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Exploratory methods to analyse output from complex environmental models Exploratory methods to analyse output from complex environmental models Adam Butler,

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Presentation on theme: "Exploratory methods to analyse output from complex environmental models Exploratory methods to analyse output from complex environmental models Adam Butler,"— Presentation transcript:

1 Exploratory methods to analyse output from complex environmental models 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. 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 Butler et al. : to appear in JRSS C

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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) x C = control run of LPJ y iC = LPJ run using i-th ensemble Prediction period (21st century) y iP = LPJ run using i-th ensemble

7 Compute discrepancies z kC = y kC - x C Fit a parametric model to z iC Use it to compute predictive distribution of z iP Predict control run to be x P = y KP - z KP,, where run K selected with probability w K Prediction

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


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