Preprints
https://doi.org/10.5194/esd-2022-15
https://doi.org/10.5194/esd-2022-15
 
22 Apr 2022
22 Apr 2022
Status: this preprint is currently under review for the journal ESD.

To weight or not to weight: assessing sensitivities of climate model weighting to multiple methods, variables, and domains

Adrienne Wootten1, Elias Massoud2, Duane Waliser3, and Huikyo Lee3 Adrienne Wootten et al.
  • 1South Central Climate Adaptation Science Center, University of Oklahoma, Norman, OK, 73019, USA
  • 2Department of Environmental Science, Policy and Management, University of California Berkeley, Berkeley, CA, 94720, USA
  • 3Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, 91109, USA

Abstract. Given the increasing use of climate projections and multi-model ensemble weighting for a diverse array of applications, this project assesses the sensitivities of climate model weighting, and their resulting ensemble means, to multiple components, such as the weighting schemes, climate variables, or spatial domains of interest. The analysis makes use of global climate models from the Coupled Model Intercomparison Project Phase 5 (CMIP5), and their statistically downscaled counterparts created with the Localized Canonical Analogs (LOCA) method. This work focuses on historical and projected future mean precipitation and daily high temperatures of the south-central United States. Results suggest that model weights and corresponding weighted projections are highly sensitive to the weighting method as well as to the selected variables and spatial domains. For instance, when estimating model weights based on Louisiana precipitation, the weighted projections show a wetter and cooler south-central domain in the future compared to other weighting schemes. Alternatively, for example, when estimating model weights based on New Mexico temperature, the weighted projections show a drier and warmer south-central domain in the future. However, when considering the entire south-central domain in estimating the model weights, the weighted future projections show a compromise in the precipitation and temperature estimates. If future impact assessments utilize weighting schemes, then our findings suggest that how the weighting scheme is derived and applied to the projections may depend on the needs of an impact assessment or adaptation plan. From the results of our analysis, we summarize our recommendations concerning multi-model ensemble weighting as follows:

  • Weighted ensemble means should be used not only for national and international assessments but also for regional impacts assessments and planning.
  • Multiple strategies for model weighting are employed when feasible, to assure that uncertainties from various sources (e.g., weighting strategy used, domain or variable of interest applied, etc.) are considered.
  • That weighting is derived for individual sub-regions (such as the NCA regions) in addition to what is derived for the continental United States.
  • That domain-specific weighting be derived using both common (e.g. precipitation) and stakeholder-specific (e.g. streamflow) variables to produce relevant analysis for impact assessments and planning.

Adrienne Wootten et al.

Status: open (until 04 Jun 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on esd-2022-15', Anonymous Referee #1, 13 May 2022 reply
  • RC2: 'Comment on esd-2022-15', Anonymous Referee #2, 17 May 2022 reply

Adrienne Wootten et al.

Adrienne Wootten et al.

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Short summary
Climate projections and multi-model ensemble weighting are increasingly used for climate assessments. This study examines the sensitivities of model weighting and multi-model ensemble means across multiple dimensions using projections in the south central United States. Model weighting and ensemble means are sensitive to the domain and variable used. This study recommends that multiple weighting strategies be used with consideration for the needs of the research project or planning exercise.
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