Abstract
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.
Highlights
The range of simulated climate responses to external radiative forcing is affected by both internal variability and intermodel differences (Hawkins and Sutton, 2009; Deser et al, 2012; Taylor et al, 2012)
We introduce a modular framework for climate model emulation of yearly land temperatures and present a specific, computationally cheap implementation called MESMER, which can create plausible temperature field time series within seconds based on a single climate model training run
The global mean temperature module contains a global mean temperature trend, which is shared by all emulations, and a global mean temperature variability term, which is modeled as an AR process and varies between individual emulations
Summary
The range of simulated climate responses to external radiative forcing is affected by both internal variability and intermodel differences (Hawkins and Sutton, 2009; Deser et al, 2012; Taylor et al, 2012). In the field of climate science, the term emulator is used for a variety of statistical models which learn from existing runs of complex climate models to infer properties of runs which have not been generated yet. There are emulators for regional-scale internal climate variability (Castruccio and Genton, 2016; Alexeeff et al, 2018; Link et al, 2019). The first attempts have been made to emulate the full dynamics of simple general circulation models (Scher, 2018; Scher and Messori, 2019)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
