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[[File:The 2011–2020 decadal mean components of the global carbon budget.png|thumb|left|2011–2020 Global carbon budget]]
[[File:The 2011–2020 decadal mean components of the global carbon budget.png|thumb|left|2011–2020 Global carbon budget]]
DGVMs are usually run in a spatially distributed mode, with simulations carried out for thousands of "cells", geographic points which are assumed to have homogeneous conditions within each cell. Simulations are carried out across a range of spatial scales, from global to landscape. Cells are usually arranged as lattice points; the distance between adjacent lattice points may be as coarse as a few degrees of latitude or longitude, or as fine as 30 arc-seconds. Simulations of the conterminous United States in the first DGVM comparison exercise (LPJ and MC1) called the VEMAP project,<ref>{{Cite journal |date=December 1995 |title=Vegetation/ecosystem modeling and analysis project- Comparing biogeography and biogeochemistry models in a continental-scale study of terrestrial ecosystem responses to climate change and C02 doubling |url=https://harvardforest1.fas.harvard.edu/publications/pdfs/VEMAP_GlobalBiogeochemicalCycles_1995.pdf |journal=Global Biogeochamical Cucles |volume=9 |issue=4 |pages=407–437}}</ref> in the 1990s used a lattice grain of one-half degree. Global simulations by the PIK group and collaborators,<ref>{{Cite journal |last=Cramer |first=Wolfgang |last2=Bondeau |first2=Alberte |last3=Woodward |first3=F. Ian |last4=Prentice |first4=I. Colin |last5=Betts |first5=Richard A. |last6=Brovkin |first6=Victor |last7=Cox |first7=Peter M. |last8=Fisher |first8=Veronica |last9=Foley |first9=Jonathan A. |last10=Friend |first10=Andrew D. |last11=Kucharik |first11=Chris |last12=Lomas |first12=Mark R. |last13=Ramankutty |first13=Navin |last14=Sitch |first14=Stephen |last15=Smith |first15=Benjamin |date=April 2001 |title=Global response of terrestrial ecosystem structure and function to CO 2 and climate change: results from six dynamic global vegetation models: ECOSYSTEM DYNAMICS, CO 2 and CLIMATE CHANGE |url=http://doi.wiley.com/10.1046/j.1365-2486.2001.00383.x |journal=Global Change Biology |language=en |volume=7 |issue=4 |pages=357–373 |doi=10.1046/j.1365-2486.2001.00383.x}}</ref> using 6 different DGVMs (HYBRID, IBIS, LPJ, SDGVM, TRIFFID, and VECODE) used the same resolution as the [[general circulation model]] (GCM) that provided the climate data, 3.75 deg longitude x 2.5 deg latitude, a total of 1631 land grid cells. Sometimes lattice distances are specified in kilometers rather than angular measure, especially for finer grains, so a project like VEMAP <ref>{{Cite web |title=Vegetation-Ecosystem Modeling and Analysis Project |url=http://www.cgd.ucar.edu/vemap/ |website=cgd.ucar.edu}}</ref> is often referred to as 50&nbsp;km grain.
DGVMs are usually run in a spatially distributed mode, with simulations carried out for thousands of "cells", geographic points which are assumed to have homogeneous conditions within each cell. Simulations are carried out across a range of spatial scales, from global to landscape. Cells are usually arranged as lattice points; the distance between adjacent lattice points may be as coarse as a few degrees of latitude or longitude, or as fine as 30 arc-seconds. Simulations of the conterminous United States in the first DGVM comparison exercise (LPJ and MC1) called the VEMAP project,<ref>{{Cite journal |date=December 1995 |title=Vegetation/ecosystem modeling and analysis project- Comparing biogeography and biogeochemistry models in a continental-scale study of terrestrial ecosystem responses to climate change and CO<sub>2</sub> doubling |url=https://harvardforest1.fas.harvard.edu/publications/pdfs/VEMAP_GlobalBiogeochemicalCycles_1995.pdf |journal=Global Biogeochamical Cucles |volume=9 |issue=4 |pages=407–437}}</ref> in the 1990s used a lattice grain of one-half degree. Global simulations by the PIK group and collaborators,<ref>{{Cite journal |last=Cramer |first=Wolfgang |last2=Bondeau |first2=Alberte |last3=Woodward |first3=F. Ian |last4=Prentice |first4=I. Colin |last5=Betts |first5=Richard A. |last6=Brovkin |first6=Victor |last7=Cox |first7=Peter M. |last8=Fisher |first8=Veronica |last9=Foley |first9=Jonathan A. |last10=Friend |first10=Andrew D. |last11=Kucharik |first11=Chris |last12=Lomas |first12=Mark R. |last13=Ramankutty |first13=Navin |last14=Sitch |first14=Stephen |last15=Smith |first15=Benjamin |date=April 2001 |title=Global response of terrestrial ecosystem structure and function to CO 2 and climate change: results from six dynamic global vegetation models: ECOSYSTEM DYNAMICS, CO 2 and CLIMATE CHANGE |url=http://doi.wiley.com/10.1046/j.1365-2486.2001.00383.x |journal=Global Change Biology |language=en |volume=7 |issue=4 |pages=357–373 |doi=10.1046/j.1365-2486.2001.00383.x}}</ref> using 6 different DGVMs (HYBRID, IBIS, LPJ, SDGVM, TRIFFID, and VECODE) used the same resolution as the [[general circulation model]] (GCM) that provided the climate data, 3.75 deg longitude x 2.5 deg latitude, a total of 1631 land grid cells. Sometimes lattice distances are specified in kilometers rather than angular measure, especially for finer grains, so a project like VEMAP <ref>{{Cite web |title=Vegetation-Ecosystem Modeling and Analysis Project |url=http://www.cgd.ucar.edu/vemap/ |website=cgd.ucar.edu}}</ref> is often referred to as 50&nbsp;km grain.


Several DGVMs appeared in the middle 1990s. The first was apparently IBIS (Foley et al., 1996), VECODE (Brovkin et al., 1997), followed by several others described below:
Several DGVMs appeared in the middle 1990s. The first was apparently IBIS (Foley et al., 1996), VECODE (Brovkin et al., 1997), followed by several others described below:

Revision as of 22:05, 2 November 2023

Example DGVM output

A Dynamic Global Vegetation Model (DGVM) is a computer program that simulates shifts in potential vegetation and its associated biogeochemical and hydrological cycles as a response to shifts in climate. DGVMs use time series of climate data and, given constraints of latitude, topography, and soil characteristics, simulate monthly or daily dynamics of ecosystem processes. DGVMs are used most often to simulate the effects of future climate change on natural vegetation and its carbon and water cycles.

Model development

DGVMs generally combine biogeochemistry, biogeography, and disturbance submodels. Disturbance is often limited to wildfires, but in principle could include any of: forest/land management decisions, windthrow, insect damage, ozone damage etc. DGVMs usually "spin up" their simulations from bare ground to equilibrium vegetation (e.g. climax community) to establish realistic initial values for their various "pools": carbon and nitrogen in live and dead vegetation, soil organic matter, etc. corresponding to a documented historical vegetation cover.

2011–2020 Global carbon budget

DGVMs are usually run in a spatially distributed mode, with simulations carried out for thousands of "cells", geographic points which are assumed to have homogeneous conditions within each cell. Simulations are carried out across a range of spatial scales, from global to landscape. Cells are usually arranged as lattice points; the distance between adjacent lattice points may be as coarse as a few degrees of latitude or longitude, or as fine as 30 arc-seconds. Simulations of the conterminous United States in the first DGVM comparison exercise (LPJ and MC1) called the VEMAP project,[1] in the 1990s used a lattice grain of one-half degree. Global simulations by the PIK group and collaborators,[2] using 6 different DGVMs (HYBRID, IBIS, LPJ, SDGVM, TRIFFID, and VECODE) used the same resolution as the general circulation model (GCM) that provided the climate data, 3.75 deg longitude x 2.5 deg latitude, a total of 1631 land grid cells. Sometimes lattice distances are specified in kilometers rather than angular measure, especially for finer grains, so a project like VEMAP [3] is often referred to as 50 km grain.

Several DGVMs appeared in the middle 1990s. The first was apparently IBIS (Foley et al., 1996), VECODE (Brovkin et al., 1997), followed by several others described below:

Groups

Several DGVMs have been developed by various research groups around the world:

The next generation of models – Earth system models (ex. CCSM,[22] ORCHIDEE,[23] JULES,[24] CTEM[25] ) – now includes the important feedbacks from the biosphere to the atmosphere so that vegetation shifts and changes in the carbon and hydrological cycles affect the climate.

DGVMs commonly simulate a variety of plant and soil physiological processes. The processes simulated by various DGVMs are summarized in the table below. Abbreviations are: NPP, net primary production; PFT, plant functional type; SAW, soil available water; LAI, leaf area index; I, solar radiation; T, air temperature; Wr, root zone water supply; PET, potential evapotranspiration; vegc, total live vegetation carbon.

process/attribute formulation/value DGVMs
shortest time step 1 hour IBIS, ED2
2 hours TRIFFID
12 hours HYBRID
1 day LPJ, SDGVM, SEIB-DGVM, MC1 fire submodel
1 month MC1 except fire submodel
1 year VECODE
photosynthesis Farquhar et al. (1980)[26] HYBRID
Farquhar et al. (1980)
Collatz et al. (1992)[27] 		IBIS, LPJ, SDGVM
Collatz et al. (1991)[28]
Collatz et al. (1992) 		TRIFFID
stomatal conductance Jarvis (1976)[29]
Stewart (1988)[30] 		HYBRID
Leuning (1995)[31] IBIS, SDGVM, SEIB-DGVM
Haxeltine & Prentice (1996)[32] LPJ
Cox et al. (1998)[33] TRIFFID
production forest NPP = f(PFT, vegc, T, SAW, P, ...)
grass NPP = f(PFT, vegc, T, SAW, P, light competition, ...) 		MC1
GPP = f(I, LAI, T, Wr, PET, CO2) LPJ
competition for light, water, and N MC1, HYBRID
for light and water LPJ, IBIS, SDGVM, SEIB-DGVM
Lotka-Volterra in fractional cover TRIFFID
Climate-dependent VECODE
establishment All PFTs establish uniformly as small individuals HYBRID
Climatically favored PFTs establish uniformly, as small individuals SEIB-DGVM
Climatically favored PFTs establish uniformly, as small LAI increment IBIS
Climatically favored PFTs establish in proportion to area available, as small individuals LPJ, SDGVM
Minimum 'seed' fraction for all PFTs TRIFFID
mortality Dependent on carbon pools HYBRID
Deterministic baseline, wind throw, fire, extreme temperatures IBIS
Deterministic baseline, self-thinning, carbon balance, fire, extreme temperatures LPJ, SEIB-DGVM, ED2
Carbon balance, wind throw, fire, extreme temperatures SDGVM
Prescribed disturbance rate for each PFT TRIFFID
Climate-dependent, based on carbon balance VECODE
Self-thinning, fire, extreme temperatures, drought MC1

References

  1. ^ "Vegetation/ecosystem modeling and analysis project- Comparing biogeography and biogeochemistry models in a continental-scale study of terrestrial ecosystem responses to climate change and CO2 doubling" (PDF). Global Biogeochamical Cucles. 9 (4): 407–437. December 1995.
  2. ^ Cramer, Wolfgang; Bondeau, Alberte; Woodward, F. Ian; Prentice, I. Colin; Betts, Richard A.; Brovkin, Victor; Cox, Peter M.; Fisher, Veronica; Foley, Jonathan A.; Friend, Andrew D.; Kucharik, Chris; Lomas, Mark R.; Ramankutty, Navin; Sitch, Stephen; Smith, Benjamin (April 2001). "Global response of terrestrial ecosystem structure and function to CO 2 and climate change: results from six dynamic global vegetation models: ECOSYSTEM DYNAMICS, CO 2 and CLIMATE CHANGE". Global Change Biology. 7 (4): 357–373. doi:10.1046/j.1365-2486.2001.00383.x.
  3. ^ "Vegetation-Ecosystem Modeling and Analysis Project". cgd.ucar.edu.
  4. ^ Sitch S, Smith B, Prentice IC, Arneth A, Bondeau A, Cramer W, Kaplan JO, Levis S, Lucht W, Sykes MT, Thonicke K, Venevsky S 2003. Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ Dynamic Global Vegetation Model. Global Change Biology 9, 161–185.
  5. ^ "LPJ & LPJML Web Distribution Portal — PIK Research Portal". Archived from the original on 2010-12-13. Retrieved 2011-01-08.
  6. ^ Foley, Jonathan A.; Prentice, I. Colin; Ramankutty, Navin; Levis, Samuel; Pollard, David; Sitch, Steven; Haxeltine, Alex (December 1996). "An integrated biosphere model of land surface processes, terrestrial carbon balance, and vegetation dynamics". Global Biogeochemical Cycles. 10 (4): 603–628. doi:10.1029/96GB02692.
  7. ^ Kucharik, Christopher J.; Foley, Jonathan A.; Delire, Christine; Fisher, Veronica A.; Coe, Michael T.; Lenters, John D.; Young-Molling, Christine; Ramankutty, Navin; Norman, John M.; Gower, Stith T. (September 2000). "Testing the performance of a dynamic global ecosystem model: Water balance, carbon balance, and vegetation structure". Global Biogeochemical Cycles. 14 (3): 795–825. doi:10.1029/1999GB001138.
  8. ^ "ORNL DAAC for Biogeochemical Dynamics". daac.ornl.gov. Retrieved 2023-09-07.
  9. ^ Bachelet, Dominique; Lenihan, James M.; Daly, Christopher; Neilson, Ronald P.; Ojima, Dennis S.; Parton, William J. (2001). "MC1: a dynamic vegetation model for estimating the distribution of vegetation and associated carbon, nutrients, and water—technical documentation. Version 1.0". Gen. Tech. Rep. PNW-GTR-508. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. 95 p. 508. doi:10.2737/PNW-GTR-508.
  10. ^ Daly, Christopher; Bachelet, Dominique; Lenihan, James M.; Neilson, Ronald P.; Parton, William; Ojima, Dennis (2000). "Dynamic Simulation of Tree-Grass Interactions for Global Change Studies". Ecological Applications. 10 (2): 449–469. doi:10.2307/2641106. ISSN 1051-0761.
  11. ^ "MC1 Dynamic Vegetation Model". fsl.orst.edu/dgvm. 2018-06-20. Retrieved 2023-09-07.
  12. ^ Friend, A. D.; Stevens, A. K.; Knox, R. G.; Cannell, M. G. R. (1997-02-14). "A process-based, terrestrial biosphere model of ecosystem dynamics (Hybrid v3.0)". Ecological Modelling. 95 (2): 249–287. doi:10.1016/S0304-3800(96)00034-8. ISSN 0304-3800.
  13. ^ Woodward, F. I.; Lomas, M. R.; Betts, R. A. (1998-01-29). Beerling, D. J.; Chaloner, W. G.; Woodward, F. I. (eds.). "Vegetation-climate feedbacks in a greenhouse world". Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences. 353 (1365): 29–39. doi:10.1098/rstb.1998.0188. ISSN 0962-8436. PMC 1692170.
  14. ^ Sato, Hisashi. "Spatially Explicit Individual Based - Dynamic Global Vegetation Model". Yokohama Institute for Earth Sciences seib-dgvm.com. Retrieved 2023-09-07.
  15. ^ "Hadley Centre: Carbon cycle models". www.metoffice.gov.uk. Archived from the original on 2001-08-22.
  16. ^ Brovkin, Victor; Ganopolski, Andrei; Svirezhev, Yuri (1997-08-15). "A continuous climate-vegetation classification for use in climate-biosphere studies". Ecological Modelling. 101 (2): 251–261. doi:10.1016/S0304-3800(97)00049-5. ISSN 0304-3800.
  17. ^ Levis, Samuel; Bonan, Gordon; Vertenstein, Mariana; Oleson, Keith (2004). The Community Land Model's Dynamic Global Vegetation Model (CLM-DGVM): Technical description and user's guide (Report). UCAR/NCAR. pp. 1505 KB. doi:10.5065/d6p26w36.
  18. ^ Moorcroft, P. R.; Hurtt, G. C.; Pacala, S. W. (November 2001). "A METHOD FOR SCALING VEGETATION DYNAMICS: THE ECOSYSTEM DEMOGRAPHY MODEL (ED)". Ecological Monographs. 71 (4): 557–586. doi:10.1890/0012-9615(2001)071[0557:AMFSVD]2.0.CO;2. ISSN 0012-9615.
  19. ^ Medvigy, D.; Wofsy, S. C.; Munger, J. W.; Hollinger, D. Y.; Moorcroft, P. R. (March 2009). "Mechanistic scaling of ecosystem function and dynamics in space and time: Ecosystem Demography model version 2". Journal of Geophysical Research: Biogeosciences. 114 (G1). doi:10.1029/2008JG000812. ISSN 0148-0227.
  20. ^ Zeng, Ning (September 2003). "Glacial-interglacial atmospheric CO2 change—The glacial burial hypothesis". Advances in Atmospheric Sciences. 20 (5): 677–693. Bibcode:2003AdAtS..20..677N. doi:10.1007/BF02915395. S2CID 15094502.
  21. ^ "UMD Earth system model". atmos.umd.edu.
  22. ^ "Community Climate System Model (CCSM) | Community Earth System Model". www.cesm.ucar.edu. Retrieved 2023-09-07.
  23. ^ "French Global Land Surface Model - Home". Archived from the original on 2008-11-11. Retrieved 2008-11-23.
  24. ^ "Joint UK Land Environment Simulator - JULES". www.jchmr.org. Archived from the original on 2007-02-02.
  25. ^ "The Canadian Terrestrial Ecosystem Model (CTEM)". CLASSIC. 2019-06-03. Retrieved 2023-09-07.
  26. ^ Farquhar, G. D.; von Caemmerer, S.; Berry, J. A. (1980). "A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species". Planta. 149 (1). Springer Science and Business Media LLC: 78–90. doi:10.1007/bf00386231. ISSN 0032-0935.
  27. ^ Collatz, GJ; Ribas-Carbo, M; Berry, JA (1992). "Coupled Photosynthesis-Stomatal Conductance Model for Leaves of C4 Plants". Functional Plant Biology. 19 (5). CSIRO Publishing: 519. doi:10.1071/pp9920519. ISSN 1445-4408.
  28. ^ Collatz, G.James; Ball, J.Timothy; Grivet, Cyril; Berry, Joseph A (1991). "Physiological and environmental regulation of stomatal conductance, photosynthesis and transpiration: a model that includes a laminar boundary layer". Agricultural and Forest Meteorology. 54 (2–4). Elsevier BV: 107–136. doi:10.1016/0168-1923(91)90002-8. ISSN 0168-1923.
  29. ^ "The interpretation of the variations in leaf water potential and stomatal conductance found in canopies in the field". Philosophical Transactions of the Royal Society of London. B, Biological Sciences. 273 (927). The Royal Society: 593–610. 1976-02-26. doi:10.1098/rstb.1976.0035. ISSN 0080-4622.
  30. ^ Stewart, J.B (1988). "Modelling surface conductance of pine forest". Agricultural and Forest Meteorology. 43 (1). Elsevier BV: 19–35. doi:10.1016/0168-1923(88)90003-2. ISSN 0168-1923.
  31. ^ LEUNING, R. (1995). "A critical appraisal of a combined stomatal-photosynthesis model for C3 plants". Plant, Cell and Environment. 18 (4). Wiley: 339–355. doi:10.1111/j.1365-3040.1995.tb00370.x. ISSN 0140-7791.
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  33. ^ Cox, P.M; Huntingford, C; Harding, R.J (1998). "A canopy conductance and photosynthesis model for use in a GCM land surface scheme". Journal of Hydrology. 212–213. Elsevier BV: 79–94. doi:10.1016/s0022-1694(98)00203-0. ISSN 0022-1694.
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