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

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.Plain Language Summary Climate models are unique tools to investigate the characteristics and behavior of the climate system. While climate models and their components are developed gradually over the years, the sixth phase of the Coupled Model Intercomparison Project (CMIP6) has been the

Tracking Improvement in Simulated Marine Biogeochemistry Between CMIP5 and CMIP6

“…The names of the ESM are given in the first line of the table where the CMIP6 ESMs are given in red cells and the CMIP5 predecessors are given in pink cells. The complexity of the marine biogeochemical models is described using (i) the trophic web, the number of living species or phytoplankton functional types; (ii) the internal physiology, the stoichiometry and the representation of internal photosynthetic pigment; (iii) the organic matter cycling, the number of organic carbon pools and their representation; (iv) the representation of marine sediments and (v) the nutrient cycling: the number of nutrients and the representation of oxygen and iron cycling [25], 10 Held et al [78], 11 Krasting et al [79], 12 Romanou et al [28], 13 Ito et al [80], 14 Jones et al [81], 15 Sellar et al [82], 16 Dufresne et al [24], 17 Boucher et al [83], 18 Watanabe et al [30], 19 Hajima et al [84], 20 Giorgetta et al [26], 21 Mauritsen et al [85], 22 Adachi et al [21], 23 Yukimoto et al [86], 24 Bentsen et al [23], 25 Seland et al [87]. Tjiputra et al [101], v Tjiputra et al [102].…”

Broad Consistency Between Observed and Simulated Trends in Sea Surface Temperature Patterns

“…We use seven single‐model initial condition large ensembles, namely, CanESM2 (Kirchmeier‐Young et al, 2016), CESM‐LE (Kay et al, 2014), CSIRO‐Mk3‐6‐0 (Jeffrey et al, 2013), GFDL‐ESM2M (Rodgers et al, 2015), IPSL‐CM6A‐LR (Boucher et al, 2020), MIROC6 (Tatebe et al, 2019), and MPI‐GE (Maher et al, 2019). The number of realizations from each single‐model ensemble are given in Figure 1.…”

Improved Near‐Surface Continental Climate in IPSL‐CM6A‐LR by Combined Evolutions of Atmospheric and Land Surface Physics

“…The focus of the present analysis is put on the processes which control the energy and moisture exchange at the surface. Main features of the near surface climate over continents in the historical simulations done with the full IPSL-CM are documented in a companion paper (Boucher et al, 2020), where the biogeochemical aspects of the land surface-atmosphere coupling are considered. largely model-dependent (Boé & Terray, 2008;Hohenegger et al, 2009).…”