Dynamic Global Vegetation Models DGVMs

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Dynamic Global Vegetation ModelsDGVMs

• Jed O. Kaplan and Stephen SitchEuropean
  Commission Joint Research Centre, Ispra,
  ItalyMet Office (JCHMR), Wallingford, U.K.

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Acknowledgments

• TERACC
• Colin Prentice
• Marie Curie Fellowships program

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Overview

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

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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

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History and development of DGVMs

• DGVM development integrated four groups of
  processes

Plant geography
D G V M
Köppen, Box, MAPSS
Biogeochemistry
Miami, TEM, Century
Biophysics
SiB, BATS, LSM
Vegetation Dynamics
JABOWA, Foret, FORSKA
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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 (1980s,
  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

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History and development of DGVMs

• Plant Physiology and Biogeochemistry
• First global relationships between environment
  and productivity 1960s
• 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)

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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)

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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

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Fundamentals and design of DGVMs

• Model architecture
• NPP
• Plant growth and vegetation dynamics
• Hydrology
• Heterotrophic respiration and SOM dynamics
• Nitrogen cycling
• Disturbance

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

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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

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Mediterranean evergreen forest
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Crown area
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Individual density
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Southern boreal forest
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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)

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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)

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N cycling

• N content (or CN 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

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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

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Evaluating DGVMs through obeservation and
experiment

• NPP
• Remotely sensed greenness
• Atmospheric CO2 concentrations
• Runoff
• CO2 and water flux measurements
• FACE experiments

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Remotely sensed greenness
Sitch et al. 2003
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Atmospheric CO2 concentrations
Sitch et al. 2003
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Runoff
Sitch et al. 2003
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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

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Holocene carbon dynamics
Ridgwell et al. 2003
Kaplan et al. 2002
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Future C cycle projections
Cramer et al. 2001
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Global wetland methane emissions 1991-2000
Kaplan et al., in prep.
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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

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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

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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

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Thank you
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Interannual variability