1. Historical trends and multi-model ensemble forecasting of extreme events Dr. Caio A. S. Coelho University of Reading, U.K. E-mail: c.a.d.s.coelho@reading.ac.uk Thanks to: David Stephenson, Mark New, Bruce Hewitson + Africa extremes workshop participants

2. 2 Talk plan What are extremes? Historical trend analysis of extremes in Africa What is going to happen to extremes in the future? - Extreme event forecasting

3. 3 What are extremes?

4. 4 Examples of wet and windy extremes Extra-tropical cyclone Hurricane Polar low Extra-tropical cyclone Convective severe storm

5. 5 Examples of dry and hot extremes Drought Wild fire Dust storm

6. 6 IPCC 2001 definitions Simple extremes: “individual local weather variables exceeding critical levels on a continuous scale” Complex extremes: “severe weather associated with particular climatic phenomena, often requiring a critical combination of variables” Extreme weather event: “an event that would normally be as rare or rarer than the 10th or 90th percentile.” Extreme climate event: “an average of a number of weather events over a certain period of time which is itself extreme (e.g. rainfall over a season)”

7. 7 Some properties of extreme events Severity large impacts (extreme losses): –Injury and loss of life –Damage to the environment –Damage to ecosystems Extremeness large values of meteorological variables: –maxima or minima –exceedance above a high threshold –exceedance above all previous recorded values (record breaker) Rarity/frequency small probability of occurrence Longevity –Acute: Having a rapid onset and following a short but severe course –Chronic: Lasting for a long period of time (> 3 months) or marked by frequent recurrence 90 th percentile

8. 8 Historical trend analysis of extremes in Africa

9. 9 Southern and West Africa workshop on weather and climate extremes Cape Town, South Africa, 31May - 4 June 2004 Organization: Expert Team on Climate Change Detection Monitoring and Indices (ETCCDMI) WMO Commission of Climatology (CCI) Climate Variability and Predictability (CLIVAR) project Aim: Derive indices from daily data to measure changes in extremes Participants: 14 countries Data: 63 stations (1961-2000) daily (minimun and maximum) temperature and precipitation New, M., B. Hewitson, D. B. Stephenson, A. Tsiga, A. Kruger, A. Manhique, B. Gomez, C. A. S. Coelho, D. N. Masisi, E. Kululanga, E. Mbambalala, F. Adesina, H. Saleh, J. Kanyanga, J. Adosi, L. Bulane, L. Fortunata, M. L. Mdoka and R. Lajoie, 2005: Evidence of trends in daily climate extremes over Southern and West Africa, Submitted to J. Geophys. Res. (Atmospheres).

10. 10 Workshop methodology Software: RClimDex ( http://cccma.seos.uvic.ca/ETCCDMI/ ) Data quality control negative precipitatoin max. temp. < min. temp. search for outliers based on threshold defined in terms of standard deviation from the long-term (1961-2000) daily mean visual inspection of time series plots Computation of climate indices using RClimDex 15 temperature indices 10 precipitation indices Trend estimation and interpretation of results

11. 11 Trends in temperature extreme indices Cold T< 10th percentile Hot T> 90th percentile Minimum Maximum Cold night frequency Cold day frequency Hot night frequency Hot day frequency Source: New et al. 2005 (submitted to ) Source: New et al. 2005 (submitted to J. Geophys. Res. (Atmospheres).)

12. 12 Summary of findings for temperature extremes in Africa Shift in the frequency distribution towards larger values Frequency of extremely cold days and nights has decreased Frequency of extremely hot days and nights has increased 10 th percentile 90 th percentile

13. 13 Trends in precipitation indices Annual total precipitation Max. n o of consec. dry days n o of days with prec. > 20 mm Annual total precip. > 95 th perc. Longest dry spell Very heavy precipitation day Source: New et al. 2005 (submitted to ) Source: New et al. 2005 (submitted to J. Geophys. Res. (Atmospheres).)

14. 14 Summary of findings for precipitation indices in Africa No trends found in many stations Only a few stations show statistically significant trends Some stations are getting drier Longest dry spells are getting longer for a few stations

15. 15 Suggestion for collaboration work Perform similar extreme indices analysis for Cuban stations Required tools: RClimDex ( http://cccma.seos.uvic.ca/ETCCDMI/ ) R ( http://www.r-project.org/ ) (both are freely available) Such study will allow us: To identify how extremes behaved in the past in Cuba To diagnose observed changes in extremes in Cuba Compare results with findings of Caribbean climate and weather extremes workshop held in Jamaica 2001

16. 16 What is going to happen to extremes in the future? Extreme event forecasting

17. 17 ENSEMBLES: ENSEMBLE-based Predictions of Climate Changes and their Impacts WP4.3: Understanding Extreme Weather and Climate Events Provision of statistical methods for identifying and forecasting extreme events and the climate regimes with which they are associated. More robust assessments of the effects of climate change on the probability of extreme events and on the characteristics of natural modes of climate variability. us! How best to make probability forecasts of extremes? multi-model ensemble  tail probabilities Need to develop: Multi-model calibration and combination approach for extremes

18. 18 Calibration and combination of multi-model ensemble seasonal forecasts: South American rainfall example

19. 19 Conceptual framework Data Assimilation “Forecast Assimilation”

20. 20 DJF rainfall anomalies for 1975/76 and 1982/83 ObsMulti-model Forecast Assimilation (mm/day) ACC=-0.09 ACC=0.32 ACC=0.59 ACC=0.56 La Nina 1975/76 El Nino 1982/83

21. 21 Summary of multi-model ensemble forecast calibration and combination Forecast assimilation: Unified framework for calibration and combination Useful approach for improving skill of South American rainfall seasonal forecasts Similar approach will be developed for extreme event forecasts in ENSEMBLES

22. 22 The EUROBRISA Project Lead Investigator: Dr Caio Coelho Key Idea: To improve seasonal forecasts in S. America: a region where there is seasonal forecast skill and useful value. Aims Strengthen collaboration and promote exchange of expertise and information between European and S. American seasonal forecasters Produce improved well-calibrated real-time probabilistic seasonal forecasts for South America Develop real-time forecast products for non-profitable governmental use (e.g. reservoir management, hydropower production, and agriculture) EUROBRISA was approved by ECMWF council in June 2005 http://www.met.rdg.ac.uk/~swr01cac/EUROBRISA InstitutionsCountryPartners CPTECBrazilCoelho, Cavalcanti, Silva Dias, Pezzi ECMWFEUAnderson, Balmaseda, Doblas-Reyes, Stockdale INMETBrazilMoura, Silveira Met OfficeUKGraham, Davey, Colman Météo FranceFranceDéqué SIMEPARBrazilGuetter Uni. of ReadingUKStephenson Uni. of Sao PauloBrazilAmbrizzi, Silva Dias CIIFENEcuadorCamacho, Santos

23. 23 Climate Analysis Group http://www.met.reading.ac.uk/cag/ Aim: develop and apply statistical analysis techniques to improve both understanding and predictive capability of weather and climate variations Main areas of interest: climate modes and regimes e.g. NAO and Asian Monsson weather and climate extremes Forecast verification, combination and calibration

24. 24 The End