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Downscaling Tools

Downscaling Tools. Introduction to LARS-WG and SDSM. LARS-WG stochastic weather generator ( http:\www.iacr.bbsrc.ac.ukmas-modelslarswg.html ). Generation of long weather time-series suitable for risk assessment Ability to extend the simulation of weather to unobserved locations

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Downscaling Tools

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  1. Downscaling Tools Introduction to LARS-WG and SDSM

  2. LARS-WG stochastic weather generator( http:\\www.iacr.bbsrc.ac.uk\mas-models\larswg.html ) • Generation of long weather time-series suitable for risk assessment • Ability to extend the simulation of weather to unobserved locations • A computationally inexpensive tool to produce climate change scenarios incorporating changes in means and in variability

  3. Parametric- e.g., WGEN Semi-parametric - e.g., LARS-WG LARS-WG stochastic weather generator( http:\\www.lars.bbsrc.ac.uk\model\larswg.html ) • Generates precipitation, min and max temperature and solar radiation • Modelling of precipitation events is based on wet/dry series • Semi-empirical distributions are used for precipitation amounts, dry/wet series and solar radiation • Temperature and solar radiation are conditioned on the wet/dry status of a day • Temperature and solar radiation are cross-correlated

  4. LARS-WG • Model calibration - SITE ANALYSIS • Model validation -QTEST • Generation of synthetic weather data - GENERATOR

  5. SITE ANALYSIS

  6. QTEST Compare observed and synthetic data to evaluate LARS-WG performance

  7. Scenario file Base scenario file GENERATOR Generate synthetic weather data: to extend time series, or for climate change studies

  8. GENERATOR

  9. Limitations of LARS-WG (and weather generators in general) ... • Temporal downscaling only • Designed for use at individual sites only (no spatial correlation) • Can only represent events in calibration data set • Generally underestimate variability

  10. SDSM • A decision support tool for assessing local climate change impacts • Facilitates the rapiddevelopment of multiple, low-cost, single-site scenarios of daily surface weather variables under current and future climate forcing • Based on a multiple regression-based method

  11. SDSM Structure • 7 steps: • Quality Control and Data Transformation • Screening of Predictor Variables • Model Calibration • Weather Generation (using observed predictors) • Statistical Analyses • Graphing Model Output • Scenario Generation (using climate model predictors)

  12. Model Verification

  13. Tmax > 25°C

  14. Cautionary Remarks • SDSM provides a parsimonious technique of scenario construction that complements other methods • SDSM should not be used uncritically as a “black box” (evaluate all relationships using independent data) • Local knowledge is an invaluable source of information when determining sensible combinations of predictors • Daily precipitation amount at individual stations is the most problematic variable to downscale • The plausibility of all SDSM scenarios depends on the realism of the climate model forcing • Try to apply multiple forcing scenarios (via different GCMs, ensemble members, time–slices, emission pathways, etc.)

  15. Projet FACC (en cours 2003-2004)Etude sur force/faiblesse de SDSM et LARS-WGpour extrêmes et variabilité climatique Coordonnateur Philippe Gachon Collaborateurs : - Ouranos : Alain Bourque, René Roy, Claude Desjarlais, Georges Desrochers, Vicky Slonosky, Diane Chaumont - EC-SMC (Qc) : Jeanna Goldstein, Jennifer Milton, Nicolas Major - McGill : VTV Nguyen, Charles Lin - INRS-ETE : André St Hilaire, Bernard Bobée, Taha Ouarda - UQAM : Peter Zwack - CCIS : Elaine Barrow - Post-Doc et étudiants : Tan Nguyen (PostDoc); Massoud Hessami (PostDoc); Mohamed Abul Kashem (PhD)

  16. 1st Objective : intercompare SDSM & LARS-WG for downscaling extremes (regional case-studies) 5 Régions à étudier (Stat. Downscaling) 1961-1990 Tmin Tmax Tmoy Precipitation tot. 2 1 4 3 5

  17. 1 2 6 4 3 5

  18. 2nd Objective Develop observed climate indices used for verification & analysis (using STARDEX software)

  19. THANK YOU FOR • YOUR ATTENTION !!

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