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Dynamic Global Vegetation Models DGVMs. Jed O. Kaplan* and Stephen Sitch° *European Commission Joint Research Centre, Ispra, Italy °Met Office (JCHMR), Wallingford, U.K. Acknowledgments. TERACC Colin Prentice Marie Curie Fellowships program. Overview. History and development

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Dynamic Global Vegetation Models DGVMs

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dynamic global vegetation models dgvms

Dynamic Global Vegetation ModelsDGVMs

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

  • Colin Prentice
  • Marie Curie Fellowships program
  • History and development
  • Fundamentals and model design
  • Evaluation
  • Example applications
  • Future research perspectives
history and development of dgvms
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
history and development of dgvms5
History and development of DGVMs
  • DGVM development integrated four groups of processes

Plant geography

Köppen, Box, MAPSS






Miami, TEM, Century



Vegetation Dynamics


history and development of dgvms6
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
history and development of dgvms7
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)
history and development of dgvms8
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)
history and development of dgvms9
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
fundamentals and design of dgvms
Fundamentals and design of DGVMs
  • Model architecture
  • NPP
  • Plant growth and vegetation dynamics
  • Hydrology
  • Heterotrophic respiration and SOM dynamics
  • Nitrogen cycling
  • Disturbance
dgvm architecture
DGVM architecture

Bonan et al. 2003



Minutes to day

  • 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
growth and dynamics
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
  • 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)
som dynamics
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)
n cycling
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
  • 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
evaluating dgvms through obeservation and experiment
Evaluating DGVMs through obeservation and experiment
  • NPP
  • Remotely sensed greenness
  • Atmospheric CO2 concentrations
  • Runoff
  • CO2 and water flux measurements
  • FACE experiments
remotely sensed greenness
Remotely sensed greenness

Sitch et al. 2003


Sitch et al. 2003

widespread applications
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
holocene carbon dynamics
Holocene carbon dynamics

Ridgwell et al. 2003

Kaplan et al. 2002

future c cycle projections
Future C cycle projections

Cramer et al. 2001

future research perspectives and priorities
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
future research perspectives and priorities31
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
future research perspectives and priorities32
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