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.
Highlights
Climate model emulators are computationally cheap devices that derive simplified statistical relationships from existing climate model runs to approximate model runs that have not been generated yet
A Modular Earth System Model Emulator with spatially Resolved output (MESMER) (Beusch et al, 2020) has 30 been developed with the ability to represent model uncertainty due to natural climate variability. It does so using a combination of pattern scaling and a natural climate variability module, to generate spatially resolved, yearly temperature realisations that emulate the properties of ESM initial-condition ensembles
We extend MESMER’s framework to include a monthly downscaling module trained for each ESM at each grid-point individually, providing realistic, spatially explicit monthly temperature fields from yearly temperature fields in a matter 350 of seconds
Summary
Climate model emulators are computationally cheap devices that derive simplified statistical relationships from existing climate model runs to approximate model runs that have not been generated yet. A Modular Earth System Model Emulator with spatially Resolved output (MESMER) (Beusch et al, 2020) has 30 been developed with the ability to represent model uncertainty due to natural climate variability It does so using a combination of pattern scaling and a natural climate variability module, to generate spatially resolved, yearly temperature realisations that emulate the properties of ESM initial-condition ensembles. Considering the importance of monthly and seasonal information in assessing the impacts of climate change (Zhao et al, 2017; Schlenker and Roberts, 2009; Wramneby et al, 2010; Stéfanon et al, 2012; Pfleiderer et al, 2019), extending the MESMER approach to grid point level monthly temperatures appears desirable Such holds additional value in assessing the 40 evolving likelihoods of future impacts, as the temperature response at monthly timescales displays heterogeneities distributive onto seasonal to monthly variabilities and uncertainties, which are otherwise unapparent at annual timescales. The structure of this study is as follows: we first introduce the framework of the emulator under Section 55 3.1 and the approach to verification of the emulator performance under Section 3.2, we provide the calibration results of the emulator and its example outputs under section 4 and verification results under section 5 after which we proceed to the conclusion and outlook under section 6
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