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Statistical Challenges in Climatology

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. Overview. History and general issues

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Statistical Challenges in Climatology

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  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 southern oscillation ‘primitive’ equations manual forecast computer forecasts 40 Tflops 10 Tbytes 1904 1922 1923 1950 2002 Vilhelm Bjerknes Lewis Fry Richardson Gilbert Walker Jule G. Charney The Earth Simulator

  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. Principal components: multi-site observations Identifies patterns of simultaneous variation Physical significance Reduces dimension Rotated, simplified etc. Climate Modes 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 Caio Coelho & Sergio Pezzulli Prior: climate-model forecast Likelihood: regression model regression model combined

  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 Control (1961-1990) Scenario – Control mm mm

  15. Mean Daily Rainfall Control (1961-1990) Scenario – Control DJF MAM DJF MAM JJA SON JJA SON mm mm

  16. Simultaneous Confidence Intervals

  17. Mean Daily Rainfall Response DJF JJA

  18. Mean Daily Rainfall Response DJF JJA

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

  20. Mean Temperature Control (1961-1990) Scenario – Control DJF MAM DJF MAM JJA SON JJA SON ºC ºC

  21. Distributional Changes

  22. Daily Rainfall Response DJF

  23. Temperature Response DJF

  24. Model Uncertainty Annual Mean Scenario Model Year

  25. Temperature: R2

  26. Temperature: Model Effects °C

  27. Temperature: Model Response °C

  28. Extreme Values

  29. Rainfall 10-DJF Return Levels Control A2 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. PRUDENCE Climate Analysis Group 9th 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 Further Information c.a.t.ferro@reading.ac.uk

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