Impact model: VEGAS

VEGAS 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

ISIMIP2b
ISIMIP2a

Confirm data for simulation round ISIMIP2b

Person responsible for model simulations in this simulation round

Min Chen: chenminbnu@gmail.com, University of Wisconsin-Madison (USA)

Fang Zhao: fangzhao@pik-potsdam.de, 0000-0002-4819-3724, Potsdam Institute for Climate Impacts Research (Germany)

Output Data

Experiments: I, II, IIa, III
Climate Drivers: None
Date: 2017-09-20

Basic information

Model Version: v2.3

Reference Paper: Main Reference: Terrestrial mechanisms of interannual CO 2 variability .

Reference Paper: Other References: Agricultural Green Revolution as a driver of increasing atmospheric CO2 seasonal amplitude.

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: not used

Input data

Simulated atmospheric climate data sets used: IPSL-CM5A-LR

Emissions data sets used: Atmospheric CO2 concentration

Land use data sets used: Historical, gridded land use (HYDE 3.2)

Other data sets used: Land-sea mask

Climate variables: tas, rsds, pr

Spin-up

Was a spin-up performed?: Yes

Spin-up design: First 300 years (first 200-year accelerated) with detrended, randomized climate data (using ISI-MIP provided random serie of detrended 1901-1930 data) with natural vegetation, followed by 300 years of random climate and constant 1860 land use and CO2.

Natural Vegetation

Natural vegetation partition: dynamic vegetation distribution

Management & Adaptation Measures

Management: prognostic planting and harvesting, varying management intensity and harvest index to simulate the tripling of crop production since 1961 (See Zeng et al. 2014)

Extreme Events & Disturbances

Key challenges: No heat stress mechanism in model.

Key model processes

Dynamic vegetation: yes

Nitrogen limitation: implicitly considered in Javis/Collatz photosynthesis

Co2 effects: yes, Javis/Collatz pohotosynthesis

Light interception: vegetation height-dependend light extinction

Light utilization: Jarvis light use efficiency

Phenology: dynamic

Water stress: Influence on photosynthesis, carbon allocation, evapotranspiration, and soil biogeochemical processes

Heat stress: no

Evapo-transpiration approach: Stomatal resistance, linked to photosynthesis; bulk transfer formula

Differences in rooting depth: Vegetation type dependent; also topography

Closed energy balance: yes

Coupling/feedback between soil moisture and surface temperature: yes

Latent heat: yes

Sensible heat: yes

Causes of mortality in vegetation models

Age/senescence: background mortality

Fire: yes. Fire module includes the effects of moisture availability, temperature, soil fuel loading, and PFT dependent resistance and captures fire contribution to interannual CO2 variability.

Drought: yes

Insects: no

Storm: no

Stochastic random disturbance: yes (random cold temperature trigger leaf falling)

Other: light competition

NBP components

Fire: yes

Land-use change: yes

Harvest: Harvest from crops and forest. 1) total aboveground crop C * harvest index (fraction of edible parts) * fraction left after loss due to weeds, failed crop etc.; harvested crop C is transported horizontally (see Zeng et a. 2014), remaining biomass sent to metabolic ltter pool; 2) sustainable wood harvest

Other processes: RH

Species / Plant Functional Types (PFTs)

List of species / pfts: Broad-leaved tree; needleleaf tree; (Whether diciduous or evergreen is dynamically determined); cold grass; warm grass; generic crop

Comments: provided by Fang Zhao (with input from Ning Zeng) 2/16

Model output specifications

Output format: NetCDF

Output per pft?: Already averaged over the whole grid

Land-use change implementation

Is crop harvest included? if so, how?: yes. Harvest occurs when maximum LAI is reached.

Is cropland soil management included? if so, how?: Yes, irrigation is introduced to moderately increase soil moisture content for crop.

Is grass harvest included? if so, how?: no

Is shifting cultivation included?: Only a single crop type (average of maize, rice and wheat) is simulated.

Is wood harvest included? if so, how?: yes, harvested wood goes into medium and long term carbon storage.

Which transition rules are applied to decide where agriculture is conducted?: transition is assumed to occur at the edge of agricultural center (according to high spatial resolution HYDE data), such that the mature forest/most dense agricultural regions are often intact.

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 after natural spin-up, no (0.3 PgC/yr) after constant 1860 land use spin up. Soil is losing carbon (thus reducing respiration and increasing NBP) due to agricultural activity that intensifies erosion. The soil erosion and subsequent transport/storage in river/marine systems cause a natural carbon sink estimated to be about 0.3 PgC y-1 that is considered to be part of the net land-atmo C flux.

What is your modeled nbp for the 1990-2000 decade? is it within 1.2 +/- 0.8 gtc/yr (1-sigma) of observed data from o2/n2 trends (keeling and manning 2014) for 1990-1999 (yes/no, provide number): NBP for 1990-2000 driven by daily GSWP3 forcing is 0.74 GtC/yr. (Note the model is sensitive to climate forcing and IPSL forcing with the same model version leads to a NBP of -0.1 GtC/yr for the same period.)

Confirm data for simulation round ISIMIP2a

Person responsible for model simulations in this simulation round

Ghassem Asrar: ghassem.asrar@pnnl.gov, 0000-0002-1066-8384, Joint Global Change Research Institute, Pacific Northwest National Laborators (USA)

Min Chen: chenminbnu@gmail.com, University of Wisconsin-Madison (USA)

Rafique Rashid: rashidbao@gmail.com, 0000-0001-9591-6588, Joint Global Change Research Institute (USA)

Ning Zeng: zeng@umd.edu, University of Maryland (USA)

Fang Zhao: fangzhao@pik-potsdam.de, 0000-0002-4819-3724, Potsdam Institute for Climate Impacts Research (Germany)

Output Data

Experiments: historical
Climate Drivers: None
Date: 2016-01-21

Basic information

Model Version: v2.3

Reference Paper: Main Reference: Terrestrial mechanisms of interannual CO 2 variability .

Reference Paper: Other References: Agricultural Green Revolution as a driver of increasing atmospheric CO2 seasonal amplitude.

Resolution

Spatial aggregation: regular grid

Horizontal resolution: 0.5°x0.5°

Temporal resolution of input data: climate variables: monthly, disaggragate to daily internally

Temporal resolution of input data: co2: annual

Temporal resolution of input data: land use/land cover: annual

Temporal resolution of input data: soil: not used

Input data

Observed atmospheric climate data sets used: GSWP3, WATCH (WFD), WATCH-WFDEI

Climate variables: tas, rsds, pr

Spin-up

Was a spin-up performed?: Yes

Spin-up design: 1. First 300 years (first 200-year accelerated) with detrended 1906 climate data (because 1701 data is 1906 detrended, and this prevents an artificial model jump), 278.72ppm CO2 (1765 level) and natural vegetation; 2. 1701-1900: randomized climate data (using ISI-MIP provided random serie of detrended 1901-1930 data), time-varying land use and co2; 3. 1901-, time varying climate, co2, land use

Natural Vegetation

Natural vegetation partition: dynamic vegetation distribution

Management & Adaptation Measures

Management: prognostic planting and harvesting, varying management intensity and harvest index to simulate the tripling of crop production since 1961 (See Zeng et al. 2014)

Key model processes

Dynamic vegetation: yes

Nitrogen limitation: implicitly considered in Javis/Collatz photosynthesis

Co2 effects: yes, Javis/Collatz pohotosynthesis

Light interception: vegetation height-dependend light extinction

Light utilization: Jarvis light use efficiency

Phenology: dynamic

Water stress: Influence on photosynthesis, carbon allocation, evapotranspiration, and soil biogeochemical processes

Heat stress: no

Evapo-transpiration approach: Stomatal resistance, linked to photosynthesis; bulk transfer formula

Differences in rooting depth: Vegetation type dependent; also topography

Closed energy balance: yes

Coupling/feedback between soil moisture and surface temperature: yes

Latent heat: yes

Sensible heat: yes

Causes of mortality in vegetation models

Age/senescence: background mortality

Fire: yes

Drought: yes

Insects: no

Storm: no

Stochastic random disturbance: yes (random cold temperature trigger leaf falling)

Other: light competition

NBP components

Fire: yes

Land-use change: yes

Other processes: RH

Species / Plant Functional Types (PFTs)

List of species / pfts: Broad-leaved tree; needleleaf tree; (Whether diciduous or evergreen is dynamically determined); cold grass; warm grass; generic crop

Comments: provided by Fang Zhao (with input from Ning Zeng) 2/16

Model output specifications

Output per pft?: Already averaged over the whole grid