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A RCM bias correction method for climatic indices

A RCM bias correction method for climatic indices expressed in a daily based frequency applied to temperature projections of the North Iberian Peninsula. Iratxe González Dr. Julia Hidalgo. TECNALIA – LABEIN. Environment Unit. Spain.

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A RCM bias correction method for climatic indices

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  1. A RCM bias correction method for climatic indices expressed in a daily based frequency applied to temperature projections of the North Iberian Peninsula Iratxe González Dr. Julia Hidalgo TECNALIA – LABEIN. Environment Unit. Spain.

  2. The goodness of fit, or bias, summarize the discrepancy between the observations and the model outputs. It should be evaluated before analysing future climatic scenarios from such models. BIAS models vs. observations Adjustment data to a probability density function Adjustment data distribution to a delta change temperature • Roossmalen et al 2009 • Kjellstrom et al 200 7 • Christensen and Christensen 2007 • Piani et al, 2009 • Collins 2007 • Planton et al, 2008 • Kjellstrom et al, 2005 • Alexander et al, 2005 • Chauvin and Devil 2005 • Seasonally frequency based indices • Daily frequency based indices

  3. USED DATA REGIONAL SERIES STATISTICAL INDICES CORRECTION BIAS RCM vs. OBSERVATIONS ANALYSIS RESULTS CONCLUSIONS OUTLINE

  4. PRUDENCE results Abanades et al. 2007 Basque Country North Iberian Peninsula. METHODOLOGY. Climate Scenarios: ENSEMBLES project Observed data: Spanish Meteorological Agency (AEMET) Time period : 1978-2000 ; 2000-2100 • Summer: • 90th percentile Tmax • days with T > 90th p Tmax • Human Comfort Index (THI) Summer: Heat wave Duration WMO • Winter: • 10th percentile Tmin • Days with T < 10th p Tmin • Number of frost days Winter: Cold wave Duration WMO

  5. METHODOLOGY. Evaluation of models

  6. ∆T (ºC) * 10 * * * * * * * * . . . * * K- pecentile 1:99th * * 1 … 99th 1th Percentiles (21 in total) (T10thp model corrected)i = (T10thp model not corrected)i – (∆T)associated to this temperature in the calibration curve Correction Seasonally indices Correction daily indices ; i- daily data METHODOLOGY. Bias correction Christensen et al 2008 Jacob et al, 2007 Kjellstrom et al 2007

  7. Obs. M. REFERENCE 22-32ºC 19-27ºC 20-27ºC CORRECTION OF MODELS: Mean Maximum TemperaturesFor Summer

  8. without correction corrected RESULTS. Bias correction 90th percentile maximum temperature

  9. RESULTS. Summer time 90th percentile Tmax Ensemble Av. 35.6ºC Spread:3.5ºC Stdv:1.41ºC Ensemble Av. 37.8ºC Spread:6.52ºC Stdv:2.54ºC Ensemble Av. 39.7ºC Spread: 4.6ºC Stdv:1.87ºC Future projection: 2000-2100 trend: 3ºC Stdv . 1.41 ºC Spread: 4.2ºC

  10. RESULTS. Summer time Heat Waves duration Obs. Av. 1.3 heat waves Freq: 13.7 days Temp: 29 ºC Ensembles Freq. 1-2 heat waves Av. 9-11 days [12%] Ensembles Freq. 1-2 heat waves Av. 13-16 days [16%] Ensembles Freq. 1-3 heat waves Av. 18-22 days [22%] Temp involved 34.7 ºC Temp involved 34.3 ºC Temp involved 34.2 ºC

  11. RESULTS. Summer time Thermal Comfort Index (THI) Ensemble Av. 67.99 Stdv:1 .64 Ensemble Av. 67.91 Stdv:1 .60 Ensemble Av. 69.34 Stdv: 3.97 (Gates, 1972)

  12. RESULTS. Winter time 10th percentile Tmin Ensemble Av. -3.04ºC Spread: 1.8ºC Stdv: 0.71ºC Ensemble Av. -1.70ºC Spread: 1.72ºC Stdv:0.68ºC Ensemble Av. - 0.72ºC Spread:1.5ºC Stdv: 0.92ºC Future scenario: 2000-2100 trend: 3ºC Stdv . 0.83 ºC Spread: 1.83ºC

  13. RESULTS. Winter time Number of frost days Ensemble Av. 20.6 d Stdv: 3.5 d Ensemble Av. 7.5 d Stdv: 3.57 d Ensemble Av. 11.5 d Stdv: 3.9 d Future scenario (2000-2100) Decrease 50% of days Respect with Ref. Period

  14. RESULTS. Winter time Cold Waves duration Obs. Av. 1.25 cold waves Freq: 11.1 days [12%] Temp: 3.3 ºC RACMO/REMO/ALADIN Freq. 1 cold wave Av. 6- 9 days [8%] HIRHAM/CLM/PROMES: 0 Ensembles Freq. 1-3 cold waves Av. 7-19 days [14%] Ensembles Freq. 1-2 cold waves Av. 6-10 days [9%] Temp involved 2.7 ºC Temp involved 3.3 ºC Temp involved 1.3ºC

  15. Conclusions & Discussions • Methodology to correct the bias based on the percentiles approach • Proposed method to correct indices when absolute values are required (daily based frequency) • Correction methodology suitable for interdisciplinary groups • Applicability for the Basque Country case study: indices indicate that for summer and winter periods the maximum and minimum temperature tend to increase. The duration of the heat episodes tend to increase and for cold episodes tend to decrease ; as well for the number of the frost days in winter.

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