Impact model: MC2-USFS-r87g5c1

Sector
Biomes
Region
global

This is MC2 dynamic global vegetation model run by the USDA Forest Service Pacific Northwest Research Station. MC2 simulates vegetation response to climate at a monthly time step. MC2 runs on a monthly time step, and represents vegetation as several plant functional types. Each gridcell simulates one tree type and one grass, which compete for light and water stored in soil.

Information for the model MC2-USFS-r87g5c1 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.

Person responsible for model simulations in this simulation round
John Kim: john.kim@usda.gov, 0000-0002-3720-7916, United States Forest Service (USA)
Person responsible for model simulations in this simulation round
John Kim: john.kim@usda.gov, 0000-0002-3720-7916, United States Forest Service (USA)
Additional persons involved: G. Stephen Pitts
Output Data
Experiments: obsclim_nat_default, counterclim_nat_default
Climate Drivers: 20CRV3, 20CRV3-ERA5, 20CRV3-W5E5, GSWP3-W5E5
Date: 2023-06-29
Basic information
Model Version: r87g5c1
Model Output License: CC0
Model Homepage: https://github.com/pacificnorthwestresearchstation/mc2
Resolution
Spatial aggregation: regular grid
Horizontal resolution: 0.5’ x 0.5’
Temporal resolution of input data: climate variables: monthly
Temporal resolution of input data: co2: annual
Temporal resolution of input data: soil: constant
Input data
Observed atmospheric climate data sets used: W5E5v2.0
Emissions data sets used: Atmospheric composition (ISIMIP3a)
Other data sets used: Land-sea mask
Additional input data sets: Global 1/2 degree elevation data.
Climate variables: huss, tasmax, tasmin, ps, pr
Additional information about input variables: huss and ps are used to compute surface vapor pressure, which is input to the model.
Spin-up
Was a spin-up performed?: Yes
Spin-up design: The model estimate initial biogeography using monthly climatology calculated from 1801-1900 ISIMIP 3a spinclim dataset, then runs 1,000 years using detrended 1801-1900 spinclim dataset. During these runs CO2 is assumed to be constant at 350 ppm and do not affect net primary production or transpiration.
Natural Vegetation
Natural vegetation partition: Each gridcell simulates a single tree and a single grass lifeform. The model estimates the initial locations of PFTs using monthly climatology.
Natural vegetation dynamics: The model simulates NPP and changes to carbon stocks, and the occurrence of fires and loss of carbon to fire. At the end of each simulated year, the model reclassifies each gridcell into one of many PFTs based on the carbon stock and climate conditions at the individual gridcell.
Soil layers: Soil input data specify % rock, sand and clay in up to 3 layers, as well as the depth to bedrock. Based on the soil input data, up to 10 soil layers are simulated. The layers are 0.15 m thick to a depth of 0.6 m and 0.3 m below that.
Management & Adaptation Measures
Management: No management is simulated.
Extreme Events & Disturbances
Key challenges: The model runs on a monthly time step using monthly climate data, so sub-monthly weather events are poorly represented. Impacts of pests, flood, logging and frost are not simulated. Fire occurrence probability for each gridcell is statistically fitted to observation, so may not meaningfully respond to a changed climate (however, the area and carbon burned can respond to climate change.)
Model set-up specifications
How do you simulate bioenergy? i.e. what pft do you simulate on bioenergy land?: Not simulated.
How do you simulate the transition from cropland to bioenergy?: Not simulated.
How do you simulate pasture (which pft)?: Not simulated.
Key model processes
Dynamic vegetation: Yes. At each time (monthly) step tree and grass growth is calculated in terms of NPP, and live carbon may be lost through fire. In each gridcell tree and grass lifeforms compete for light and water. At the end of each year, the gridcell is reclassified into one of many PFTs, which in turn affect fire occurrence and effects.
Nitrogen limitation: Not simulated.
Co2 effects: For both simulated trees and grasses, total potential production is enhanced by a coefficient of atmospheric CO2 concentration, which equals 1 at 350 ppm and 1.25 when CO2 concentration reaches 700 ppm. Effects are linear between those two points. (Total potential production may be further reduced by climate and site conditions before NPP is derived.) Similarly, potential transpiration rate equals 1.0 at 350 ppm and is reduced to 0.75 when CO2 concentration reaches 700 ppm.
Light interception: Only the effect of tree shading grass is simulated, using Beers law, which reduced grass production.
Light utilization: Not simulated.
Phenology: Leaf phenology (deciduous vs. needleleaf, and evergreen vs. broadleaf) is estimated as a function of long-term temperature and precipitation. Seasonal leaf phenology is not explicitly simulated, and is an emergent effect as climatic conditions are sufficient temperature and moisture allow NPP.
Water stress: Reductions in available soil water and precipitation, as well as increases in PET, reduces NPP.
Heat stress: NPP is reduced for trees when monthly temperature reaches 30 degC for needleleaf trees; 35 degC for deciduous broadleaf trees; 45 for evergreen broadleaf trees; 32 degC for C3 grass; and 45 degC for C4 grass.
Evapo-transpiration approach: PET is calculated as a function of average monthly maximum and minimum temperatures from the equations of Linacre (1977). Snow is evaporated as a function of PET. Bare soil water evaporation and interception by the canopy are functions of aboveground biomass, rainfall, and PET. Water is transpired from each soil layer as a function of live biomass, ensuring that total evapotranspiration does not exceed PET.
Differences in rooting depth: Rooting depth is not explicitly modeled, but transpiration algorithm assumes it is the depth to bedrock, specified in our own soil input data layer, and does not change.
Root distribution over depth: Not explicitly simulated.
Closed energy balance: Not simulated.
Coupling/feedback between soil moisture and surface temperature: Not simulated.
Latent heat: Not simulated.
Sensible heat: Not simulated.
How do you compute soil organic carbon during land use (do you mix the previous pft soc into agricultural soc)?: Not simulated.
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?: No.
How do you to treat biofuel npp and biofuel harvest?: No.
Does non-harvested crop npp go to litter in your output?: No.
Causes of mortality in vegetation models
Age/senescence: Different parts of live wood senesces at fix rates that do not change with climate change.
Fire: Fire occurrence is fitted to GFED4 fire data per MC2 PFT, and will not change with climate change. Area burned within a cell is a function of two fuel indices: BUI and FFMC, and can increase under a hotter climate.
Drought: Drought effects are not directly simulated. However, drought can deplete available water in the soil, which will reduce NPP.
Insects: Not simulated.
Storm: Not simulated.
Stochastic random disturbance: Not simulated.
NBP components
Fire: Yes, fire is simulated. Live biomass is either burned or just killed by fire. Burned biomass is removed from simulation without accounting for it any further. Killed biomass is moved to the dead carbon pools.
Land-use change: Not simulated.
Harvest: Not simulated.
Species / Plant Functional Types (PFTs)
List of species / pfts: ColdBarren (COLD_BARREN); Tundra (TUNDRA); BorealDeciduousForest (BOREAL_DECIDUOUS_FOREST); BorealNeedleleafForest (BOREAL_NEEDLELEAF_FOREST); BorealWoodland (BOREAL_WOODLAND); SubalpineForest (SUBALPINE_FOREST); MaritimeEvergreenNeedleleafForest (MARITIME_EN_FOREST); MesicTemperateNeedleleafForest (MESIC_TEMPERATE_NEEDLELEAF_FOREST); TemperateDeciduousBroadleafForest (TEMPERATE_DB_FOREST); CoolMixedForest (COOL_MIXED_FOREST); TemperateWarmMixedForest (TEMPERATE_WARM_MIXED_FOREST); TemperateEvergreenNeedleleafWoodland (TEMPERATE_EN_WOODLAND); TemperateDeciduousBroadleafWoodland (TEMPERATE_DB_WOODLAND); TemperateCoolMixedWoodland (TEMPERATE_COOL_MIXED_WOODLAND); TemperateWarmMixedWoodland (TEMPERATE_WARM_MIXED_WOODLAND); C3Shrub (C3_SHRUB); C3Grass (C3_GRASS); TemperateDesert (TEMPERATE_DESERT); SubtropicalEvergreenNeedleleafForest (SUBTROPICAL_EN_FOREST); SubtropicalDeciduousBroadleafForest (SUBTROPICAL_DB_FOREST); WarmEvergreenBroadleafForest (WARM_EB_FOREST); SubtropicalMixedForest (SUBTROPICAL_MIXED_FOREST); SubtropicalEvergreenNeedleleafWoodland (SUBTROPICAL_EN_WOODLAND); SubtropicalDeciduousBroadleafWoodland (SUBTROPICAL_DB_WOODLAND); SubtropicalEvergreenBroadleafWoodland (SUBTROPICAL_EB_WOODLAND); SubtropicalMixedWoodland (SUBTROPICAL_MIXED_WOODLAND); C4Shrub (C4SHRUB); C4Grass (C4GRASS); SubtropicalDesert (SUBTROPICAL_DESERT); TropicalEvergreenBroadleafForest (TROPICAL_EB_FOREST); TropicalDeciduousWoodland (TROPICAL_DECIDUOUS_WOODLAND); TropicalSavanna (TROPICAL_SAVANNA); TropicalShrubland (TROPICAL_SHRUBLAND); TropicalGrassland (TROPICAL_GRASSLAND); TropicalDesert (TROPICAL_DESERT); MoistTemperateNeedleleafFore (MOIST_TEMPERATE_NEEDLELEAF_FORE); SubalpineMeadow (SUBALPINE_MEADOW); NaturalBarren (NATURAL_BARREN); DryTemperateNeedleleafForest (DRY_TEMPERATE_NEEDLELEAF_FOREST); XericNeedleleafWoodland (XERIC_NEEDLELEAF_WOODLAND); SubtropicalEvergreenBroadleafForest (SUBTROPICAL_EB_FOREST); SubtropicalDeciduousBroadleafEvergreenBroadleafForest (SUBTROPICAL_DB_EB_FOREST); TropicalDeciduousBroadleafEvergreenBroadleafForest (TROPICAL_DB_EB_FOREST);
Model output specifications
Output format: Output value applies to entire gridcell.
Output per pft?: No.
Land-use change implementation
Is crop harvest included? if so, how?: No.
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.
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.
Fire modules
Aggregation of reported burnt area: Burnt area is calculated as fraction of gridcell.
Land-use classes allowed to burn: All vegetated gridcells.
Included fire-ignition factors: Vegetation type (PFT).
Is fire ignition implemented as a random process?: Yes.
Is human influence on fire ignition and/or suppression included? how?: No.
How is fire spread/extent modelled?: Fraction of gridcell burned is calculated as a function of two fuel indices, BUI and FFMC. (No cell-to-cell spread is modelled).
Are deforestation or land clearing fires included?: No.
What is the minimum burned area fraction at grid level?: 0.000001
Person responsible for model simulations in this simulation round
John Kim: john.kim@usda.gov, 0000-0002-3720-7916, United States Forest Service (USA)
Ellynne Kutschera: Ellen.Kutschera@usda.gov, 0000-0002-4196-2869, Forest Service, Pacific Northwest Research Station (USA)
Output Data
Experiments: historical_nosoc_co2, picontrol_nosoc_co2, rcp26_nosoc_2005co2, rcp26_nosoc_co2, rcp60_nosoc_2005co2, rcp60_nosoc_co2, rcp85_nosoc_2005co2, rcp85_nosoc_co2
Climate Drivers: HadGEM2-ES, GFDL-ESM2M, IPSL-CM5A-LR, MIROC5
Date: 2024-08-15
Basic information
Model Output License: CC0
Resolution
Spatial aggregation: regular grid
Horizontal resolution: 0.5’ x 0.5’
Temporal resolution of input data: climate variables: monthly
Temporal resolution of input data: co2: annual
Temporal resolution of input data: soil: constant
Input data
Observed atmospheric climate data sets used: W5E5v2.0
Emissions data sets used: Atmospheric composition (ISIMIP3a)
Other data sets used: Land-sea mask
Additional input data sets: Global 1/2 degree elevation data.
Climate variables: huss, tasmax, tasmin, ps, pr
Additional information about input variables: huss and ps are used to compute surface vapor pressure, which is input to the model.
Spin-up
Was a spin-up performed?: Yes
Spin-up design: The model estimate initial biogeography using monthly climatology calculated from 1801-1900 ISIMIP 3a spinclim dataset, then runs 1,000 years using detrended 1801-1900 spinclim dataset. During these runs CO2 is assumed to be constant at 350 ppm and do not affect net primary production or transpiration.
Natural Vegetation
Natural vegetation partition: Each gridcell simulates a single tree and a single grass lifeform. The model estimates the initial locations of PFTs using monthly climatology.
Natural vegetation dynamics: The model simulates NPP and changes to carbon stocks, and the occurrence of fires and loss of carbon to fire. At the end of each simulated year, the model reclassifies each gridcell into one of many PFTs based on the carbon stock and climate conditions at the individual gridcell.
Soil layers: Soil input data specify % rock, sand and clay in up to 3 layers, as well as the depth to bedrock. Based on the soil input data, up to 10 soil layers are simulated. The layers are 0.15 m thick to a depth of 0.6 m and 0.3 m below that.
Management & Adaptation Measures
Management: No management is simulated.
Extreme Events & Disturbances
Key challenges: The model runs on a monthly time step using monthly climate data, so sub-monthly weather events are poorly represented. Impacts of pests, flood, logging and frost are not simulated. Fire occurrence probability for each gridcell is statistically fitted to observation, so may not meaningfully respond to a changed climate (however, the area and carbon burned can respond to climate change.)
Model set-up specifications
How do you simulate bioenergy? i.e. what pft do you simulate on bioenergy land?: Not simulated.
How do you simulate the transition from cropland to bioenergy?: Not simulated.
How do you simulate pasture (which pft)?: Not simulated.
Key model processes
Dynamic vegetation: Yes. At each time (monthly) step tree and grass growth is calculated in terms of NPP, and live carbon may be lost through fire. In each gridcell tree and grass lifeforms compete for light and water. At the end of each year, the gridcell is reclassified into one of many PFTs, which in turn affect fire occurrence and effects.
Nitrogen limitation: Not simulated.
Co2 effects: For both simulated trees and grasses, total potential production is enhanced by a coefficient of atmospheric CO2 concentration, which equals 1 at 350 ppm and 1.25 when CO2 concentration reaches 700 ppm. Effects are linear between those two points. (Total potential production may be further reduced by climate and site conditions before NPP is derived.) Similarly, potential transpiration rate equals 1.0 at 350 ppm and is reduced to 0.75 when CO2 concentration reaches 700 ppm.
Light interception: Only the effect of tree shading grass is simulated, using Beers law, which reduced grass production.
Light utilization: Not simulated.
Phenology: Leaf phenology (deciduous vs. needleleaf, and evergreen vs. broadleaf) is estimated as a function of long-term temperature and precipitation. Seasonal leaf phenology is not explicitly simulated, and is an emergent effect as climatic conditions are sufficient temperature and moisture allow NPP.
Water stress: Reductions in available soil water and precipitation, as well as increases in PET, reduces NPP.
Heat stress: NPP is reduced for trees when monthly temperature reaches 30 degC for needleleaf trees; 35 degC for deciduous broadleaf trees; 45 for evergreen broadleaf trees; 32 degC for C3 grass; and 45 degC for C4 grass.
Evapo-transpiration approach: PET is calculated as a function of average monthly maximum and minimum temperatures from the equations of Linacre (1977). Snow is evaporated as a function of PET. Bare soil water evaporation and interception by the canopy are functions of aboveground biomass, rainfall, and PET. Water is transpired from each soil layer as a function of live biomass, ensuring that total evapotranspiration does not exceed PET.
Differences in rooting depth: Rooting depth is not explicitly modeled, but transpiration algorithm assumes it is the depth to bedrock, specified in our own soil input data layer, and does not change.
Root distribution over depth: Not explicitly simulated.
Closed energy balance: Not simulated.
Coupling/feedback between soil moisture and surface temperature: Not simulated.
Latent heat: Not simulated.
Sensible heat: Not simulated.
How do you compute soil organic carbon during land use (do you mix the previous pft soc into agricultural soc)?: Not simulated.
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?: No.
How do you to treat biofuel npp and biofuel harvest?: No.
Does non-harvested crop npp go to litter in your output?: No.
Causes of mortality in vegetation models
Age/senescence: Different parts of live wood senesces at fix rates that do not change with climate change.
Fire: Fire occurrence is fitted to GFED4 fire data per MC2 PFT, and will not change with climate change. Area burned within a cell is a function of two fuel indices: BUI and FFMC, and can increase under a hotter climate.
Drought: Drought effects are not directly simulated. However, drought can deplete available water in the soil, which will reduce NPP.
Insects: Not simulated.
Storm: Not simulated.
Stochastic random disturbance: Not simulated.
NBP components
Fire: Yes, fire is simulated. Live biomass is either burned or just killed by fire. Burned biomass is removed from simulation without accounting for it any further. Killed biomass is moved to the dead carbon pools.
Land-use change: Not simulated.
Harvest: Not simulated.
Species / Plant Functional Types (PFTs)
List of species / pfts: ColdBarren (COLD_BARREN); Tundra (TUNDRA); BorealDeciduousForest (BOREAL_DECIDUOUS_FOREST); BorealNeedleleafForest (BOREAL_NEEDLELEAF_FOREST); BorealWoodland (BOREAL_WOODLAND); SubalpineForest (SUBALPINE_FOREST); MaritimeEvergreenNeedleleafForest (MARITIME_EN_FOREST); MesicTemperateNeedleleafForest (MESIC_TEMPERATE_NEEDLELEAF_FOREST); TemperateDeciduousBroadleafForest (TEMPERATE_DB_FOREST); CoolMixedForest (COOL_MIXED_FOREST); TemperateWarmMixedForest (TEMPERATE_WARM_MIXED_FOREST); TemperateEvergreenNeedleleafWoodland (TEMPERATE_EN_WOODLAND); TemperateDeciduousBroadleafWoodland (TEMPERATE_DB_WOODLAND); TemperateCoolMixedWoodland (TEMPERATE_COOL_MIXED_WOODLAND); TemperateWarmMixedWoodland (TEMPERATE_WARM_MIXED_WOODLAND); C3Shrub (C3_SHRUB); C3Grass (C3_GRASS); TemperateDesert (TEMPERATE_DESERT); SubtropicalEvergreenNeedleleafForest (SUBTROPICAL_EN_FOREST); SubtropicalDeciduousBroadleafForest (SUBTROPICAL_DB_FOREST); WarmEvergreenBroadleafForest (WARM_EB_FOREST); SubtropicalMixedForest (SUBTROPICAL_MIXED_FOREST); SubtropicalEvergreenNeedleleafWoodland (SUBTROPICAL_EN_WOODLAND); SubtropicalDeciduousBroadleafWoodland (SUBTROPICAL_DB_WOODLAND); SubtropicalEvergreenBroadleafWoodland (SUBTROPICAL_EB_WOODLAND); SubtropicalMixedWoodland (SUBTROPICAL_MIXED_WOODLAND); C4Shrub (C4SHRUB); C4Grass (C4GRASS); SubtropicalDesert (SUBTROPICAL_DESERT); TropicalEvergreenBroadleafForest (TROPICAL_EB_FOREST); TropicalDeciduousWoodland (TROPICAL_DECIDUOUS_WOODLAND); TropicalSavanna (TROPICAL_SAVANNA); TropicalShrubland (TROPICAL_SHRUBLAND); TropicalGrassland (TROPICAL_GRASSLAND); TropicalDesert (TROPICAL_DESERT); MoistTemperateNeedleleafFore (MOIST_TEMPERATE_NEEDLELEAF_FORE); SubalpineMeadow (SUBALPINE_MEADOW); NaturalBarren (NATURAL_BARREN); DryTemperateNeedleleafForest (DRY_TEMPERATE_NEEDLELEAF_FOREST); XericNeedleleafWoodland (XERIC_NEEDLELEAF_WOODLAND); SubtropicalEvergreenBroadleafForest (SUBTROPICAL_EB_FOREST); SubtropicalDeciduousBroadleafEvergreenBroadleafForest (SUBTROPICAL_DB_EB_FOREST); TropicalDeciduousBroadleafEvergreenBroadleafForest (TROPICAL_DB_EB_FOREST);
Model output specifications
Output format: Output value applies to entire gridcell.
Output per pft?: No.
Land-use change implementation
Is crop harvest included? if so, how?: No.
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.
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.
Fire modules
Aggregation of reported burnt area: Burnt area is calculated as fraction of gridcell.
Land-use classes allowed to burn: All vegetated gridcells.
Included fire-ignition factors: Vegetation type (PFT).
Is fire ignition implemented as a random process?: Yes.
Is human influence on fire ignition and/or suppression included? how?: No.
How is fire spread/extent modelled?: Fraction of gridcell burned is calculated as a function of two fuel indices, BUI and FFMC. (No cell-to-cell spread is modelled).
Are deforestation or land clearing fires included?: No.
What is the minimum burned area fraction at grid level?: 0.000001