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David Galbraith, Peter Levy, Stephen Sitch , Chris Huntingford , Patrick Meir, Peter Cox

Quantifying the contributions of different environmental drivers to Amazon dieback predictions in 3 Dynamic Global Vegetation Models. David Galbraith, Peter Levy, Stephen Sitch , Chris Huntingford , Patrick Meir, Peter Cox. Amazon Dieback Predictions: A brief history.

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David Galbraith, Peter Levy, Stephen Sitch , Chris Huntingford , Patrick Meir, Peter Cox

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  1. Quantifying the contributions of different environmental drivers to Amazon dieback predictions in 3 Dynamic Global Vegetation Models David Galbraith, Peter Levy, Stephen Sitch, Chris Huntingford, Patrick Meir, Peter Cox

  2. Amazon Dieback Predictions: A brief history White and Friend (1999) Cox et al. (2000) Sitch et al. (2008) Betts et al. (2004)

  3. Aims 1) Separate the contributions of four environmental variables to Amazon dieback in 3 DGVMs: • precipitation • relative humidity • temperature • carbon dioxide 2) Identify which carbon cycle process responses most affect the simulated loss of Amazon vegetation carbon in different models? • Direct effect on Plant respiration • Direct effect on GPP • Indirect effect on GPP

  4. The Expected Storyline Shallow Rooting Depths in DGVMS High Sensitivity to Reduced Rainfall Loss of Rainforest Carbon/Cover INCORPORATION OF MORE REALISTIC DROUGHT ADAPTATION SYSTEMS WOULD LEAD TO MORE RESILIENT FOREST AND LESS DIEBACK

  5. Methods 3 DGVMS (HYLAND, LPJ, TRIFFID) IMOGEN GCM ANALOGUE + CRU BASELINE CLIMATOLOGY 4 IPCC SCENARIOS 16 Factorial Simulations per model/scenario combination

  6. HYLAND LPJ TRIFFID Predicted Change in Vegetation C in 2100 HYLAND LPJ TRIFFID A1F1 Precipitation Only Simulations with CO2 held constant A1F1 Temperature Only A1F1 exceptCO2

  7. HYLAND LPJ TRIFFID Predicted Change in Vegetation C to 2100 A1F1Precipitation + CO2 Simulations with varying CO2 A1F1 Temperature + CO2 A1F1 Scenario

  8. Contributions of Environmental Drivers to Dieback Contribution of climate drivers to Amazon VegC loss in 4 IPCC scenarios

  9. Sensitivity of Amazon Veg C to Precipitation Reduction and Temperature Increase Linear Precipitation Reduction (%) by 2100 Linear Temperature Increase (°C) by 2100 Temperature a more important driver of dieback in models than hitherto demonstrated – but what are the mechanisms?

  10. Indirect vs. Direct Effects of Temperature 1) VPD calculated using ‘baseline temperature’; plant physiology routines use ‘updated temperature’ 2) Substitution of default plant respiration dependency on temperature with one where NPP = 0.5*GPP Lloyd and Farquhar 2008

  11. Temperature Mechanisms: Results A1F1 HYLAND LPJ TRIFFID Similar outcome, yet very different mechanisms Is temperature effect actually a drought effect?

  12. Relating Models to Reality: Drought Sensitivity An issue of parameterisation or process representation? Drought experiments suggest the forest is more vulnerable to drought than some models predict Fisher et al. 2008

  13. Relating Models to Reality: Temperature Sensitivity Exceedance of photosynthetic temperature optima But what about acclimation responses ? (to both photosynthesis and respiration) A2 B1 IMOGEN (IMOGEN/HADCM3 PATTERNS) Plant processes (photosynthesis and respiration) seem to adapt to long-term changes in temperature (Figure from Yadvind

  14. A) What causes Amazon dieback in DGVMs?:Conclusions • Similar result despite divergence of mechanisms across models • Rising temperature a more important driver of dieback than hitherto shown • Simple model assumptions can have a big impact on • the result • Next Steps • What difference does unique treatment of the Amazon make? • Drought response in DGVMs – a question of improved parameterisation or process representation?

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