Impact model: SSiB4/TRIFFID-Fire

ISIMIP3a

Person responsible for model simulations in this simulation round

Huilin Huang: mgh4rc@virginia.edu, 0000-0002-7328-6738, University of Virginia (USA)

Output Data

Experiments: (*) obsclim_histsoc, counterclim_histsoc
Climate Drivers: GSWP3-W5E5
Date: 2022-09-23

Basic information

Model Homepage: https://github.com/hhllbao93/SSiB4-TRIFFID-Fire

Reference Paper: Main Reference: Huang, H., Xue, Y., Li, F., and Liu, Y. et al. Modeling long-term fire impact on ecosystem characteristics and surface energy using a process-based vegetation–fire model SSiB4/TRIFFID-Fire v1.0. Geosci. Model Dev.,13,6029–6050,2020

Reference Paper: Other References: Huilin Huang, Yongkang Xue, Ye Liu, Fang Li, and Gregory S. Okin et al. Modeling the short-term fire effects on vegetation dynamics and surface energy in southern Africa using the improved SSiB4/TRIFFID-Fire model. Geosci. Model Dev.,,,2021

Resolution

Spatial aggregation: regular grid

Horizontal resolution: 0.5’ x 0.5’

Temporal resolution of input data: climate variables: monthly

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: <a href='https://www.isimip.org/gettingstarted/input-data-bias-adjustment/details/114/'>W5E5v2.0</a>, <a href='https://www.isimip.org/gettingstarted/input-data-bias-adjustment/details/113/'>W5E5v1.0</a>

Socio-economic data sets: <a href='https://www.isimip.org/gettingstarted/input-data-bias-adjustment/details/125/'>National historical Gross Domestic Product (GDP)</a>, <a href='https://www.isimip.org/gettingstarted/input-data-bias-adjustment/details/123/'>Population (national and gridded, historical, ISIMIP3a)</a>

Land use data sets: <a href='https://www.isimip.org/gettingstarted/input-data-bias-adjustment/details/82/'>Historical, gridded land use</a>

Other data sets: <a href='https://www.isimip.org/gettingstarted/input-data-bias-adjustment/details/135/'>Lightning</a>, <a href='https://www.isimip.org/gettingstarted/input-data-bias-adjustment/details/41/'>Land-sea mask</a>

Climate variables: <abbr title="Near-surface specific humidity">huss</abbr>, <abbr title="Near-surface wind speed">sfcWind</abbr>, <abbr title="daily mean temperature">tas</abbr>, <abbr title="long wave downwelling radiation">rlds</abbr>, <abbr title="sea-level pressure">psl</abbr>, <abbr title="short wave downwelling radiation">rsds</abbr>, <abbr title="total precipitation">pr</abbr>

Spin-up

Was a spin-up performed?: Yes

Spin-up design: Spin-up experiments have been done for 1851-1900 using cycling climate forcing from 1901-1910 and transient CO2 conditions.

Natural Vegetation

Natural vegetation partition: For each grid, SSiB4/TRIFFID-Fire simulates the fraction of 7 plant functional types (PFTs) + the bare soil: BET: Broadleaf evergreen tree NET: Needleleaf evergreen tree DBT: Deciduous broadleaf tree C3: C3 grass C4: C4 grass SHR: shrub TUN: tundra shrub

Natural vegetation dynamics: In each grid, the fraction of each PFTs is updated every 10 days related to the Net Primary Productivity

Soil layers: 3 layers

Soil

Soil layers: 3 layers

Routing

Runoff routing: No

Land Use

Land-use change effects: Cropland changes

Dams & Reservoirs

Dam and reservoir implementation: No

Calibration

Was the model calibrated?: No

Vegetation

How is vegetation represented?: We consider the dynamic fraction cover of the following PFTs BET: Broadleaf evergreen tree NET: Needleleaf evergreen tree DBT: Deciduous broadleaf tree C3: C3 grass C4: C4 grass SHR: shrub TUN: tundra shrub