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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/gmd-2018-294
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2018-294
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Model evaluation paper 14 Jan 2019

Model evaluation paper | 14 Jan 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Model Development (GMD).

Climate projections of a multi-variate heat stress index: the role of downscaling and bias correction

Ana Casanueva1, Sven Kotlarski1, Sixto Herrera2, Andreas M. Fischer1, Tord Kjellstrom3,4, and Cornelia Schwierz1 Ana Casanueva et al.
  • 1Federal Office of Meteorology and Climatology MeteoSwiss, Zurich, Switzerland
  • 2Meteorology Group, University of Cantabria, Santander, Spain
  • 3Centre for Technological Research and Innovation (CETRI), Limmasol, Cyprus
  • 4Australian National University, Canberra, Australia

Abstract. Along with the higher demand of bias-corrected data for climate impact studies, the number of available data sets has largely increased in the recent years. For instance, the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) constitutes a framework for consistently projecting the impacts of climate change across affected sectors and spatial scales. These data are very attractive for any impact application since they offer worldwide bias-corrected data based on Global Climate Models (GCMs). Complementary, the CORDEX initiative has incorporated experiments based on regionally-downscaled bias-corrected data by means of debiasing and quantile mapping (QM) methods. In light of this situation, it is challenging to distil the most accurate and useful information for climate services, but at the same time it creates a perfect framework for intercomparison and sensitivity analyses.

In the present study, the trend-preserving ISIMIP method and empirical QM are applied to climate model simulations that were carried out at different spatial resolutions (CMIP5 GCM and EURO-CORDEX Regional Climate Models (RCMs), at approximately 150 km, 50 km and 12 km horizontal resolution, respectively) in order to assess the role of downscaling and bias correction in a multi-variate framework. The analysis is carried out for the wet bulb globe temperature (WBGT), a heat stress index that is commonly used in the context of working people and labour productivity. WBGT for shaded conditions depends on air temperature and dew point temperature, which in this work are individually bias-corrected prior to the index calculation. Our results show that the added value of RCMs with respect to the driving GCM is limited after bias correction. The two bias correction methods are able to adjust the central part of the WBGT distribution, but some added value of QM is found in WBGT percentiles and in the intervariable relationships. The evaluation in present climate of such multivariate indices should be performed with caution since biases in the individual variables might compensate, thus leading to better performance for the wrong reason. Climate change projections of WBGT reveal a larger increase of summer mean heat stress for the GCM than for the RCMs, related to the well-known reduced summer warming of the EURO-CORDEX RCMs. These differences are lowered after QM, since this bias correction method modifies the change signals and brings the results for GCM and RCMs closer to each other. We also highlight the need of large ensembles of simulations to assess the feasibility of the derived projections.

Ana Casanueva et al.
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Short summary
Given the large number of available data sets and products currently produced for climate impact studies, it is challenging to distil the most accurate and useful information for climate services. This work presents a comparison of methods widely use to generate climate projections, from different sources and at different spatial resolutions, in order to assess the role of downscaling and statistical postprocessing (bias correction).
Given the large number of available data sets and products currently produced for climate impact...
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