Dynamic Global Vegetation Models DGVMs

1. Dynamic Global Vegetation ModelsDGVMs Jed O. Kaplan* and Stephen Sitch°*European Commission Joint Research Centre, Ispra, Italy°Met Office (JCHMR), Wallingford, U.K.

2. Acknowledgments • TERACC • Colin Prentice • Marie Curie Fellowships program

3. Overview • History and development • Fundamentals and model design • Evaluation • Example applications • Future research perspectives

4. History and development of DGVMs • Impetus for the development of a DGVM • Terrestrial biosphere provides critical services to humanity: food, water, shelter, psychological benefits • Biosphere plays a major role in the global carbon cycle with a timescale relevant to human activities (mean residence time of ~20yr) • Anthropogenic alteration of the atmosphere and biosphere has have been very large since industrialization

5. History and development of DGVMs • DGVM development integrated four groups of processes Plant geography Köppen, Box, MAPSS D G V M Biogeochemistry Miami, TEM, Century Biophysics SiB, BATS, LSM Vegetation Dynamics JABOWA, Foret, FORSKA

6. History and development of DGVMs • Plant geography • First observations of relationship between vegetation and climate from von Humboldt and Schimper (19th century) • Empirical schemes from Köppen, Holdridge followed by the works of Shugart and Emanuel (1980’s, including the first 2xCO2 scenario). • The PFT concept outlined by Raunkiaer (1st half of 20th century) and developed by Box (1981) into the first predictive biogeography models • Woodward, Prentice, Nielson et al. all developed biogeography models at the end of the ‘80s

7. History and development of DGVMs • Plant Physiology and Biogeochemistry • First global relationships between environment and productivity 1960’s • IBP, Walter, and Lieth (Miami Model) • TBMs to simulate NPP beginning early 90s • TEM, Century, Forest/BIOME-BGC, CASA, DOLY • Hybrid models (BIOME2-3-4)

8. History and development of DGVMs • Vegetation dynamics • Exposition of the gap/mosaic idea (early 20th century) • Development of “Gap models”: JABOWA, FORET, LINKAGES, FORSKA, SORTIE • Challenge for computational efficiency in order to look at larger spatial scales • Development of statistical representation for individual dynamics (e.g. ED model)

9. History and development of DGVMs • Biophysics • Climate modelling called for a realistic representation of the land surface, particularly roughness, albedo, heat and water transfer • Led to the development of SVAT (80s, 90s) • SiB, BATS first explicit SVAT, followed by many others with higher complexity • DGVMs as a SVAT: IBIS, Triffid • Later included carbon feedbacks

10. Fundamentals and design of DGVMs • Model architecture • NPP • Plant growth and vegetation dynamics • Hydrology • Heterotrophic respiration and SOM dynamics • Nitrogen cycling • Disturbance

11. DGVM architecture Bonan et al. 2003 Daily Annual Minutes to day

12. NPP • Leaf-level photosynthesis using Farquhar et al. or derivatives (Collatz et al., Haxeltine & Prentice, etc.) • C uptake is optimized relative to water availability through canopy conductance, incorporating photosynthesis, canopy biophysics, and hydrology • Light uptake and nutrient distribution simplified to one canopy level (exceptionally more) • Autotrophic respiration function of temperature (Q10 or Arrehenius function) or canopy C:N ratio

13. Growth and dynamics • Driven by NPP • Allocated to leaves, stems, roots • Establishment and mortality are parameterized boundary conditions • Use the “population average” • Expressed through allocation to state variables of fractional coverage, individual size, density • Flexible allocation in response to changing environmental conditions

14. Mediterranean evergreen forest

15. Crown area

16. Individual density

17. Southern boreal forest

18. Hydrology • One, two or multi-layered soil characterization (reliable data is a limitation) • Two layers is usually minimum for bringing out distinctions between trees and grass • Parameterizations for saturated vertical flow, runoff, and drainage • Exceptionally, DGVMs may explicitly simulate snow, frost, and permafrost, wetlands, and horizontal transport of water (among others)

19. SOM dynamics • Dead organic matter partitioned into rate-specific pools based on litter quality • Two to three pools for simpler models, eight or more for DGVMs with Century scheme • Respiration often represented as a function of temperature and moisture (Q10 or Arrhenius)

20. N cycling • N content (or C:N ratio) carried as a state variable in each biomass compartment • Simple scaling of gross uptake based on optimization hypothesis • Or simulation of actual soil N mineralization and immobilization (Century-based schemes) • N-fixation generally not considered

21. Disturbance • Major natural disturbances are fire, windthrow, disease, insects • Most models only consider fire • Fire modeled as a probability function of fuel availability, moisture, and stochastic processes • Human-induced fire may be included

22. Evaluating DGVMs through obeservation and experiment • NPP • Remotely sensed greenness • Atmospheric CO2 concentrations • Runoff • CO2 and water flux measurements • FACE experiments

23. Remotely sensed greenness Sitch et al. 2003

24. Atmospheric CO2 concentrations Sitch et al. 2003

25. Runoff Sitch et al. 2003

26. Widespread applications • Holocene changes in atmospheric CO2 • Boreal greening and contemporary carbon cycle • Future carbon cycle projections • Carbon-climate feedbacks to future climate change • Land-use change effects

27. Holocene carbon dynamics Ridgwell et al. 2003 Kaplan et al. 2002

28. Future C cycle projections Cramer et al. 2001

29. Global wetland methane emissions 1991-2000 Kaplan et al., in prep.

30. Future research perspectives and priorities • Plant functional types • To now, PFT classification has been arbitrary, without a standard parameter set • More PFTs may help to better simulate ecosystem response to change • Nitrogen cycle • Much more can be done • Plant dispersal and migration • Not considered, yet a common criticism

31. Future research perspectives and priorities • Multiple nutrient limitations • Going beyond N - deposition and cycling of P,K,S… • Agricultural crops and forest management • Crop models (PFTs) may be incoporated into a DGVM • Forest management can be prescribed • Grazers and pests • Insect outbreaks are major source of disturbance • Grazers: natural and anthropogenic

32. Future research perspectives and priorities • Simulating total atmospheric composition • Wetlands • Wetland PFTs • Modified hydrology schemes • Horizontal routing of water • Biogenic trace gases and aerosols • Emissions of BVOC, black carbon, aerosols • Models exist which may be incorporated into DGVMs

33. Thank you

34. Interannual variability