Abstract
This paper presents a novel statistical downscaling method based on a non-linear classification technique known as self-organizing maps (SOMs) and has therefore been named SOM-SD. The relationship between large-scale atmospheric circulation and local-scale surface variable was constructed in a relatively simple and transparent manner. For a specific atmospheric state, an ensemble of possible values was generated for the predictand following the Monte Carlo method. Such a stochastic simulation is essential to explore the uncertainties of climate change in the future through a series of random re-sampling experiments. The novel downscaling method was evaluated by downscaling daily precipitation over Southeast Australia. The large-scale predictors were extracted from the daily NCAR/NCEP reanalysis data, while the predictand was high-resolution gridded daily observed precipitation (1958–2008) from the Australian Bureau of Meteorology. The results showed that the method works reasonably well across a variety of climatic zones in the study area. Overall, there was no particular zone that stands out as a climatic entity where the downscaling skill in reproducing all statistical indices was consistently lower or higher across seasons than the other zones. The method displayed a high skill in reproducing not only the climatologic statistical properties of the observed precipitation, but also the characteristics of the extreme precipitation events. Furthermore, the model was able to reproduce, to a certain extent, the inter-annual variability of precipitation characteristics.
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References
Barry RG, Perry AH (2001) Synoptic climatology and its applications. In: Barry RG, Carleton AM (eds) Synoptic and Dynamic Climatology. Routledge, London, pp 547–603
Benestad RE, Hanssen-Bauer, Chen DL (2008) Empirical-Statistical Downscaling. Sciences, New York
Bergant K, Kajfez-Bogataj L (2005) N-PLS regression as empirical downscaling tool in climate change studies. Theor Appl Climatol 81:11–23
Bronstert A, Niehoff D, Burger G (2002) Effects of climate and land-use change on storm runoff generation: present knowledge and modeling capabilities. Hydrol Process 16(2):509–529
Cassano EN, Lynch AH, Cassano JJ, Koslow MR (2006a) Classification of synoptic patterns in the western Arctic associated with extreme events at Barrow, Alaska, USA. Clim Res 30:83–97
Cassano JJ, Uotila P, Lynch AH (2006b) Changes in synoptic weather patterns in the polar regions in the twentieth and twenty-first centuries part 1: Arctic. Int J Climatol 26:1027–1049
Christensen JH, Hewitson B, Busuioc A, Chen A et al (2007) Regional climate projections. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (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 University Press, Cambridge, United Kingdom and New York
Daley R (1991) Atmospheric Data Assimilation. Cambridge University Press, Cambridge, 457
Davison AC, Hinkley D (2006) Bootstrap methods and their application (8th ed.). Cambridge: Cambridge Series in Statistical and Probabilistic Mathematics
Fowler HJ, Blenkinsop S, Tebaldi C (2007) Linking climate change modeling to impacts studies: recent advances in downscaling techniques for hydrological modeling. Int J Climatol 27:1547–1578
Frias MD, Zorita E, Femandez J, Rodriguez-Puebla C (2006) Testing statistical downscaling methods in simulated climates. Geophys Res Lett 33:L19807. doi:10.1029/2006GL027453
Gabriel KR, Neumann J (1962) A Markov chain model for daily rainfall occurrence at Tel Aviv. J R Meteorol Soc 88:90–95
Haylock MR, Cawley GC, Harpham C, Wilby RL, Goodess CM (2006) Downscaling heavy precipitation over the UK: a comparison of dynamical and statistical methods and their future scenarios. Int J Climatol 26:1397–1415
Hewitson BC, Crane RG (2002) Self organizing maps: applications to synoptic climatology. Clim Res 22:13–26
Hewitson BC, Crane RG (2006) Consensus between GCM climate change projections with empirical downscaling: precipitation downscaling over South Africa. Int J Climatol 26:1315–1337
Hidalgo H G, Dettinger M D, Cayan D R (2008) Downscaling with constructed analogues: daily precipitation and temperature fields over the United States. California Energy Commission, PIER Energy–Related Environmental Research. EC–500–2007–123
Hutchinson MF, McIntyre S, Hobbs RJ, Stein JL, Garnett S, Kinloch J (2005) Integrating a global agro-climatic classification with bioregional boundaries in Australia. Glob Ecol Biogeogr 14:197–212
Imbert A, Benestad RE (2005) An improvement of analog model strategy for more reliable local climate change scenarios. Theor Appl Climatol 82:245–255. doi:10.1007/s00704-005-0133-4
Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Derber J, Gandin L, Iredell M, Saha S, White G, Woollen J, Zhu Y, Chelliah M, Ebisuzaki W, Higgins W, Janowiak J, Mo K, Ropelewski C, Wang J, Leetma A, Reynolds R, Jenne R, Joseph D (1996) The NCEP/NCAR 40-year reanalysis project. Bull Am Meteorol Soc 77:437–471
Kistler R, Kalnay E, CollinsW SS, White G, Woollen J, ChelliahM EW, Kanmitsu M, Kousky V, van den Dool H, Jenne R, Fiorino M (2001) The NCEP/NCAR 50-year reanalysis: monthly means CD-ROM and documentation. Bull Am Meteorol Soc 82:247–267
Kohonen T (1982) Self-organized formation of topologically correct feature maps. Biol Cybern 43:59–69
Kohonen T (2001) Self-organizing maps. Springer, Berlin, p 501
Lall U, Sharma A (1996) A nearest neighbor bootstrap for resampling of hydrologic time series. Water Resour Res 32(3):679–693
Leung LR, Wigmosta MS (1999) Potential climate chance impacts on mountain watersheds in the Pacific Northwest. J Am Water Resour Assoc 35:1463–1471
Lynch AH, Uotila P, Cassano JJ (2006) Changes in synoptic weather patterns in the polar regions in the twentieth and twenty-first centuries, part 2: Antarctic. Int J Climatol 26:1181–1199
Mullan AB, Wratt DS, Renwick JA (2001) Transient model scenarios of climate change for New Zealand. Weather Climate 21:3–34
Nishiyama K, Endo S, Jinno K, Uvo CB, Olsson J, Berndtsson R (2007) Identification of typical synoptic patterns causing heavy rainfall in the rainy season in Japan by a self-organizing map. Atmos Res 83:185–200
Schuenemann K, Cassano J, Finnis J (2009) Forcing of precipitation over Greenland: synoptic climatology for 1961–99. J Hydrometeorol 10:60–78. doi:10.1175/2008JHM1014.1
Semenov MA, Barrow EM (1997) Use of a stochastic weather generator in the development of climate change scenarios. Clim Change 35:397–414
Timbal B, Fernandez E, Li Z (2009) Generalization of a statistical downscaling model to provide local climate change projections for Australia. Environ Soft Model 24:341–358. doi:10.1016/j.envsoft.
von Storch H, Hewitson B, Mearns L (2000) Review of empirical downscaling techniques. In: T, Iversen, and BAK, Høiskar (eds), Regional climate development under global warming. General Technical Report 4. http://regclim.met.no/rapport4/presentation02/presentation02.htm
Vrac M, Stein M, Hayhoe K (2007) Statistical downscaling of precipitation through nonhomogeneous stochastic weather typing. Clim Res 34:169–184
Wetterhall F, Halldin S, Xu C-Y (2005) Statistical precipitation downscaling in central Sweden with the analogue method. J Hydrol 306:174–190
Wetterhall F, Halldin S, Xu C-Y (2007) Seasonality properties of four statistical-downscaling methods in central Sweden. Theor Appl Climatol 87(1–4):123–137
Wilby RL, Charles SP, Zorita E, Timbal B, Whetton P, Mearns LO (2004) Guidelines for use of climate scenarios developed from statistical downscaling methods, supporting material of the Intergovernmental Panel on Climate Change, available from the DDC of IPCC TGCIA, 27
Wood AW, Leung LR, Sridhar V et al (2004) Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs. Clim Change 62:189–216
Zangl G (2004) The sensitivity of simulated orographic precipitation to model components other than cloud microphysics. Quart J Roy Meteor Soc 130:1857–1875
Zorita E, von Storch H (1999) The analog method as a simple downscaling technique: comparison with more complicated methods. J Climate 12:2474–2489
Acknowledgments
This research was supported by the Asia Pacific Network for Global Change Research (APN) CAPaBLE project, CRP2008-02CMY. The authors would like to thank the Australian Bureau of Meteorology for providing the high-quality meteorological data.
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Yin, C., Li, Y., Ye, W. et al. Statistical downscaling of regional daily precipitation over southeast Australia based on self-organizing maps. Theor Appl Climatol 105, 11–26 (2011). https://doi.org/10.1007/s00704-010-0371-y
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DOI: https://doi.org/10.1007/s00704-010-0371-y