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A new spatially distributed added value index for regional climate models: the EURO-CORDEX and the CORDEX-CORE highest resolution ensembles

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Abstract

The added value of using regional climate models (RCMs) to downscale data from general circulation models (GCMs) has often been questioned and researched. Although several studies have used different methods to identify (and in some cases quantify) the added value, there is still a need to find a general metric that quantifies the added value of any variable. This paper builds on past studies to propose a new metric of added value in the simulation of present-day climate which measures the difference in the probability density functions (PDFs) at each grid-cell between a model and an observation source, and then compares the results of the RCM and GCM in order to spatially compute the added value index. The same method is also adapted to quantify the climate change downscaling signal in a way that is consistent with the present-day metric. These new metrics are tested on the daily precipitation output from the EURO-CORDEX and CORDEX-CORE projection ensembles and reveal an overall positive added value of RCMs, especially at the tail-end of the distribution. Higher added value is obtained in areas of complex topography and coast-lines, as well as in tropical regions. Areas with large added value in present-day climate are consistent with areas of significant climate change downscaling signal in the RCP 8.5 far future simulations, and when the analysis is repeated at a low-resolution. The use of different resolution observations shows that the added value tends to decrease when models are compared to low-resolution observation datasets.

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Acknowledgements

The RegCM simulations for the ICTP institute have been completed thanks to the support of the CINECA supercomputing center, Bologna, Italy and the ISCRA projects HP10BDU7TR and HP10BQCFJ2. The authors would like to thank Graziano Giuliani and Ivan Girotto for their constant support in the preparation of the simulations used in this paper. The authors would also like to thank the CMIP5 and EURO-CORDEX community, as well as the ESGF for providing access to their database where most of the data is available. The majority of the EURO-CORDEX contributions have funded by the “Producing RegIoNal ClImate Projections Leading to European Services" (PRINCIPLES, C3S_34b Lot2) as part of the Copernicus Climate Change Service (C3S). The work was also partially supported by the EUCP H2020 project (GA number: 776613). Im E-S and Nguyen-Xuan T were supported by “Research Program for Agricultural Science and Technology Development" (Project no. PJ014882) funded by the Rural Development Administration, Republic of Korea. The study was also supported by the Oak Ridge Leadership Computing Facility and the National Climate-Computing Research Center at the Oak Ridge National Laboratory, the Climate Change Research Center, Institute of Atmospheric Physics; the Chinese Academy of Sciences, Beijing, China and University of Chinese Academy of Sciences, Beijing, China; the Yingkou Meteorological Bureau, Yingkou, China; the National Center for Atmospheric Research, Boulder, CO, USA; and the Climate Service Center Germany (GERICS), Helmholtz-Zentrum Geesthacht, Hamburg, Germany; all of whom provided access to their simulation data. The observations were provided by MeteoSwiss, Santander meteorology group, Meteo-France, Met office UK, METNO Norway, SMHI, Hungarian Meteorological Service, DWD Germany, and CETEMPS University of L’Aquila. The CPC Global Unified Precipitation data was provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their Web site at https://www.esrl.noaa.gov/psd/.

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Authors and Affiliations

  1. The Abdus Salam International Center for Theoretical Physics (ICTP), Strada Costiera 11, 34135, Trieste, Italy

    James M. Ciarlo`, Erika Coppola, Adriano Fantini, Filippo Giorgi, Russell H. Glazer, Jose Abraham Torres Alavez, Taleena Sines, Emanuela Pichelli, Francesca Raffaele & Sushant Das

  2. National Institute of Oceanography and Applied Geophysics (OGS), Borgo Grotta Gigante 42/C, 34010 Sgonico, Italy

    James M. Ciarlo`

  3. Climate Change Research Center, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

    XueJie Gao

  4. University of Chinese Academy of Sciences, Beijing, China

    XueJie Gao

  5. Yingkou Meteorological Bureau, Yingkou, China

    Yao Tong

  6. National Center for Atmospheric Research, Boulder, CO, USA

    Melissa Bukovsky

  7. Oak Ridge National Laboratory, Oak Ridge, TN, USA

    Moetasim Ashfaq

  8. The Hong Kong University of Science and Technology, Hong Kong, China

    Eun-Soon Im & Thanh Nguyen-Xuan

  9. Climate Service Center Germany (GERICS), Helmholtz-Zentrum Geesthacht, Hamburg, Germany

    Claas Teichmann, Armelle Remedio, Thomas Remke, Katharina Bülow, Torsten Weber, Lars Buntemeyer, Kevin Sieck, Diana Rechid & Daniela Jacob

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Appendix

Appendix

The method described in Sect. 2 is based on the difference between two PDFs, therefore the selection of the PDF bin-size is a very important process. Since a smaller bin-size is analogous to a higher horizontal resolution, it should allow a better representation of the details of the PDF. However, the effect of varying the bin-size on this new added value method requires some testing.

One example simulation for all RCMs used in this study (each driven by a different GCM and compared to the EURO4M data-set) was used to assess the dependence of the added value on the bin-size (Figs. 11, 12). The results show a decrease in magnitude of the added value as the bin-size increases. This happens as a result of aggregating a larger number of events and thus smearing out the details of the distributions. The sign of the added value changes in some cases, but in these cases the magnitude of the added value is very low. As a result of this test, in order to obtain the best possible resolution of the PDFs and the most informative outcome from this new method, the bin-size of 1 mm/day is used.

Fig. 11
figure 11

Added value for some model examples at different PDF (with full distribution) bin sizes compared to EURO4M at 0.11°

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Fig. 12
figure 12

Added value of 99–100 percentile interval for some model examples at different PDF bin sizes compared to EURO4M at 0.11°

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Ciarlo`, J.M., Coppola, E., Fantini, A. et al. A new spatially distributed added value index for regional climate models: the EURO-CORDEX and the CORDEX-CORE highest resolution ensembles. Clim Dyn 57, 1403–1424 (2021). https://doi.org/10.1007/s00382-020-05400-5

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