Although there is no 'standard' approach to downscaling, (i.e. obtaining finer resolution scenarios of climate change from coarser resolution GCM output), software is available which can be used to undertake spatial and temporal downscaling.
The Statistical Downscaling Model (SDSM) permits the spatial downscaling of daily predictor-predictand relationships using multiple linear regression techniques. The predictor variables provide daily information concerning the large-scale state of the atmosphere, whilst the predictand describes conditions at the site scale. The software reduces the task of statistically downscaling daily weather series into a number of discrete processes:
- Preliminary screening of potential downscaling predictor variables - identifies those large-scale predictor variables which are significantly correlated with observed station (predictand) data. A number of variables derived from mean sea level pressure fields are included, e.g. air flow strength, meridional and zonal components of air flow, vorticity etc. (see Statistical Downscaling Input, in Download Data section);
- Assembly and calibration of statistical downscaling model(s) - the large-scale predictor variables identified in (1) are used in the determination of multiple linear regression relationships between these variables and the local station data. Statistical models may be built on a monthly, seasonal or annual basis. Information regarding the amount of variance explained by the model(s) and the standard error is given in order to determine the viability of spatial downscaling for the variable and site in question;
- Synthesis of ensembles of current weather data using observed predictor variables - once statistical downscaling models have been determined they can be verified by using an independent data set of observed predictors. The stochastic component of SDSM allows the generation of up to 100 ensembles of data which have the same statistical characteristics but which vary on a day-to-day basis;
- Generation of ensembles of future weather data using GCM-derived predictor variables - provision of the appropriate GCM-derived predictor variables allows the generation of ensembles of future weather data by using the statistical relationships calculated in (2);
- Diagnostic testing/analysis of observed data and climate change scenarios - it is possible to calculate the statistical characteristics of both the observed and synthetic data in order for easy comparison and thus determination of the effect of spatial downscaling.
To access predictor data, please visit the CMIP5 Statistical Downscaling Input pages.
SDSM is a decision support tool for assessing local climate change impacts using a robust statistical downscaling technique. It is a hybrid of a stochastic weather generator and regression-based downscaling methods and facilitates the rapid development of multiple, low-cost, single-site scenarios of daily surface weather variables under current and future climate forcing.
SDSM is designed to help users identify those large-scale climate variables (the predictors), which explain most of the variability in the climate (the predictand) at a particular site, and statistical models are then built based on this information. Statistical models are built using daily observed data – local climate data for a specific location for the predictand and larger-scale NCEP data for the predictors – and these models are then used with GCM-derived predictors to obtain daily weather data at the site in question for a future time period.
Where can I get SDSM?
You may download the software, user manual and a demonstration data set for free from the SDSM website.
How do I prepare my own data for use in SDSM?
It may be best to set up a new directory for each site you wish to downscale using SDSM. This directory should contain both the observed daily data (i.e., the predictand) and the observed (i.e., National Centre for Environmental Prediction (NCEP)), and GCM-derived predictors. You will need to supply the predictand information, but predictor information is available through CCDS (see CMIP5 Statistical Downscaling Input).
Adjusted and homogenized observed daily data for Canada is available through Environment and Climate Change Canada.
How do I use SDSM?
Please refer to the user manual for step-by-step instructions on using SDSM.
References for SDSM (tool and predictors)
Barrow, E., B. Maxwell and P. Gachon, 2004: Climate Variability and Change in Canada: Past, Present and Future, Climate Change Impacts Scenarios Project, National Report, Environment Canada, Meteorological Service of Canada, Adaptation Impacts Research Group, Atmospheric and Climate Sciences Directorate publication, Canada, 114 pp, ISBN: 0-662-38497-0.
Choux M., (2005): Development of new predictor variables for the statistical downscaling of precipitation. Degree Master of Engineering, Department of Civil Engineering and Applied Mechanics, McGill University. (Dec. 2005).
Conway, D., Wilby, R.L. and Jones, P.D. (1996): Precipitation and air flow indices over the British Isles. Climate Research 7: 169-183.
Dibike, Y., P. Gachon, A. St-Hilaire, T.B.M.J. Ouarda, and VTV Nguyen, 2007: Uncertainty analysis of statistically downscaled temperature and precipitation regimes in northern Canada. Theoretical and Applied Climatology (in press).
Gachon, P., A. St-Hilaire, T. Ouarda, VTV Nguyen, C. Lin, J. Milton, D. Chaumont, J. Goldstein, M. Hessami, T.D. Nguyen, F. Selva, M. Nadeau, P. Roy, D. Parishkura, N. Major, M. Choux & A. Bourque, 2005: A first evaluation of the strength and weaknesses of statistical downscaling methods for simulating extremes over various regions of eastern Canada. Sub-component, Climate Change Action Fund (CCAF), Environment Canada, Final report, Montréal, Québec, Canada, 209 pp.
Goldstein, J., J. Milton, N. Major, P. Gachon, and D. Parishkura, 2004: Climate extremes indices and their links with future water availability: Case study for summer of 2001, article published in the proceeding of the 57th Annual Conference of the Canadian Water Resources Association. Montréal, Canada, June 16-18 2004, 7pp.
Hassan, H., Aramaki, T., Hanaki, K., Matsuo, T. and Wilby, R.L. (1998): Lake stratification and temperature profiles simulated using downscaled GCM output. Journal of Water Science and Technology 38: 217-226.
Hessami M., T.B.M.J. Ouarda, P. Gachon, A. St-Hilaire, F. Selva and B. Bobée, 2004: Evaluation of statistical downscaling methods over several regions of eastern Canada, article published in the proceeding of the 57th Annual Conference of the Canadian Water Resources Association. Montréal, Québec, Canada. June 16-18, 2004, 9 pp.
Jones, P.D., Hulme, M. and Briffa, K.R. (1993): A comparison of Lamb circulation types with an objective classification scheme. International Journal of Climatology 13: 655-663.
Kalnay, E., Kanamitsu, M., Kistler, R. et al. (1996): The NCEP/NCAR 40-year reanalysis project. Bulletin of the American Meteorological Society 77: 437-471.
Nguyen T., V.T.V. Nguyen, P. Gachon and A. Bourque, 2004a: An assessment of statistical downscaling methods for generating daily precipitation and temperatures extremes in the greater Montréal region, article published in the proceeding of the 57th Annual Conference of the Canadian Water Resources Association. Montréal, Québec, Canada. June 16-18, 2004, 10 pp.
Nguyen VTV, Nguyen TD, Gachon P. 2004b: An Evaluation of Statistical Downscaling Method for Simulating Daily Precipitation and Extreme Temperature Series at a Local Site, 14th Congress of the APD-International Association of Hydraulic Engineering and Research, Hongkong, December 15-18, 2004, pp. 1911-1916.
Nguyen VTV, Nguyen TD, Gachon P., 2006: On the linkage of large-scale climate variability with local characteristics of daily precipitation and temperature extremes: an evaluation of statistical downscaling methods. Advances in Geosciences (WSPC/SPI-B368) 4(16): 1-9.
Nguyen, T-D., V-T-V. Nguyen, and P. Gachon, 2007: A spatial-temporal downscaling approach for construction of intensity-duration-frequency curves in consideration of GCM-based climate change scenarios, in 'Advances in Geosciences, Vol. 6: Hydrological Sciences', N. Park et al. (Eds.), World Scientific Publishing Company, pp. 11-21.
Wilby, R.L. and Dettinger, M.D. (2000): Streamflow changes in the Sierra Nevada, CA, simulated using a statistically downscaled General Circulation Model scenario of climate change. In: Linking Climate Change to Land Surface Change, McLaren, S.J. and Kniveton, D.R. (Eds.), Kluwer Academic Publishers, Netherlands, pp. 91-121.
Wilby, R.L., and Wigley, T.M.L., (2000): Precipitation predictors for downscaling: observed and General Circulation Model relationships. International Journal of Climatology 20: 641-661.
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 and Modelling Software 17: 145-157.
Wilby, R.L., Hassan, H. and Hanaki, K. (1998): Statistical downscaling of hydrometeorological variables using general circulation model output. Journal of Hydrology 205: 1-19.