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Earth System Dynamics An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/esd-2019-34
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esd-2019-34
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 02 Jul 2019

Research article | 02 Jul 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Earth System Dynamics (ESD).

Emulating Earth System Model temperatures: from global mean temperature trajectories to grid-point level realizations on land

Lea Beusch, Lukas Gudmundsson, and Sonia I. Seneviratne Lea Beusch et al.
  • Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland

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 global spatially and temporally correlated land temperature field time series at a negligible computational cost which closely resemble ESM runs spanning from 1870 to 2099. For this purpose, we develop a modular framework that consists of (i) a global mean temperature emulator, (ii) a local mean temperature emulator, and (iii) a local residual temperature variability emulator. We first show that to successfully mimic single-model initial-condition ensembles of yearly temperature, 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 super-ensemble, i.e., a large ensemble that closely resembles a multi-model initial-condition ensemble. Furthermore, the thereby emerging ESM-specific emulator calibration parameters provide essential insights on inter-model divergences across a broad range of scales which can be viewed as a model ID of core properties of each ESM. Our results highlight that, for temperature at the spatio-temporal 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 then be extended to super-ensembles with geographically-explicit temperature emulators like the one presented here.

Lea Beusch et al.
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Lea Beusch et al.
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Latest update: 22 Jul 2019
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Short summary
Earth System Models (ESMs) are invaluable to study the climate system but are expensive to run. Here, we present a statistical tool which emulates ESMs at a negligible computational cost by creating stochastic realizations of land temperature field time series. While our realizations closely resemble ESM simulations not employed during training, our model needs to be retrained when considering different ESMs, highlighting the importance of using multiple ESMs to study climate signal uncertainty.
Earth System Models (ESMs) are invaluable to study the climate system but are expensive to run....
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