Impact of climate uncertainty upon trends in outputs generated by an ecosystem model

1. 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

2. 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

3. The Impacts model: LPJ “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/

4. Drivers Fluxes (daily) Vegetation Dynamics (annual)

5. LPJ Vegetation Model simulations • Driven by climate and soils inputs LPJ simulates: • Daily: carbon and water fluxes • Annually: vegetation dynamics and competition amongst 10 Plant Functional Types (PFTs) • 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

6. LPJ Inputs/drivers • 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)

7. Tropical Broadleaf Evergreen Tree (FPC)

8. C3 Grasses (FPC)

9. Sources of LPJ Model Uncertainty • Model inputs: future climate uncertainty • Representation of mechanisms driving model processes (Cramer et al. 2001; Smith et al. 2001- tests different formulations of relevant processes)- generally use most up-to date formulations from literature • Parameters within the model (Zaehle et al. 2005, GBC)

10. LPJ Parameter uncertainty: 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)

11. NBP = NEE+Biob Uc=full uncertainty range C=excluding unrealistic parameters NPP accounting for parameter uncertainty LPJ Parameter uncertainty:Zaehle et al (2005)

12. 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

13. Future Climate Uncertainty based onIPCC 4th Assessment GCM simulations

14. IPCC-AR4 simulations

15. GCMs contributing to SRES A2

16. CO2 concentrations

17. 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 1901-2002 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

18. Global mean temperature anomaly relative to 61-90

19. …we focus on globally averaged values of these variables… 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 Net Primary Production Net Ecosystem Production Plant Functional Type Heterotrophic respiration Vegetation carbon Soil carbon Fire carbon Run-off Evapotranspiration

20. 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

21. 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 fairly computationally intensive to run • No useful observational data to validate LPJ against

22. 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: Output for past or future year t using run i of GCM I: Assume conditional independence in both cases

23. Latent trends Model trends in true signalt and GCM biasesYIt - t as independent random walks: e.g.  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

24. Results - temperature

26. NPP

27. 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)

28. Inter-ensemble variability

29. 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 Apply analysis to output from newer version of LPJ Apply a similar analysis at the regional scale Extend approach to other variables, especially PFT Incorporate information on multiple scenarios

30. 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

31. Bayesian analogue of the DLM Problems: Lack of identifiability Bias terms are not really AR(1)

32. A Bayesian ASK-like model Problems: Lack of fit Unconstrained estimation leads to weights outside range [0,1]

33. 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?

34. 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

37. 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?

38. Contact us Adam Butler adam@bioss.ac.uk Ruth Doherty ruth.doherty@ed.ac.uk Glenn Marion glenn@bioss.ac.uk