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Impact of climate uncertainty upon trends in outputs generated by an ecosystem model Adam Butler & Glenn Marion, Biomathematics & Statistics Scotland • Ruth Doherty, Edinburgh University • Jonathan Rougier,University of Durham Probabilistic Climate Impacts workshop, September 2006 Some background Aims To quantify uncertainties in projections of global and regional vegetation trends for the 21st century from the LPJ ecosystem model, based on future climate uncertainty BIOSS Public body providing quantitative consultancy & research to support biological science Funded by ALARM: a 5 year EU project to assess risks of environmental change upon European biodiversity The LPJ Ecosystem Model “The Lund-Potsdam-Jena Dynamic Global Vegetation Model (LPJ) combines process-based, large-scale representations of terrestrial vegetation dynamics and land-atmosphere carbon and water exchanges in a modular framework…” http://www.pik-potsdam.de/lpj/ Drivers Fluxes (daily) Vegetation Dynamics (annual) LPJ-Lund Potsdam Jena Vegetation Model Based on climate and soils inputs LPJ simulates: Vegetation dynamics and competition amongst 10 Plant Functional Types (PFTs) Vegetation and soil carbon and water fluxes Average grid-cell basis with a 1-year time-step Spin-up period of 1000 years to develop equilibrium vegetation and soil structure at start of simulation LPJ-Lund Potsdam Jena Vegetation Model Inputs: Soils: FAO global soils dataset: 9 types inc coarse-fine range (CRU) Climate: monthly temperature, precipitation, solar radiation CO2: provided for 1901-1998; updated to 2002 from CDIAC Model output scale determined by driving climate Acknowledgements: LPJ code- Ben Smith, Stephen Sitch, Sybil Schapoff CRU data- David Viner (CRU), GCM data (PCMDI) Tropical Broadleaf Evergreen Tree (FPC) C3 Grasses (FPC) LPJ Model Uncertainty Model inputs: future climate uncertainty Representation of mechanisms driving model processes (Cramer et al. 2001; Smith et al. 2001tests different formulations of relevant processes)generally use most up-to date formulations from literature Parameters within the model (Zaehle et al. 2005, GBC) Zaehle et al. 2005 Latin hypercube sampling Assume uniform PDF for each parameter Exclude unrealistic parameter combinations Simulations at sites representing major biomes (81) 400 model runs (61-90 CRU climatology and HadCM2 1860-2100) Identified 14 functionally important parameters Differences in parameter importance in water-limited regions Estimated uncertainty range of modelled results: 61-90: NPP=43.1 –103.3 PgC/yr; cf. 44.4-66.3 Cramer et al. (2001) Zaehle et al (2005) NPP accounting for parameter uncertainty NBP = NEE+Biob Uc=full uncertainty range C=excluding unrealistic parameters Increases in 2050s due to increased CO2 and WUE, thereafter a decline Parameter uncertainty increases in the future Uncertainty estimates in NBP/NPP comparable to those obtain from uncertainty amongst 6 DGVMs Future Climate Uncertainty based on IPCC 4th Assessment GCM simulations IPCC-AR4 simulations GCMs contributing to SRES A2 CO2 concentrations Investigating the effect of Future Climate Uncertainty for LPJ predictions Perform 19 separate runs of LPJ at the global scale one run using CRU data for 1900-2002 at 0.5o x 0.5o results from 18 simulations from 9 GCMs for the period 1850-2100 (20th Century and A2) running at the native scale of each GCM GCMs with multiple ensembles CCCMA-CGCM3, MPI-ECHAM5, NCAR-CCSM3 GCMs with single ensemble member CNRM-CM3, CSIRO-MK3, GFDL-MK2, MRI-CGCM2-3, UKMO-HADCM3, UKMO-HADGEM Global mean temperature anomaly relative to 61-90 LPJ Outputs For each grid cell LPJ produces annual values for: Net Primary Production Net Ecosystem Production Plant Functional Type Heterotrophic respiration Vegetation carbon Soil carbon Fire carbon Run-off Evapotranspiration …we focus on globally averaged values of these variables… Statistical approach • Statistical post-processing of LPJ output • Analyse trends in global annual mean NPP based on outputs from 19 runs of the LPJ model • Runs forced using a total of 18 ensembles from 9 GCMs, and using gridded CRU data • Analysis (partially) deals with climate uncertainty, but does not deal with parameter or structural uncertainties in the LPJ model Motivating factors • Statistical pre-processing of LPJ inputs is tough: would need to describe month-to-month trends in three climate variables for each location • GCMs are each run at different spatial resolutions, all of which differ from the resolution of the CRU data • LPJ is computationally intensive to run • No useful observational data to validate LPJ against Time series model Use a hierarchical time series model to draw inferences about “true” response of LPJ model to projected climate changes based on the 19 runs Output from past year t using CRU data: xt ~ N(t , vt ) Output for past or future year t using run i of GCM I: yit ~ N(zIt , ) Assume conditional independence in both cases Latent trends Model trends in true signal t and GCM biases YIt - t as independent random walks: e.g. t ~ N(t 1 , s t ) allows process variability to change linearly over time Can fit as a Dynamic Linear Model using the Kalman filter – easy to implement in R (sspir package) Parameter estimation by numerical max likelihood Results - temperature NPP Assumptions • Observational errors are IID and unbiased • Inter-ensemble variabilities for a given GCM are IID • Random walk model can provide a good description of actual trends • Levels of variability do not change over the course of the runs (except for a jump at present day) Inter-ensemble variability Future work - methodology Explore impacts of making different assumptions about the biases in the GCM responses Explore impacts of varying levels of inter-ensemble variability and observation error Explore links between this and a regression-based (ASK-like) approach Deal with uncertainty in estimation of parameters in time series model – e.g. a fully Bayesian analysis BUGS BUGS: free software for fitting a vast range of statistical models via Bayesian inference Provides an environment for exploring the impacts of different assumptions Allows for the use of informative priors [http://www-fis.iarc.fr/bugs/wine/winbugs.jpg] http://mathstat.helsinki.fi/openbugs http://www.mrc-bsu.cam.ac.uk/bugs Bayesian analogue of the DLM z It t bIt t 2t 1 t 2 ~ N (0, ) Problems: Lack of identifiability Bias terms are not really AR(1) bI ,t I bI ,t 1 ~ N (0, I ) A Bayesian ASK-like model M t wI z It bt I 1 zIt 2zI ,t 1 zI ,t 2 ~ N (0, I ) Problems: Lack of fit Unconstrained estimation leads to weights outside range [0,1] bt bt 1 ~ N (0, ) Open questions – statistical methodology • What assumptions can we make about the biases in GCM responses and in the observational data? • How reasonable is the assumption that future variability is related to past variability, and how far can we weaken this assumption? • How should we best deal with small numbers of ensembles & unknown levels of “observational error”? Can we ellicit more prior information? Future work - application Apply analysis to output from newer version of LPJ Apply a similar analysis at the regional scale Extend approach to other variables, especially PFT Analyse outputs from multiple SRES scenarios Open questions - application Should LPJ be run at the native spatial scale of the data/GCM that is being used to force it ? LPJ includes stochastic modules – switched off here, but how could we best deal with these…? For a limited number of runs what experimental design would enable us to best reflect the different elements of climate and impact uncertainty? Context: the ALARM project Assessing impacts of environmental change upon biodiversity at the European scale Modules: climate change, environmental chemicals, invasive species, pollination Relies heavily upon climate and land use projections Impacts assessed using either via mechanistic models (e.g. LPJ) or through extrapolation from current data Contact us Adam Butler [email protected] Ruth Doherty [email protected] Glenn Marion [email protected]