Climatic Change

, Volume 125, Issue 1, pp 23–38 | Cite as

Present and future climatologies in the phase I CREMA experiment

  • Erika Coppola
  • Filippo Giorgi
  • Francesca Raffaele
  • Ramon Fuentes-Franco
  • Graziano Giuliani
  • Marta LLopart-Pereira
  • Ashu Mamgain
  • Laura Mariotti
  • Gulilat Tefera Diro
  • Csaba Torma
Article

Abstract

We provide an overall assessment of the surface air temperature and precipitation present day (1976–2005) and future (2070–2099) ensemble climatologies in the Phase I CREMA experiment. This consists of simulations performed with different configurations (physics schemes) of the ICTP regional model RegCM4 over five CORDEX domains (Africa, Mediterranean, Central America, South America, South Asia), driven by different combinations of three global climate models (GCMs) and two greenhouse gas (GHG) representative concentration pathways (RCP8.5 and RCP4.5). The biases (1976–2005) in the driving and nested model ensembles compared to observations show a high degree of spatial variability and, when comparing GCMs and RegCM4, similar magnitudes and more similarity for precipitation than for temperature. The large scale patterns of change (2070–2099 minus 1976–2005) are broadly consistent across the GCM and RegCM4 ensembles and with previous analyses of GCM projections, indicating that the GCMs selected in the CREMA experiment are representative of the more general behavior of current GCMs. The RegCM4, however, shows a lower climate sensitivity (reduced warming) than the driving GCMs, especially when using the CLM land surface scheme. While the broad patterns of precipitation change are consistent across the GCM and RegCM4 ensembles, greater differences are found at sub-regional scales over the various domains, evidently tied to the representation of local processes. This paper serves to provide a reference view of the behavior of the CREMA ensemble, while more detailed and process-based analysis of individual domains is left to companion papers of this special issue.

Supplementary material

10584_2014_1137_MOESM3_ESM.pdf (84 kb)
Table S1(PDF 83 kb)
10584_2014_1137_MOESM4_ESM.pdf (91 kb)
Table S2(PDF 90 kb)
10584_2014_1137_MOESM5_ESM.pdf (80 kb)
Table S3(PDF 80 kb)
10584_2014_1137_MOESM6_ESM.pdf (80 kb)
Table S4(PDF 80 kb)
10584_2014_1137_MOESM7_ESM.pdf (82 kb)
Table S5(PDF 81 kb)
10584_2014_1137_MOESM8_ESM.pdf (56 kb)
Table S6(PDF 55 kb)
10584_2014_1137_MOESM9_ESM.pdf (81 kb)
Table S7(PDF 80 kb)
10584_2014_1137_MOESM1_ESM.pdf (1.6 mb)
Figure S1(PDF 1591 kb)
10584_2014_1137_MOESM2_ESM.pdf (3.8 mb)
Figure S2(PDF 3881 kb)

Reference

  1. Adam JC, Lettenmaier DP (2003) Adjustment of global gridded precipitation for systematic bias. J Geophys Res 108:4257. doi:10.1029/2002/JD002499 CrossRefGoogle Scholar
  2. Castro CL, Pielke Sr RA, Leoncini G (2005) Dynamical downscaling: assessment of value retained and added using the regional atmospheric modeling system (RAMS). J Geophys Res 110, D05108. doi:10.1029/2004JD004721
  3. Christensen JH, Hewitson B, Busuioc A, Chen A, Gao X, Held I, Jones R, Koli RK, Kwon WT, Laprise R, Rueda VM, Mearns L, Menéndez CG, Räisänen J, Rinke A, Sarr A, Whetton P (2007) Regional climate projections. climate change 2007: the physical science basis. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (eds) Contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, pp 847–940Google Scholar
  4. Dickinson RE, Henderson-Sellers A, Kennedy PJ (1993) Biosphere-atmosphere transfer scheme (BATS) version 1e as coupled to the NCAR Community Climate Model, NCAR Tech. Note NCAR/TN-387+STR Natl. Cent. for Atmos. Res. Boulder, ColoGoogle Scholar
  5. Di Luca A, de Elia R, Laprise R (2013) Potential for small scale added value of RCM’s downscaled climate change signal. Clim Dyn 40:601–618CrossRefGoogle Scholar
  6. Emanuel KA, Zivkovic-Rothman M (1999) Development and evaluation of a convection scheme for use in climate models. J Atmos Sci 56:1766–1782CrossRefGoogle Scholar
  7. Fredrik Boberg and Jens H. Christensen (2012) Overestimation of Mediterranean summer temperature projections due to model deficiencies Nature Climate Change 2, 433–436 doi:10.1038/nclimate1454
  8. Giorgi F. (2013) The introduction to the CREMA experiment special issues. Climatic Change, this issue.Google Scholar
  9. Giorgi F, Bi X (2005) Updated regional precipitation and temperature changes for the 21st century from ensembles of recent AOGCM simulations. Geophys Res Lett 32, L21715CrossRefGoogle Scholar
  10. Giorgi F, Coppola E (2010) Does the model regional bias affect the projected regional climate change? an analysis of global model projections. Climatic Change Letters 100:787–795CrossRefGoogle Scholar
  11. Giorgi F, Mearns LO (1999) Introduction to special section: regional climate modeling revisited. J Geophys Res 104:6335–6352Google Scholar
  12. Giorgi F, Shields C (1999) Tests of precipitation parameterizations available in the latest version of the NCAR regional climate model (RegCM) over the continental U.S. J Geophys Res 104:6353–6375CrossRefGoogle Scholar
  13. Giorgi F, Marinucci MR, Bates GT (1993a) Development of a second generation regional climate model (REGCM2). Part I: boundary layer and radioactive transfer processes. Mon Weather Rev 121:2794–2813CrossRefGoogle Scholar
  14. Giorgi F, Marinucci MR, Bates GT, DeCanio G (1993b) Development of a second generation regional climate model (REGCM2). Part II: convective processes and assimilation of lateral boundary conditions. Mon Weather Rev 121:2814–2832CrossRefGoogle Scholar
  15. Giorgi F, Huang Y, Nishizawa K, Fu C (1999) A seasonal cycle simulation over eastern Asia and its sensitivity to radioactive transfer and surface processes. J Geophys Res 104:6403–6423CrossRefGoogle Scholar
  16. Giorgi F, Pal JS, Bi X, Sloan L, Elguindi N, Solmon F (2006) Introduction to the TAC special issue: The RegCNET network. Theor Appl Climatol 86:1–4CrossRefGoogle Scholar
  17. Giorgi F, Jones C, Asrar G (2009) Addressing climate information needs at the regional scale: the CORDEX framework. WMO Bull 58:175–183Google Scholar
  18. Giorgi F et al (2012) RegCM4: model description and preliminary tests over multiple CORDEX domains. Clim Res 52:7–29CrossRefGoogle Scholar
  19. Grell G (1993) Prognostic evaluation of assumptions used by cumulus parameterizations. Mon Wea Rev 121:764–787Google Scholar
  20. Huffman GJ et al (2007) The TRMM multi satellite precipitation analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scale. J Hydrometeorol 8:38–55CrossRefGoogle Scholar
  21. Jacob, D., et al. (2007) An intercomparison of regional climate models for Europe: design of the experiments and model performance. Clim. Change, doi:10.1007/s10584-006-9213-4
  22. Knutti, R. and J. Sedlacek (2012) Robustness and uncertainties in the new CMIP5 climate model projections, Nature Climate Change, published online, doi:10.1038/nclimate1716.Google Scholar
  23. Leduc M, Laprise R (2009) Regional climate model sensitivity to domain size. Clim Dyn 32:833–854CrossRefGoogle Scholar
  24. Llopart M., Da Rocha R. P., S.V. Cuadra, E. Coppola, F. Giorgi (2013) Land surface feedbacks and climate change over South America as projected by RegCM4. Climatic Change. This issue.Google Scholar
  25. Mariotti L.; Coppola E.; Sylla M. B.; et al., (2011) Regional climate model simulation of projected 21st century climate change over an all-Africa domain: Comparison analysis of nested and driving model results, Journal of Geophysical Research-Atmospheres, doi:10.1029/2010JD015068
  26. Mitchell T, Jones D (2005) An improved method of constructing a database of monthly climate observations and associated high-resolution grids. Int J Climatol 25:693–712CrossRefGoogle Scholar
  27. Nikulin G (2012) Precipitation climatology in an ensemble of CORDEX Africa regional climate simulations. J Clim 25:6057–6078. doi:10.1175/JCLI-D-11-00375.1 CrossRefGoogle Scholar
  28. Nogherotto R, Coppola E, Giorgi F, Mariotti L (2013) Impact of Congo basin deforestation on the African monsoon. Atmos Sci Lett. doi:10.1002/asl.416 Google Scholar
  29. Oleson KW, Dai Y, Bonan G, Bosilovich M, Dickinson R, Dirmeyer P, Hoffman F, Houser P, Levis S, Niu G-Y, Thornton P, Vertenstein M, Yang Z-L, Zeng X (2004) Technical description of the Community Land Model (CLM). NCAR Technical Note NCAR/TN - 461+STR. National Center for Atmospheric Research. Boulder, CO, p 173Google Scholar
  30. Pal JS, Giorgi F, Bi X, Elguindi N, Solmon F, Gao X, Francisco R, Zakey A, Winter J, Ashfaq M, Syed F, Bell J, Diffenbaugh N, Karmacharya J, Konare A, Martinez Castro D, Porfirio da Rocha R, Sloan L, Steiner A (2007) Regional climate modeling for the developing world: the ICTP RegCM3 and RegCNET. Bull Am Meteorol Soc 88:1395–1409CrossRefGoogle Scholar
  31. Pan Z, Takle ES, Otieno F (2001) Evaluation of uncertainties in regional climate change simulations. J Geophys Res 106(D16):17735–17752CrossRefGoogle Scholar
  32. Small E, Giorgi F, Sloan LC (1999) Regional climate model simulation of precipitation in central Asia: mean and inter annual variability. J Geophys Res 104:6563–6582CrossRefGoogle Scholar
  33. Sylla MB, Giorgi F, Coppola E, Mariotti L (2012) Uncertainties in daily rainfall over Africa: assessment of gridded observation products and evaluation of a regional climate model simulation. Int J Climatol. doi:10.1002/joc.3551 Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Erika Coppola
    • 1
  • Filippo Giorgi
    • 1
  • Francesca Raffaele
    • 1
  • Ramon Fuentes-Franco
    • 2
  • Graziano Giuliani
    • 1
  • Marta LLopart-Pereira
    • 3
  • Ashu Mamgain
    • 4
  • Laura Mariotti
    • 1
  • Gulilat Tefera Diro
    • 1
  • Csaba Torma
    • 1
  1. 1.Abdus Salam International Centre for Theoretical PhysicsTriesteItaly
  2. 2.Center for Scientific Research and Higher Education at EnsenadaBajaMexico
  3. 3.Department of Atmospheric SciencesUniversity of São PauloSão PauloBrazil
  4. 4.Centre for Atmospheric Sciences, Indian Institute of Technology DelhiNew DelhiIndia

Personalised recommendations