Preprints
https://doi.org/10.5194/esd-2022-31
https://doi.org/10.5194/esd-2022-31
 
03 Aug 2022
03 Aug 2022
Status: this preprint is currently under review for the journal ESD.

Performance based sub-selection of CMIP6 models for impact assessments in Europe

Tamzin Emily Palmer1, Carol F. McSweeney1, Ben B. B. Booth1, Matthew D. K. Priestley2, Paolo Davini3, Lukas Brunner4, Leonard Borchert5,6, and Matthew B. Menary6 Tamzin Emily Palmer et al.
  • 1Met Office Hadley Centre, FitzRoy Rd, Exeter, Devon, EX1 3PB, UK
  • 2College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK
  • 3Consiglio Nazionale delle Ricerchere, Istituto di Scienze dell’Atmosfera e del Clima (CNR-ISAC), Torino, Italy
  • 4Department of Meteorology and Geophysics, University of Vienna, Vienna, Austria
  • 5Climate Statistics and Climate Extremes, Centre for Earth System Research and Sustainability (CEN), Universität Hamburg, Germany
  • 6Laboratoire de Météorologie Dynamique (LMD) at École Normale Supérieure (ENS), Paris, France

Abstract. We have created a performance-based assessment of CMIP6 models for Europe that can be used to inform the sub-selection of models for this region. Our assessment covers criteria indicative of the ability of individual models to capture a range of large-scale processes that are important for the representation of present-day European climate. We use this study to provide examples of how this performance-based assessment may be applied to multi-model ensemble of CMIP6 models to a) filter the ensemble for performance against these climatological/ processed-based criteria and, b) create a smaller sub-set of models based on performance, that also maintains model diversity and the filtered projection range as far as possible.

Filtering by excluding the least realistic models leads to higher sensitivity models remaining in the ensemble as an emergent consequence of the assessment. This results in both the 25th percentile and the median of the projected temperature range being shifted toward greater warming for the filtered set of models. We also weight the unfiltered ensemble against global trends. In contrast this shifts both the distribution of towards less warming. This highlights a tension for regional model selection in terms of selection based on regional climate processes versus the global mean warming trend.

Tamzin Emily Palmer et al.

Status: open (until 14 Sep 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on esd-2022-31', Anonymous Referee #1, 03 Aug 2022 reply
  • AC1: 'Comment on esd-2022-31', Tamzin Palmer, 11 Aug 2022 reply

Tamzin Emily Palmer et al.

Tamzin Emily Palmer et al.

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Short summary
We carry out an assessment of an ensemble of general climate models (CMIP6), based on the ability of the models to represent the key physical process that are important for representing the European climate. Filtering the models with the assessment leads to more of the models with less global warming being removed, and this shifts the lower part of the projected temperature range towards greater warming. This is in contrast to the affect of weighting the ensemble using global temperature trends.
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