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Impact model: LPJ-GUESS

Sector
Biomes
Region
global

LPJ-GUESS is a Dynamic Global Vegetation Model (DGVM) applied in the biome sector. The model version is v3.1, described in Smith et al 2014, http://www.biogeosciences.net/11/2027/2014/.

Information for the model LPJ-GUESS 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
Almut Arneth: almut.arneth@kit.edu, 0000-0001-6616-0822, Karlsruhe Institute of Technology (Germany)
Matthew Forrest: matthew.forrest@senckenberg.de, 0000-0003-1858-3489, Senckenberg Biodiversity and Climate Research Centre (BiK-F) (Germany)
Thomas Hickler: thomas.hickler@senckenberg.de, 0000-0002-4668-7552, Senckenberg Biodiversity and Climate Research Centre (BiK-F) (Germany)
Jörg Steinkamp: joerg.steinkamp@uni-mainz.de, 0000-0002-7861-8789, Senckenberg Biodiversity and Climate Research Centre (BiK-F); now at: Data Center, Johannes Gutenberg-University Mainz (Germany)
Additional persons involved: matthew.forrest@senckenberg.de
Output Data
Experiments: I, Ia, II, IIa, IIb, III, IIIa, IIIb, IIIc, IV, V, VI, VII
Climate Drivers: None
Date: 2018-01-19
Basic information
Model Version: 3.1
Model Output License: CC BY 4.0
Reference Paper: Main Reference: Smith et al. et al. Implications of incorporating N-cycling and {N} limitations on primary production in an individual-based dynamic vegetation model. 2014
Resolution
Spatial aggregation: regular grid
Horizontal 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
Simulated atmospheric climate data sets used: IPSL-CM5A-LR, HadGEM2-ES, GFDL-ESM2M, MIROC5
Emissions data sets used: Atmospheric CO2 concentration
Land use data sets used: Future land-use patterns, Historical, gridded land use (HYDE 3.2)
Additional input data sets: Soiltypes: Haxeltine, A. and Prentice, I. C.: BIOME3: An equilibrium terrestrial biosphere model based on ecophysiological constraints, resource availability, and competition among plant functional types, Global Biogeochemical Cycles, 10(4), 693–709, doi:10.1029/96GB02344, 1996. Nitrogen deposition: Lamarque, J.-F., Kyle, G. P., Meinshausen, M., Riahi, K., Smith, S. J., Vuuren, D. P. van, Conley, A. J. and Vitt, F.: Global and regional evolution of short-lived radiatively-active gases and aerosols in the Representative Concentration Pathways, Climatic Change, 109(1-2), 191–212, doi:10.1007/s10584-011-0155-0, 2011.Lamarque, J.-F., Dentener, F., McConnell, J., Ro, C.-U., Shaw, M., Vet, R., Bergmann, D., Cameron-Smith, P., Dalsoren, S., Doherty, R., Faluvegi, G., Ghan, S. J., Josse, B., Lee, Y. H., MacKenzie, I. A., Plummer, D., Shindell, D. T., Skeie, R. B., Stevenson, D. S., Strode, S., Zeng, G., Curran, M., Dahl-Jensen, D., Das, S., Fritzsche, D. and Nolan, M.: Multi-model mean nitrogen and sulfur deposition from the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP): evaluation of historical and projected future changes, Atmos. Chem. Phys., 13(16), 7997–8018, doi:10.5194/acp-13-7997-2013, 2013.
Climate variables: tas, rsds, pr
Exceptions to Protocol
Exceptions: The default LPJ-GUESS N deposition data was used. This is also derived from the La Marque dataset so is broadly consistent with the protocol. *However* since the transient time series were used, the N deposition is historical/transient even if the land use scenario is 1860soc. Fortunately, this does not affect the future and extended_future times periods as they do have appropriate N deposition available in the standard LPJ-GUESS data input.
Spin-up
Was a spin-up performed?: Yes
Spin-up design: 500 years, recycling detrended first 30 years; CO2 of first years
Natural Vegetation
Natural vegetation partition: Dynamic vegetation distribution - all vegetation is included in the patch (but in a non-spatially explicit way).
Natural vegetation dynamics: Gap model with stochastic establishment and mortality. Establishment of shade intolerant PFTs limited under low-light conditions. Disturbance events come from fire and a stochastic generic-patch destroying disturbance with a mean return time of 100 years.
Management & Adaptation Measures
Management: Deforestation effects including by using a 'grass-for-crops' approach where some where tree establishment was forbidden in some patches based on the crop+pasture fraction. Therefore no specific physiological representation of crops or management.
Key model processes
Dynamic vegetation: Yes
Nitrogen limitation: Yes
Co2 effects: Yes, Farquhar/Collatz photosynthesis
Light interception: Big-leaf approach
Light utilization: Farquhar/Collatz photosynthesis
Phenology: Differentiation between evergreen (constant leaf coverage over the year), raingreen (maximum leaf coverage until water stress threshold) and summergreen (budburst and senescence controlled by base temperature, leaf cover increases with accumulated heat sum) trees
Water stress: Influence on photosynthesis, allocation (roots/leaf), triggers leaf abscission in raingreen trees
Heat stress: Reduction of photosynthesis at high temperatures only (PFT varying)
Evapo-transpiration approach: PET: Priestley-Taylor (modified for transpiration)
Differences in rooting depth: Fixed per PFT
Root distribution over depth: Trees and grasses differ, with 60% and 90% of roots in the upper soil layer (50cm) respectively.
Closed energy balance: No
Coupling/feedback between soil moisture and surface temperature: No
Latent heat: No
Sensible heat: No
Causes of mortality in vegetation models
Age/senescence: Yes
Fire: GlobFIRM (Thonicke et al 2001, GEB), the de facto fire model in LPJ-GUESS, uses soil moisture of the upper soil layer as a proxy for fuel moisture. Soil moisture is updated daily based on precipitation (excluding interception by the vegetation canopy); snow melt and evapotranspiration. The daily soil moisture values are used to calculate a daily fire probability using an empirical relationship; and these values are summed at the end of the year to calculate an annual fire season length. From this annual fire season length the annual area burnt is calculated using another empirical relationship. There is also a threshold amount of litter which is required for a gridcell to burn, providing a plant-productivity (and so further climatic) constraint on fire occurrance.
Drought: Not directly, but water limitation will reduce productivity and therefore increase growth efficiency mortality (see below).
Insects: No
Storm: No
Stochastic random disturbance: Yes, mean return time of 100 years.
Other: Growth efficiency mortality, based on the ratio of GPP to leaf area, with the strength of the effect depening the shade-tolerance class. See Sitch et al 2003 (http://onlinelibrary.wiley.com/doi/10.1046/j.1365-2486.2003.00569.x/abstract) for equation and Smith et al 2014 (http://www.biogeosciences.net/11/2027/2014/) for parameters.
NBP components
Fire: 1) yes 2) burnt area fraction calculated at the end of each year, biomass C and above-ground litter C released to atmosphere
Land-use change: All biomass transferred to litter pools on land use change; no slash-and-burn or other treatment
Harvest: No
Species / Plant Functional Types (PFTs)
List of species / pfts: Boreal needleleaved evergreen (BNE); Boreal shade intolerant needleleaved evergreen (BINE); Boreal needleleved summergreen (BNS); Temperate broadleaved summergreen (TeBS); shade intolerant broadleaved summergreen (IBS); Temperate broadleved evergreen (TeBE); Tropical broadleaved evergreen (TrBE); Tropical shade intolerant broadleaved evergreen (TrIBE); Tropical broadleaved raingreen (TrBR); C3 grass (C3G); C4 grass (C4G); C3 agricultural grass (C3G_agr); C4 agricultural grass (C4G_agr); * The last two PFTs are physiologically identical to the previous two but output separately*
Comments: "Carbon pools: Vegetation (VegC); Litter (LitterC); Soil (SoilC); Total (Total); Carbon fluxes: Net primary production (Veg); Reproduction (Repr); Soil (Soil); Fire (Fire); Establishment (Est); Net ecosystem exchange (NEE);"
Model output specifications
Output format: Per land gridcell.
Output per pft?: No. For PFT-level output, output is per unit are of gridcell, *not* per unit of PFT.
Considerations: Grid-cell totals have to be calculated by hand by summing up the pfts. PFT fraction not normalized, sum can be larger than 1, if so scale all PFT values by 1/(sum of all PFT fraction). *Do not multiply with pft-fraction to calculate global totals*.
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?: Net transitions (ie natural stands are not deforested if possible)
Fire modules
Aggregation of reported burnt area: Calculated in the model as annual output, hence total area affected within one year
Land-use classes allowed to burn: Natural vegetation, ISIMIP-Pasture (managed pastures, rangeland), cropland and urban areas are allowed to burn. Urban Land is treated as natural vegetation.
Included fire-ignition factors: Natural ignition once soil moisture threshold for available litter associated with plant productivity is reached
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?: Empirical relationship between the length of the fire season and the annual area burned, where the length of the fire season is derived from the number of fires initialized in the considered year.
Are deforestation or land clearing fires included?: No
What is the minimum burned area fraction at grid level?: 0.001
Person responsible for model simulations in this simulation round
Almut Arneth: almut.arneth@kit.edu, 0000-0001-6616-0822, Karlsruhe Institute of Technology (Germany)
Matthew Forrest: matthew.forrest@senckenberg.de, 0000-0003-1858-3489, Senckenberg Biodiversity and Climate Research Centre (BiK-F) (Germany)
Thomas Hickler: thomas.hickler@senckenberg.de, 0000-0002-4668-7552, Senckenberg Biodiversity and Climate Research Centre (BiK-F) (Germany)
Mikhail Mishurov: mikhail.mishurov@nateko.lu.se, Institutionen für naturgeografi och ekosystemvetenskap (Sweden)
Jörg Steinkamp: joerg.steinkamp@uni-mainz.de, 0000-0002-7861-8789, Senckenberg Biodiversity and Climate Research Centre (BiK-F); now at: Data Center, Johannes Gutenberg-University Mainz (Germany)
Output Data
Experiments: historical
Climate Drivers: None
Date: 2016-04-29
Basic information
Model Version: 3.1
Model Output License: CC0
Reference Paper: Main Reference: Smith et al. et al. Implications of incorporating N-cycling and {N} limitations on primary production in an individual-based dynamic vegetation model. 2014
Resolution
Spatial aggregation: regular grid
Horizontal 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
Observed atmospheric climate data sets used: GSWP3, WATCH (WFD), WATCH-WFDEI
Additional input data sets: Soiltypes:Haxeltine, A. and Prentice, I. C.: BIOME3: An equilibrium terrestrial biosphere model based on ecophysiological constraints, resource availability, and competition among plant functional types, Global Biogeochemical Cycles, 10(4), 693–709, doi:10.1029/96GB02344, 1996.Nitrogen deposition:Lamarque, J.-F., Kyle, G. P., Meinshausen, M., Riahi, K., Smith, S. J., Vuuren, D. P. van, Conley, A. J. and Vitt, F.: Global and regional evolution of short-lived radiatively-active gases and aerosols in the Representative Concentration Pathways, Climatic Change, 109(1-2), 191–212, doi:10.1007/s10584-011-0155-0, 2011.Lamarque, J.-F., Dentener, F., McConnell, J., Ro, C.-U., Shaw, M., Vet, R., Bergmann, D., Cameron-Smith, P., Dalsoren, S., Doherty, R., Faluvegi, G., Ghan, S. J., Josse, B., Lee, Y. H., MacKenzie, I. A., Plummer, D., Shindell, D. T., Skeie, R. B., Stevenson, D. S., Strode, S., Zeng, G., Curran, M., Dahl-Jensen, D., Das, S., Fritzsche, D. and Nolan, M.: Multi-model mean nitrogen and sulfur deposition from the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP): evaluation of historical and projected future changes, Atmos. Chem. Phys., 13(16), 7997–8018, doi:10.5194/acp-13-7997-2013, 2013.
Climate variables: tas, rsds, pr
Spin-up
Was a spin-up performed?: Yes
Spin-up design: 500 years, recycling detrended first 30 years; CO2 of first years
Natural Vegetation
Natural vegetation partition: dynamic vegetation distribution
Management & Adaptation Measures
Management: No forest cover in fraction of patches equivalent to crop/pasture fraction
Key model processes
Dynamic vegetation: yes
Nitrogen limitation: yes
Co2 effects: yes, Farquhar/Collatz photosynthesis
Light interception: big-leaf approach
Light utilization: Farquhar/Collatz photosynthesis
Phenology: differentiation between evergreen (constant leaf coverage over the year), raingreen (maximum leaf coverage until water stress threshold) and summergreen (budburst and senescence controlled by base temperature, leaf cover increases with accumulated heat sum) trees
Water stress: influence on photosynthesis, allocation (roots/leaf), triggers leaf abscission in raingreen trees
Heat stress: no
Evapo-transpiration approach: PET: Priestley-Taylor (modified for transpiration)
Differences in rooting depth: no
Root distribution over depth: trees and grasses differ
Closed energy balance: no
Coupling/feedback between soil moisture and surface temperature: no
Latent heat: no
Sensible heat: no
Causes of mortality in vegetation models
Age/senescence: yes
Fire: yes
Drought: no
Insects: no
Storm: no
Stochastic random disturbance: yes
NBP components
Fire: 1) yes 2) burnt area fraction calculated at the end of each year, biomass C and above-ground litter C released to atmosphere
Land-use change: all biomass transferred to litter pools on land use change; no slash-and-burn or other treatment
Harvest: no
Species / Plant Functional Types (PFTs)
List of species / pfts: Boreal needleleaved evergreen (BNE); Boreal shade intolerant needleleaved evergreen (BINE); Boreal needleleved summergreen (BNS); Temperate broadleaved summergreen (TeBS); shade intolerant broadleaved summergreen (IBS); Temperate broadleved evergreen (TeBE); Tropical broadleaved evergreen (TrBE); Tropical shade intolerant broadleaved evergreen (TrIBE); Tropical broadleaved raingreen (TrBR); C3 grass (C3G); C4 grass (C4G);
Comments: "Carbon pools: Vegetation (VegC); Litter (LitterC); Soil (SoilC); Total (Total); Carbon fluxes: Net primary production (Veg); Reproduction (Repr); Soil (Soil); Fire (Fire); Establishment (Est); Net ecosystem exchange (NEE);"
Model output specifications
Output per pft?: Yes, grid-cell totals have to be calculated by hand by summing up the pfts; pft fraction not normalized, sum can be larger than 1, do not multiply with pft-fraction to calculate global totals
Considerations: consider all PFTs
Fire modules
Aggregation of reported burnt area: Calculated in the model as annual output, hence total area affected within one year
Land-use classes allowed to burn: Natural vegetation, ISIMIP-Pasture (managed pastures, rangeland), cropland and urban areas are allowed to burn. Urban Land is treated as natural vegetation.
Included fire-ignition factors: Natural ignition once soil moisture threshold for available litter associated with plant productivity is reached
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?: Empirical relationship between the length of the fire season and the annual area burned, where the length of the fire season is derived from the number of fires initialized in the considered year.
Are deforestation or land clearing fires included?: No.
What is the minimum burned area fraction at grid level?: 0.001




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