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Dynamic Global Vegetation Models DGVMs: 

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 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 DGVMs: 

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

History and development of DGVMs: 

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 DGVMs: 

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 DGVMs: 

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 DGVMs: 

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 Daily Annual


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

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

Mediterranean evergreen forest: 

Mediterranean evergreen forest

Crown area: 

Crown area

Individual density: 

Individual density

Southern boreal forest: 

Southern boreal forest


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)

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


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

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

Atmospheric CO2 concentrations: 

Atmospheric CO2 concentrations Sitch et al. 2003


Runoff 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

Global wetland methane emissions 1991-2000: 

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

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 priorities: 

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 priorities: 

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

Thank you: 

Thank you

Interannual variability: 

Interannual variability

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