Assessment of multiple daily precipitation statistics in ERA-Interim driven Med-CORDEX and EURO-CORDEX experiments against high resolution observations

  • Adriano Fantini
  • Francesca Raffaele
  • Csaba Torma
  • Sara Bacer
  • Erika Coppola
  • Filippo Giorgi
  • Bodo Ahrens
  • Clotilde Dubois
  • Enrique Sanchez
  • Marco Verdecchia
Article
  • 264 Downloads

Abstract

We assess the statistics of different daily precipitation indices in ensembles of Med-CORDEX and EURO-CORDEX experiments at high resolution (grid spacing of ~0.11°, or RCM11) and medium resolution (grid spacing of ~0.44°, or RCM44) with regional climate models (RCMs) driven by the ERA-Interim reanalysis of observations for the period 1989–2008. The assessment is carried out by comparison with a set of high resolution observation datasets for nine European subregions. The statistics analyzed include quantitative metrics for mean precipitation, daily precipitation probability density functions (PDFs), daily precipitation intensity, frequency, 95th percentile and 95th percentile of dry spell length. We assess an ensemble including all Med-CORDEX and EURO-CORDEX models together and others including the Med-CORDEX and EURO-CORDEX separately. For the All Models ensembles, the RCM11 one shows a remarkable performance in reproducing the spatial patterns and seasonal cycle of mean precipitation over all regions, with a consistent and marked improvement compared to the RCM44 ensemble and the ERA-Interim reanalysis. A good consistency with observations by the RCM11 ensemble (and a substantial improvement compared to RCM44 and ERA-Interim) is found also for the daily precipitation PDFs, mean intensity and, to a lesser extent, the 95th percentile. A general improvement by the RCM11 models is also found when the data are upscaled and intercompared at the 0.44° and 1.5° resolutions. For some regions the RCM11 ensemble overestimates the occurrence of very high intensity events while for one region the models underestimate the occurrence of the most intense extremes. The RCM11 ensemble still shows a general tendency to underestimate the dry day frequency and 95th percentile of dry spell length over wetter regions, with only a marginal improvement compared to the lower resolution models. This indicates that the problem of the excessive production of low precipitation events found in many climate models persists also at relatively high resolutions, at least in wet climate regimes. Concerning the Med-CORDEX and EURO-CORDEX ensembles we find that their performance is of similar quality over the Mediterranean regions analyzed. Finally, we stress the need of consistent and quality checked fine scale observation datasets for the assessment of RCMs run at increasingly high horizontal resolutions.

Keywords

Regional climate model Daily precipitation Med-CORDEX EURO-CORDEX Model validation Extremes 

Supplementary material

382_2016_3453_MOESM1_ESM.pdf (1.7 mb)
Fig. S1Taylor diagrams of the ensemble mean seasonal precipitation in the different analysis regions for the ERA-Interim, RCM44 and RCM11 (both EURO-CORDEX and Med-CORDEX models) ensembles with respect to the corresponding regional observation datasets, at the 0.44° resolution. (PDF 1787 kb)
382_2016_3453_MOESM2_ESM.pdf (45 kb)
Fig. S2Mean precipitation Bias (model minus observations, % of observed values, upper panels) and RMSE (mm/day, lower panels) for December–January–February (DJF, on the left) and June–July–August (JJA, on the right) for the different analysis regions, the ERA-Interim, and the RCM44 and RCM11 ensemble average (EURO-CORDEX Models and Med-CORDEX models). All the values indicate Bias and RMSE obtained without the under-catch gauge correction (see text). (PDF 45 kb)
382_2016_3453_MOESM3_ESM.pdf (75 kb)
Fig. S3Probability Density Function of daily precipitation intensity (mm/day) over the different analysis regions in the ERA-Interim reanalysis, regional observation datasets, RCM44 and RCM11 ensembles (All Models), all at the 0.44° resolution. For the RCM44 and RCM11 ensembles both the individual model values (circles) and their ensemble mean (continuous line) are shown. The PDFs include daily data for the different regional analysis periods (see Sect. 2). (PDF 75 kb)
382_2016_3453_MOESM4_ESM.pdf (80 kb)
Fig. S4Same as Figure S3 but at the 1.5° resolution. (PDF 79 kb)
382_2016_3453_MOESM5_ESM.pdf (1.7 mb)
Fig. S5Taylor diagrams of the ensemble mean values of the four indices described in Sect. 2 in the different analysis regions for the ERA-Interim, RCM44 and RCM11 (both EURO-CORDEX and Med-CORDEX models) ensembles with respect to the corresponding regional observation datasets, at the 0.44° resolution. (PDF 1773 kb)
382_2016_3453_MOESM6_ESM.pdf (6.3 mb)
Fig. S6Ensemble mean precipitation over the analysis regions for ERA-Interim reanalysis, RCM44 and RCM11 (only Med-CORDEX models) ensembles, regional observations. Upper panels: December–January–February (DJF); Lower panels: June–July–August (JJA). The last column on the right shows the precipitation bias at each resolution. Units are mm/day and the mean is taken over the different regional analysis periods (see Sect. 2). (PDF 6430 kb)
382_2016_3453_MOESM7_ESM.pdf (6.5 mb)
(PDF 6620 kb)
382_2016_3453_MOESM8_ESM.pdf (17.2 mb)
Fig. S7Same as Figure S6 but for EURO-CORDEX models ensembles. (PDF 17567 kb)
382_2016_3453_MOESM9_ESM.pdf (17.6 mb)
(PDF 18013 kb)
382_2016_3453_MOESM10_ESM.pdf (13 kb)
Fig. S8Annual cycle of ensemble mean value of the SDII index over the different analysis regions for the RCM44 and RCM11 ensembles (EURO-CORDEX and Med-CORDEX models), along with the ERA-Interim and observed SDII, at the 1.50° resolution. For the model ensembles, both the mean (thick line) and intermodel standard deviations range (shaded area) are shown. Units are mm/day and the mean is taken over the different analysis periods (see Sect. 2). (PDF 13 kb)
382_2016_3453_MOESM11_ESM.pdf (14 kb)
Fig. S9Same as Figure S8, but for the index Psum > R95-obs. Units are mm/year. (PDF 14 kb)
382_2016_3453_MOESM12_ESM.pdf (14 kb)
Fig. S10Same as Figure S8, but for the index DDF. Units are % days/year. (PDF 13 kb)

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Adriano Fantini
    • 1
    • 2
  • Francesca Raffaele
    • 2
  • Csaba Torma
    • 2
    • 3
    • 4
  • Sara Bacer
    • 5
  • Erika Coppola
    • 2
  • Filippo Giorgi
    • 2
  • Bodo Ahrens
    • 6
  • Clotilde Dubois
    • 7
    • 8
  • Enrique Sanchez
    • 9
  • Marco Verdecchia
    • 10
  1. 1.Department of Mathematics and GeosciencesUniversity of TriesteTriesteItaly
  2. 2.Abdus Salam ICTPTriesteItaly
  3. 3.Department of MeteorologyEötvös Loránd UniversityBudapestHungary
  4. 4.HAS Post-Doctoral Research ProgramBudapestHungary
  5. 5.Atmospheric Chemistry DepartmentMax Planck Institute for ChemistryMainzGermany
  6. 6.Goethe-Universitaet Frankfurt a.M.Frankfurt/MainGermany
  7. 7.Météo-FranceToulouseFrance
  8. 8.Mercator OcéanRamonville-Saint-AgneFrance
  9. 9.Universidad de Castilla-La ManchaToledoSpain
  10. 10.Department of Physical and Chemical SciencesUniversity of L’AquilaL’AquilaItaly

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