Skip to main content

Advertisement

Log in

CGCM3 predictors used for daily temperature and precipitation downscaling in Southern Québec, Canada

  • Original Paper
  • Published:
Theoretical and Applied Climatology Aims and scope Submit manuscript

Abstract

This study provides some guidance on the choice of predictor variables from both reanalysis products and the third version of the Canadian Coupled Global Climate Model (CGCM3) outputs for regression-based statistical downscaling models (SDMs) for climate change application in southern Québec (Canada). Twenty CGCM3 grid points and four surface observation sites in the study area were employed. Twenty-five deseasonalized predictors and four deseasonalized predictands (daily maximum and minimum temperatures, precipitation occurrence and wet day precipitation amount) were used to investigate correlation coefficients among predictors and to evaluate their predictive ability when used in a multiple linear regression (MLR) downscaling model. The basic statistical characteristics of vorticity at 1,000-, 850- and 500-hPa levels, U-component of velocity at 1,000-hPa level, temperature at 2 m (T 2) and wind direction at 1,000- and 500-hPa level of CGCM3 showed a larger difference with those of the NCEP reanalysis data. Therefore, those seven variables require high caution to be included as predictors in statistical downscaling models. Specific humidity at 1,000-, 850- and 500-hPa levels, geopotential height at 850- and 500-hPa levels and T 2 were the most sensitive predictors for future climate conditions (i.e. A1B and A2 emission scenarios). Specific humidity and geopotential height at different levels and T 2 were important explainable predictors for the daily temperatures. Mean sea level pressure, specific humidity, U and V components and divergence showed potential as predictors for daily precipitation. Spatial explained variance of MLRs between predictors of every different CGCM3 grid points and the four predictands showed large values at the CGCM3 grid points located near the observation sites, whereas relatively small values were shown at the CGCM3 grid points located more than 400 km from the sites. The explained variance of the downscaled predictands by predictors of three or four CGCM3 grid points located near the observation site produced 2–5% larger R-squares than those by predictors of the nearest grid point. The results illustrated that the use of predictors from more than one AOGCM grid points located near the observation site can increase the skill of the MLR downscaling models.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Abbreviations

AOGCM:

Atmosphere–Ocean Global Climate Model

CCCma:

Canadian Centre for Climate Modeling and Analysis

CGCM:

Canadian Coupled Global Climate Model

GP:

Grid point

NCAR:

National Center for Atmospheric Research

NCEP:

National Centers for Environmental Prediction

P amount :

Daily precipitation amount

P occ :

Daily precipitation occurrence

SDM:

Statistical downscaling model

T max :

Daily maximum temperature

T min :

Daily minimum temperature

References

  • Bates BC, Charles SP, Hughes JP (1998) Stochastic downscaling of numerical climate model simulations. Env Mod Soft 13:325–331

    Article  Google Scholar 

  • Benestad RE (2001) The cause of warming over Norway in the ECHAM4/OPYC3 GHG integration. Int J Climatol 21:371–387

    Article  Google Scholar 

  • Boer GJ (1995) A hybrid moisture variable suitable for spectral GCMs. Research activities in atmospheric and oceanic modelling. Report No. 21, WMO/TD-No. 665, World Meteorological Organization, Geneva

  • 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 Climatol 28:449–464

    Article  Google Scholar 

  • Cannon AJ, Whitfield PH (2002) Downscaling recent streamflow conditions in British Columbia, Canada using ensemble neural network models. J Hydrol 259:136–151

    Article  Google Scholar 

  • Cavazos T, Hewitson BC (2005) Performance of NCEP variables in statistical downscaling of daily precipitation. Clim Res 28:95–107

    Article  Google Scholar 

  • Charles S, Bates B, Smith I, Hughes J (2004) Statistical downscaling of daily precipitation from observed and modeled atmospheric fields. Hydrol Process 18:1374–1394

    Article  Google Scholar 

  • Crane RG, Hewitson BC (1998) Doubled CO2 precipitation changes for the Susquehanna basin: down-scaling from the GENESIS general circulation model. Int J Climatol 18:65–76

    Google Scholar 

  • DAI CGCM3 Predictors (2008) Sets of predictor variables derived from CGCM3 T47 and NCEP/NCAR reanalysis, version 1.1 April 2008, Montreal, 15 pp

  • Dibike Y, Gachon P, St-Hilaire A, Ouarda T, Nguyen VTV (2008) Uncertainty analysis of statistically downscaled temperature and precipitation regimes in northern Canada. Theor Appl Climatol 91:149–170

    Article  Google Scholar 

  • Fealy R, Sweeney J (2007) Statistical downscaling of precipitation for a selection of sites in Ireland employing a generalised linear modelling approach. Int J Climatol 27:2083–2094

    Article  Google Scholar 

  • Flato GM, Boer GJ (2001) Warming asymmetry in climate change simulations. Geophys Res Lett 28:195–198

    Article  Google Scholar 

  • Fowler HJ, Blenkinsop S, Tebaldi C (2007) Review: linking climate change modeling to impacts studies: recent advances in downscaling techniques for hydrological modeling. Int J Climatol 27:1547–1578

    Article  Google Scholar 

  • Gachon P, Dibike Y (2007) Temperature change signals in northern Canada: convergence of statistical downscaling results using two driving GCMs. Int J Climatol 27:1623–1641

    Article  Google Scholar 

  • Gachon P, Laprise R, Zwack P, Saucier FJ (2003) The effects of interactions between surface forcings in the development of a model-simulated polar low in Hudson Bay. Tellus Series A-Dynamic Meteorology and Oceanography 55(1):61–87

    Article  Google Scholar 

  • Gerardin V, McKenney D (2001) Une classification climatique du Québec à partir de modèles de distribution spatiale de données climatiques mensuelles: Vers une définition des bioclimats du Québec, Contribution du Service de la cartographie écologique. No 60. Ministère de l'environnement, Direction du patrimoine écologique et du développement durable, Québec (in French)

  • Harding A, Gachon P, Nguyen VTV (2010) Replication of atmospheric oscillations and their patterns in Atmosphere–Ocean Global Climate Model derived predictors. Int J Climatol. doi:10.1002/joc.2191

  • Hellstrom C, Chen D, Achberger C, Raisanen J (2001) Comparison of climate change scenarios for Sweden based on statistical and dynamical downscaling of monthly precipitation. Clim Res 19:45–55

    Article  Google Scholar 

  • Helsel RD, Hirsch RM (1992) Statistical methods in water resources. Elsevier Science, New York

    Google Scholar 

  • Hessami M, Gachon P, Ouarda TBMJ, St-Hilaire A (2008) Automated regression-based statistical downscaling tool. Env Mod Soft 23:813–834

    Article  Google Scholar 

  • Hewitson BC, Crane RG (1996) Climate downscaling: techniques and applications. Clim Res 7:85–95

    Article  Google Scholar 

  • Hughes JP, Guttorp P (1994) A class of stochastic models for relating synoptic atmospheric patterns to regional hydrologic phenomena. Water Resour Res 30(5):1535–1546

    Article  Google Scholar 

  • Huth R (1999) Statistical downscaling in central Europe: evaluation of methods and potential predictors. Clim Res 13:91–101

    Article  Google Scholar 

  • Huth R (2002) Statistical downscaling of daily temperature in central Europe. J Clim 15(13):1731–1742

    Article  Google Scholar 

  • Huth R (2004) Sensitivity of local daily temperature change estimates to the selection of downscaling models and predictors. J Clim 17(3):640–652

    Article  Google Scholar 

  • IPCC (2001) In: Houghton JT, Ding Y, Griggs DJ, Noguer M, van der Linden PJ, Dai X, Maskell K, Johnson CA (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 University Press, Cambridge

  • IPCC (2007) Climate change 2007: the physical science basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) Summary for Policymakers. Available from: http://www.ipcc.ch (accessed 10.03.07)

  • Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Gandin L, Iredell M, Saha S, White G, Woollen J, Zhu Y, Chelliah M, Ebisuzaki W, Higgins W, Janowiak J, Mo KC, Ropelewski C, Wang J, Leetmaa A, Reynolds R, Jenne R, Joseph D (1996) The NCEP/NCAR 40-year reanalysis project. Bull Amer Meteor Soc 77:437–471

    Article  Google Scholar 

  • Kistler R, Kalnay E, Collins W, Saha S, White G, Woollen J, Chelliah M, Ebisuzaki W, Kanamitsu M, Kousky V, Dool H, Jenne R, Fiorino M (2001) The NCEP/NCAR 50-year reanalysis. Bull Amer Meteor Soc 82(2):247–267

    Article  Google Scholar 

  • Linderson ML, Achberger C, Chen D (2004) Statistical downscaling and scenario construction of precipitation in Scania, southern Sweden. Nordic Hydrol 35:261–278

    Google Scholar 

  • Mekis É, Hogg WD (1999) Rehabilitation and analysis of Canadian daily precipitation time series. Atmos Ocean 37(1):53–85

    Article  Google Scholar 

  • Murphy J (1999) An evaluation of statistical and dynamical techniques for downscaling local climate. J Clim 12(8):2256–2284

    Article  Google Scholar 

  • Nakicenovic N, Alcamo J, Davis G, de Vries B, Fenhann J, Gaffin S, Gregory K, Grübler A, Jung TY, Kram T, La Rovere EL, Michaelis L, Mori S, Morita T, Pepper W, Pitcher H, Price L, Riahi K, Roehrl A, Rogner HH, Sankovski A, Schlesinger M, Shukla P, Smith S, Swart R, van Rooijen S, Victor N, Dadi Z (2000): IPCC special report on emissions scenarios. Cambridge University Press, Cambridge, 599 pp

  • Rummukainen M. 1997. Methods for statistical downscaling of GCM simulations. SWECLIM Rep. 80, SMHI, 29

  • Schoof JT, Pryor SC, Robeson SM (2007) Downscaling daily maximum and minimum temperatures in the Midwestern USA: a hybrid empirical approach. Int J Climatol 27:439–454

    Article  Google Scholar 

  • Trigo RM, Palutikof JP (2001) Precipitation scenarios over Iberia: a comparison between direct GCM output and different downscaling techniques. J Clim 14:4422–4446

    Article  Google Scholar 

  • Tripathi S, Srinivas V, Nanjundiah RS (2006) Downscaling of precipitation for climate change scenarios: a support vector machine approach. J Hydrol 330:621–640

    Article  Google Scholar 

  • Verseghy DL, McFarlane NA, Lazare M (1993) A Canadian land surface scheme for GCMs. II. Vegetation model and coupled runs. Int J Climatol 13:347–370

    Article  Google Scholar 

  • Vincent LA, Zhang X, Bonsal BR, Hogg WD (2002) Homogenization of daily temperatures over Canada. J Clim 15:1322–1334

    Article  Google Scholar 

  • Wilby RL, Wigley TML (2000) Precipitation predictors for downscaling: observed and general circulation model relationships. Int J Climatol 20:641–661

    Article  Google Scholar 

  • Wilby RL, Hay LE, Leavesley GH (1999) A comparison of downed scaled and raw GCM output: implications for climate change scenarios in the San Juan River basin, Colorado. J Hydrol 225:67–91

    Article  Google Scholar 

  • Wilby RL, Dawson CW, Barrow EM (2002) SDSM—a decision support tool for the assessment of regional climate change impacts. Env Mod Soft 17:147–159

    Google Scholar 

  • Wilby RL, Tomlinson OJ, Dawson CW (2003) Multisite simulation of precipitation by conditional resampling. Clim Res 23:183–194

    Article  Google Scholar 

  • Wilby RL, Charles SP, Zorita E, Timbal B, Whetton P, Mearns LO (2004) Guidelines for use of climate scenarios developed from statistical downscaling methods. Technical report. Data Distribution Centre of the IPCC http://www.ipcc-data.org/guidelines/index.html

  • Zar JH (1999) Biostatistical analysis, 4th edn. Prentice-Hall, New Jersey, p 423

    Google Scholar 

  • Zhang GJ, McFarlane NA (1995) Sensitivity of climate simulations to the parameterization of cumulus convection in the CCC-GCM. Atmos Ocean 33:407–446

    Article  Google Scholar 

Download references

Acknowledgements

We acknowledge the financial support provided by the National Science and Engineering Research Council (NSERC) of Canada. We are also grateful to Lucie Vincent and to Eva Mekis from Environment Canada for providing observed data sets of homogenized temperatures and rehabilitated precipitation. The authors would like to acknowledge also the Data Access and Integration (DAI; see http://quebec.ccsn.ca/DAI/) team for providing the predictors data and technical support. The DAI data download gateway is made possible through collaboration among the Global Environmental and Climate Change Centre (GEC3), the Adaptation and Impacts Research Section (AIRS) of Environment Canada and the Drought Research Initiative (DRI).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Dae Il Jeong.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Jeong, D.I., St-Hilaire, A., Ouarda, T.B.M.J. et al. CGCM3 predictors used for daily temperature and precipitation downscaling in Southern Québec, Canada. Theor Appl Climatol 107, 389–406 (2012). https://doi.org/10.1007/s00704-011-0490-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00704-011-0490-0

Keywords

Navigation