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  3. Impact model: LPJmL

Impact model: LPJmL

Sector
Permafrost
Region
regional

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
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: Sitch S, Smith B, Prentice I, Arneth A, Bondeau A, Cramer W, Kaplan J, Levis S, Lucht W, Sykes M, Thonicke K, Venevsky S et al. Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model. Global Change Biology,9,161-185,2003
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: Reservoir operation starts at reservoir-specific first year of operation, and reservoir characteristics do not change afterwards.
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, rlds, rsds, pr
Additional information about input variables: lwnet derived from tas and rlds
Exceptions to Protocol
Exceptions: Permafrost outputs are taken from the biome sector simulations which include human influences such as landuse, irrigation, reservoir operation, whereas the protocol requests simulations wiithout human influences (nosoc).
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 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
Key model processes
Dynamic vegetation: yes
Nitrogen limitation: no
Person responsible for model simulations in this simulation round
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 Output License: CC BY 4.0
Reference Paper: Main Reference: Sitch S, Smith B, Prentice I, Arneth A, Bondeau A, Cramer W, Kaplan J, Levis S, Lucht W, Sykes M, Thonicke K, Venevsky S et al. Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model. Global Change Biology,9,161-185,2003
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
Climate variables: tas, rlds, rsds, pr
Additional information about input variables: lwnet 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
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




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