1 / 16

Statistical Downscaling of Climate Extremes

Statistical Downscaling of Climate Extremes. Xuebin Zhang, Jiafeng Wang, Elaine Barrow Supported by CCAF. Outline. Introduction Extreme value modeling Downscaling extreme precipitation. How often will this occur in the future?. Climate change?. Source: Natural Resources of Canada.

hedy
Download Presentation

Statistical Downscaling of Climate Extremes

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Statistical Downscaling of Climate Extremes Xuebin Zhang, Jiafeng Wang, Elaine Barrow Supported by CCAF

  2. Outline • Introduction • Extreme value modeling • Downscaling extreme precipitation

  3. How often will this occur in the future? Climate change? Source: Natural Resources of Canada

  4. Projected return period of 1990’s H20yr for 2080, A2 forcing scenario, JFM Wang, X. L. and V. R. Swail, 2003: Historical and possible future changes of wave heights in northern hemisphere oceans. Atmosphere Ocean Interactions – Vol. 2, ed. W. Perrie, WIT Press - Ashurst Lodge, Ashurst, Southampton, UK (in press).

  5. Projected return period of 1990’s H20yr for 2080, A2 forcing scenario, JFM Wang, X. L., F. W. Zwiers and V. R. Swail, 2003: North Atlantic Ocean Wave Climate Change Scenarios for the 21st century. J. Climate (submitted).

  6. Statistical downscaling • Regression • Condition/Assumption • Good relationship • Relationship valid in the future • Advantages • Easy to use • Can have good skill • Problems • Inherent problems from large-scale field

  7. Generalized Linear Model • Simple linear regression not valid for extremes • GLM considers extreme value distribution • Software available • S-plus functions (Coles, 2001) • NCAR extreme-tool-kits (based on R, Rick Katz) • Home grow FORTRAN codes

  8. Modeling Extreme Values GEV distribution function Introduce co-variates Regression coefficients Estimated by MLE Use r largest values to improve model fitting

  9. Data • Daily precipitation over N. America • 3 largest precipitation amounts in DJFM • NCEP reanalysis SLP • CGCM2 IS92a Runs

  10. Procedures • 3 leading rotated PCs from Obs SLP as co-variates • Project SLP changes (2050-2099 minus 1950-1999) to the observed EOFs • Projected changes in GEV distribution parameters • Projected changes in the risk of 20-yr return values

  11. Projected SLP change for 2050-99

  12. EOF1

  13. Projected return period for 2050-2099 for current 20-yr return value

  14. Projected SLP change (2050-2099) SLP anomalies during El Nino years (1951-2000)

  15. Summary • Possible to assess the changes in risk using statistical downscaling • Need to understand caveat • Good relation between large-scale field and the variable • Inherent problems in GCM • Examples show success

More Related