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

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.

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

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  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 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/

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

  5. 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

  6. 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)

  7. Tropical Broadleaf Evergreen Tree (FPC)

  8. C3 Grasses (FPC)

  9. 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. 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 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 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

  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

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