Climate Scenario Development and Applications for Local/Regional Climate Change Impact Assessments: An Overview for the Non-Climate Scientist: Part I: Scenario Development Using Downscaling Methods Climate scenario development and applications I

Geography Compass

Volumn 5, Issue 6, 2011, Pages 275-300

  Winkler, Julie A ^a   Guentchev, Galina S ^b   Perdinan, S ^a   Tan, Pang Ning ^c   Zhong, Sharon ^a   Liszewska, Malgorzata ^d   Abraham, Zubin ^c   Niedźwiedź, Tadeusz ^e   Ustrnul, Zbigniew ^f  

^a MICHIGAN STATE UNIVERSITY   (United States)

^b UCAR CLIVAR Postdocs Applying Climate Expertise (PACE) Program   (United States)

^c Michigan State University   (United States)

^d UNIVERSITY OF WARSAW   (Poland)

^e UNIVERSITY OF SILESIA   (Poland)

^f JAGIELLONIAN UNIVERSITY   (Poland)

Author keywords

[No Author keywords available]

Indexed keywords

CLIMATE CHANGE; DOWNSCALING; EMPIRICAL ANALYSIS; ENVIRONMENTAL IMPACT ASSESSMENT; ENVIRONMENTAL PLANNING; PLANNING METHOD;

EID: 79958008882     PISSN: None     EISSN: 17498198     Source Type: Journal    
DOI: 10.1111/j.1749-8198.2011.00425.x     Document Type: Article

References (99)

1. American Meteorological Society (2000). Glossary of meteorology. Glickman, T. S. (managing ed.). Boston: American Meteorological Society, 855 pp.
2. Anthes, R. A. and Warner, T. T. (1978). Development of hydrodynamic models suitable for air pollution and other mesometeorological studies. Monthly Weather Review 106, pp. 1045-1078.
3. Baede, A. M. (2007). Annex I: glossary. In: Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K. B., Tignor, M. and Miller, H. L. (eds) Climate change 2007: the physical science basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press, pp. 941-954.
4. Bardossy, A., Duckstein, L. and Bogárdi, I. (1995). Fuzzy rule-based classification of atmospheric circulation patterns. International Journal of Climatology 15, pp. 1087-1097.
5. Beckmann, B.-R. and Buishand, T. A. (2002). Statistical downscaling relationships for precipitation in the Netherlands and north Germany. International Journal of Climatology 22, pp. 15-32.
6. Benestad, R. E. (2001). A comparison between two empirical downscaling strategies. International Journal of Climatology 21, pp. 1645-1668.
7. Benestad, R. E., Hanssen-Bauer, I. and Chen, D. (2008). Empirical-statistical downscaling. Singapore: World Scientific Publishing Company, 228 pp.
8. Bergant, K. and Kaijfež-Bogataj, L. (2005). N-PLS regression as empirical downscaling tool in climate change studies. Theoretical and Applied Climatology 81, pp. 11-23.
9. Buishand, T. A., Shabalova, M. V. and Brandsma, T. (2004). On the choice of the temporal aggregation level for statistical downscaling of precipitation. Journal of Climate 17, pp. 1816-1827.
10. Buonomo, E., Jones, R. G., Huntingford, C. and Hannaford, J. (2007). On the robustness of changes in extreme precipitation over Europe from two high resolution climate change simulations. Quarterly Journal of the Royal Meteorological Society 133, pp. 65-81.
11. Busuioc, A., Chen, D. and Hellström, C. (2001). Performance of statistical downscaling models in GCM validation and regional climate change estimates: application for Swedish precipitation. International Journal of Climatology 21, pp. 557-578.
12. Carter, T. R., et al. (2001). Developing and applying scenarios. In: McCarthy, J. J., Canziani, O. F., Leary, N. A., Dokken, D. J. and White, K. S. (eds) Climate change 2001: impacts, adaptation, and vulnerability. Contribution of Working Group II to the Third Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press, pp. 145-190.
13. Carter, T. R., et al. (2007). New assessment methods and the characterization of future conditions. In: Parry, M. L., Canziani, O. F., Palutikof, J. P., van der Linden, P. J. and Hanson, C. E. (eds) Climate change 2007: impacts, adaptation and vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press, pp. 133-171.
14. Caya, D. and Laprise, R. (1999). A semi-implicit semi-lagrangian regional climate model: The Canadian RCM. Monthly Weather Review 127, pp. 341-362.
15. CCSP (2008). Climate models: an assessment of strengths and limitations. A Report by the U.S. Climate Change Science Program and the Subcommittee on Global Change Research [Bader D. C., Covey C., Gutowski W. J., Held I. M., Kunkel K. E., Miller R. L., Tokmakian R. T. and Zhang M. H. authors]. Washington, DC: Department of Energy Office of Biological and Environmental Research, 124 pp.
16. Cheng, H. and Tan, P. N. (2008). Semi-supervised learning with data calibration for long-term time series forecasting. In: Proceedings of the ACM SIGKDD International Conference on Data Mining (KDD-2008), Las Vegas, NV. New York: ACM Press.
17. Christensen, J. H., Carter, T. R. and Giorgi, F. (2002). PRUDENCE employs new methods to assess European climate change. EOS, Transactions, American Geophysical Union 83, p. 147.
18. Christensen, J. H. and van Meijgaard, E. (1992). On the construction of a regional atmospheric climate model. DMI Technical Report 92-14, 26 pp.
19. Christensen, J. H., et al. (2007). Regional climate projections. In: Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K. B., Tignor, M. and Miller, H. L. (eds) Climate change 2007: the physical science basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press, pp. 847-940.
20. Conway, D. and Jones, P. D. (1998). The use of weather types and air flow indices for GCM downscaling. Journal of Hydrology 212-213, pp. 348-361.
21. Daly, C. (2006). Guidelines for assessing the suitability of spatial climate data sets. International Journal of Climatology 26, pp. 707-721.
22. Déqué, M., et al. (2007). An intercomparison of regional climate simulations for Europe: assessing uncertainties in model projections. Climatic Change 81 (Suppl. 1), pp. 53-70.
23. Dubrovsky, M., Buchtele, J. and Zalud, Z. (2004). High-frequency and low-frequency variability in stochastic daily weather generator and its effect on agriculture and hydrologic modeling. Climatic Change 63, pp. 145-179.
24. Easterling, D. R. (1999). Development of regional climate scenarios using a downscaling approach. Climatic Change 41, pp. 615-634.
25. Fealy, R. and Sweeney, J. (2007). Statistical downscaling of precipitation for a selection of sites in Ireland employing a generalized linear modeling approach. International Journal of Climatology 27, pp. 2083-2094.
26. Fowler, H. J., Blenkinsop, S. and Tebaldi, C. (2007). Linking climate change modeling to impact studies: recent advances in downscaling techniques for hydrological modeling. International Journal of Climatology 27, pp. 1547-1578.
27. Fowler, H. J. and Wilby, R. L. (2007). Editorial: Beyond the downscaling comparison study. International Journal of Climatology 27, pp. 1543-1545.
28. Früh, B., et al. (2011). Estimation of climate change impacts on the urban heat load using an urban climate model and regional climate projections. Journal of Applied Meteorology and Climatology 50, pp. 167-184.
29. Giorgi, F. (2006). Regional climate modeling: status and perspectives. Journal de Physique IV France 139, pp. 101-118.
30. Giorgi, F. and Bates, G. T. (1989). The climatological skill of a regional model over complex terrain. Monthly Weather Review 117, pp. 2325-2347.
31. Giorgi, F. and Mearns, L. O. (1999). Introduction to special issue: regional climate modeling revisited. Journal of Geophysical Research 104, pp. 6335-6352.
32. Giorgi, F., et al. (2001). Regional climate information - evaluation and projections. In: Houghton, J. T., Ding, Y., Griggs, D. J., Noguer, M., van der Linden, P. J., Dai, X., Maskell, K. and Johnson, C. A. (eds) Climate change 2001: the scientific basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press, pp. 583-638.
33. Glahn, H. R. (1985). Statistical weather forecasting. In: Murphy, A. H. and Katz, R. W. (eds) Probability, statistics and decision making in the atmospheric sciences. Boulder: Westview Press, pp. 289-366.
34. Goodess, C. M. and Palutikof, J. (1998). Development of daily rainfall scenarios for southeast Spain using a circulation-type approach to downscaling. International Journal of Climatology 18, pp. 1051-1083.
35. Grell, G., Dudhia, J. and Stauffer, D. (1994). A description of the fifth-generation Penn State/NCAR Mesoscale Model (MM5). NCAR Technical note NCAR/TN-398+STR. Boulder: National Center for Atmospheric Research, 117 pp.
36. Guentchev, G. S., Piromsopa, K. and Winkler, J. A. (2009). Estimating changes in temperature variability in a future climate for the Great Lakes region. In: Pryor, S. C. (ed.) Climate variability, predictability and change in the Midwestern USA. Bloomington: Indiana University Press, pp. 66-75.
37. Hanssen-Bauer, I., et al. (2005). Statistical downscaling of climate scenarios over Scandinavia. Climate Research 29, pp. 255-268.
38. Hay, L. E., Clark, M. P., Pagowski, M. and Leavesley, G. H. (2006). One-way coupling of an atmospheric and a hydrologic model in Colorado. Journal of Hydrometeorology 7, pp. 569-589.
39. Haylock, M. R., et al. (2006). Downscaling heavy precipitation over the UK: a comparison of dynamical and statistical methods and their future scenarios. International Journal of Climatology 26 (10), pp. 1397-1415.
40. Hewitson, B. C. and Crane, R. G. (1996). Climate downscaling: techniques and application. Climate Research 7, pp. 85-95.
41. Hewitson, B. C. and Crane, R. G. (2006). Consensus between GCM climate change projections with empirical downscaling: precipitation downscaling over South Africa. International Journal of Climatology 26, pp. 1315-1337.
42. Huth, R. (1999). Statistical downscaling in central Europe: evaluation of methods and potential predictors. Climate Research 13, pp. 91-101.
43. Huth, R. and Kysely, J. (2000). Constructing site-specific climate change scenarios on a monthly scale using statistical downscaling. Theoretical and Applied Climatology 66, pp. 13-27.
44. Jacob, D. and Podzun, R. (1997). Sensitivity studies with the regional climate model REMO. Meteorology and Atmospheric Physics 63, pp. 119-129.
45. Jones, R. G., Murphy, J. M. and Noguer, M. (1995). Simulations of climate change over Europe using a nested regional climate model I: assessment of control climate, including sensitivity to location of lateral boundaries. Quarterly Journal of the Royal Meteorological Society 121, pp. 1413-1449.
46. Kaas, E. and Frich, P. (1995). Diurnal temperature range and cloud cover in the Nordic countries: observed trends and estimates for the future. Atmospheric Research 37, pp. 211-238.
47. Karl, T. R., et al. (1990). A method of relating general circulation model simulated climate to the observed local climate. Part I: Seasonal statistics. Journal of Climate 3, pp. 1053-1079.
48. Katz, R. W. (1996). Use of conditional stochastic models to generate climate change scenarios. Climatic Change 32, pp. 237-255.
49. Katz, R. W. and Parlange, M. B. (1993). Effects of an index of atmospheric circulation on stochastic properties of precipitation. Water Resources Research 29, pp. 2335-2344.
50. Khan, M. S., Coulibaly, P. and Dibike, Y. (2006). Uncertainty analysis of statistical downscaling methods. Journal of Hydrology 319, pp. 357-382.
51. Laprise, R. (2008). Regional climate modeling. Journal of Computational Physics 227, pp. 3641-3666.
52. Leduc, M. and Laprise, R. (2009). Regional climate model sensitivity to domain size. Climate Dynamics 32, pp. 833-854.
53. Leung, L. R., et al. (2004). Mid-century ensemble regional climate change scenarios for the western United States. Climatic Change 62, pp. 75-113.
54. Li, X. and Sailor, D. (2000). Application of tree-structured regression for regional precipitation prediction using GCM output. Climate Research 16, pp. 17-30.
55. Liang, X.-Z., Kunkel, K. E. and Samel, A. N. (2001). Development of a regional climate model for U.S. Midwest applications. Part 1: sensitivity to buffer zone treatment. Journal of Climate 14, pp. 4363-4378.
56. Lüthi, D., et al. (1996). Interannual variability and regional climate simulations. Theoretical and Applied Climatology 53, pp. 185-209.
57. Maraun, D., et al. (2010). Precipitation downscaling under climate change: recent developments to bridge the gap between dynamical models and the end user. Reviews of Geophysics 48, pp. 1-34.
58. Matulla, C., Scheifinger, H., Menzel, A. and Koch, E. (2003). Exploring two methods for statistical downscaling of Central European phenological time series. International Journal of Biometeorology 48, pp. 56-64.
59. Matulla, C., et al. (2008). Influence of similarity measures on the performance of the analog method for downscaling daily precipitation. Climate Dynamics 30, pp. 133-144.
60. Mearns, L. O., et al. (2001). Climate scenario development. In: Houghton, J. T., Ding, Y., Griggs, D. J., Noguer, M., van der Linden, P. J., Dai, X., Maskell, K. and Johnson, C. A. (eds) Climate change 2001: the scientific basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press, pp. 739-768.
61. Mearns, L. O., et al. (2003). Guidelines for use of climate scenarios developed from regional climate model experiments. Data Distribution Centre of the Intergovernmental Panel on Climate Change. [Online]. Retrieved on 18 March 2011 from.
62. Menzel, L. and Burger, G. (2002). Climate change scenarios and runoff response in the Mulde catchment (Southern Elbe, Germany). Journal of Hydrology 267, pp. 53-64.
63. Mitchell, T. D., et al. (2004). A comprehensive set of high-resolution grids of monthly climate for Europe and the globe: The observed record (1901-2000) and 16 scenarios (2001-2100). Tyndall Centre for Climate Change Research Working Paper 55. [Online]. Retrieved on 18 March 2011 from.
64. Murphy, J. (2000). Predictions of climate change over Europe using statistical and dynamic downscaling techniques. International Journal of Climatology 20, pp. 489-501.
65. Najac, J., Lac, C. and Terray, L. (2011). Impact of climate change on surface winds in France using a statistical-dynamical downscaling method with mesoscale modelling. International Journal of Climatology 31, pp. 415-430.
66. Notaro, M., et al. (forthcoming). 21st century Wisconsin snow projections based on an operational snow model driven by statistically downscaled climate data. International Journal of Climatology, doi.
67. Palutikof, J. P., Winkler, J. A., Goodess, C. M. and Andresen, J. A. (1997). The simulation of daily temperature time series from GCM output. Part I: Comparison of model data with observations. Journal of Climate 10, pp. 2497-2513.
68. Pan, Z., et al. (2001). Evaluation of uncertainties in regional climate change simulations. Journal of Geophysical Research 106, pp. 17735-17752.
69. Panagoulia, D., Bárdossy, A. and Lourmas, G. (2006). Diagnostic statistics of daily rainfall variability in an evolving climate. Advances in Geosciences 7, pp. 349-354.
70. Pielke, R. A., et al. (1992). A comprehensive meteorological modeling system - RAMS. Meteorological and Atmospheric Physics 49, pp. 69-91.
71. Plummer, D. A., et al. (2006). Climate and climate change over North America as simulated by the Canadian regional climate model. Journal of Climate 19, pp. 3112-3132.
72. Qian, B., Gameda, S. and Hayhoe, H. (2008). Performance of stochastic weather generators LARS-WG and AAFC-WG for reproducing daily extremes of diverse Canadian climates. Climate Research 37, pp. 17-33.
73. Reichert, B. K., Bengtsson, L. and Akesson, O. (1999). A statistical modeling approach for the simulation of local paleoclimatic proxy records using GCM output. Journal of Geophysical Research 104 (D16), pp. 19071-19083.
74. Rummukainen, M. (2010). State-of-the art with regional climate models. WIREs Climate Change 1, pp. 82-96.
75. Salathé, E. P., Mote, P. W. and Wiley, M. W. (2007). Review of scenario selection and downscaling methods for the assessment of climate change impacts on hydrology in the United States Pacific Northwest. International Journal of Climatology 27, pp. 1611-1621.
76. Schmidli, J., Frei, C. and Vidale, P. L. (2006). Downscaling from GCM precipitation: a benchmark for dynamical and statistical downscaling methods. International Journal of Climatology 26, pp. 679-689.
77. Schoof, J. T. and Pryor, S. C. (2001). Downscaling temperature and precipitation: a comparison of regression-based methods and artificial neural networks. International Journal of Climatology 21, pp. 773-790.
78. Semenov, M. A. (2008). Simulation of extreme weather events by a stochastic weather generator. Climate Research 35, pp. 203-212.
79. Semenov, M. A. and Barrow, E. M. (1997). Use of a stochastic weather generator in the development of climate change scenarios. Climatic Change 35, pp. 397-414.
80. Skamarock, W. C., et al. (2005). A Description of the Advanced Research WRF Version 2. NCAR/TN-468+STR. Boulder, CO. [Online]. Retrieved on 18 March 2011 from.
81. von Storch, H., Zorita, E. and Cubasch, U. (1993). Downscaling of global climate change estimates to regional scales: an application to Iberian rainfall in wintertime. Journal of Climate 6, pp. 1161-1171.
82. Tabor, K. and Williams, J. W. (2010). Globally downscaled climate projections for assessing the conservation impacts of climate change. Ecological Applications 20, pp. 554-565.
83. Themeβl, M. J., Gobiet, A. and Leuprecht, A. (forthcoming). Empirical-statistical downscaling and error correction of daily precipitation from regional climate models. International Journal of Climatology, doi.
84. Tolika, K., et al. (2007). Simulation of seasonal precipitation and raindays over Greece: a statistical downscaling technique based on artificial neural networks (ANNs). International Journal of Climatology 27, pp. 861-881.
85. Tripathi, S., Srinivas, V. V. and Nanjundiah, R. S. (2006). Downscaling of precipitation for climate change scenarios: a support vector machine approach. Journal of Hydrology 330, pp. 621-640.
86. Wetterhall, F., Halldin, S. and Xu, C. (2005). Statistical precipitation downscaling in central Sweden with the analogue method. Journal of Hydrology 306, pp. 174-190.
87. Widmann, M., Bretherton, C. S. and Salathe, E. P., Jr (2003). Statistical precipitation downscaling over the northwestern United States using numerically simulated precipitation as a predictor. Journal of Climate 16, pp. 799-816.
88. Wilby, R. L., Dawson, C. W. and Barrow, E. M. (2002). SDSM - a decision support tool for the assessment of regional climate change impacts. Environmental Modelling and Software 17, pp. 147-159.
89. Wilby, R. L. and Wigley, T. M. L. (1997). Downscaling general circulation model output: a review of methods and limitations. Progress in Physical Geography 21, pp. 530-548.
90. Wilby, R. L., et al. (2004). Guidelines for use of climate scenarios developed from statistical downscaling methods. 27 pp. Available from the DDC of IPCC TGCIA. [Online]. Retrieved on 18 March 2011 from.
91. Wilks, D. S. (1992). Adapting stochastic weather generation algorithms for climate change studies. Climatic Change 22, pp. 67-84.
92. Wilks, D. S. (2006). Statistical methods in the atmospheric sciences. 2nd ed. Burlington, MA: Academic Press, 627 pp.
93. Wilks, D. S. (2010). Use of stochastic weather generators for precipitation downscaling. WIREs Climate Change 1, pp. 898-907.
94. Winkler, J. A., Palutikof, J. P., Andresen, J. A. and Goodess, C. M. (1997). The simulation of daily temperature time series from GCM output. Part II: Sensitivity analysis of an empirical transfer function methodology. Journal of Climate 10, pp. 2514-2532.
95. Winkler, J. A., et al. (2010). A conceptual framework for multi-regional climate change assessments for international market systems with long-term investments. Climatic Change 103, pp. 445-470.
96. Winkler, J. A., et al. (forthcoming). The development and communication of an ensemble of local-scale climate scenarios: an example from the Pileus Project. In: Dietz, T. and Bidwell, D. (eds) Climate change in the Great Lakes Region: navigating an uncertain future, East Lansing: Michigan State University Press.
97. Xu, C. Y. (1999). From GCMs to river flow: a review of downscaling methods and hydrologic modeling approaches. Progress in Physical Geography 23, pp. 229-249.
98. Yarnal, B., Comrie, A. C., Frakes, B. and Brown, D. P. (2001). Developments and prospects in synoptic climatology. International Journal of Climatology 21, pp. 1923-1950.
99. Zorita, E. and von Storch, H. (1999). The analogue method as a simple statistical downscaling technique: comparison with more complicated methods. Journal of Climate 12, pp. 2474-2488.