Preprints
https://doi.org/10.5194/egusphere-2023-3004
https://doi.org/10.5194/egusphere-2023-3004
09 Jan 2024
 | 09 Jan 2024
Status: this preprint is open for discussion.

Distribution-based pooling for combination and multi-model bias correction of climate simulations

Mathieu Vrac, Denis Allard, Grégoire Mariéthoz, Soulivanh Thao, and Lucas Schmutz

Abstract. For investigating, assessing and anticipating climate change, tens of Global Climate Models (GCM)s have been designed, each modeling the Earth system slightly differently. To extract a robust signal from the diverse simulations and outputs, models are typically gathered into multi-model ensembles (MMEs). Those are then summarised in various ways, including (possibly weighted) multi-model means, medians or quantiles. In this work, we introduce a new probability aggregation method termed "α-pooling" which builds an aggregated Cumulative Distribution Function (CDF) designed to be closer to a reference CDF over the calibration (historical) period. The aggregated CDFs can then be used to perform bias adjustment of the raw climate simulations, hence performing a "multi-model bias correction". In practice, each CDF is first transformed according to a non-linear transformation that depends on a parameter α. Then, a weight is assigned to each transformed CDF. This weight is an increasing function of the CDF closeness to the reference transformed CDF. Key to the α-pooling is a parameter α that describes the type of transformation, and hence the type of aggregation, generalising both linear and log-linear pooling methods. We first establish that α-pooling is a proper aggregation method verifying some optimal properties. Then, focusing on climate models simulations of temperature and precipitation over Western Europe, several experiments are run in order to assess the performance of α-pooling against methods currently available, including multi-model means and weighted variants. A reanalyses-based evaluation as well as a perfect model experiment and a sensitivity analysis to the set of climate models are run. Our findings demonstrate the superiority of the proposed pooling method, indicating that α-pooling presents a robust and efficient way to combine GCMs' CDFs. The results of this study also show that the CDFs pooling strategy for "multi-model bias correction" is a credible alternative to usual GCM-by-GCM bias correction methods, by allowing to handle and consider several climate models at once.

Mathieu Vrac, Denis Allard, Grégoire Mariéthoz, Soulivanh Thao, and Lucas Schmutz

Status: open (until 13 Mar 2024)

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  • RC1: 'Comment on egusphere-2023-3004', Anonymous Referee #1, 01 Feb 2024 reply
Mathieu Vrac, Denis Allard, Grégoire Mariéthoz, Soulivanh Thao, and Lucas Schmutz
Mathieu Vrac, Denis Allard, Grégoire Mariéthoz, Soulivanh Thao, and Lucas Schmutz

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Short summary
We aim to combine multiple Global Climate Models (GCMs) to enhance the robustness of future projections. We introduce a novel approach, called "α-pooling", aggregating the Cumulative Distribution Functions (CDFs) of the models into a CDF more aligned with historical data. The new CDFs allow us to perform bias adjustment of all the raw climate simulations at once. Experiments on European temperature and precipitation demonstrate the superiority of this approach over conventional techniques.