Emulating Earth system model temperatures with MESMER: from global mean temperature trajectories to grid-point-level realizations on land

Abstract

Earth system models (ESMs) are invaluable tools to study the climate system's response to specific greenhouse gas emission pathways. Large single-model initial-condition and multi-model ensembles are used to investigate the range of possible responses and serve as input to climate impact and integrated assessment models. Thereby, climate signal uncertainty is propagated along the uncertainty chain and its effect on interactions between humans and the Earth system can be quantified. However, generating both single-model initial-condition and multi-model ensembles is computationally expensive. In this study, we assess the feasibility of geographically explicit climate model emulation, i.e., of statistically producing large ensembles of land temperature field time series that closely resemble ESM runs at a negligible computational cost. For this purpose, we develop a modular emulation framework which consists of (i) a global mean temperature module, (ii) a local temperature response module, and (iii) a local residual temperature variability module. Based on this framework, MESMER, a Modular Earth System Model Emulator with spatially Resolved output, is built. We first show that to successfully mimic single-model initial-condition ensembles of yearly temperature from 1870 to 2100 on grid-point to regional scales with MESMER, it is sufficient to train on a single ESM run, but separate emulators need to be calibrated for individual ESMs given fundamental inter-model differences. We then emulate 40 climate models of the Coupled Model Intercomparison Project Phase 5 (CMIP5) to create a “superensemble”, i.e., a large ensemble which closely resembles a multi-model initial-condition ensemble. The thereby emerging ESM-specific emulator parameters provide essential insights on inter-model differences across a broad range of scales and characterize core properties of each ESM. Our results highlight that, for temperature at the spatiotemporal scales considered here, it is likely more advantageous to invest computational resources into generating multi-model ensembles rather than large single-model initial-condition ensembles. Such multi-model ensembles can be extended to superensembles with emulators like the one presented here.