1 / 19

Predicting indices of climate extremes using eigenvectors of SST and MSLP

This study predicts climate extremes in SE England using eigenvectors of Nth Atlantic SST and MSLP, and various rainfall indices. The model, built through multiple linear regression and cross-validation, shows promising skill in hindcasting. LEPS scores are used to assess forecast accuracy.

hilarym
Download Presentation

Predicting indices of climate extremes using eigenvectors of SST and MSLP

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. Predicting indices of climate extremes using eigenvectors of SST and MSLP Malcolm Haylock, CRU

  2. Predictands 33 rainfall indices calculated seasonally for 27 stations in SE England • 601R Mean climatological precipitation (mm/day) • precXXp XXth percentile of rainday amounts (mm/day) • fracXXp Fraction of total precipitation above annual XXth percentile • 606R10 No. of days precip >= 10mm • 641CDD Max no. consecutive dry days • 642CWD Max no. consecutive wet days • pww Mean wet-day persistence • persist_dd Mean dry-day persistence • persist_corr Correlation for spell lengths • wet_spell_mean mean wet spell lengths (days) • wet_spell_perc median wet spell lengths (days) • wet_spell_sd standard deviation wet spell lengths (days) • dry_spell_mean mean dry spell lengths (days) • dry_spell_perc median dry spell lengths (days) • dry_spell_sd standard deviation dry spell lengths (days) • 643R3d Greatest 3-day total rainfall • 644R5d Greatest 5-day total rainfall • 645R10d Greatest 10-day total rainfall • 646SDII Simple Daily Intensity (rain per rainday) • 691R90N No. of events > long-term 90th percentile • 692R90T % of total rainfall from events > long-term 90th percentile

  3. Predictors • Eigenvectors of Nth Atlantic SST and MSLP • Calculated using all months together with seasonal cycle removed • Significant components rotated (VARIMAX) • 9 SST • 9 MSLP

  4. SST Scores PC: 1 4 3 2 1 0 -1 -2 -3 1960 1970 1980 1990 2000 2010

  5. SST Scores PC: 2 6 4 2 0 -2 -4 -6 1960 1970 1980 1990 2000 2010

  6. SST Scores PC: 3 3 2 1 0 -1 -2 -3 -4 1960 1970 1980 1990 2000 2010

  7. SST Scores PC: 1 4 3 2 1 0 -1 -2 -3 -4 1960 1970 1980 1990 2000 2010

  8. SST Scores PC: 2 4 3 2 1 0 -1 -2 -3 -4 1960 1970 1980 1990 2000 2010

  9. SST Scores PC: 3 4 3 2 1 0 -1 -2 -3 -4 1960 1970 1980 1990 2000 2010

  10. The Model • 1960-2000 • Multiple linear regression using singular value decomposition • Best predictors selected using cross-validation • For each combination of predictors (2n): • Remove a year • Find MLR coefficients • Hindcast missing year • Assess skill using all hindcasts

  11. Skill of model • Build model using all years except 1979-93 then hindcast these years and compare • Double cross-validation • For each year in 1960-2000: • Remove a year • Use cross-validation to find best model • Hindcast missing year • Assess skill using all hindcasts

  12. pf abs(pf - pv) LEPS=1- abs(pf - pv) 1 is perfect forecast 0 is worst possible forecast pv Obs. Forecast

  13. If = LEPS'(perfect forecast) …LEPS • For single forecast • LEPS' = LEPS - LEPS(climatology)= abs(pv - 0.5) - abs(pf - pv) • For set of forecasts If = LEPS'(worst case) 100 = all perfect forecasts 0 = all climatology -100 = all worst case forecasts

  14. SST only. LEPS(hindcast) vs LEPS(dx-val)

  15. Hindcast LEPS(MSLP) vs LEPS(SST)

  16. Where to... • NW England • Other European stations • Combined SST and MSLP (trim predictors) • Other predictors?

More Related