Preprints
https://doi.org/10.5194/esd-2021-59
https://doi.org/10.5194/esd-2021-59

  03 Sep 2021

03 Sep 2021

Review status: this preprint is currently under review for the journal ESD.

MESMER-M: an Earth System Model emulator for spatially resolved monthly temperatures

Shruti Nath1,2, Quentin Lejeune1, Lea Beusch2, Carl-Friedrich Schleussner1,3, and Sonia I. Seneviratne2 Shruti Nath et al.
  • 1Climate Analytics, Berlin, Germany
  • 2Institute of Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland
  • 3Integrative Research Institute on Transformations of Human-Environment Systems (IRI THESys) and Geography Department, Humboldt-Universität zu Berlin, Berlin, Germany

Abstract. The degree of trust placed in climate model projections is commensurate to how well their uncertainty can be quantified, particularly at timescales relevant to climate policy makers. On interannual to decadal timescales, model uncertainty due to internal variability dominates and is imperative to understanding near-term and seasonal climate events, but hard to quantify owing to the computational constraints on producing large ensembles. To this extent, emulators are valuable tools for approximating climate model runs, allowing for exploration of the model uncertainty space surrounding select climate variables at a significantly reduced computational cost. Most emulators, however, operate at annual to seasonal timescales, leaving out monthly information that may be essential to assessing climate impacts. This study extends the framework of an existing spatially resolved, annual-scale Earth System Model (ESM) emulator (MESMER, Beusch et al. 2020) by a monthly downscaling module (MESMER-M), thus providing local monthly temperatures from local yearly temperatures. We first linearly represent the mean response of the monthly temperature cycle to yearly temperatures using a simple harmonic model, thus maintaining month to month correlations and capturing changes in intra-annual variability. We then construct a month-specific local variability module which generates spatio-temporally correlated residuals with month and yearly temperature dependent skewness incorporated within. The performance of the resulting emulator is demonstrated on 38 different ESMs from the 6th phase of the Coupled Model Intercomparison Project (CMIP6). The emulator is furthermore benchmarked using a simple Gradient Boosting Regressor based, physical model trained on biophysical information. We find that while regional-scale, biophysical feedbacks may induce non-uniformities in the yearly to monthly temperature downscaling relationship, statistical emulation of regional effects shows comparable skill to approaches with physical representation. Thus, MESMER-M is able to generate ESM-like, large initial-condition ensembles of spatially explicit monthly temperature fields, thereby providing monthly temperature probability distributions which are of critical value to impact assessments. 

Shruti Nath et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on esd-2021-59', Anonymous Referee #1, 30 Sep 2021
  • RC2: 'Comment on esd-2021-59', Anonymous Referee #2, 18 Oct 2021
  • RC3: 'Comment on esd-2021-59', Anonymous Referee #3, 19 Oct 2021

Shruti Nath et al.

Shruti Nath et al.

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Short summary
Uncertainty within climate model projections on interannual timescales are largely affected by natural climate variability. Emulators are valuable tools for approximating climate model runs, allowing for easy exploration of such uncertainty spaces. This study takes a first step at building a spatially resolved, monthly temperature emulator that takes local yearly temperatures as the sole input, thus providing monthly temperature distributions which are of critical value to impact assessments.
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