Impact model: CARAIB

CARAIB (CARbon Assimilation In the Biosphere) dynamic vegetation model is a process-based model which calculates carbon and water fluxes between the atmosphere and the terrestrial biosphere. It simulates the major processes of the plant development (establishment, growth, decease) as well as their geographic distributions (Plant Functional Types or species) in response to climate change. Its various modules describe respectively (i) soil hydrology, (ii) photosynthesis/stomatal regulation, (iii) carbon allocation and plant growth, (iv) litter/soil carbon dynamics, (v) vegetation cover dynamics, (vi) seed dispersal, and (vii) fire disturbance. Originally dedicated to natural plant types, CARAIB includes now the representation of agricultural plants, crops and meadows. CARAIB is one of the 8 global models following the ISIMIP2a protocol which form the base of simulations for the ISIMIP2a biome sector outputs; for a full technical description of the ISIMIP2a Simulation Data from Biomes Sector, see this DOI link: http://doi.org/10.5880/PIK.2017.002

Sector
Biomes
Region
global
Contact Person

Information for the model CARAIB 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.

Basic information
Reference Paper: Main Reference: Dury M., Hambuckers A., Warnant P., Henrot A.-J., Favre E., Ouberdous M., François L. et al. et al. Responses of European forest ecosystems to 21st century climate: assessing changes in interannual variability and fire intensityiForest – Biogeosciences and Forestry,4,82-99,2011
Reference Paper: Other References:
  • Minet J., Laloy E., Tychon B., François L. et al. et al. Bayesian inversions of a dynamic vegetation model at four European grassland sites.Biogeosciences,12,2809-2829,2015
  • Warnant P., François L., Strivay D., Gérard J. et al. et al. CARAIB: A global model of terrestrial biological productivity. Global Biogeochemical Cycles,8,255-270,1994
Person Responsible For Model Simulations In This Simulation Round: Louis François (louis.francois@ulg.ac.be), Alexandra-Jane Henrot (alexandra.henrot@ulg.ac.be), Marie Dury (marie.dury@ulg.ac.be), Ingrid Jacquemin (ingrid.jacquemin@ulg.ac.be), Guy Munhoven (guy.munhoven@ulg.ac.be)
Output Data
Experiments: I, Ia, II, IIa, IIb, III, IIIa, IIIb, IV, V, VI, VII
Climate Drivers: IPSL-CM5A-LR, HadGEM2-ES, GFDL-ESM2M, MIROC5
Date: 2018-04-19
Resolution
Spatial Aggregation: regular grid
Spatial Resolution: 0.5°x0.5°
Temporal Resolution Of Input Data: Climate Variables: daily
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 sets used
Simulated Atmospheric Climate Data Sets Used: IPSL-CM5A-LR, GFDL-ESM2M, MIROC5
Emissions Data Sets Used: CO2 concentration
Land Use Data Sets Used: Historical, gridded land use (HYDE 3.2)
Other Data Sets Used: Land-sea mask
Climate Variables: tasmax, tas, tasmin, hurs, sfcWind, rsds, pr
Additional Input Data Sets: average pressure was evaluated from elevation data
Exceptions to Protocol
Exceptions: monthly ouputs only, yearly outputs for PFT level results
Spin-up
Was A Spin-Up Performed?: Yes
Spin-Up Design: Spin-up from 1461 to 1661 using piControl climatic fields and 1661 CO2.
Natural Vegetation
Natural Vegetation Partition: Natural vegetation is calculated in a dynamic mode in the model.
Management & Adaptation Measures
Management: Land use and land cover change from year to year, as a function of the land use and land cover data provided. In addition to the 26 natural PFTs, there are 16 crop PFTs distributed on the crop fraction of the pixel and 2 grass PFTs (C3 grass and C4 grass) distributed on the pasture fraction of the pixel. No management is included, except for crops where sowing and harvesting dates are evaluated from crop-specific relationships with climate (e.g., GDD5). In the version used for ISIMIP2b, the crops products are cut but not harvested: they are incorporated in the litter.
Extreme Events & Disturbances
Key Challenges: Effect of extreme events on mortality is not easy to quantify. In the model, this is crudely represented through reduction of the characteristic times of the reservoirs when a temperature or a hydric stress occurs. In its current formulation, the model probably overestimates stress-induced mortality, but this is very difficult to constrain with data.
Model output specifications
Output Format: written per grid-cell area
Output Per Pft?: Output per unit area of a PFT, so weighting by fractional coverage of the PFT (frac) is required
Considerations: Weighting by fractional coverage (frac) is necessary. Be cautious, however, the sum of frac can be 0 (no vegetation in any storey), 1 (vegetation in only one of the two storeys) or 2 (vegetation in both storeys). This is because we assume that over- and under-storey vegetation can overlap. Intermediate values for this sum (e.g., between 1 and 2) are not normally possible, except for rounding errors. This means that bare soil is not accounted in frac and that, once at least one PFT is present, vegetation covers the pixel. However, the LAI of the PFTs may be small and in that case you can see the soil among the PFTs. It means tha to calculate a bare soil cover, you need to combine frac with projected LAI for each PFT. However, the grid box average of any vegetation attribute (e.g. GPP) is obtained by weighting PFT values by frac (even if the sum can be 2, since in this case we must add the values of the under- and over-storeys which are both present at the same spot in the pixel).
Key model processes
Dynamic Vegetation: yes
Nitrogen Limitation: no, but downregulation of Vcmax under high CO2 is implemented
Co2 Effects: yes Farqhuar/Collatz photosynthesis
Light Interception: Big leaf approach with separation of sun and shaded leaves. Moreover, the time step is divided into a cloud covered portion when only diffuse radiation is present and a sunny portion when both direct and diffuse radiation is present. Photosynthesis is calculated twice during the sunny portion (sun and shaded leaves) and once in the cloudy portion (shaded leaves -> diffuse rad only). The time step for photosynthesis (and respiration) calculation is 2 hours, so that the diurnal cycle is represented.
Light Utilization: Farquhar/Collatz photosynthesis
Phenology: Phenology is regulated purely by evolution of LAI that is determined by temperatures for which the increase of LAI is possible: leaf growth (resp. leaf fall) is initiated when the air temperature is above (resp. below) a prescribed threshold. This threshold is PFT-dependent.
Evapo-Transpiration Approach: PET: Penman
Root Distribution Over Depth: A budget of one single soil layer corresponding to the root zone is performed. Soil hydraulic conductivity is calculated from soil texture, using the parameterization of Saxton et al. (1986). Soil water can vary from wilting point to saturation.
Permafrost: no
Closed Energy Balance: yes
Latent Heat: yes
Sensible Heat: yes
Causes of mortality in vegetation models
Age: yes
Fire: yes The fire module developed in CARAIB (Dury et al., 2011) is based on the approach implemented in the dynamic global vegetation CTEM model (Canadian Terrestrial Ecosystem Model, Arora and Boer, 2005). On a given grid cell, the emergence of a fire is conditioned by the three factors of the fire triangle: the availability of fuel (biomass, litter), the combustibility of the fuel (soil moisture) and the presence of a source of ignition (natural or anthropogenic). If one of these factors is missing, the fire cannot occur. The three constraints, considered in terms of probability, allow to calculate a probability of fire occurrence (Pf = Pb.Pm.Pi). Pb is the probability of fire conditioned on the available above-ground biomass (leaf, stem and litter pools averaged over all PFTs), Pm is the probability of fire conditioned on the soil moisture in the root zone and Pi is the fire occurrence probability linked to ignition source. The natural ignition constraint is represented by a “lightning scalar” linked to cloud-to-ground lightning frequency (flashes km2 month-1, Arora and Boer 2005). Here, only natural fires are simulated. Once the probability of fire occurrence is established, the area burned on the grid cell can be calculated. It is taken elliptical in shape with point of ignition at one of the foci. The fire spread rate is a function of soil moisture and wind speed. Fire duration controls the maximum size of this ellipse that will be reached. It depends on an extinguishing probability parameter set to a fixed value for natural fires. Arora VK, Boer GJ (2005). Fire as an interactive component of dynamic vegetation models. Journal of Geophysical Research-Biogeosciences 110. - doi: 10.1029/2005JG000042 Dury M, Hambuckers A, Warnant P, Henrot A, Favre E, Ouberdous M, François L (2011). Responses of European forest ecosystems to 21st century climate: assessing changes in interannual variability and fire intensity. iForest 4: 82-99.
Drought: yes
Insects: no
Storm: no
Stochastic Random Disturbance: no
Other: cold temperatures, light competition between storeys
NBP components
Fire: 1) yes
Species / Plant Functional Types (PFTs)
List Of Species / Pfts: C3 herbs (humid) (c3hh); C3 herbs (dry) (c3dh); C4 herbs (c4h); Broadleaved summergreen arctic shrubs (brsuas); Broadleaved summergreen boreal or temperate cold shrubs (brsutecds); Broadleaved summergreen temperate warm shrubs (brsutewms); Broadleaved evergreen boreal or temperate cold shrubs (brevtecds); Broadleaved evergreen temperate warm shrubs (brevtewms); Broadleaved evergreen xeric shrubs (brevxs); Subdesertic shrubs (sds); Tropical shrubs (trs); Needleleaved evergreen boreal or temperate cold trees (ndevtecdt); Needleleaved evergreen temperate cool trees (ndevteclt); Needleleaved evergreen trees, drought-tolerant (ndevtedtt); Needleleaved evergreen trees, drought-tolerant, thermophilous (ndevtedttht); Needleleaved evergreen subtropical trees, drought-intolerant (ndevstdit); Needleleaved summergreen boreal or temperate cold trees (ndsutecdt); Needleleaved summergreen subtropical swamp trees (ndsustswt); Broadleaved evergreen trees, drought tolerant (brevdtt); Broadleaved evergreen trees, drought-tolerant, thermophilous (brevdttht); Broadleaved evergreen subtropical trees, drought-intolerant (brevstdit); Broadleaved summergreen boreal or temperate cold trees (brsutecdt); Broadleaved summergreen temperate cool trees (brsuteclt); Broadleaved summergreen temperate warm trees (brsutewmt); Broadleaved raingreen tropical trees (brrgtrt); Broadleaved evergreen tropical trees (brevtrt); Maize-temperate (maizetec); Maize-tropical (maizetrc); Oil crops-groundnuts oil (groundnutc) Oil crops-rapeseed (oilrapeseedc); Soybeans (soybeanc); Sunflower (sunflowerc); Other crops-temperate (othertec); Other crops-tropical (othertrc); Pulses-temperate (pulsestec); Pulses-tropical (pulsestrc) Rice (ricec); Sugarcane (sugarcanec); Temperate cereals (cerealtec); Temperate roots (roottec); Tropical cereals (cerealtrc) Tropical roots roottrc C3 herbs - Pastures c3p C4 herbs - Pastures c4p
Fire modules
Aggregation of reported burnt area: daily output, monthly sum of daily values, assuming that a burned fraction cannot burn twice a year
Land-use classes allowed to burn: Natural vegetation is allowed to burn but not ISIMIP-Pasture (managed pastures, rangeland), cropland and urban land. Urban Land is treated as bare soil.
Included fire-ignition factors: availability of fuel, combustibility of fuel (soil moisture), presence of natural ignition source (lightning)
Is fire ignition implemented as a random process?: no, forced by lightning flash data
Is human influence on fire ignition and/or suppression included?: No
How is fire spread/extent modelled?: function of soil moisture and wind speed
Are deforestation or land clearing fires included?: no
What is the minimum burned area fraction at grid level?: 0
Basic information
Reference Paper: Main Reference: Dury M., Hambuckers A., Warnant P., Henrot A.-J., Favre E., Ouberdous M., François L. et al. et al. Responses of European forest ecosystems to 21st century climate: assessing changes in interannual variability and fire intensityiForest – Biogeosciences and Forestry,4,82-99,2011
Person Responsible For Model Simulations In This Simulation Round: Louis François (louis.francois@ulg.ac.be), Alexandra-Jane Henrot (alexandra.henrot@ulg.ac.be), Marie Dury (marie.dury@ulg.ac.be), Ingrid Jacquemin (ingrid.jacquemin@ulg.ac.be), Guy Munhoven (guy.munhoven@ulg.ac.be)
Output Data
Experiments: historical
Climate Drivers: GSWP3, PGMFD v.2 (Princeton), WATCH (WFD), WATCH+WFDEI
Date: 2016-11-16
Resolution
Spatial Aggregation: regular grid
Spatial Resolution: 0.5°x0.5°
Temporal Resolution Of Input Data: Climate Variables: daily
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 sets used
Observed Atmospheric Climate Data Sets Used: GSWP3, PGMFD v.2 (Princeton), WATCH (WFD), WATCH+WFDEI
Emissions Data Sets Used: CO2 concentration
Land Use Data Sets Used: Historical, gridded land use (HYDE 3.0)
Other Data Sets Used: Land-sea mask
Climate Variables: tasmax, tas, tasmin, wind, rhs, rsds, pr
Additional Input Data Sets: average pressure was evaluated from elevation data
Exceptions to Protocol
Exceptions: monthly ouputs only, yearly outputs for PFT level results
Spin-up
Was A Spin-Up Performed?: Yes
Spin-Up Design: Spin-up from 1765 to 1900 using detrended 1901-1930 climatic fields and 1765-1900 CO2.
Natural Vegetation
Natural Vegetation Partition: Natural vegetation is calculated in a dynamic mode in the model.
Management & Adaptation Measures
Management: No management included, except for crops where sowing and harvesting dates are evaluated from crop-specific relationships with climate (e.g., GDD5).
Extreme Events & Disturbances
Key Challenges: Effect of extreme events on mortality is not easy to quantify. In the model, this is crudely represented through reduction of the characteristic times of the reservoirs when a temperature or a hydric stress occurs. In its current formulation, the model probably overestimates stress-induced mortality, but this is very difficult to constrain with data.
Model output specifications
Output Format: written per grid-cell
Output Per Pft?: Output per unit area of a PFT, so weighting by fractional coverage of the PFT (frac) is required
Considerations: Weighting by fractional coverage (frac) is necessary. Be cautious, however, the sum of frac can be 0 (no vegetation in any storey), 1 (vegetation in only one of the two storeys) or 2 (vegetation in both storeys). This is because we assume that over- and under-storey vegetation can overlap. Intermediate values for this sum (e.g., between 1 and 2) are not normally possible, except for rounding errors. This means that bare soil is not accounted in frac and that, once at least one PFT is present, vegetation covers the pixel. However, the LAI of the PFTs may be small and in that case you can see the soil among the PFTs. It means tha to calculate a bare soil cover, you need to combine frac with projected LAI for each PFT. However, the grid box average of any vegetation attribute (e.g. GPP) is obtained by weighting PFT values by frac (even if the sum can be 2, since in this case we must add the values of the under- and over-storeys which are both present at the same spot in the pixel).
Key model processes
Dynamic Vegetation: yes
Nitrogen Limitation: no, but downregulation of Vcmax under high CO2 is implemented
Co2 Effects: yes Farqhuar/Collatz photosynthesis
Light Interception: big leaf approach with separation of sun and shaded leaves
Light Utilization: Farquhar/Collatz photosynthesis
Evapo-Transpiration Approach: PET: Penman
Permafrost: no
Closed Energy Balance: yes
Latent Heat: yes
Sensible Heat: yes
Causes of mortality in vegetation models
Age: yes
Fire: yes
Drought: yes
Insects: no
Storm: no
Stochastic Random Disturbance: no
Other: cold temperatures, light competition between storeys
NBP components
Fire: 1) yes
Species / Plant Functional Types (PFTs)
List Of Species / Pfts: C3 herbs (humid) (c3hh); C3 herbs (dry) (c3dh); C4 herbs (c4h); Broadleaved summergreen arctic shrubs (brsuas); Broadleaved summergreen boreal or temperate cold shrubs (brsutecds); Broadleaved summergreen temperate warm shrubs (brsutewms); Broadleaved evergreen boreal or temperate cold shrubs (brevtecds); Broadleaved evergreen temperate warm shrubs (brevtewms); Broadleaved evergreen xeric shrubs (brevxs); Subdesertic shrubs (sds); Tropical shrubs (trs); Needleleaved evergreen boreal or temperate cold trees (ndevtecdt); Needleleaved evergreen temperate cool trees (ndevteclt); Needleleaved evergreen trees, drought-tolerant (ndevtedtt); Needleleaved evergreen trees, drought-tolerant, thermophilous (ndevtedttht); Needleleaved evergreen subtropical trees, drought-intolerant (ndevstdit); Needleleaved summergreen boreal or temperate cold trees (ndsutecdt); Needleleaved summergreen subtropical swamp trees (ndsustswt); Broadleaved evergreen trees, drought tolerant (brevdtt); Broadleaved evergreen trees, drought-tolerant, thermophilous (brevdttht); Broadleaved evergreen subtropical trees, drought-intolerant (brevstdit); Broadleaved summergreen boreal or temperate cold trees (brsutecdt); Broadleaved summergreen temperate cool trees (brsuteclt); Broadleaved summergreen temperate warm trees (brsutewmt); Broadleaved raingreen tropical trees (brrgtrt); Broadleaved evergreen tropical trees (brevtrt); Maize-temperate (maizetec); Maize-tropical (maizetrc); Oil crops-groundnuts oil (groundnutc) Oil crops-rapeseed (oilrapeseedc); Soybeans (soybeanc); Sunflower (sunflowerc); Other crops-temperate (othertec); Other crops-tropical (othertrc); Pulses-temperate (pulsestec); Pulses-tropical (pulsestrc) Rice (ricec); Sugarcane (sugarcanec); Temperate cereals (cerealtec); Temperate roots (roottec); Tropical cereals (cerealtrc) Tropical roots roottrc C3 herbs - Pastures c3p C4 herbs - Pastures c4p
Fire modules
Aggregation of reported burnt area: daily output, monthly sum of daily values, assuming that a burned fraction cannot burn twice a year
Land-use classes allowed to burn: Natural vegetation is allowed to burn but not ISIMIP-Pasture (managed pastures, rangeland), cropland and urban land. Urban Land is treated as bare soil.
Included fire-ignition factors: availability of fuel, combustibility of fuel (soil moisture), presence of natural ignition source (lightning)
Is fire ignition implemented as a random process?: no, forced by lightning flash data
Is human influence on fire ignition and/or suppression included?: No
How is fire spread/extent modelled?: function of soil moisture and wind speed
Are deforestation or land clearing fires included?: no
What is the minimum burned area fraction at grid level?: 0