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Archive
by Torma, C., Giorgi, F. and Coppola, E.
Abstract:
We present an analysis of the added value (AV) of downscaling via regional climate model (RCM) nesting with respect to the driving global climate models (GCMs). We analyze ensembles of driving GCM and nested RCM (two resolutions, 0.44° and 0.11°) simulations for the late 20th and late 21st centuries from the CMIP5, EURO-CORDEX, and MED-CORDEX experiments, with a focus on the Alpine region. Different metrics of AV are investigated, measuring aspects of precipitation where substantial AV can be expected in mountainous terrains: spatial pattern of mean precipitation, daily precipitation intensity distribution, and daily precipitation extremes tails. Comparison with a high-quality, fine-scale (5 km) gridded observational data set shows substantial AV of RCM downscaling for all metrics selected, and results are mostly improved compared to the driving GCMs also when the RCM fields are upscaled at the scale of the GCM resolution. We also find consistent improvements in the high-resolution (0.11°) versus medium-resolution (0.44°) RCM simulations. Finally, we find that the RCM downscaling substantially modulates the GCM-produced precipitation change signal in future climate projections, particularly in terms of fine-scale spatial pattern associated with the complex topography of the region. Our results thus point to the important role that high-resolution nested RCMs can play in the study of climate change over areas characterized by complex topographical features.
Reference:
Torma, C., Giorgi, F. and Coppola, E., 2015: Added value of regional climate modeling over areas characterized by complex terrain - Precipitation over the AlpsJournal of Geophysical Research: Atmospheres, 120, 3957-3972.
Bibtex Entry:
@Article{Torma2015,
  Title                    = {Added value of regional climate modeling over areas characterized by complex terrain - Precipitation over the Alps},
  Author                   = {Torma, C. and Giorgi, F. and Coppola, E.},
  Journal                  = {Journal of Geophysical Research: Atmospheres},
  Year                     = {2015},

  Month                    = {May},
  Number                   = {9},
  Pages                    = {3957-3972},
  Volume                   = {120},

  __markedentry            = {[hymexw:]},
  Abstract                 = {We present an analysis of the added value (AV) of downscaling via regional climate model (RCM) nesting with respect to the driving global climate models (GCMs). We analyze ensembles of driving GCM and nested RCM (two resolutions, 0.44° and 0.11°) simulations for the late 20th and late 21st centuries from the CMIP5, EURO-CORDEX, and MED-CORDEX experiments, with a focus on the Alpine region. Different metrics of AV are investigated, measuring aspects of precipitation where substantial AV can be expected in mountainous terrains: spatial pattern of mean precipitation, daily precipitation intensity distribution, and daily precipitation extremes tails. Comparison with a high-quality, fine-scale (5 km) gridded observational data set shows substantial AV of RCM downscaling for all metrics selected, and results are mostly improved compared to the driving GCMs also when the RCM fields are upscaled at the scale of the GCM resolution. We also find consistent improvements in the high-resolution (0.11°) versus medium-resolution (0.44°) RCM simulations. Finally, we find that the RCM downscaling substantially modulates the GCM-produced precipitation change signal in future climate projections, particularly in terms of fine-scale spatial pattern associated with the complex topography of the region. Our results thus point to the important role that high-resolution nested RCMs can play in the study of climate change over areas characterized by complex topographical features.},
  Copublication            = {3: 3 It},
  Doi                      = {10.1002/2014JD022781},
  ISSN                     = {2169-8996},
  Keywords                 = {Regional climate change; Extreme events; Regional modeling; Climate change and variability; Climate impact; added value; regional climate modeling; complex topography;},
  Owner                    = {hymexw},
  Timestamp                = {2016.01.07},
  Url                      = {http://dx.doi.org/10.1002/2014JD022781}
}