Climate Uncertainty Impact on Ecosystem Model Trends

1. Impact of climate uncertainty upon trends in outputs generated by an ecosystem model Ruth Doherty, Edinburgh University Adam Butler & Glenn Marion, BioSS ALARM meeting, Athens, January 2007

2. Acknowledgements LPJ code: Ben Smith, Stephen Sitch, Sybil Schapoff CRU data: David Viner GCM data: PCMDI Statistical methods: Jonathan Rougier, Chris Glasbey Uncertainty analysis: Bjoern Reineking, Stijn Bierman

3. Aim Quantify uncertainties in projections of global & regional vegetation trends for the 21st century from the LPJ ecosystem model, based on future climate uncertainty within the SRES A2 scenario

4. The LPJ model http://www.pik-potsdam.de/lpj/ “…the Lund-Potsdam-Jena Dynamic Global Vegetation Model combines process-based, large-scale representations of terrestrial vegetation dynamicsandland-atmosphere carbon and water exchanges in a modular framework”

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

6. Sources of uncertainty in LPJ • Zaehle et al. (2005) analysed parameter uncertainty: used Latin hypercube sampling to sample uniformly over values of 14 functionally important parameters • Cramer et al. (2001) and Smith et al. (2001) analysed structural uncertainty, by looking at alternative parameterisations of processes within LPJ • Estimated uncertainty range (NPP, 1961-1990): 43 –103 PgC/yr in Zaehle et al., 2005 44 – 66 PgC/yr in Cramer et al., 2001 Zaehle et al.: uncertainty range increases in future

7. Climate uncertainty LPJ is driven by climate, CO2 and soils data. Future climate inputs are uncertain, due to: • Future emissions: choice of SRES scenario • Choice of GCM (climate model) • Intra-model uncertainty for each GCM

10. Climate model runs • GCM simulations from the IPCC 4th Assessment • We consider only SRES scenarioA2 • We use 17 ensemble runs, from a total of 9 GCMs • GCMs with multiple ensembles CCCMA-CGCM3, MPI-ECHAM5,NCAR-CCSM3 • GCMs with a single run CNRM-CM3,CSIRO-MK3,GFDL-MK2, MRI-CGCM2-3,UKMO-HADCM3, UKMO-HADGEM

11. LPJ model runs • We run LPJ 18 times at a global scale • Soil inputs: FAO global soils dataset, with 9 types • CO2 inputs • Climate inputs: • monthly temperature, precipitation, solar radiation • control run: gridded 0.5o x 0.5o CRU data for 1900-2001 • other runs: GCM model runs for 1900-2098, with LPJ run at native spatial scale of the GCM • Spin-up period of 1000 years at start of each run • Run on average grid-cell basis with 1-year time-step

12. LPJ Outputs Daily: carbon and water fluxes Annual: vegetation dynamics and competition amongst 10 Plant Functional Types Spatial scale of outputs varies, depending on scale of the climate data / model used to provide the inputs We analyse trends from 2002 to 2098 in global annual values of vegetation carbon, soil carbon & NPP

13. Systematic biases • LPJ runs using GCMs exhibit systematic biases – presumably related to coarse spatial scale • By calibrating against the LPJ control run we can use a statistical model to describe the statistical properties of these biases over the period 1900-2001 • This model can then, along with the LPJ runs under scenario A2, be used to predict the response of the LPJ model to climate over the 21st century

14. Statistical methodology Past t = years 1900,…,2001 k = GCM run 1,…,17 We have data on: xt = LPJ control run ykt = LPJ run using GCM run k bkt = xt - ykt (bias in run k) Assume bkt = k + ekt, where: ekt is AR(1): ekt ~ N(k ek,t-1,k2) vague priors on k, k ,k,ek,1899 Future t = years 2002,…,2098 k = GCM run 1,…,17 We have data on: ykt = LPJ run with GCM k Predict Xt = BKt +yKt K is randomly chosen GCM run: K = k with probability 1/17 BKt is predicted using the fitted AR(1) model for {bkt}

15. Statistical assumptions • Historical biases between the control & GCM-forced runs can be described by a simple time series model • Future biases have the same distributional properties as historical biases • The future LPJ runs provide equal information about year-to-year variations in vegetation characteristics • The control run of LPJ rovides an error-free and unbiased representation of current vegetation

16. Results Fit using LinBUGS (http://mathstat.helsinki.fi/openbugs): free software for fitting a vast range of statistical models via Bayesian inference Can obtain similar results using ARIMA() function in R: but this does not account for estimation uncertainty

17. Simulated data

21. Annual global NPP

24. Annual global soil carbon

27. Annual global vegetation carbon

30. Diagnostics • Does the AR model describe historical biases well? • Model checking: • plot of residuals from model, sample autocorrelations, estimates for k • sensitivity of predictions to value of K • Possible extensions: • long-term linear or quadratic trends • higher-order terms in an ARIMA model • model responsesy1t,…,y17,t as covariates

31. Annual global NPP

36. Future work • Improve time series model for bias terms • Investigate possible reasons for systematic bias • Apply a similar analysis at the regional scale • Analyse outputs from the other SRES scenarios • Incorporate global satellite data on NPP…?

37. Open questions • How reasonable is the assumption that future biases are related to past biases? • Should we assign equal weights to model runs? • Should we run LPJ at the native spatial scale of the climate model that is being used to force it? • We use statistical post-processing – could we use statistical methods to generate climate inputs for LPJ? • LPJ can be run with stochastic modules – how could we incorporate uncertainty from these?

38. Contact us Adam Butler adam@bioss.ac.uk Ruth Doherty ruth.doherty@ed.ac.uk Glenn Marion glenn@bioss.ac.uk File: created 11 December, last modified 13 December, author Adam Butler