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Exploratory methods to analyse output from complex environmental models

Exploratory methods to analyse output from complex environmental models. Adam Butler, Biomathematics and Statistics Scotland www.bioss.ac.uk/staff/adam.html ICMS/SPRUCE workshop, March 2007. Statistical post-processing.

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Exploratory methods to analyse output from complex environmental models

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  1. Exploratory methods to analyse output from complex environmental models Adam Butler, Biomathematics and Statistics Scotland www.bioss.ac.uk/staff/adam.html 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 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

  4. 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 predictfuture values of the control run

  5. 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

  6. Prediction 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

  7. 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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