Exploratory methods to analyse Exploratory methods to analyse output from complex environmental output from complex environmental

modelsmodels

Adam Butler, Biomathematics and Statistics Scotlandwww.bioss.ac.uk/staff/adam.html

ICMS/SPRUCE workshop, March 2007

Statistical post-processingStatistical 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

Past trends in North Sea storm surges Past trends in North Sea storm surges ..

Analyse output from a single run of a storm surge model -

reconstructed North Sea surge elevations for the years 1955-2000

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

Butler et al. : to appear in JRSS C

Projecting trends in global vegetation 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

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

Compute discrepancies zkC = ykC - xC

Fit a parametric model to ziC

Use it to compute predictive distribution of ziP

Predict control run to be xP = yKP - zKP,,

where run K selected with probability wK

Prediction

The ALARM projectThe 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