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, 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. 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

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

5. Intergovernmental Panel on Climate Change www.ipcc.ch
PRUDENCE
European climate
30-year control simulation,
30-year scenario simulation,
Gigatonnes of Carbon
A2 = yellow, B1 = green, B2 = blue, IS92a = black, A1F1 = red dot, A1T = red dash, A1B = brown
Prediction of Regional scenarios and Uncertainties for Defining European Climate change risks and Effects
Intergovernmental Panel on Climate Change

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. 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

10. Data Assimilation State Observation Solution
Assumptions, approximations and choice of
Prediction (3DVAR) and smoothing (4DVAR)

11. Forecast Calibration Caio Coelho & Sergio Pezzulli
climate model
Caio Coelho & Sergio Pezzulli
Prior: climate-model forecast
Likelihood: regression model
regression model
combined
Wt | Ft ~ N(mean[Ft], sd[Ft])
Ft | Wt ~ N(a + bWt, cV)

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

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

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

15. Further Information Climate Analysis Group Data Assimilation
Research Centre
PRUDENCE
9th International Meeting on Statistical Climatology, Cape Town, May 2004