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Impact model: LPJmL

Sector
Biomes
Region
global

The LPJmL model simulates potential natural vegetation, represented by 9 plant-funtional types (PFTs), as well as agriculture, represented by 12 crop-functional types (CFTs) and managed grasslands, and biomass for bioenergy plantations represented by 3 biomass-functional types (BFTs). The composition of natural vegetation is determined dynamically. BFTs, CFTs and managed grasslands are grown on prescribed areas with a distinction between irrigated and rainfed agriculture.

Information for the model LPJmL 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
Sebastian Ostberg: ostberg@pik-potsdam.de, 0000-0002-2368-7015, Potsdam Institute for Climate Impact Research (Germany)
Sibyll Schaphoff: sibylls@pik-potsdam.de, 0000-0003-1677-8282, Potsdam Institute for Climate Impact Research (Germany)
Output Data
Experiments: I, II, IIa, III, IV, V, VI, VII, VIII
Climate Drivers: None
Date: 2018-01-18
Basic information
Model Version: ISIMIP2b version differs from ISIMIP2a version.
Model Output License: CC BY 4.0
Reference Paper: Main Reference: BONDEAU A, SMITH P, ZAEHLE S, SCHAPHOFF S, LUCHT W, CRAMER W, GERTEN D, LOTZE-CAMPEN H, MÜLLER C, REICHSTEIN M, SMITH B et al. Modelling the role of agriculture for the 20th century global terrestrial carbon balance. Global Change Biology,13,679-706,2007
Reference Paper: Other References:
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
Additional temporal resolution information: Lake and river fractions: constant; reservoirs: one reservoir information per grid cell that does not change after first year of operation
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)
Other data sets used: Drainage direction map for river routing, GRanD reservoirs & dams
Climate variables: tas, rsds, pr
Additional information about input variables: lwnet: long wave net radiation, derived from rlds and tas
Spin-up
Was a spin-up performed?: Yes
Spin-up design: 5000 years of PNV spin-up, followed by 161 years of land-use spin-up. Both spin-ups recycle 200 years of piControl climate data and use a constant CO2 concentration of 286.23 ppm (value for 1860). The land-use spin-up uses transient land-use patterns from the period 1700-1860.
Natural Vegetation
Natural vegetation partition: dynamic vegetation composition
Natural vegetation dynamics: The establishment of PFTs in natural vegetation is constrained by climatic suitability and the density of the existing vegetation, whereas their mortality depends on climatic stress (i.e., heat), plant density and growth efficiency.
Management & Adaptation Measures
Management: Crop sowing dates determined dynamically following Waha et al. 2012. “Climate-Driven Simulation of Global Crop Sowing Dates.” Global Ecology and Biogeography 21 (2): 247–59. doi:10.1111/j.1466-8238.2011.00678.x. Note: This differs from LPJmL settings in the agriculture sector.
Key model processes
Dynamic vegetation: yes
Nitrogen limitation: no
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: aggregated degree-day sum over PFT-specific temperature base, only on boreal trees
Evapo-transpiration approach: PET: Priestley-Taylor (modified for transpiration)
Differences in rooting depth: no
Root distribution over depth: PFT-specific following Jobbagy & Jackson 2000
Closed energy balance: yes
Coupling/feedback between soil moisture and surface temperature: yes
Latent heat: yes
Sensible heat: yes, but only for soil temperature
Causes of mortality in vegetation models
Age/senescence: no
Fire: annual burnt grid fraction based on length of fire season, mortality based on PFT-specific fire resistence; 2) daily fire probability depends on litter moisture
Drought: no
Insects: no
Storm: no
Stochastic random disturbance: no
Other: light competition
Remarks: growth efficiency, climatic stress (heat threshold, long-term mean outside bioclimatic limits), plant density (FPC>1)
NBP components
Fire: yes, burnt area fraction calculated at the end of each year, burnt 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: 1) C from storage organ added to annual harvest flux, remaining biomass sent to litter; 2) no forest harvest; 3) 75% of leaf biomass harvested (added to harvest flux) whenever leaf biomass exceeds 100g/m2
Other processes: heterotrophic respiration
Comments: note that monthly NBP is derived by subtracting annual fire and annual harvest fluxes as well as monthly RH from monthly NPP, i.e. annual values more reliable than seasonal cycle
Species / Plant Functional Types (PFTs)
List of species / pfts: tropical broad-leaved evergreen tree; tropical broad-leaved raingreen tree; temperate needle-leaved evergreen tree; temperate broad-leaved evergreen tree; temperate broad-leaved summergreen tree; boreal needle-leaved evergreen tree; boreal broad-leaved summergreen tree; temperate herbaceous (C3 perennial grass); tropical herbaceous (C4 perennial grass); temperate cereals (whe); rice (ric), maize (mai), tropical cereals (mil); pulses (pea), temperate roots (sgb), tropical roots (cas); oil crops sunflower (sun); oil crops soybean (soy); oil crops groundnut (nut); oil crops rapeseed (rap); sugarcane (sug)
Comments: Crop groups: temperate cereals: barley, rye, ryefor, triticale, wheat; tropical cereals: fonio, millet, sorghum, sorghum for forage; pulses: bambara, bean, broad bean, chick pea, cow pea, green bean, green broad bean, green pea, lentil, lupin, pea, pigeon pea, pulses (various), string bean, vetch; temperate roots: beet for forage, carrot, carrot for forage, potato, roots (various), sugarbeet, swede for forage, turnip for forage; tropical roots: cassava, sweet potato, taro, yam, yautia; results for managed grasslands abbreviated with "mgr", contain a mixture of C3 and C4 grass based on bioclimatic limits; all remaining crop areas (others) simulated as managed grasslands - do not analyse, especially others_irrigated
Model output specifications
Output format: per grid-cell area
Output per pft?: grid-cell totals provided for all variables that are also provided differentiated by PFT; NPP and GPP: for PNV types normalized to area covered by all natural vegetation, for crops normalized to fraction covered by crop
Considerations: Do not analyse GPP/NPP of "others rainfed/irrigated". GPP/NPP on set-aside stands is included in grid-cell total output, but not in PFT-specific outputs.
Land-use change implementation
Is crop harvest included? if so, how?: Yes; "storage organ" carbon pool added to harvest flux
Is cropland soil management included? if so, how?: No
Is grass harvest included? if so, how?: Yes (on pasture areas according to land-use input dataset); 75% of leaf biomass harvested whenever leaf biomass reaches threshold of 100g C/m2
Is shifting cultivation included?: No
Is wood harvest included? if so, how?: No
Which transition rules are applied to decide where agriculture is conducted?: In case of net land-use expansion land is taken from PNV stand
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 and urban areas are allowed to burn but not ISIMIP-Pasture (managed pastures, rangeland) and cropland. 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
Sebastian Ostberg: ostberg@pik-potsdam.de, 0000-0002-2368-7015, Potsdam Institute for Climate Impact Research (Germany)
Sibyll Schaphoff: sibylls@pik-potsdam.de, 0000-0003-1677-8282, Potsdam Institute for Climate Impact Research (Germany)
Output Data
Experiments: historical
Climate Drivers: None
Date: 2016-05-02
Basic information
Model Version: The model version used in ISIMIP2a lacks some of the features included in ISIMIP2b.
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: 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, PGMFD v2.1 (Princeton), WATCH (WFD), WATCH-WFDEI
Emissions data sets used: Atmospheric CO2 concentration
Land use data sets used: Historical, gridded land use (HYDE 3.0)
Other data sets used: Drainage direction map for river routing
Additional input data sets: While dams in LPJmL are based on GRanD, locations of some dams may differ from the version provided by ISIMIP because a different algorithm was used to assign dam coordinates to grid cells.
Climate variables: tas, rsds, pr
Additional information about input variables: lwnet was derived from tas and rlds
Spin-up
Was a spin-up performed?: Yes
Spin-up design: 5000 years of PNV spin-up, followed by 390 years of land-use spin-up, both recycling 120-year random climate sequence (taken from 1901-1930), spin-up leads into transient run 1901 - end of climate dataset; 278 ppm CO2 used before 1765, after 1765 CO2 from provided input file
Natural Vegetation
Natural vegetation partition: dynamic vegetation distribution
Natural vegetation dynamics: The establishment of PFTs in natural vegetation is constrained by climatic suitability and the density of the existing vegetation, whereas their mortality depends on climatic stress (i.e., heat), plant density and growth efficiency.
Management & Adaptation Measures
Management: crop sowing dates computed internally but fixed after 1960 in biome, permafrost and water sector runs
Key model processes
Dynamic vegetation: yes
Nitrogen limitation: no
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: aggregated degree-day sum over PFT-specific temperature base, only on boreal trees
Evapo-transpiration approach: PET: Priestley-Taylor (modified for transpiration)
Differences in rooting depth: no
Root distribution over depth: PFT-specific following Jobbagy & Jackson 2000
Closed energy balance: yes
Coupling/feedback between soil moisture and surface temperature: yes
Latent heat: yes
Sensible heat: yes, but only for soil temperature
Causes of mortality in vegetation models
Age/senescence: no
Fire: 1) annual burnt grid fraction based on length of fire season, mortality based on PFT-specific fire resistence; 2) daily fire probability depends on litter moisture
Drought: no
Insects: no
Storm: no
Stochastic random disturbance: no
Other: light competition
Remarks: growth efficiency, climatic stress (heat threshold, long-term mean outside bioclimatic limits), plant density (FPC>1)
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: 1) C from storage organ added to annual harvest flux, remaining biomass sent to litter; 2) no forest harvest; 3) 50% of leaf biomass harvested (added to harvest flux) whenever biomass increment exceeds 100g/m2
Other processes: RH
Comments: note that monthly NBP is derived by substracting annual fire and annual harvest fluxes as well as monthly RH from monthly NPP, i.e. annual values more reliable than seasonal cycle
Species / Plant Functional Types (PFTs)
List of species / pfts: tropical broad-leaved evergreen tree; tropical broad-leaved raingreen tree; temperate needle-leaved evergreen tree; temperate broad-leaved evergreen tree; temperate broad-leaved summergreen tree; boreal needle-leaved evergreen tree; boreal broad-leaved summergreen tree; temperate herbaceous (C3 perennial grass); tropical herbaceous (C4 perennial grass); temperate cereals (whe); rice (ric), maize (mai), tropical cereals (mil); pulses (pea), temperate roots (sgb), tropical roots (cas); oil crops sunflower (sun); oil crops soybean (soy); oil crops groundnut (nut); oil crops rapeseed (rap); sugarcane (sug)
Comments: Crop groups: temperate cereals: wheat, barley, rye, oat; tropical cereals: millet, sorghum; pulses: lentils, temperate roots: sugar beet, tropical roots: cassava; results for managed grasslands abbreviated with "mgr", contain a mixture of C3 and C4 grass based on bioclimatic limits; all remaining crop areas (others) simulated as managed grasslands - do not analyse, especially others_irrigated
Model output specifications
Output format: per grid-cell area
Output per pft?: only fractional cover provided by PFT, all other outputs provided as grid-cell totals
Considerations: PFT-specific GPP and NPP outputs retracted for all PFTs because of erroneous values for some PFTs.
Land-use change implementation
Is crop harvest included? if so, how?: Yes; "storage organ" carbon pool added to harvest flux
Is cropland soil management included? if so, how?: No
Is grass harvest included? if so, how?: Yes, 50% of leaf biomass harvested (added to harvest flux) whenever biomass increment exceeds 100g/m2
Is shifting cultivation included?: No
Is wood harvest included? if so, how?: No
Which transition rules are applied to decide where agriculture is conducted?: In case of net land-use expansion land is taken from PNV stand
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 and urban areas are allowed to burn but not ISIMIP-Pasture (managed pastures, rangeland) and cropland. 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
Sebastian Ostberg: ostberg@pik-potsdam.de, 0000-0002-2368-7015, Potsdam Institute for Climate Impact Research (Germany)
Sibyll Schaphoff: sibylls@pik-potsdam.de, 0000-0003-1677-8282, Potsdam Institute for Climate Impact Research (Germany)
Output Data
Experiments: historical, rcp26, rcp45, rcp60, rcp85
Climate Drivers: None
Date: 2013-12-17
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 and urban areas are allowed to burn but not ISIMIP-Pasture (managed pastures, rangeland) and cropland. 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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