1. Statistical Challenges in Climatology Chris Ferro Climate Analysis Group Department of Meteorology University of Reading ‘Climate is what we expect, weather is what we get.’ Mark Twain (?) RSS Birmingham Local Group, Coventry, 11 December 2003

2. Overview History and general issues Examples of research topics Climate change simulations Concluding remarks

3. History Jule G. Charney Vilhelm Bjerknes The Earth SimulatorLewis Fry Richardson 1950 computer forecasts 1922 manual forecast ‘primitive’ equations 19042002 Gilbert Walker 40 Tflops 10 Tbytes southern oscillation 1923

4. General Issues Dependent Nonstationary Huge datasets Limited data space and time: many scales space and time: periodicities, shocks, external forcings station, satellite, simulation short record, no replication

5. Examples of Research Topics Observations Climate modes Numerical models Data assimilation Forecast calibration Other topics

6. Observations Buoys Field Stations Ships & Aircraft Satellites Radiosondes Palaeo-records homogeneity, missing data, errors and outliers network design and adaptive observations statistical models to reconstruct past climates

7. Climate Modes Principal components: multi-site observations Identifies patterns of simultaneous variation Physical significance Reduces dimension Rotated, simplified etc. North Atlantic Oscillation, courtesy of Abdel Hannachi

8. General Circulation Models Differential equations Physical schemes External forcings Initial conditions Numerical scheme Deterministic output: temp, precip, wind, pressure etc.

9. Data Assimilation State Observation Solution Assumptions, approximations, choice of

10. Forecast Calibration climate model combined regression model Caio Coelho & Sergio Pezzulli Prior: climate-model forecast Likelihood: regression model

11. Other Topics Model validation Forecast verification Statistical downscaling Climate change attribution Stochastic models of processes

12. Climate Change Simulations The PRUDENCE project Temperature and precipitation Distributional changes Extreme values Model uncertainty

13. PRUDENCE European Climate 30-year control simulation, 1961-1990 30-year A2 scenario simulation, 2071-2100 10 high-resolution regional models 6 global models From www.ipcc.ch

14. Mean Daily Rainfall mm Control (1961-1990)Scenario – Control

15. Mean Daily Rainfall DJFMAM SONJJA Control (1961-1990)Scenario – Control DJFMAM JJASON mm

16. Simultaneous Confidence Intervals

17. Mean Daily Rainfall Response DJFJJA

18. Mean Daily Rainfall Response DJFJJA

19. Mean Temperature ºCºC Control (1961-1990)Scenario – Control ºCºC

20. Mean Temperature Control (1961-1990)Scenario – Control ºCºC ºCºC SONJJA SON DJFMAMDJFMAM

21. Distributional Changes

22. Daily Rainfall Response DJF

23. Temperature Response DJF

24. Model Uncertainty ScenarioYearModelAnnual Mean Global model12 Regional model12345…12 Controlxxxxx A2 Scenarioxxxxx B2 Scenario

25. Temperature: R 2

26. Temperature: Model Effects °C°C

27. Temperature: Model Response °C°C

28. Extreme Values

29. Rainfall 10-DJF Return Levels ControlA2 Scenario / Control

30. GEV Parameter Estimates

31. Scale-change Model p-value

32. Concluding Remarks Need for sophisticated statistical techniques to help to analyse large amount of complex data. ‘There is, to-day, always a risk that specialists in two subjects, using languages full of words that are unintelligible without study, will grow up not only, without knowledge of each other’s work, but also will ignore the problems which require mutual assistance.’ Sir Gilbert Walker, 1927

33. Further Information PRUDENCE Climate Analysis Group 9 th International Meeting on Statistical Climatology, Cape Town, 24-28 May 2004 prudence.dmi.dk www.met.rdg.ac.uk/cag www.csag.uct.ac.za/IMSC c.a.t.ferro@reading.ac.uk