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Climate Dynamics

, Volume 43, Issue 12, pp 3201–3217 | Cite as

Comparison of statistically downscaled precipitation in terms of future climate indices and daily variability for southern Ontario and Quebec, Canada

  • Carlos F. GaitanEmail author
  • William W. Hsieh
  • Alex J. Cannon
Article

Abstract

Given the coarse resolution of global climate models, downscaling techniques are often needed to generate finer scale projections of variables affected by local-scale processes such as precipitation. However, classical statistical downscaling experiments for future climate rely on the time-invariance assumption as one cannot know the true change in the variable of interest, nor validate the models with data not yet observed. Our experimental setup involves using the Canadian regional climate model (CRCM) outputs as pseudo-observations to estimate model performance in the context of future climate projections by replacing historical and future observations with model simulations from the CRCM, nested within the domain of the Canadian global climate model (CGCM). In particular, we evaluated statistically downscaled daily precipitation time series in terms of the Peirce skill score, mean absolute errors, and climate indices. Specifically, we used a variety of linear and nonlinear methods such as artificial neural networks (ANN), decision trees and ensembles, multiple linear regression, and k-nearest neighbors to generate present and future daily precipitation occurrences and amounts. We obtained the predictors from the CGCM 3.1 20C3M (1971–2000) and A2 (2041–2070) simulations, and precipitation outputs from the CRCM 4.2 (forced with the CGCM 3.1 boundary conditions) as predictands. Overall, ANN models and tree ensembles outscored the linear models and simple nonlinear models in terms of precipitation occurrences, without performance deteriorating in future climate. In contrast, for the precipitation amounts and related climate indices, the performance of downscaling models deteriorated in future climate.

Keywords

Statistical downscaling Nonlinear methods Climate extremes Precipitation Future evaluation Artificial neural networks 

Notes

Acknowledgments

The authors would like to acknowledge the data access integration (DAI) Team for providing the data (CGCM3 and CRCM 4.2) and technical support. The DAI Portal (http://loki.qc.ec.gc.ca/DAI/) is made possible through collaboration among the Global Environmental and Climate Change Centre (GEC3), the Adaptation and Impacts Research Division (AIRD) of Environment Canada, and the drought research initiative (DRI). The Ouranos Consortium (in Quebec) provides access of the CRCM. We are grateful for the support from a Special Research Opportunity grant awarded by the Canadian Natural Sciences and Engineering Research Council, and to Prof. Van Nguyen for leading the multi-university project. This work is part of the “Probabilistic assessment of regional changes in climate variability and extremes” project funded by the Natural Sciences and Engineering Research Council of Canada through a Special Research Opportunity (NSERC-SRO) grant. The project aims to develop high-resolution climate change information with the Canadian GCMs and different downscaling methodologies.

References

  1. Benestad RE, Chen D, Hanssen-Bauer I (2008) Empirical-statistical downscaling. World Scientific, SingaporeCrossRefGoogle Scholar
  2. Bishop CM (2006) Pattern recognition and machine learning. Springer, CambridgeGoogle Scholar
  3. Bourdages L, Huard D (2010) Climate change scenario over Ontario based on the Canadian regional climate model (CRCM4.2). Ouranos, MontrealGoogle Scholar
  4. Breiman L (1996) Bagging predictors. Mach Learn 24:123–140Google Scholar
  5. Bronaugh D (2012) PCIC implementation of Climdex routines. version 0.4–1. Victoria, BC. http://cran.r-project.org/web/packages/climdex.pcic/index.html
  6. Bürger G, Murdock TQ, Werner AT, Sobie SR, Cannon AJ (2012) Downscaling extremes—an intercomparison of multiple statistical methods for present climate. J Clim 25(12):4366–4388. doi: 10.1175/jcli-d-11-00408.1 CrossRefGoogle Scholar
  7. Busuioc A, Tomozeiu R, Cacciamani C (2008) Statistical downscaling model based on canonical correlation analysis for winter extreme precipitation events in the Emilia-Romagna region. Int J Clim 28(4):449–464. doi: 10.1002/joc.1547 CrossRefGoogle Scholar
  8. Chen ST, Yu PS, Tang YH (2010) Statistical downscaling of daily precipitation using support vector machines and multivariate analysis. J Hydrol 385(1–4):13–22. doi: 10.1016/J.Jhydrol.01.021 CrossRefGoogle Scholar
  9. Crane R, Hewitson B (1998) Doubled CO2 precipitation changes for the Susquehanna Basin: down-scaling from the Genesis general circulation model. Int J Clim 18:65–76CrossRefGoogle Scholar
  10. DAI_Team (2009) Climatological maps from the Canadian regional climate model & the Canadian global climate model simulations over North America for the current (1961–1990) & future (2041–2070) periods. Environment CanadaGoogle Scholar
  11. Darlington RB (1990) Regression and linear models. Chapter 18. McGraw-Hill, New YorkGoogle Scholar
  12. de Elia R, Cote H (2010) Climate and climate change sensitivity to model configuration in the Canadian RCM over North America. Meteorologische Zeitschrift 19(4):1–15Google Scholar
  13. de Elía R, Caya D, Côté H, Frigon A, Biner S, Giguère M, Paquin D, Harvey R, Plummer D (2008) Evaluation of uncertainties in the CRCM-simulated North American climate. Clim Dyn 30(2–3):113–132. doi: 10.1007/s00382-007-0288-z CrossRefGoogle Scholar
  14. Déqué et al. (2007) An intercomparison of regional climate simulations for Europe: assessing uncertainties in model projections. Clim Change 81(1):53–70. doi: 10.1007/s10584-006-9228-x
  15. Deser C, Phillips A, Bourdette V, Teng H (2010) Uncertainty in climate change projections: the role of internal variability. Clim Dyn 38(3–4):527–546. doi: 10.1007/s00382-010-0977-x Google Scholar
  16. Dibike YB, Coulibaly P (2006) Temporal neural networks for downscaling climate variability and extremes. Neural Netw 19(2):135–144. doi: 10.1016/j.neunet.2006.01.003 CrossRefGoogle Scholar
  17. Fowler HJ, Blenkinsop S, Tebaldi C (2007) Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling. Int J Clim 27(12):1547–1578. doi: 10.1002/Joc.1556 CrossRefGoogle Scholar
  18. Frías MD, Zorita E, Fernández J, Rodríguez-Puebla C (2006) Testing statistical downscaling methods in simulated climates. Geophys Res Lett 33(19). doi: 10.1029/2006gl027453
  19. Friedman JH (1991) Multivariate adaptive regression splines (with discussion). Ann Stat 19(1):14Google Scholar
  20. Furrer EM, Katz RW (2007) Generalized linear modeling approach to stochastic weather generators. Clim Res 34:129–144CrossRefGoogle Scholar
  21. Gangopadhyay S, Clark M, Rajagopalan B (2005) Statistical downscaling using K-nearest neighbors. Water Res Res 41:W02024. DOI: 10.1029/2004wr003444
  22. Harpham C, Wilby R (2005) Multi-site downscaling of heavy daily precipitation occurrence and amounts. J Hydrol 312(1–4):235–255. doi: 10.1016/j.jhydrol.2005.02.020 CrossRefGoogle Scholar
  23. Hashmi MZ, Shamseldin AY, Melville BW (2011) Comparison of SDSM and LARS-WG for simulation and downscaling of extreme precipitation events in a watershed. Stoch Environ Res Risk Asses 25(4):475–484. doi: 10.1007/S00477-010-0416-X CrossRefGoogle Scholar
  24. Hastie T, Tibshirani R, Friedman JH (2009) The elements of statistical learning, 2nd edn. Springer, BerlinCrossRefGoogle Scholar
  25. Haylock MR, Cawley GC, Harpham C, Wilby RL, Goodess CM (2006) Downscaling heavy precipitation over the United Kingdom: a comparison of dynamical and statistical methods and their future scenarios. Int J Clim 26(10):1397–1415. doi: 10.1002/joc.1318 CrossRefGoogle Scholar
  26. Hill T, Lewicki P (2006) Statistics: methods and applications: a comprehensive reference for science, industry and data mining. StatSoft, TulsaGoogle Scholar
  27. Hsieh WW (2009) Machine learning methods in the environmental sciences: neural networks and kernels. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  28. Huth R (1999) Statistical downscaling in central Europe: evaluation of methods and potential predictors. Clim Res 13(2):91–101CrossRefGoogle Scholar
  29. Huth R (2003) Sensitivity of local daily temp change estimates to the selection of downscaling models and predictors. J Clim 17:640–652CrossRefGoogle Scholar
  30. Huth R, Kysely J, Dubrovsky M (2001) Time structure of observed, GCM-simulated, downscaled, and stochastically generated daily temperature series. J Clim 14(20):4047–4061CrossRefGoogle Scholar
  31. Huth R, Kysely J, Dubrovsky M (2003) Simulation of surface air temperature by GCMs, statistical downscaling and weather generator: higher-order statistical moments. Stud Geophys Geod 47:203–216CrossRefGoogle Scholar
  32. Iizumi T, Nishimori M, Dairaku K, Adachi SA, Yokozawa M (2011) Evaluation and intercomparison of downscaled daily precipitation indices over Japan in present-day climate: strengths and weaknesses of dynamical and bias correction-type statistical downscaling methods. J Geophys Res Atmos 116. doi: 10.1029/2010jd014513
  33. IPCC (2000) Emissions scenarios. A special report of working group III of the intergovernmental panel on climate change. Cambridge University Press, CambridgeGoogle Scholar
  34. James G, Witten D, Hastie T, Tibshirani R (2013) An introduction to statistical learning: with applications in R. Springer Texts in Statistics. Springer, New YorkCrossRefGoogle Scholar
  35. Jekabsons G (2010) AresLab, version 1.5. Riga, Latvia. http://www.cs.rtu.lv/jekabsons/
  36. Jeong DI, St-Hilaire A, Ouarda TBMJ, Gachon P (2012a) CGCM3 predictors used for daily temperature and precipitation downscaling in Southern Quebec, Canada. Theor Appl Climatol 107(3–4):389–406. doi: 10.1007/S00704-011-0490-0 CrossRefGoogle Scholar
  37. Jeong DI, St-Hilaire A, Ouarda TBMJ, Gachon P (2012b) Comparison of transfer functions in statistical downscaling models for daily temperature and precipitation over Canada. Stoch Environ Res Risk Assess 26(5):633–653. doi: 10.1007/S00477-011-0523-3 CrossRefGoogle Scholar
  38. Jeong DI, St-Hilaire A, Ouarda TBMJ, Gachon P (2012c) Multisite statistical downscaling model for daily precipitation combined by multivariate multiple linear regression and stochastic weather generator. Clim Change 114(3–4):567–591. doi: 10.1007/S10584-012-0451-3 CrossRefGoogle Scholar
  39. Kallache M, Vrac M, Naveau P, Michelangeli PA (2011) Nonstationary probabilistic downscaling of extreme precipitation. J Geophys Res 116(D5). doi: 10.1029/2010jd014892
  40. Karl TR, Wang WC, Schlesinger ME, Knight RW, Portman D (1990) A method of relating general circulation model simulated climate to observed local climate. Part I: seasonal. J Clim 3(10):1053–1079CrossRefGoogle Scholar
  41. Karl TR, Nicholls N, Ghazi A (1999) CLIVAR/GCOS/WMO workshop on indices and indicators for climate extremes—workshop summary. Clim Change 42(1):3–7CrossRefGoogle Scholar
  42. Khan M, Coulibaly P, Dibike Y (2006) Uncertainty analysis of statistical downscaling methods. J Hydrol 319(1–4):357–382. doi: 10.1016/j.jhydrol.2005.06.035 CrossRefGoogle Scholar
  43. Laprise R (2008) Regional climate modelling. J Comput Phys 227(7):3641–3666. doi: 10.1016/j.jcp.2006.10.024 CrossRefGoogle Scholar
  44. Lucas-Picher P, Caya D, de Elía R, Laprise R (2008) Investigation of regional climate models’ internal variability with a ten-member ensemble of 10-year simulations over a large domain. Clim Dyn 31(7–8):927–940. doi: 10.1007/s00382-008-0384-8 CrossRefGoogle Scholar
  45. MacKay DJC (1992) Bayesian interpolation. Neural Comput 4(3):415–447CrossRefGoogle Scholar
  46. Maraun D (2012) Nonstationarities of regional climate model biases in European seasonal mean temperature and precipitation sums. Geophys Res Lett 39(6). doi: 10.1029/2012gl051210
  47. Mearns LO, Giorgi F, Whetton P, Pabon D, Hulme M, Lal M (2003) Guidelines for use of climate scenarios developed from regional climate model experiments (trans: IPCC DDCot). IPCC Technical Report. IPCCGoogle Scholar
  48. Mearns LO, Gutowski WJ, Jones R, Leung R, McGinnis S, Nunes A, Qian Y (2007) The North American regional climate change assessment program dataset. Boulder, CO. doi: 10.5065/D6RN35ST
  49. Murphy J (1999) An evaluation of statistical and dynamical techniques for downscaling local climate. J Clim 12:2256–2284. doi:10.1175/1520-0442(1999)012<2256:AEOSAD>2.0.CO;2Google Scholar
  50. Murphy KP (2012) Machine Learning: a probabilistic perspective. Adaptive computation and machine learning series. MIT Press, CambridgeGoogle Scholar
  51. Music B, Caya D (2007) Evaluation of the hydrological cycle over the Mississippi river basin as simulated by the Canadian regional climate model (CRCM). J Hydrometeorol 8(5):969–988. doi: 10.1175/jhm627.1 CrossRefGoogle Scholar
  52. Music B, Sykes C (2011) CRCM diagnostics for future water resources in OPG priority watersheds. In: Consortium on Regional Climatology and Adaptation to Climate Change. Ontario, Canada. http://www.ouranos.ca/media/publication/175_MusicSykes2011.pdf
  53. Peterson TC (2005) Report on the activities of the working group on climate change detection and related rapporteurs 1998–2001. WMO, GeneveGoogle Scholar
  54. Rowell, DP (2006) A demonstration of the uncertainty in projections of UK climate change resulting from regional model formulation. Clim Change 79(3–4):243–257Google Scholar
  55. Schmidli J, Goodess CM, Frei C, Haylock MR, Hundecha Y, Ribalaygua J, Schmith T (2007) Statistical and dynamical downscaling of precipitation: An evaluation and comparison of scenarios for the European Alps. J Geophys Res 112(D4). doi: 10.1029/2005jd007026
  56. Schoof JT, Pryor SC (2001) Downscaling temperature and precipitation: a comparison of regression-based methods and artificial neural networks. Int J Clim 21(7):773–790. doi: 10.1002/joc.655 CrossRefGoogle Scholar
  57. Semenov MA, Donatelli M, Stratonovitch P, Chatzidaki E, Baruth B (2010) ELPIS: a dataset of local-scale daily climate scenarios for Europe. Clim Res 44(1):3–15. doi: 10.3354/cr00865 CrossRefGoogle Scholar
  58. Tebaldi C, Knutti R (2007) The use of the multi-model ensemble in probabilistic climate projections. Philos Trans A Math Phys Eng Sci 365(1857):2053–2075. doi: 10.1098/rsta.2007.2076 CrossRefGoogle Scholar
  59. Teutschbein C, Seibert J (2010) Regional climate models for hydrological impact studies at the catchment scale: a review of recent modeling strategies. Geogr Compass 4(7):834–860Google Scholar
  60. Teutschbein C, Seibert J (2013) Is bias correction of regional climate model (RCM) simulations possible for non-stationary conditions? Hydrol Earth Syst Sci 17(12):5061–5077. doi: 10.5194/hess-17-5061-2013 CrossRefGoogle Scholar
  61. Tomassetti B, Verdecchia M, Giorgi F (2009) NN5: a neural network based approach for the downscaling of precipitation fields—model description and preliminary results. J Hydrol 367(1–2):14–26. doi: 10.1016/j.jhydrol.2008.12.017 CrossRefGoogle Scholar
  62. Trigo RM, Palutikof JP (2001) Precipitation scenarios over Iberia: a comparison between direct GCM output and different downscaling techniques. J Clim 14(23):4422–4446. doi:10.1175/1520- 0442(2001)014<4422:Psoiac>2.0.Co;2Google Scholar
  63. von Storch H, Zorita E, Cubasch U (1993) Downscaling of global climate change estimates to regional scales: an application to Iberian rainfall in wintertime. J Clim 6:1161–1171. doi:10.1175/1520-0442(1993)006<1161:DOGCCE>2.0.CO;2Google Scholar
  64. Vrac M, Stein ML, Hayhoe K, Liang XZ (2007) A general method for validating statistical downscaling methods under future climate change. Geophys Res Lett 34(18). doi: 10.1029/2007gl030295
  65. Wigley TML, Jones PD, Briffa KR, Smith G (1990) Obtaining subgrid scale information from coarse resolution general circulation model output. J Geophys Res 95(D2):1943–1953Google Scholar
  66. Wilby RL, Wigley TML (1997) Downscaling general circulation model output: a review of methods and limitations. Prog Phys Geogr 21(4):530–548. doi: 10.1177/030913339702100403 CrossRefGoogle Scholar
  67. Wilby RL, Wigley TML, Conway D, Jones PD, Hewitson BC, Main J, Wilks DS (1998) Statistical downscaling of general circulation model output: a comparison of methods. Water Resour Res 34(11):2995–3008CrossRefGoogle Scholar
  68. Wilby RL, Conway D, Jones PD (2002) Prospects for downscaling seasonal precipitation variability using conditioned weather generator parameters. Hydrol Process 16(6):1215–1234. doi: 10.1002/Hyp.1058 CrossRefGoogle Scholar
  69. Wilby et al. (2004) Guidelines for the use of climate scenarios developed from statistical downscaling models. IPCC task group on data and scenario support for impact and climate analysis (TGICA). http://ipcc-ddc.cru.uea.ac.uk/guidelines/StatDown_Guide.pdf
  70. Wilks DS (2006) On “field significance” and the false discovery rate. J Appl Meteorol Clim 45:1181–1189CrossRefGoogle Scholar
  71. Wilks DS (2010) Use of stochastic weather generators for precipitation downscaling. Wires Clim Change 1(6):898–907. doi: 10.1002/Wcc.85 CrossRefGoogle Scholar
  72. Wilks DS (2011) Statistical methods in the atmospheric sciences. International geophysics series, vol 100, 3rd edn. Academic Press, LondonGoogle Scholar
  73. Willmott CJ, Matsuura K (2005) Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Clim Res 30:79–82CrossRefGoogle Scholar
  74. Willmott CJ, Robeson SM, Matsuura K (2012) A refined index of model performance. Int J Clim 32(13):2088–2094. doi: 10.1002/joc.2419 CrossRefGoogle Scholar
  75. Zhang XB, Alexander L, Hegerl GC, Jones P, Tank AK, Peterson TC, Trewin B, Zwiers FW (2011) Indices for monitoring changes in extremes based on daily temperature and precipitation data. Wires Clim Change 2(6):851–870. doi: 10.1002/Wcc.147 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Carlos F. Gaitan
    • 1
    • 3
    Email author
  • William W. Hsieh
    • 1
  • Alex J. Cannon
    • 1
    • 2
  1. 1.Department of Earth, Ocean and Atmospheric Sciences, 2020-2207 Main MallUniversity of British ColumbiaVancouverCanada
  2. 2.Pacific Climate Impacts ConsortiumUniversity of VictoriaVictoriaCanada
  3. 3.South Central Climate Science CenterNOAA-GFDLPrincetonUSA

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