1. Exploratory methods to analyse output from complex environmental models
Adam Butler, Biomathematics and Statistics Scotland
ICMS/SPRUCE workshop, March 2007

2. Statistical post-processing
Use conventional statistical methods - including smoothing techniques - to analyse outputs from process-based models
“Static”, in contrast with “dynamic” emulation/GP approaches
Provides an approach to the exploratory analysis of aspects of uncertainty and inadequacy in highly complex models, by allowing us to make use of information from a small # of runs

3. Past trends in North Sea storm surges .
Butler et al. : to appear in JRSS C
Analyse output from a single run of a storm surge model reconstructed North Sea surge elevations for the years
Compare spatial and temporal trends in storm surge magnitude and frequency with those seen in observational sea level data
Analysis based on an extreme value model
Use nonparametric regression (local likelihood) to allow parameters to vary over space and time in a smooth way

5. Projecting trends in global vegetation .
Doherty et al. : in preparation
Quantify impact of climate uncertainty upon projections generated by the Lund-Potsdam-Jena Dynamic Global Vegetation Model
Run LPJ once using gridded climate data for the 20th century (“control run”), then eighteen more times using climate projections for the 20th and 21st centuries generated by ensemble runs from nine different Atmosphere-Ocean General Circulation Models
Ignore inadequancies of LPJ; focus on using the climate model projections to predict future values of the control run

6. Annual global vegetation carbon
Calibration period (20th century)
xC = control run of LPJ
yiC = LPJ run using i-th ensemble
Prediction period (21st century)
yiP = LPJ run using i-th ensemble

7. Prediction Compute discrepancies zkC = ykC - xC
Fit a parametric model to ziC
Use it to compute predictive distribution of ziP
Prediction
Predict control run to be xP = yKP - zKP,, where run K selected with probability wK

8. The ALARM project
Integrated European Union research project to develop a set of tools for biodiversity risk assessment
>50 organisations, >200 scientists and social scientists... BioSS provides statistical support for the project
Focus is on assessing risks from multiple environmental pressures at multiple scales, and on risk communication…
Evidence comes from species atlas data, local experimental data, process-based models and expert opinion