CMIP6 simulations with the compact Earth system model OSCAR v3.1

Abstract

Abstract. Reduced-complexity models, also called simple climate models or compact models, provide an alternative to Earth system models (ESMs) with lower computational costs, although at the expense of spatial and temporal information. It remains important to evaluate and validate these reduced-complexity models. Here, we evaluate a recent version (v3.1) of the OSCAR model using observations and results from ESMs from the current Coupled Model Intercomparison Project 6 (CMIP6). The results follow the same post-processing used for the contribution of OSCAR to the Reduced Complexity Model Intercomparison Project (RCMIP) Phase 2 regarding the identification of stable configurations and the use of observational constraints. These constraints succeed in decreasing the overestimation of global surface air temperature over 2000–2019 with reference to 1961–1900 from 0.60±0.11 to 0.55±0.04 K (the constraint being 0.54±0.05 K). The equilibrium climate sensitivity (ECS) of the unconstrained OSCAR is 3.17±0.63 K, while CMIP5 and CMIP6 models have ECSs of 3.2±0.7 and 3.7±1.1 K, respectively. Applying observational constraints to OSCAR reduces the ECS to 2.78±0.47 K. Overall, the model qualitatively reproduces the responses of complex ESMs, although some differences remain due to the impact of observational constraints on the weighting of parametrizations. Specific features of OSCAR also contribute to these differences, such as its fully interactive atmospheric chemistry and endogenous calculations of biomass burning, wetlands CH4 and permafrost CH4 and CO2 emissions. Identified main points of needed improvements of the OSCAR model include a low sensitivity of the land carbon cycle to climate change, an instability of the ocean carbon cycle, the climate module that is seemingly too simple, and the climate feedback involving short-lived species that is too strong. Beyond providing a key diagnosis of the OSCAR model in the context of the reduced-complexity models, this work is also meant to help with the upcoming calibration of OSCAR on CMIP6 results and to provide a large group of CMIP6 simulations run consistently within a probabilistic framework.