaume, Masa Kageyama, Myriam Khodri, Gerhard Krinner, Nicolas Lebas, Guillaume Levavasseur, Claire Lévy, Laurent Li, François Lott, Thibaut Lurton, Sebastiaan Luyssaert, Gurvan Madec, Jean Baptiste Madeleine, Fabienne Maignan, Marion Marchand, Olivier Marti, Lidia Mellul, Yann Meurdesoif, Juliette Mignot, Ionela Musat, Catherine Ottlé, Philippe Peylin, Yann Planton, Jan Polcher, Catherine Rio, Nicolas Rochetin, Clément Rousset, Pierre Sepulchre, Adriana Sima, Didier Swingedouw, Rémi Thiéblemont, Abdoul Khadre Traore, Martin Vancoppenolle, Jessica Vial, Jérôme Vialard, Nicolas Viovy, Nicolas Vuichard

*Corresponding author for this work

    Research output: Contribution to JournalArticleAcademicpeer-review

    Abstract

    This study presents the global climate model IPSL-CM6A-LR developed at Institut Pierre-Simon Laplace (IPSL) to study natural climate variability and climate response to natural and anthropogenic forcings as part of the sixth phase of the Coupled Model Intercomparison Project (CMIP6). This article describes the different model components, their coupling, and the simulated climate in comparison to previous model versions. We focus here on the representation of the physical climate along with the main characteristics of the global carbon cycle. The model's climatology, as assessed from a range of metrics (related in particular to radiation, temperature, precipitation, and wind), is strongly improved in comparison to previous model versions. Although they are reduced, a number of known biases and shortcomings (e.g., double Intertropical Convergence Zone [ITCZ], frequency of midlatitude wintertime blockings, and El Niño–Southern Oscillation [ENSO] dynamics) persist. The equilibrium climate sensitivity and transient climate response have both increased from the previous climate model IPSL-CM5A-LR used in CMIP5. A large ensemble of more than 30 members for the historical period (1850–2018) and a smaller ensemble for a range of emissions scenarios (until 2100 and 2300) are also presented and discussed.

    Original languageEnglish
    Article numbere2019MS002010
    Pages (from-to)1-52
    Number of pages52
    JournalJournal of Advances in Modeling Earth Systems
    Volume12
    Issue number7
    Early online date28 May 2020
    DOIs
    Publication statusPublished - Jul 2020

    Funding

    The authors are grateful to the developers of the OASIS software and the Earth System model components not in the author list. This work was undertaken in the framework of the L-IPSL LABEX and the IPSL Climate Graduate School EUR. As such it benefited from the French state aid managed by the ANR under the ?Investissements d'avenir? program with the reference ANR-11-IDEX-0004-17-EURE-0006. It also benefited from Belmont project GOTHAM, under Grant ANR-15-JCLI-0004-01, the ANR project ARISE under Grant ANR-18-CE01-0012, ANR project CONVERGENCE, under Grant ANR-13-MONU-0008 and MOPGA/Investissements d'Avenir project Archange, under Grant ANR-18-MPGA-0001. The CMIP6 project at IPSL used the HPC resources of TGCC under the allocations 2016-A0030107732, 2017-R0040110492, and 2018-R0040110492 (project gencmip6) provided by GENCI (Grand ?quipement National de Calcul Intensif). This study benefited from the ESPRI (Ensemble de Services Pour la Recherche l'IPSL) computing and data center (https://mesocentre.ipsl.fr) which is supported by CNRS, Sorbonne Universit?, ?cole Polytechnique, and CNES and through national and international grants. Support from the European Commission's Horizon 2020 Framework Programme is acknowledged, under Grant Agreement number 641816 for the ?Coordinated Research in Earth Systems and Climate: Experiments, kNowledge, Dissemination and Outreach (CRESCENDO)? project (11/2015-10/2020) and under Grant Agreement number 820829 for the ?Constraining uncertainty of multidecadal climate projections (CONSTRAIN)? project. Peter Gleckler and colleagues from the Program for Climate Model Diagnosis and Intercomparison (PCMDI) are acknowledged for their contribution to the performance metrics package. For the analyses we have used Python, CDO (https://code.mpimet.mpg.de/projects/cdo), NCL (http://ncl.ucar.edu/), R (https://www.R-project.org/) and took advantage of the CliMAF Python library (Climate Model Assessment Framework, https://github.com/rigoudyg/climaf).

    FundersFunder number
    ESPRI
    European Commission's Horizon 2020 framework programme641816, 11/2015‐10/2020, 820829
    TGCC2018-R0040110492, 2016-A0030107732, 2017-R0040110492
    Centre National d’Etudes Spatiales
    Centre National de la Recherche Scientifique
    Grand Équipement National De Calcul Intensif
    National Chemical Laboratory
    Sorbonne Université
    École polytechnique

      Keywords

      • climate metrics
      • climate model
      • climate sensitivity
      • CMIP6
      • IPSL-CM6A-LR

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