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Impact model: VISIT

Sector
Biomes
Region
global

VISIT is a process-based terrestrial ecosystem model, focusing on atmosphere–ecosystem trace gas exchange. VISIT is one of the global models following the ISIMIP3a protocol which form the base of simulations for the ISIMIP3a biome sector outputs.

Information for the model VISIT is provided for the simulation rounds shown in the tabs below. Click on the appropriate tab to get the information for the simulation round you are interested in.

Confirm data for simulation round ISIMIP3b
Person responsible for model simulations in this simulation round
Akihiko Ito: itoh@nies.go.jp, 0000-0001-5265-0791, National Institute for Environmental Studies (NIES), Japan (Japan)
Kazuya Nishina: nishina.kazuya@nies.go.jp, 0000-0002-8820-1282, National Institute for Environmental Studies (NIES) & Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Japan (Japan)
Output Data
Experiments: (*) ssp585_nat_default, historical_2015soc_default, ssp126_2015soc_default, ssp126_nat_default, picontrol_histsoc_default, ssp585_nat_2015co2, ssp370_2015soc_2015co2, ssp370_2015soc_default, picontrol_2015soc_default, historical_nat_default, ssp126_2015soc_2015co2, picontrol_1850soc_default, ssp585_2015soc_default, ssp370_nat_default, picontrol_nat_default, ssp585_2015soc_2015co2, historical_histsoc_default
Climate Drivers: GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, UKESM1-0-LL
Date: 2021-10-12
Basic information
Model Version: Monthly ISIMIP3b
Simulation Round Specific Description: To be available publicly
Reference Paper: Main Reference: Ito, A. et al. Disequilibrium of terrestrial ecosystem CO2 budget caused by disturbance-induced emissions and non-CO2 carbon export flows: a global model assessment. Earth System Dynamics,10,685–709,
Resolution
Spatial aggregation: regular grid
Horizontal resolution: 0.5°x0.5°
Temporal resolution of input data: climate variables: monthly
Temporal resolution of input data: co2: annual
Temporal resolution of input data: land use/land cover: annual
Temporal resolution of input data: soil: constant
Input data
Simulated atmospheric climate data sets used: MRI-ESM2-0 (hist-nat, ssp119, ssp245, ssp460, ssp534-over), MPI-ESM1-2-HR (ssp245), IPSL-CM6A-LR (ssp119, ssp245, ssp460), GFDL-ESM4 (ssp245), UKESM1-0-LL (ssp119, ssp245)
Climate variables: huss, tas, rsds, pr
Spin-up
Spin-up design: Spin-up of 3000 years was performed at all grids, using constant atmospheric CO2, land use at the first simulation year, and repeated first 50-year climate data.
Natural Vegetation
Natural vegetation partition: Most dominant natural vegetation types was considered at each grid.
Natural vegetation dynamics: No vegetation dynamics was considered.
Natural vegetation cover dataset: Ramankutty, N. & Foley, J. A. Estimating historical changes in global land cover: croplands from 1700 to 1992. Global Biogeochem. Cycles 13, 997-1027 (1999).
Soil layers: Top and bottom layers were considered. Top layer represents surface litter, and bottom represents mineral soil layer.
Management & Adaptation Measures
Management: In cropland fraction, agricultural management from planting to harvesting was considered.
Extreme Events & Disturbances
Key challenges: Wildfire and its impacts on carbon cycle was simulated using an empirical scheme.
Model set-up specifications
How do you simulate bioenergy? i.e. what pft do you simulate on bioenergy land?: No bioenergy-related process was included.
How do you simulate the transition from cropland to bioenergy?: No transition from cropland to bioenergy was considered.
How do you simulate pasture (which pft)?: Pasture was implicitly included into grasslands.
Key model processes
Dynamic vegetation: No.
Nitrogen limitation: VISIT contains nitrogen cycle, but nitrogen limitation on production and decomposition was not activated (off) in the present simulations.
Co2 effects: CO2 affects directly stomatal conductance and photosynthetic rates, leading to many indirect impacts on carbon and hydrological cycles.
Light interception: Canopy light interception was calculated using Lambert-Beer law as a function of leaf area index and attenuation constant.
Light utilization: Leaf-level light utilization of photosynthesis was used.
Phenology: In deciduous forests and grasslands, leaf phenology was simulated as functions of cumulative temperature and drought stress.
Water stress: Water stress on photosynthesis and microbial decomposition were included in empirical manners.
Heat stress: No heat stress was considered, except photosynthetic decline above optimal temperature.
Evapo-transpiration approach: Penman-Monteith with soil water limitation.
Differences in rooting depth: Biome-specific rooting depth by Zeng (2001). Zeng, X. Global vegetation root distribution for land modeling. Journal of Hydrometeorology 2, 525-530 (2001).
Root distribution over depth: Biome-specific rooting depth by Zeng (2001). Zeng, X. Global vegetation root distribution for land modeling. Journal of Hydrometeorology 2, 525-530 (2001).
Closed energy balance: Not strictly, because sensible heat and ground heat fluxes were not calculated.
Coupling/feedback between soil moisture and surface temperature: Soil physical coupling / feedback was not simulated.
Latent heat: Simply converted from evapotranspiration rate.
Sensible heat: Not calculated.
How do you compute soil organic carbon during land use (do you mix the previous pft soc into agricultural soc)?: The present model did not mix the previous SOC into agricultural SOC. They were separately simulated, and then land-use change should dilute or condense carbon stock per area.
Do you separate soil organic carbon in pasture from natural grass?: No.
Do you harvest npp of crops? do you including grazing? how does harvested npp decay?: In cropland, a certain fraction (yield) of crop was harvested, but we did not consider their decay.
How do you to treat biofuel npp and biofuel harvest?: No biofuel.
Does non-harvested crop npp go to litter in your output?: Yes, a part of crop residue went to litter.
Causes of mortality in vegetation models
Age/senescence: No age structure was considered. Senescence was simulated as turnover of biomass.
Fire: Wild fire was considered in an empirical manner, and certain fraction of biomass was burnt.
Drought: Drought affects vegetation productivity but did not increase mortality.
Insects: No.
Storm: No.
Stochastic random disturbance: No.
Other: No.
NBP components
Fire: Yes, an empirical fire scheme was used.
Land-use change: Yes, an empirical land-use scheme was used.
Harvest: Wood harvest and crop harvest were considered. Their fate was not considered (i.e., go out from system).
Species / Plant Functional Types (PFTs)
List of species / pfts: 0 water; 1 tropical & subtropical evergreen forest; 2 tropical montane forest; 3 tropical & subtropical dry forest; 4 mid-latitude mixed forest; 5 mid-latitude broad-leaved forest; 6 semiarid wood or low forest; 7 coniferous evergreen forest; 8 southern taiga; 9 main evergreen taiga; 10 main deciduous taiga; 11 northern evergreen taiga; 12 northern deciduous taiga; 13 second growth woods; 14 second growth field; 15 succulent & thorn wood; 16 tropical savanna, woodland; 17 mediterranean-type dry wood; 18 heath & moorland; 19 warm or hot shrub & grassland; 20 tibetan meadow & siberian highland; 21 tundra; 22 wooded tundra; 23 warm or hot wetlands; 24 cool bog & mire; 25 shore & hinterland; 26 cool semi-desert scrub; 27 non-polar desert; 28 non-polar sand desert; 29 paddyland; 30 cool cropland; 31 warm cropland; 32 irrigated; 33 antarctica;
Model output specifications
Output format: NETCDF, per gird-cell
Output per pft?: No.
Land-use change implementation
Is crop harvest included? if so, how?: Yes, a certain fraction (harvest yield) of existing biomass.
Is cropland soil management included? if so, how?: No.
Is grass harvest included? if so, how?: No.
Is shifting cultivation included?: No.
Is wood harvest included? if so, how?: No, in the present simulation. By using LUH2, we could include wood harvest in other studies.
Which transition rules are applied to decide where agriculture is conducted?: Not applicable.
Carbon-cycle benchmarking
Does your model reach a (near) steady state after spin up (characterized by nbp of < 0.2 pgc y-1)? (yes/no, provide number): Yes. in 1860-1879, NBP of < 0.2 PgC y-1.
What is your modeled nbp for the 1990-2000 decade? is it within 1.2 +/- 0.8 gtc/yr (1-sigma) of observed data from o2/n2 trends (keeling and manning 2014) for 1990-1999 (yes/no, provide number): Mean NBP in 1990-2000 was 1.95 Pg C yr-1.
Fire modules
Aggregation of reported burnt area: No aggregation.
Land-use classes allowed to burn: Natural vegetation.
Included fire-ignition factors: Natural stochastic ignition on the basis of fuel dryness.
Is fire ignition implemented as a random process?: No (statisic).
Is human influence on fire ignition and/or suppression included? how?: No.
How is fire spread/extent modelled?: Empirically, functions of fuel load and dryness.
Are deforestation or land clearing fires included?: Implicitly, land-use induced fast turnover (within 1 year) corresponds to human fires.
What is the minimum burned area fraction at grid level?: No minimum burnt area was considered.
Confirm data for simulation round ISIMIP3a
Person responsible for model simulations in this simulation round
Akihiko Ito: itoh@nies.go.jp, 0000-0001-5265-0791, National Institute for Environmental Studies (NIES), Japan (Japan)
Kazuya Nishina: nishina.kazuya@nies.go.jp, 0000-0002-8820-1282, National Institute for Environmental Studies (NIES) & Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Japan (Japan)
Output Data
Experiments: (*) counterclim_nat_default, obsclim_1901soc_1901co2, obsclim_histsoc_default, obsclim_2015soc_default, obsclim_nat_default, obsclim_2015soc_1901co2, obsclim_1901soc_default, obsclim_histsoc_1901co2, counterclim_histsoc_default, counterclim_1901soc_default, counterclim_2015soc_default
Climate Drivers: GSWP3-W5E5
Date: 2022-03-08
Basic information
Model Version: Application year 2022
Simulation Round Specific Description: To be available publicly
Reference Paper: Main Reference: Ito, A. et al. Disequilibrium of terrestrial ecosystem CO2 budget caused by disturbance-induced emissions and non-CO2 carbon export flows: a global model assessment. Earth System Dynamics,10,685–709,
Resolution
Spatial aggregation: regular grid
Horizontal resolution: 0.5°x0.5°
Temporal resolution of input data: climate variables: monthly
Temporal resolution of input data: co2: annual
Temporal resolution of input data: land use/land cover: annual
Temporal resolution of input data: soil: constant
Input data
Observed atmospheric climate data sets used: GSWP3-W5E5 (ISIMIP2a)
Land use data sets used: Historical, gridded land use
Other human influences data sets used: N-Fertilizer (ISIMIP2), Nitrogen deposition
Climate variables: huss, tas, rsds, pr
Spin-up
Was a spin-up performed?: Yes
Spin-up design: Spin-up of 3000 years was performed at all grids, using constant atmospheric CO2, land use at the first simulation year, and repeated first 50-year climate data.
Natural Vegetation
Natural vegetation partition: Most dominant natural vegetation types was considered at each grid.
Natural vegetation dynamics: No vegetation dynamics was considered.
Natural vegetation cover dataset: Ramankutty, N. & Foley, J. A. Estimating historical changes in global land cover: croplands from 1700 to 1992. Global Biogeochem. Cycles 13, 997-1027 (1999).
Soil layers: Top and bottom layers were considered. Top layer represents surface litter, and bottom represents mineral soil layer.
Management & Adaptation Measures
Management: In cropland fraction, agricultural management from planting to harvesting was considered.
Extreme Events & Disturbances
Key challenges: Wildfire and its impacts on carbon cycle was simulated using an empirical scheme.
Model set-up specifications
How do you simulate bioenergy? i.e. what pft do you simulate on bioenergy land?: No bioenergy-related process was included.
How do you simulate the transition from cropland to bioenergy?: No transition from cropland to bioenergy was considered.
How do you simulate pasture (which pft)?: Pasture was implicitly included into grasslands.
Key model processes
Dynamic vegetation: No.
Nitrogen limitation: VISIT contains nitrogen cycle, but nitrogen limitation on production and decomposition was not activated (off) in the present simulations.
Co2 effects: CO2 affects directly stomatal conductance and photosynthetic rates, leading to many indirect impacts on carbon and hydrological cycles.
Light interception: Canopy light interception was calculated using Lambert-Beer law as a function of leaf area index and attenuation constant.
Light utilization: Leaf-level light utilization of photosynthesis was used.
Phenology: In deciduous forests and grasslands, leaf phenology was simulated as functions of cumulative temperature and drought stress.
Water stress: Water stress on photosynthesis and microbial decomposition were included in empirical manners.
Heat stress: No heat stress was considered, except photosynthetic decline above optimal temperature.
Evapo-transpiration approach: Penman-Monteith with soil water limitation.
Differences in rooting depth: Biome-specific rooting depth by Zeng (2001). Zeng, X. Global vegetation root distribution for land modeling. Journal of Hydrometeorology 2, 525-530 (2001).
Root distribution over depth: Biome-specific rooting distribution by Zeng (2001). Zeng, X. Global vegetation root distribution for land modeling. Journal of Hydrometeorology 2, 525-530 (2001).
Closed energy balance: Not strictly, because sensible heat and ground heat fluxes were not calculated.
Coupling/feedback between soil moisture and surface temperature: Soil physical coupling / feedback was not simulated.
Latent heat: Simply converted from evapotranspiration rate.
Sensible heat: Not calculated.
How do you compute soil organic carbon during land use (do you mix the previous pft soc into agricultural soc)?: The present model did not mix the previous SOC into agricultural SOC. They were separately simulated, and then land-use change should dilute or condense carbon stock per area.
Do you separate soil organic carbon in pasture from natural grass?: No.
Do you harvest npp of crops? do you including grazing? how does harvested npp decay?: In cropland, a certain fraction (yield) of crop was harvested, but we did not consider their decay.
How do you to treat biofuel npp and biofuel harvest?: No biofuel.
Does non-harvested crop npp go to litter in your output?: Yes, a part of crop residue went to litter.
Causes of mortality in vegetation models
Age/senescence: No age structure was considered. Senescence was simulated as turnover of biomass.
Fire: Wild fire was considered in an empirical manner, and certain fraction of biomass was burnt.
Drought: Drought affects vegetation productivity but did not increase mortality.
Insects: No.
Storm: No.
Stochastic random disturbance: No.
Other: No.
NBP components
Fire: Yes, an empirical fire scheme was used.
Land-use change: Yes, an empirical land-use scheme was used.
Harvest: Wood harvest and crop harvest were considered. Their fate was not considered (i.e., go out from system).
Species / Plant Functional Types (PFTs)
List of species / pfts: 0 water; 1 tropical & subtropical evergreen forest; 2 tropical montane forest; 3 tropical & subtropical dry forest; 4 mid-latitude mixed forest; 5 mid-latitude broad-leaved forest; 6 semiarid wood or low forest; 7 coniferous evergreen forest; 8 southern taiga; 9 main evergreen taiga; 10 main deciduous taiga; 11 northern evergreen taiga; 12 northern deciduous taiga; 13 second growth woods; 14 second growth field; 15 succulent & thorn wood; 16 tropical savanna, woodland; 17 mediterranean-type dry wood; 18 heath & moorland; 19 warm or hot shrub & grassland; 20 tibetan meadow & siberian highland; 21 tundra; 22 wooded tundra; 23 warm or hot wetlands; 24 cool bog & mire; 25 shore & hinterland; 26 cool semi-desert scrub; 27 non-polar desert; 28 non-polar sand desert; 29 paddyland; 30 cool cropland; 31 warm cropland; 32 irrigated; 33 antarctica;
Model output specifications
Output format: NETCDF, per gird-cell
Output per pft?: No.
Land-use change implementation
Is crop harvest included? if so, how?: Yes, a certain fraction (harvest yield) of existing biomass.
Is cropland soil management included? if so, how?: No.
Is grass harvest included? if so, how?: No.
Is shifting cultivation included?: No.
Is wood harvest included? if so, how?: No, in the present simulation. By using LUH2, we could include wood harvest in other studies.
Which transition rules are applied to decide where agriculture is conducted?: Not applicable.
Carbon-cycle benchmarking
Does your model reach a (near) steady state after spin up (characterized by nbp of < 0.2 pgc y-1)? (yes/no, provide number): Yes. in 1860-1879, NBP of < 0.2 PgC y-1.
What is your modeled nbp for the 1990-2000 decade? is it within 1.2 +/- 0.8 gtc/yr (1-sigma) of observed data from o2/n2 trends (keeling and manning 2014) for 1990-1999 (yes/no, provide number): Mean NBP in 1990-2000 was 1.95 Pg C yr-1.
Fire modules
Aggregation of reported burnt area: No aggregation.
Land-use classes allowed to burn: Natural vegetation.
Included fire-ignition factors: Natural stochastic ignition on the basis of fuel dryness.
Is fire ignition implemented as a random process?: No (statisic).
Is human influence on fire ignition and/or suppression included? how?: No.
How is fire spread/extent modelled?: Empirically, functions of fuel load and dryness.
Are deforestation or land clearing fires included?: Implicitly, land-use induced fast turnover (within 1 year) corresponds to human fires.
What is the minimum burned area fraction at grid level?: No minimum burnt area was considered.
Person responsible for model simulations in this simulation round
Akihiko Ito: itoh@nies.go.jp, 0000-0001-5265-0791, National Institute for Environmental Studies (NIES), Japan (Japan)
Output Data
Experiments: I, II, IIa, III, IV, V, VI, VII
Climate Drivers: None
Date: 2018-11-06
Basic information
Model Version: VISITa
Model Output License: CC BY 4.0
Reference Paper: Other References:
Resolution
Spatial aggregation: regular grid
Horizontal resolution: 0.5°x0.5°
Temporal resolution of input data: climate variables: monthly
Temporal resolution of input data: co2: annual
Temporal resolution of input data: land use/land cover: annual
Temporal resolution of input data: soil: constant
Input data
Simulated atmospheric climate data sets used: IPSL-CM5A-LR, HadGEM2-ES, GFDL-ESM2M, MIROC5
Observed atmospheric climate data sets used: EWEMBI
Land use data sets used: Future land-use patterns, Historical, gridded land use (HYDE 3.2)
Climate variables: hurs, tas, rsds, pr
Spin-up
Was a spin-up performed?: Yes
Spin-up design: Using climate forcing data (e.g., GSWP3) for the period 1901–1930, the spin-up simulation was conducted for 3000 years, so that carbon budget at each grid comes sufficiently close to an equilibrium. The same protocol was applied for all ISI-MIP simulations.
Natural Vegetation
Natural vegetation partition: The present version of VISITa considers only the dominant natural vegetation type for each grid, using a prescribed natural vegetation data.
Natural vegetation cover dataset: Olson's vegetation map (Olson et al., 1983) with modification by the potential vegetation data by Ramankutty and Foley (1999)
Extreme Events & Disturbances
Key challenges: In VISIT, drought-induced mortality and windthrow are not included, although they have potentially great impacts on long-term ecosystem dynamics. Only fire effect was included in a simple and area-avaraged manner.
Additional comments: This model accounts for lateral displacement of soil organic carbon by erosion and leaching of dissolved organic carbon. So, these small terms should be included to close the carbon balance in the model.
Model set-up specifications
How do you simulate bioenergy? i.e. what pft do you simulate on bioenergy land?: Not applicable
How do you simulate the transition from cropland to bioenergy?: Not applicable
How do you simulate pasture (which pft)?: No
Key model processes
Dynamic vegetation: No
Nitrogen limitation: No
Co2 effects: Empirical (Michaelis-Menten type) CO2 response
Light interception: Lambert-Beer type light interception
Light utilization: Non linear, Michaelis-Menten type, light response curve
Phenology: A simple phenology scheme. Growing-degree-day is used to determine leaf onset in deciduous biomes.
Water stress: Empirical (Miechaelis-Menten type) water-limitation curve
Heat stress: Photosynthesis rate drops down under heat stree
Evapo-transpiration approach: Penman-Monteith equation, with a limitation by soil water availability
Differences in rooting depth: Considered: Zeng, X. (2001), Global vegetation root distribution for land modeling, Journal of Hydrometeorology, 2, 525-530.
Root distribution over depth: Considered: Zeng, X. (2001), Global vegetation root distribution for land modeling, Journal of Hydrometeorology, 2, 525-530.
Closed energy balance: Not exactly
Coupling/feedback between soil moisture and surface temperature: No
Latent heat: Converted from evapotranspiration
Sensible heat: Not explicitly (i.e. net radiation minus latent heat)
Do you separate soil organic carbon in pasture from natural grass?: No
How do you to treat biofuel npp and biofuel harvest?: Not applicable
Does non-harvested crop npp go to litter in your output?: Yes. A certain amount of non-harvested NPP goes to litter.
Causes of mortality in vegetation models
Age/senescence: Yes (base senescence rate).
Fire: Burnt area is estimated for each grid at monthly time-step, using the parameterization of Thonicke et al. (2001). Biomass burning emission is estimated using carbon stock simulated by the VISIT model and emission factors by Hoelzemann et al. (2004). We assume that fire occurs only in natural biomes.
Drought: No
Insects: No
Storm: No
Stochastic random disturbance: No
Other: No
NBP components
Fire: Prognostic fire scheme (Thonicke et al., 2001) is implemented.
Land-use change: Land-use change is included. Associated emissions are calculated using the Grand-Slam Protocol.
Harvest: No. (If harvest data is available, the model can consider this effect.)
Species / Plant Functional Types (PFTs)
List of species / pfts: VISIT uses biome types (after Olson et al. 1983) instead of PFTs as listed below: 0:water, 1:tropical & subtropical evergreen forest, 2:tropical montane forest, 3:tropical & subtropical dry forest, 4:mid-latitude mixed forest, 5:mid-latitude broad-leaved forest, 6:semiarid wood or low forest, 7:coniferous evergreen forest, 8:southern taiga, 9:main evergreen taiga, 10:main deciduous taiga, 11:northern evergreen taiga, 12:northern deciduous taiga, 13:second growth woods, 14:second growth field, 15:succulent & thorn wood, 16:tropical savanna, woodland, 17:mediterranean-type dry wood, 18:heath & moorland, 19:warm or hot shrub & grassland, 20:tibetan meadow & siberian highland, 21:tundra, 22:wooded tundra, 23:warm or hot wetlands, 24:cool bog & mire, 25:shore & hinterland, 26:cool semi-desert scrub, 27:non-polar desert, 28:non-polar sand desert, 29:paddyland, 30:cool cropland, 31:warm cropland, 32:irrigated, 33:antarctica
Model output specifications
Output format: Grid-cell
Output per pft?: No
Land-use change implementation
Is crop harvest included? if so, how?: Yes. A constant harvest index is used.
Is cropland soil management included? if so, how?: No.
Is grass harvest included? if so, how?: No.
Is shifting cultivation included?: No.
Is wood harvest included? if so, how?: This process is data-driven. In ISI-MIP2b experiments, this process was unactivated.
Carbon-cycle benchmarking
Does your model reach a (near) steady state after spin up (characterized by nbp of < 0.2 pgc y-1)? (yes/no, provide number): Yes. About 0.1 Pg C y-1.
What is your modeled nbp for the 1990-2000 decade? is it within 1.2 +/- 0.8 gtc/yr (1-sigma) of observed data from o2/n2 trends (keeling and manning 2014) for 1990-1999 (yes/no, provide number): Average NBP in 1990-2000 is 1.0 Pg C y-1, when driven by CRU TS3.25.
Fire modules
Aggregation of reported burnt area: Simulated annual burned area is disaggregated into monthly values.
Land-use classes allowed to burn: Natural vegetation, ISIMIP-Pasture (managed pastures, rangeland) and urban areas are allowed to burn but not cropland. Urban Land is treated as natural vegetation.
Included fire-ignition factors: Natural ignition based on availability of fuel, combustibility of fuel (soil moisture)
Is fire ignition implemented as a random process?: No.
Is human influence on fire ignition and/or suppression included? how?: No.
How is fire spread/extent modelled?: Fire extent is an empirical function of soil moisture and fuel load.
Are deforestation or land clearing fires included?: No.
What is the minimum burned area fraction at grid level?: 0
Person responsible for model simulations in this simulation round
Akihiko Ito: itoh@nies.go.jp, 0000-0001-5265-0791, National Institute for Environmental Studies (NIES), Japan (Japan)
Kazuya Nishina: nishina.kazuya@nies.go.jp, 0000-0002-8820-1282, National Institute for Environmental Studies (NIES) & Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Japan (Japan)
Output Data
Experiments: historical
Climate Drivers: None
Date: 2016-05-17
Basic information
Model Version: VISITa
Model Output License: CC BY 4.0
Reference Paper: Other References:
Resolution
Spatial aggregation: regular grid
Horizontal resolution: 0.5°x0.5°
Temporal resolution of input data: climate variables: monthly
Temporal resolution of input data: co2: annual
Temporal resolution of input data: land use/land cover: annual
Temporal resolution of input data: soil: constant
Input data
Observed atmospheric climate data sets used: GSWP3, PGMFD v2.1 (Princeton), WATCH (WFD), WATCH-WFDEI
Climate variables: tas, rhs, rsds, pr
Spin-up
Was a spin-up performed?: Yes
Spin-up design: Using climate forcing data (e.g., GSWP3) for the period 1901–1930, the spin-up simulation was conducted for 3000 years, so that carbon budget at each grid comes sufficiently close to an equilibrium. The same protocol was applied for all ISI-MIP simulations.
Natural Vegetation
Natural vegetation partition: The present version of VISITa considers only the dominant natural vegetation type for each grid, using a prescribed natural vegetation data.
Natural vegetation cover dataset: Olson's vegetation map (Olson et al., 1983) with modification by the potential vegetation data by Ramankutty and Foley (1999)
Extreme Events & Disturbances
Key challenges: In VISIT, drought-induced mortality and windthrow are not included, although they have potentially great impacts on long-term ecosystem dynamics. Only fire effect was included in a simple and area-avaraged manner.
Additional comments: This model accounts for lateral displacement of soil organic carbon by erosion and leaching of dissolved organic carbon. So, these small terms should be included to close the carbon balance in the model.
Key model processes
Co2 effects: Empirical (Miechaelis-Menten type) CO2 response
Light interception: Lambert-Beer type light interception
Phenology: A simple phenology scheme. Growing-degree-day is used to determine leaf onset in deciduous biomes.
Water stress: Empirical (Miechaelis-Menten type) water-limitation curve
Evapo-transpiration approach: Penman-Monteith equation, with a limitation by soil water availability
Differences in rooting depth: Considered: Zeng, X. (2001), Global vegetation root distribution for land modeling, Journal of Hydrometeorology, 2, 525-530.
Root distribution over depth: Considered: Zeng, X. (2001), Global vegetation root distribution for land modeling, Journal of Hydrometeorology, 2, 525-530.
Latent heat: Converted from evapotranspiration
NBP components
Fire: Prognostic fire scheme (Thonicke et al., 2001) is implemented.
Species / Plant Functional Types (PFTs)
List of species / pfts: VISIT uses biome types (after Olson et al. 1983) instead of PFTs as listed below: 0:water, 1:tropical & subtropical evergreen forest, 2:tropical montane forest, 3:tropical & subtropical dry forest, 4:mid-latitude mixed forest, 5:mid-latitude broad-leaved forest, 6:semiarid wood or low forest, 7:coniferous evergreen forest, 8:southern taiga, 9:main evergreen taiga, 10:main deciduous taiga, 11:northern evergreen taiga, 12:northern deciduous taiga, 13:second growth woods, 14:second growth field, 15:succulent & thorn wood, 16:tropical savanna, woodland, 17:mediterranean-type dry wood, 18:heath & moorland, 19:warm or hot shrub & grassland, 20:tibetan meadow & siberian highland, 21:tundra, 22:wooded tundra, 23:warm or hot wetlands, 24:cool bog & mire, 25:shore & hinterland, 26:cool semi-desert scrub, 27:non-polar desert, 28:non-polar sand desert, 29:paddyland, 30:cool cropland, 31:warm cropland, 32:irrigated, 33:antarctica
Model output specifications
Output format: Grid-cell
Output per pft?: No
Fire modules
Aggregation of reported burnt area: Simulated annual burned area is disaggregated into monthly values.
Land-use classes allowed to burn: Natural vegetation, ISIMIP-Pasture (managed pastures, rangeland) and urban areas are allowed to burn but not cropland. Urban Land is treated as natural vegetation.
Included fire-ignition factors: Natural ignition based on availability of fuel, combustibility of fuel (soil moisture)
Is fire ignition implemented as a random process?: No.
Is human influence on fire ignition and/or suppression included? how?: No.
How is fire spread/extent modelled?: Fire extent is an empirical function of soil moisture and fuel load.
Are deforestation or land clearing fires included?: No.
What is the minimum burned area fraction at grid level?: 0
Person responsible for model simulations in this simulation round
Noda Hibiki: noda.hibiki@nies.go.jp, National Institute for Environmental Studies (Japan)
Akihiko Ito: itoh@nies.go.jp, 0000-0001-5265-0791, National Institute for Environmental Studies (NIES), Japan (Japan)
Kazuya Nishina: nishina.kazuya@nies.go.jp, 0000-0002-8820-1282, National Institute for Environmental Studies (NIES) & Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Japan (Japan)
Output Data
Experiments: historical, rcp26, rcp45, rcp60, rcp85
Climate Drivers: None
Date: 2013-12-17
Basic information
Model Version: Monthly 0.5° version
Reference Paper: Main Reference: Ito A, Inatomi M et al. Water-Use Efficiency of the Terrestrial Biosphere: A Model Analysis Focusing on Interactions between the Global Carbon and Water Cycles. Journal of Hydrometeorology,13,681-694,2011
Reference Paper: Other References:
Resolution
Spatial aggregation: regular grid
Horizontal resolution: 0.5°x0.5°
Temporal resolution of input data: climate variables: monthly
Temporal resolution of input data: co2: annual
Temporal resolution of input data: land use/land cover: annual
Temporal resolution of input data: soil: constant
Input data
Simulated atmospheric climate data sets used: GCM atmospheric climate data (Fast Track)
Emissions data sets used: CO2 concentration
Climate variables: tas, rhs, rsds, pr
Spin-up
Was a spin-up performed?: Yes
Spin-up design: Repetition for 100 times using bias-corrected 30-year climate data and constant atmospheric CO2 concentration.
Natural Vegetation
Natural vegetation cover dataset: Olson JS, Watts JA, Allison LJ (1983) Carbon in live vegetation of major world ecosystems. Oak Ridge National Laboratory.
Model set-up specifications
How do you simulate bioenergy? i.e. what pft do you simulate on bioenergy land?: No bioenergy
How do you simulate the transition from cropland to bioenergy?: No bioenergy
How do you simulate pasture (which pft)?: No pasture (natural grassland only)
Fire modules
Aggregation of reported burnt area: Simulated annual burned area is disaggregated into monthly values.
Land-use classes allowed to burn: Natural vegetation, ISIMIP-Pasture (managed pastures, rangeland) and urban areas are allowed to burn but not cropland. Urban Land is treated as natural vegetation.
Included fire-ignition factors: Natural ignition based on availability of fuel, combustibility of fuel (soil moisture)
Is fire ignition implemented as a random process?: No.
Is human influence on fire ignition and/or suppression included? how?: No.
How is fire spread/extent modelled?: Fire extent is an empirical function of soil moisture and fuel load.
Are deforestation or land clearing fires included?: No.
What is the minimum burned area fraction at grid level?: 0




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