VALUE: A framework to validate downscaling approaches for climate change studies

Earth's Future

Volumn 3, Issue 1, 2015, Pages 1-14

  Maraun, Douglas ^a   Widmann, Martin ^b   Gutiérrez, José M ^c   Kotlarski, Sven ^d   Chandler, Richard E ^e   Hertig, Elke ^f   Wibig, Joanna ^g   Huth, Radan ^h   Wilcke, Renate A I ⁱ  

^a GEOMAR HELMHOLTZ CENTRE FOR OCEAN RESEARCH KIEL   (Germany)

^b UNIVERSITY OF BIRMINGHAM   (United Kingdom)

^c INSTITUTO DE FÍSICA DE CANTABRIA CSIC UC   (Spain)

^d ETH Zurich   (Switzerland)

^e UNIVERSITY COLLEGE LONDON   (United Kingdom)

^f UNIVERSITY OF AUGSBURG   (Germany)

^g UNIVERSITY OF LODZ   (Poland)

^h CHARLES UNIVERSITY   (Czech Republic)

ⁱ SWEDISH METEOROLOGICAL AND HYDROLOGICAL INSTITUTE   (Sweden)

Author keywords

Bias Correction; Downscaling; Dynamical Downscaling; Regional Climate Modelling; Statistical Downscaling; Validation

Indexed keywords

ASSESSMENT METHOD; CLIMATE CHANGE; CLIMATE MODELING; CLIMATOLOGY; DOWNSCALING; REGIONAL CLIMATE; STAKEHOLDER;

EUROPE;

EID: 85000401554     PISSN: None     EISSN: 23284277     Source Type: Journal    
DOI: 10.1002/2014EF000259     Document Type: Article

References (47)

1. Barsugli, J., et al., (2013), The practitioner's dilemma: How to assess the credibility of downscaled climate projections, Eos Trans. AGU, 94 (46), 424-425.
2. Brands, S., J. Gutiérrez, and, S. Herrera, (2012), On the use of reanalysis data for downscaling, J. Clim., 25, 2517-2526, doi: 10.1175/JCLI-D-11-00251.1.
3. Brown, C., Y. Ghile, M. Laverty, and, K. Li, (2012), Decision scaling: Linking bottom-up vulnerability analysis with climate projections in the water sector, Water Resour. Res., 48, W09537, doi: 10.1029/2011WR011212.
4. Casanueva, A., S. Herrera, J. Fernández, M. Frías, and, J. Gutiérrez, (2013), Evaluation and projection of daily temperature percentiles from statistical and dynamical downscaling methods, Nat. Hazards Earth Syst. Sci., 13, 2089-2099, doi: 10.5194/nhess-13-2089-2013.
5. Charles, S., B. Bates, P. Whetton, and, J. Hughes, (1999), Validation of downscaling models for changed climate conditions: Case study of southwestern Australia, Clim. Res., 12, 1-14.
6. Davison, A. C., (2003), Statistical Models, Cambridge Series in Statistical and Probabilistic Mathematics, Cambridge Univ. Press, Cambridge U. K.
7. Dee, D., et al., (2011), The ERA-Interim reanalysis: Configuration and performance of the data assimilation system, Q. J. R. Meteorol. Soc., 137, 553-597.
8. Déqué, M., D. P. Rowell, D. Luthi, F. Giorgi, J. H. Christensen, B. Rockel, D. Jacob, E. Kjellström, M. de Castro, and, B. van den Hurk, (2007), An intercomparison of regional climate simulations for Europe: Assessing uncertainties in model projections, Clim. Change, 81, 53-70.
9. Deser, C., R. Knutti, S. Solomon, and, A. Phillips, (2012), Communication of the role of natural variability in future North American climate, Nat. Clim. Change, 2, 775-779.
10. Deser, F., B. Rockel, H. von Storch, J. Winterfeldt, and, M. Zahn, (2011), Regional climate models add value to global model data. a review and selected examples, Bull. Am. Meteorol. Soc., 92, 1181-1192.
11. Dessai, S., (2009), Do we need better predictions to adapt to a changing climate?, Eos Trans. AGU, 90, 111-112., doi: 10.1029/2009EO130003
12. Eden, J., M. Widmann, D. Grawe, and, S. Rast, (2012), Skill, correction, and downscaling of GCM-simulated precipitation, J. Clim., 25, 3970-3984.
13. European Commission (2013), An EU Strategy on adaptation to climate change, http://ec.europa.eu/clima/policies/adaptation.
14. Fowler, H. J., S. Blenkinsop, and, C. Tebaldi, (2007), Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling, Int. J. Climatol., 27, 1547-1578.
15. Frías, M., E. Zorita, J. Fernández, and, C. Rodríguez-Puebla, (2006), Testing statistical downscaling methods in simulated climates, Geophys. Res. Lett., 33, L19,807., doi: 10.1029/2006GL027453
16. Giorgi, F., C. Jones, and, G. Asrar, (2009), Addressing climate information needs at the regional level: The CORDEX framework, WMO Bull., 58 (3), 175-183.
17. Goodess, C., et al., (2010), An intercomparison of statistical downscaling methods for Europe and European regions-Assessing their performance with respect to extreme weather events and the implications for climate change applications, Tech. rep., Climatic Research Unit.
18. Hawkins, E., and, R. Sutton, (2009), The potential to narrow uncertainty in regional climate predictions, Bull. Am. Meteorol. Soc., 90 (8), 1095-1107.
19. Hewitson, B., J. Daron, R. Crane, M. Zermoglio, and, C. Jack, (2013), Interrogating empirical-statistical downscaling, Clim. Change, 122, 539-554.
20. Jacob, D., et al., (2014), EURO-CORDEX: New high-resolution climate change projections for European impact research, Reg. Environ. Change, 14, 563-578.
21. Jolliffe, I. T., and, D. B. Stephenson, (Eds) (2003), Forecast Verification: A Practitioner's Guide in Atmospheric Science, Wiley, Chichester, U.K.
22. Keeley, S., R. Sutton, and, L. Shaffrey, (2012), The impact of North Atlantic sea surface temperature errors on the simulation of North Atlantic European region climate, Q. J. R. Meteorol. Soc., 138, 1774-1783.
23. Kendon, E., N. Roberts, H. Fowler, M. Roberts, S. Chan, and, C. Senior, (2014), Heavier summer downpours with climate change revealed by weather forecast resolution model, Nat. Clim. Change, 4, 570-576.
24. Klein Tank, A., et al., (2002), Daily dataset of 20th-century surface air temperature and precipitation series for the european climate assessment, Int. J. Climatol., 22 (12), 1441-1453.
25. Klein Tank, A., F. Zwiers, and, X. Zhang, (2009), Guidelines on analysis of extremes in a changing climate in support of informed decisions for adaptation, Climate data and monitoring wcdmp-no. 72, World Meteorological Organisation.
26. Kotlarski, S., et al., (2014), Regional climate modelling on European scales: A joint standard evaluation of the EURO-CORDEX RCM ensemble, Geosci. Model Dev., 7, 1297-1333.
27. Kundzewicz, Z., and, E. Stakhiv, (2010), Are climate models "ready for prime time" in water resources management applications, or is more research needed?, Hydrol. Sci. J., 55, 1085-1089.
28. Lempert, R., N. Nakicenovic, D. Sarewitz, and, M. Schlesinger, (2004), Characterising climate-change uncertainties for decision-makers, Clim. Change, 65, 1-9.
29. Maraun, D., (2012), Nonstationarities of regional climate model biases in European seasonal mean temperature and precipitation sums, Geophys. Res. Lett., 39, L06,706., doi: 10.1029/2012GL051210
30. Maraun, D., (2013), When will trends in European mean and heavy daily precipitation emerge?, Environ. Res. Lett., 8, 014,004.
31. Maraun, D., et al., (2010), Precipitation downscaling under climate change: Recent developments to bridge the gap between dynamical models and the end user, Rev. Geophys., 48, RG3003, doi: 10.1029/2009RG000314.
32. Mearns, L., W. Gutowski, R. Jones, L.-Y. Leung, S. McGinnis, A. Nunes, and, Y. Qian, (2009), A regional climate change assessment program for North America, Eos Trans. AGU, 90 (36), 311-312.
33. Mearns, L., et al., (2012), The North American Regional Climate Change Assessment Program. Overview of Phase I results, Bull. Am. Meteorol. Soc., 93, 1337-1362.
34. Prudhomme, C., R. Wilby, S. Crooks, A. Kay, and, N. Reynard, (2010), Scenario-neutral approach to climate change impact studies: Application to flood risk, J. Hydrol., 390, 198-209.
35. Räisänen, J., and, O. Räty, (2013), Projections of daily mean temperature variability in the future: cross-validation tests with ENSEMBLES regional climate simulations, Clim. Dyn., 41, 1553-1568.
36. Rummukainen, M., (2010), State-of-the-art with regional climate models, Wiley Int. Rev. Clim. Change, 1, 82-96, doi: 10.1002/wcc.8.
37. Segond, M.-L., H. Wheater, and, C. Onof, (2007), The significance of spatial rainfall representation for flood runoff estimation: A numerical evaluation based on the Lee catchment, J. Hydrol., 347, 116-131.
38. Stainforth, D., M. Allen, E. Tredger, and, L. Smith, (2007), Confidence, uncertainty and decision-support relevance in climate predictions, Philos. Trans. R. Soc. A, 365, 2145-2161.
39. UNFCCC (2008), Compendium on methods and tools to evaluate impacts of, and vulnerability and adaptation to climate change.
40. United Nations (1992), United Nations Framework Convention on Climate Change.
41. van der Linden, P., and, J. F. B. Mitchell, (2009), ENSEMBLES: Climate change and its impacts: Summary of research and results from the ENSEMBLES project, Tech. rep., Met Office Hadley Centre.
42. Vrac, M., M. L. Stein, K. Hayhoe, and, X. Z. Liang, (2007), A general method for validating statistical downscaling methods under future climate change, Geophys. Res. Lett., 34, L18,701, doi: 10.1029/2007GL030295.
43. Wilby, R., (2010), Opinion: Evaluating climate model outputs for hydrological applications, Hydrol. Sci. J., 55, 1090-1093.
44. Wilby, R., and, S. Dessai, (2010), Robust adaptation to climate change, Weather, 65, 180-185.
45. Woollings, T., (2013), Dynamical influences on European climate: An uncertain future, Philos. Trans. R. Soc. A, 368, 3733-3756.
46. Worldbank Independent Evaluation Group (2012), Adapting to climate change: Assessing the World Bank Group Experience Phase III.
47. Zwiers, F., L. Alexander, G. Hegerl, T. Knutson, P. Naveau, N. Nicholls, C. Schär, S. Seneviratne, and, X. Zhang, (2011), Community paper on climate extremes. Challenges in estimating and understanding recent changes in the frequency and intensity of extreme climate and weather events.