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Statistical Challenges in Climatology

Statistical Challenges in Climatology. Chris Ferro Climate Analysis Group Department of Meteorology University of Reading. Also featuring… David Stephenson, Abdel Hannachi, Sergio Pezzulli, Cristina Carollo (ESSC), Barbara Casati, Caio Coelho, Pascal Mailier, Tim Mosedale, Fotis

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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 Also featuring… David Stephenson, Abdel Hannachi, Sergio Pezzulli, Cristina Carollo (ESSC), Barbara Casati, Caio Coelho, Pascal Mailier, Tim Mosedale, Fotis Panagiotopoulos, Matt Sapiano and Neeraj Teeluk (UCL) Young Statisticians’ Meeting, Cambridge, 14-15 April 2003

  2. Statistical Climatology? ‘primitive’ equations manual forecasts computer forecasts 1904 1922 1950 2002 Vilhelm Bjerknes Lewis Fry Richardson Jule G. Charney The Earth Simulator Speed = 40 Tflops Memory = 10Tbytes

  3. 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 General Issues

  4. parameters errors structure Differential Equations Numerical Scheme Initial Conditions External Forcings Circulation Model deterministic sensitivity estimation

  5. Climate Change • PRUDENCE • European climate • 30-year control simulation, 1961-1990 • 30-year scenario simulation, 2071-2100 Intergovernmental Panel on Climate Change www.ipcc.ch

  6. Mean Winter Precipitation mm/day mm/day

  7. Mean Winter Precipitation mm/day • Two-sample block bootstrap simultaneously at each grid point • accounts for temporal dependence • preserves spatial structure

  8. Mean Winter Precipitation mm/day • Two-sample block bootstrap simultaneously at each grid point • accounts for temporal dependence • preserves spatial structure

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

  10. Data Assimilation State Observation Solution • Assumptions, approximations and choice of

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

  12. Forecast ‘Verification’ Barbara Casati false wet : false dry Wavelet decomposition identifies contributions to the forecast performance measure from different spatial scales.

  13. Multivariate methods Stochastic models Statistical models identify climate modes investigate climate dynamics attribute climate change downscale simulated data Other Topics

  14. Conclusions • Huge amount of complex data produced • Frustrated by inadequate statistical methods • Sophisticated techniques required • Collaboration and education

  15. Climate Analysis Group Data Assimilation Research Centre PRUDENCE 9th International Meeting on Statistical Climatology, Cape Town, May 2004 www.met.rdg.ac.uk/cag www.darc.nerc.ac.uk www.dmi.dk/f+u/klima/prudence www.csag.uct.ac.za/IMSC Further Information

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