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

This study assesses uncertainties in global and regional vegetation trends for the 21st century using the LPJ ecosystem model and future climate projections. It explores the impact of climate uncertainty on ecosystem outputs and models probabilistic climate impacts.

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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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