LMDZ6A: The Atmospheric Component of the IPSL Climate Model With Improved and Better Tuned Physics

Hourdin, F.; Rio, Catherine; Grandpeix, Jean‐Yves; Madeleine, Jean‐Baptiste; Chéruy, F.; Rochetin, Nicolas; Jam, Arnaud; Musat, Ionela; Idelkadi, Abderrahmane; Fairhead, Laurent; Foujols, Marie-Alice; Mellul, Lidia; Traoré, Abdoul Khadre; Dufresne, Jean-Louis; Boucher, Oliviér; Lefebvre, Marie‐Pierre; Millour, Ehouarn; Vignon, Étienne; Jouhaud, Jean; Diallo, F. B.; Lott, François; Gastineau, Guillaume; Caubel, Arnaud; Meurdesoif, Yann; Ghattas, Joséfine

doi:10.1029/2019ms001892

Cited by 119 publications

(138 citation statements)

References 121 publications

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“…The ocean component is the NOAA GFDL Modular Ocean Model (MOM) version 5 (Griffies, 2014) with the same configuration as the ocean model component of ACCESS1.0 and ACCESS1.3 (Bi et al, 2013). Sea ice is simulated using the LANL CICE4.1 model (Hunke and Lipscomb, 2010). Coupling of the ocean and sea ice to the atmosphere is through the OASIS-MCT coupler (Valcke, 2013).…”

Section: Land Oceanmentioning

confidence: 99%

Carbon–concentration and carbon–climate feedbacks in CMIP6 models and their comparison to CMIP5 models

et al. 2020

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Abstract. Results from the fully and biogeochemically coupled simulations in which CO2 increases at a rate of 1 % yr−1 (1pctCO2) from its preindustrial value are analyzed to quantify the magnitude of carbon–concentration and carbon–climate feedback parameters which measure the response of ocean and terrestrial carbon pools to changes in atmospheric CO2 concentration and the resulting change in global climate, respectively. The results are based on 11 comprehensive Earth system models from the most recent (sixth) Coupled Model Intercomparison Project (CMIP6) and compared with eight models from the fifth CMIP (CMIP5). The strength of the carbon–concentration feedback is of comparable magnitudes over land (mean ± standard deviation = 0.97 ± 0.40 PgC ppm−1) and ocean (0.79 ± 0.07 PgC ppm−1), while the carbon–climate feedback over land (−45.1 ± 50.6 PgC ∘C−1) is about 3 times larger than over ocean (−17.2 ± 5.0 PgC ∘C−1). The strength of both feedbacks is an order of magnitude more uncertain over land than over ocean as has been seen in existing studies. These values and their spread from 11 CMIP6 models have not changed significantly compared to CMIP5 models. The absolute values of feedback parameters are lower for land with models that include a representation of nitrogen cycle. The transient climate response to cumulative emissions (TCRE) from the 11 CMIP6 models considered here is 1.77 ± 0.37 ∘C EgC−1 and is similar to that found in CMIP5 models (1.63 ± 0.48 ∘C EgC−1) but with somewhat reduced model spread. The expressions for feedback parameters based on the fully and biogeochemically coupled configurations of the 1pctCO2 simulation are simplified when the small temperature change in the biogeochemically coupled simulation is ignored. Decomposition of the terms of these simplified expressions for the feedback parameters is used to gain insight into the reasons for differing responses among ocean and land carbon cycle models.

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Section: Land Oceanmentioning

confidence: 99%

Carbon–concentration and carbon–climate feedbacks in CMIP6 models and their comparison to CMIP5 models

et al. 2020

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“…One of the most important legacies of this group for the atmospheric modeling community is an ensemble of test cases that connect observations, LES and SCM, and which sample many typical situations over the globe, thought to be of importance for the climate system (e.g., Siebesma & Cuijpers, 1995;Brown et al, 2002;Duynkerke et al, 2004). As such, this framework has been increasingly used in model development (e.g., Hourdin et al, 2013;Gettelman et al, 2019;Hourdin et al, 2020;Roehrig et al, -9-…”

Section: Systemmentioning

confidence: 99%

Process‐Based Climate Model Development Harnessing Machine Learning: I. A Calibration Tool for Parameterization Improvement

Couvreux

Hourdin

Williamson

et al. 2021

J Adv Model Earth Syst

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Atmospheric global or regional circulation models used either for numerical weather prediction (NWP) or climate studies encompass a dynamical core and a physical component. The dynamical core computes the spatio-temporal evolution of atmospheric state variables by solving a discrete version of the fluid dynamic equations. The physical component quantifies the impact on the resolved variables of radiative, thermodynamical, and chemical processes, as well as dynamical processes that occur at scales smaller than the computational grid. These processes are handled by a suite of sub-models, most often referred to as parameterizations, which provide source terms in the resolved-scale equations. Parameterizations (e.g., turbulence, convection, radiation, microphysics) are often based on a mixture of physical principles and heuristic description of the involved processes, of their interactions and of their impact on the larger resolved scales. Although it is difficult to trace back the origin of the term "parameterization" in climate modeling, it semantically points to the fact that the sub-models summarize the processes as functions of the model state vector x (typically the value of zonal and meridional wind, temperature, and water phases at each point of the three-dimensional [3D] model grid) that depends on some free parameters. These free parameters arise from the simplification of the complex nature of the subgrid processes (e.g., assuming a bulk thermal plume instead of a population of plumes, stationarity). The atmospheric model can be summarized as      () (,)

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“…Yearly evolution of long-lived greenhouse gases concentrations and total solar irradiance (TSI) correspond to the historical requirements of the CMIP5 project, with notably reduced TSI to consider the volcanic radiative forcing based on an updated version of Sato et al (1993). Tropospheric aerosols, tropospheric and stratospheric ozone were prepared as described in Szopa et al (2013) and land use changes processed from crop and pasture datasets developed by Hurtt et al (2011), as described in Dufresne et al (2013). Unless otherwise expressed, comparison between the historical run Table 2.…”

Section: Comparison Of Ipsl-cm5a and Ipsl-cm5a2 With Observationsmentioning

confidence: 99%

IPSL-CM5A2 – an Earth system model designed for multi-millennial climate simulations

et al. 2020

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Abstract. Based on the fifth phase of the Coupled Model Intercomparison Project (CMIP5)-generation previous Institut Pierre Simon Laplace (IPSL) Earth system model, we designed a new version, IPSL-CM5A2, aiming at running multi-millennial simulations typical of deep-time paleoclimate studies. Three priorities were followed during the setup of the model: (1) improving the overall model computing performance, (2) overcoming a persistent cold bias depicted in the previous model generation and (3) making the model able to handle the specific continental configurations of the geological past. These developments include the integration of hybrid parallelization Message Passing Interface – Open Multi-Processing (MPI-OpenMP) in the atmospheric model of the Laboratoire de Météorologie Dynamique (LMDZ), the use of a new library to perform parallel asynchronous input/output by using computing cores as “I/O servers” and the use of a parallel coupling library between the ocean and the atmospheric components. The model, which runs with an atmospheric resolution of 3.75∘×1.875∘ and 2 to 0.5∘ in the ocean, can now simulate ∼100 years per day, opening new possibilities towards the production of multi-millennial simulations with a full Earth system model. The tuning strategy employed to overcome a persistent cold bias is detailed. The confrontation of a historical simulation to climatological observations shows overall improved ocean meridional overturning circulation, marine productivity and latitudinal position of zonal wind patterns. We also present the numerous steps required to run IPSL-CM5A2 for deep-time paleoclimates through a preliminary case study for the Cretaceous. Namely, specific work on the ocean model grid was required to run the model for specific continental configurations in which continents are relocated according to past paleogeographic reconstructions. By briefly discussing the spin-up of such a simulation, we elaborate on the requirements and challenges awaiting paleoclimate modeling in the next years, namely finding the best trade-off between the level of description of the processes and the computing cost on supercomputers.

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LMDZ6A: The Atmospheric Component of the IPSL Climate Model With Improved and Better Tuned Physics

Cited by 119 publications

References 121 publications

Carbon–concentration and carbon–climate feedbacks in CMIP6 models and their comparison to CMIP5 models

Carbon–concentration and carbon–climate feedbacks in CMIP6 models and their comparison to CMIP5 models

Process‐Based Climate Model Development Harnessing Machine Learning: I. A Calibration Tool for Parameterization Improvement

IPSL-CM5A2 – an Earth system model designed for multi-millennial climate simulations

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