Table of Contents Author Guidelines Submit a Manuscript
Advances in Meteorology
Volume 2014, Article ID 473167, 14 pages
http://dx.doi.org/10.1155/2014/473167
Research Article

A Regionalization of Downscaled GCM Data Considering Geographical Features in a Mountainous Area

1Columbia Water Center, Columbia University, New York City, NY 10027, USA
2Centre Eau Terre Environment, INRS490, rue de la Couronne, QC, Canada G1K 9A9
3Department of Civil Engineering, Inha University, Nam-Gu, Incheon 402-751, Republic of Korea

Received 19 April 2014; Revised 8 August 2014; Accepted 10 August 2014; Published 8 September 2014

Academic Editor: Richard Anyah

Copyright © 2014 Soojun Kim et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. T. G. Huntington, “Evidence for intensification of the global water cycle: review and synthesis,” Journal of Hydrology, vol. 319, no. 1–4, pp. 83–95, 2006. View at Publisher · View at Google Scholar · View at Scopus
  2. IPCC, The First Assessment Report (FAR), Cambridge University Press, Cambridge, UK, 1990.
  3. IPCC, The Second Assessment Report: Climate Change 1995 (SAR), Cambridge University Press, Cambridge, UK, 1995.
  4. IPCC, The Third Assessment Report: Climate Change 2001(TAR), Cambridge University Press, New York, NY, USA, 2001.
  5. IPCC, The Fourth Assessment Report: Climate Change 2007 (AR4), Cambridge University Press, New York, NY, USA, 2007.
  6. IPCC, The Fifth Assessment Report: Climate Change 2013 (AR5), Cambridge University Press, Great Britain, Cambridge, UK, 2013.
  7. F. Giorgi, B. Hewitson, J. Christensen et al., “Regional climate information—evaluation and projections,” in Climate Change 2001: The Scientific Basis, J. T. Houghton, Y. Ding, D. J. Griggs et al., Eds., p. 944, Cambridge University Press, Cambridge, UK, 2001. View at Google Scholar
  8. L. O. Mearns, F. Giorgi, P. Whetton, D. Pabon, M. Hulme, and M. Lal, “Guidelines for use of climate scenarios developed from regional climate model experiments,” Tech. Rep., Data Distribution Centre of the Intergovernmental Panel on Climate Change, Norwich, UK, 2003. View at Google Scholar
  9. E. P. Maurer, A. W. Wood, J. C. Adam, D. P. Lettenmaier, and B. Nijssen, “A long-term hydrologically based dataset of land surface fluxes and states for the conterminous United States,” The Americam Meteorological Society, vol. 15, no. 22, pp. 3237–3251, 2002. View at Google Scholar · View at Scopus
  10. H. J. Fowler, S. Blenkinsop, and C. Tebaldi, “Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling,” International Journal of Climatology, vol. 27, no. 12, pp. 1547–1578, 2007. View at Publisher · View at Google Scholar · View at Scopus
  11. C. Frei, R. Schöll, S. Fukutome, J. Schmidli, and P. L. Vidale, “Future change of precipitation extremes in Europe: Intercomparison of scenarios from regional climate models,” Journal of Geophysical Research, vol. 111, no. D6, Article ID D06105, 2006. View at Publisher · View at Google Scholar
  12. J. W. Hurrell, G. J. Holland, and W. G. Large, “The nested regional climate model: an approach toward prediction across scales,” in Proceedings of the AGU Fall Meeting, vol. 89, Eos, Transactions American Geophysical Union, 2008. View at Google Scholar
  13. L. O. Mearns, W. Gutowski, R. Jones et al., “A regional climate change assessment program for North America,” Eos, Transactions American Geophysical Union, vol. 90, no. 36, p. 311, 2009. View at Publisher · View at Google Scholar
  14. M. Hulme, G. J. Jenkins, X. Lu et al., Climate Change Scenarios for the United Kingdom: The UKCIP02 Scientific Report, Tyndall Centre for Climate Change Research, University of East Anglia, Norwich, UK, 2002.
  15. J. H. Christensen, T. R. Carter, M. Rummukainen, and G. Amanatidis, “Evaluating the performance and utility of regional climate models: the PRUDENCE project,” Climatic Change, vol. 81, no. 1, pp. 1–6, 2007. View at Publisher · View at Google Scholar · View at Scopus
  16. X. Gao, J. S. Pal, and F. Giorgi, “Projected changes in mean and extreme precipitation over the Mediterranean region from a high resolution double nested RCM simulation,” Geophysical Research Letters, vol. 33, no. 3, Article ID L03706, 2006. View at Publisher · View at Google Scholar · View at Scopus
  17. F. Giorgi, X. Bi, and J. S. Pal, “Mean, interannual variability and trends in a regional climate change experiment over Europe. I. Present-day climate (1961–1990),” Climate Dynamics, vol. 22, no. 6-7, pp. 733–756, 2004. View at Publisher · View at Google Scholar · View at Scopus
  18. J. Marengo and T. Ambrizzi, “Use of regional climate models in impact assessments and adaptations studies from continental to regional and local scales: The CREAS (Regional Climate Change Scenarios for South America) initiative in South America,” in Proccedings of the 8th International Conference on Southern Hemisphere Meteorology and Oceanography (ICSHMO '06), pp. 291–296, 2006.
  19. R. L. Wilby, S. P. Charles, E. Zorita, B. Timbal, P. Whetton, and L. O. Mearns, “Guidelines for the use of climate scenarios developed from statistical downscaling methods,” Tech. Rep., IPCC Task Group on Data and Scenario Support for Impact and Climate Analysis (TGICA), Norwich, UK, 2004. View at Google Scholar
  20. C. M. Goodess, C. Anagnostopoulo, A. Bardossy et al., “An intercomparison of statistical downscaling methods for Europe and European regions—assessing their performance with respect to extreme temperature and precipitation events,” Tech. Rep. CRU RP11, University of East Anglia, Climate Research Unit Research Publications, Norfolk, UK, 2012. View at Google Scholar
  21. I. Hanssen-Bauer and E. J. Førland, “Long-term trends in precipitation and temperature in the Norwegian Arctic: can they be explained by changes in atmospheric circulation patterns?” Climate Research, vol. 10, no. 2, pp. 143–153, 1998. View at Publisher · View at Google Scholar · View at Scopus
  22. C. Hellström, D. Chen, C. Achberger, and J. Räisänen, “Comparison of climate change scenarios for Sweden based on statistical and dynamical downscaling of monthly precipitation,” Climate Research, vol. 19, no. 1, pp. 45–55, 2001. View at Publisher · View at Google Scholar · View at Scopus
  23. U. Cubasch, H. von Storch, J. Waszkewitz, and E. Zorita, “Estimates of climate change in southern Europe derived from dynamical climate model output,” Climate Research, vol. 7, no. 2, pp. 129–149, 1996. View at Publisher · View at Google Scholar · View at Scopus
  24. J. W. Kidson and C. S. Thompson, “A comparison of statistical and model-based downscaling techniques for estimating local climate variations,” Journal of Climate, vol. 11, no. 4, pp. 735–753, 1998. View at Publisher · View at Google Scholar · View at Scopus
  25. I. Hanssen-Bauer, E. J. Førland, J. E. Haugen, and O. E. Tveito, “Temperature and precipitation scenarios for Norway: comparison of results from dynamical and empirical downscaling,” Climate Research, vol. 25, no. 1, pp. 15–27, 2003. View at Publisher · View at Google Scholar · View at Scopus
  26. J.-L. Chu, H. Kang, C.-Y. Tam, C.-K. Park, and C.-T. Chen, “Seasonal forecast for local precipitation over northern Taiwan using statistical downscaling,” Journal of Geophysical Research, vol. 113, no. 12, Article ID D12118, 2008. View at Publisher · View at Google Scholar · View at Scopus
  27. E. Zorita and H. von Storch, “The analog method as a simple statistical downscaling technique: comparison with more complicated methods,” Journal of Climate, vol. 12, no. 8, pp. 2474–2489, 1999. View at Google Scholar · View at Scopus
  28. Yuval and W. W. Hsieh, “An adaptive nonlinear scheme for precipitation forecasts using neural networks,” Weather and Forecasting, vol. 18, no. 2, pp. 303–310, 2003. View at Publisher · View at Google Scholar
  29. Y. B. Dibike and P. Coulibaly, “Temporal neural networks for downscaling climate variability and extremes,” Neural Networks, vol. 19, no. 2, pp. 135–144, 2006. View at Publisher · View at Google Scholar · View at Scopus
  30. A. Pasini and R. Langone, “Attribution of precipitation changes on a regional scale by neural network modeling: a case study,” Water, vol. 2, no. 3, pp. 321–332, 2010. View at Publisher · View at Google Scholar
  31. T. R. Karl, W. C. Wang, M. E. Schlesinger, R. W. Knight, and D. Portman, “A method of relating general circulation model simulated climate to the observed local climate. Part I: seasonal statistics,” Journal of Climate, vol. 3, no. 10, pp. 1053–1079, 1990. View at Publisher · View at Google Scholar
  32. T. M. L. Wigley, P. D. Jones, K. R. Briffa, and G. Smith, “Obtaining sub-grid-scale information from coarse-resolution general circulation model output,” Journal of Geophysical Research, vol. 95, no. 2, pp. 1943–1953, 1990. View at Publisher · View at Google Scholar · View at Scopus
  33. H. Von Storch, E. Zorita, and U. Cubasch, “Downscaling of global climate change estimates to regional scales: an application to Iberian rainfall in wintertime,” Journal of Climate, vol. 6, no. 6, pp. 1161–1171, 1993. View at Publisher · View at Google Scholar · View at Scopus
  34. A. Busuioc, D. Chen, and C. Hellström, “Temporal and spatial variability of precipitation in Sweden and its link with the large-scale atmospheric circulation,” Tellus, vol. 53, no. 3, pp. 348–367, 2001. View at Publisher · View at Google Scholar · View at Scopus
  35. R. Huth, “Statistical downscaling of daily temperature in central Europe,” Journal of Climate, vol. 15, no. 13, pp. 1731–1742, 2002. View at Publisher · View at Google Scholar · View at Scopus
  36. R. Tomozeiu, C. Cacciamani, V. Pavan, A. Morgillo, and A. Busuioc, “Climate change scenarios of surface temperature in Emilia-Romagna (Italy) obtained using statistical downscaling,” Theoretical and Applied Climatology, vol. 90, no. 1-2, pp. 25–47, 2006. View at Google Scholar
  37. M. D. Frías, E. Zorita, J. Fernández, and C. Rodríguez-Puebla, “Testing statistical downscaling methods in simulated climates,” Geophysical Research Letters, vol. 33, no. 19, Article ID L19807, 2006. View at Publisher · View at Google Scholar · View at Scopus
  38. M. S. Kyoung, H. S. Kim, B. Sivakumar, V. P. Singh, and K. S. Ahn, “Dynamic characteristics of monthly rainfall in the Korean Peninsula under climate change,” Stochastic Environmental Research and Risk Assessment, vol. 25, no. 4, pp. 613–625, 2011. View at Publisher · View at Google Scholar · View at Scopus
  39. L. E. Hay and M. P. Clark, “Use of statistically and dynamically downscaled atmospheric model output for hydrologic simulations in three mountainous basins in the western United States,” Journal of Hydrology, vol. 282, no. 1–4, pp. 56–75, 2003. View at Publisher · View at Google Scholar · View at Scopus
  40. J. Schmidli, C. M. Goodess, C. Frei et al., “Statistical and dynamical downscaling of precipitation: an evaluation and comparison of scenarios for the European Alps,” Journal of Geophysical Research, vol. 112, no. 4, Article ID D04105, 2007. View at Publisher · View at Google Scholar · View at Scopus
  41. S. Spak, T. Holloway, B. Lynn, and R. Goldberg, “A comparison of statistical and dynamical downscaling for surface temperature in North America,” Journal of Geophysical Research D: Atmospheres, vol. 112, no. 8, Article ID D08101, 2007. View at Publisher · View at Google Scholar · View at Scopus
  42. E. D. Gutmann, R. M. Rasmussen, C. Liu et al., “A comparison of statistical and dynamical downscaling of winter precipitation over complex terrain,” Journal of Climate, vol. 25, no. 1, pp. 262–281, 2012. View at Publisher · View at Google Scholar · View at Scopus
  43. S.-T. Chen, P.-S. Yu, and Y.-H. Tang, “Statistical downscaling of daily precipitation using support vector machines and multivariate analysis,” Journal of Hydrology, vol. 385, no. 1–4, pp. 13–22, 2010. View at Publisher · View at Google Scholar · View at Scopus
  44. M. Kyoung, Assessment of climate change effect on standardized precipitation index and frequency-based precipitation [Doctoral dissertation], Inha University, Incheon, Korea, 2010.
  45. Y. A. Kwon, W. T. Kwon, K. O. Boo, and Y. E. Choi, “Future projections on subtropical climate regions over south korea using SRES A1B data,” The Korean Geographical Society, vol. 42, no. 3, pp. 355–367, 2007. View at Google Scholar
  46. R. J. Kuligowski and A. P. Barros, “Localized precipitation forecasts from a numerical weather prediction model using artificial neural networks,” Weather and Forecasting, vol. 13, no. 4, pp. 1195–1205, 1998. View at Publisher · View at Google Scholar · View at Scopus
  47. C. M. Bishop, Neural Networks for Pattern Recognition, Clarendon Press, London, UK, 2000. View at MathSciNet
  48. P. Picton, Neural Networks, Palgrave, Basingstoke, UK, 2nd edition, 2000.
  49. W. Hsieh, Machine Learning Methods in the Environmental Sciences, Cambridge University Press, Cambridge, UK, 2009.
  50. S. Haupt, A. Pasini, and C. Marzban, Artificial Intelligence Methods in the Environmental Sciences, Springer, 2009.
  51. P. Goovaerts, Geostatistics for Natural Resources Evaluation, Oxford University Press, New York, NY, USA, 1997.
  52. R. L. Wilby, C. W. Dawson, and E. M. Barrow, “SDSM—a decision support tool for the assessment of regional climate change impacts,” Environmental Modelling and Software, vol. 17, no. 2, pp. 147–159, 2002. View at Google Scholar · View at Scopus