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Modeling long-term fire impact on ecosystem characteristics and surface energy using a process-based vegetation–fire model SSiB4/TRIFFID-Fire v1.0

02 Dec 2020-Geoscientific Model Development (Copernicus GmbH)-Vol. 13, Iss: 12, pp 6029-6050

Abstract: . Fire is one of the primary disturbances to the distribution and ecological properties of the world’s major biomes and can influence the surface fluxes and climate through vegetation-climate interactions. This study incorporates a fire model of intermediate complexity to a biophysical model with dynamic vegetation, SSiB4/TRIFFID (The Simplified Simple Biosphere Model coupled with the Top-down Representation of Interactive Foliage and Flora Including Dynamics Model). This new model, SSiB4/TRIFFID-Fire, updating fire impact on the terrestrial carbon cycle every 10 days, is then used to simulate the burned area during 1948–2014. The simulated global burned area in 2000–2014 is 471.9 Mha yr−1, close to the estimate, 478.1 Mha yr−1, in Global Fire Emission Database v4s (GFED4s) with a spatial correlation of 0.8. The SSiB4/TRIFFID-Fire reproduces temporal variations of the burned area at monthly to interannual scales. Specifically, it captures the observed decline trend in northern African savanna fire and accurately simulates the fire seasonality in most major fire regions. The simulated fire carbon emission is 2.19 Pg yr−1, slightly higher than the GFED4s (2.07 Pg yr−1). The SSiB4/TRIFFID-Fire is applied to assess long-term fire impact on ecosystem characteristics and surface energy budget by comparing model runs with and without fire (FIRE-ON minus FIRE-OFF). The FIRE-ON simulation reduces tree cover over 6.14 % of the global land surface, accompanied by a decrease in leaf area index and vegetation height by 0.13 m2 m−2 and 1.27 m, respectively. The surface albedo and sensible heat are reduced throughout the year, while latent heat flux decreases in the fire season but increases in the rainy season. Fire results in an increase in surface temperature over most fire regions.
Topics: Vegetation (54%), Biosphere model (53%)

Summary (6 min read)

1 Introduction

  • Wildfire, whether natural or human-made, is one of the primary ecosystem disturbances and it plays a major role in the terrestrial biogeochemical cycles and ecological succession across spatial and temporal scales (Sousa, 1984; Bowman et al., 2009).
  • Fire emissions contribute to increases in greenhouse gases and cloud condensation nuclei through geochemistry processes (Scholes et al., 1996), affecting radiative forcing, the hydrology cycle (Ward et al., 2012; Jiang Published by Copernicus Publications on behalf of the European Geosciences Union.
  • Many empirically determined parameters are included in the complicated process-based models, which leads to large uncertainties.
  • Moreover, current fire models have deficiencies in simulating the peak fire month, fire season length, and interannual variability, as reported by the Fire Model Intercomparison Project (FireMIP; Hantson et al., 2020; Li et al., 2019).
  • The SSiB4/TRIFFID-Fire model updates fire-induced carbon loss every 10 d, which has been rarely employed in current process-based fire models, and is used to provide a quantitative assessment of fire impact on ecosystem characteristics and surface energy at subseasonal, seasonal, interannual, and long-term scales.

2.1 Land and vegetation model

  • The Simplified Simple Biosphere Model (SSiB, Xue et al., 1991; Zhan et al., 2003) is a biophysical model which simulates fluxes of surface radiation, momentum, sensible and Geosci.
  • Model Dev., 13, 6029–6050, 2020 https://doi.org/10.5194/gmd-13-6029-2020.
  • Latent heat, runoff, soil moisture, surface temperature, and vegetation gross and net primary productivity (GPP and NPP) based on energy and water balance.
  • The SSiB was coupled with a dynamic vegetation model, the Top-down Representation of Interactive Foliage and Flora Including Dynamics Model , to calculate leaf area index (LAI), canopy height, and PFT fractional coverage according to the carbon balance (Cox, 2001; Zhang et al., 2015; Harper et al., 2016; Liu et al., 2019).
  • The authors have improved the PFT competition strategy and plant physiology processes to make the SSiB4/TRIFFID suitable for seasonal, interannual, and decadal studies (Zhang et al., 2015; Liu et al., 2019).

2.2 Fire model and modifications

  • A process-based fire model of intermediate complexity has been implemented in the SSiB4/TRIFFID, called SSiB4/TRIFFID-Fire.
  • The fire model developed by Li et al. (2012, 2013) was first built on the model platform of CLM-DGVM and has been incorporated in IAP-DGVM (Zeng et al., 2014), CLM4.5 (Oleson et al., 2013), CLM5 (Lawrence et al., 2019), LM3 in Earth system model GFDLESM (Rabin et al., 2018; Ward et al., 2018), AVIM in Climate System Model BCC-CSM (Weiping Li, personal communication, 2016), E3SM Land Model (ELM; Ricciuto et al., 2018), NASA GEOS catchment-CN4.5 model (Zeng et al., 2019), and DLEM (Yang et al., 2014), and it has been partly used in GLASS-CTEM (Melton and Arora, 2016).
  • The following briefly describes the fire schemes adapted from Li et al. (2012, 2013), Li and Lawrence (2017), and their own modifications.
  • The fire model is comprised of three parts: fire occurrence, fire spread, and fire impact.
  • The basic equation is that the burned area in a grid cell (Ab, km2 s−1) is determined by the number of fires per time step (Nf, count s−1) and the average spread area per fire (a, km2 count−1): Ab =Nfa. (1).

2.2.1 Fire occurrence

  • Nf is the product of the number of potential ignition counts due to both natural causes (In, count s−1 km−2) and human ignitions (Ia, count s−1 km−2), fuel availability (fb), fuel combustibility (fm), and human suppression factors (feo).
  • The authors only consider non-crop fire by excluding the cropland fraction from burning: Nf = (In+ Ia)fbfmfeo(1− )Ag, (2) where Ag is the land area of the grid cell (km2).
  • Fires in the croplands are excluded here due to their small extent within the major fire regions and their relatively low intensity (Bistinas et al., 2014).
  • The number of natural ignitions is related to lightning flashes (Il, count s−1); cloud-to-ground lightning fraction, 1 5.16+2.16 cos[3,min(60,λ)] , which depends on latitude λ (Prentice and Mackerras, 1977); and ignition efficiency (ψ = 0.22).
  • The anthropogenic ignition, Ia, is parameterized using the number of potential anthropogenic ignitions by a person (α = 1.35× 10−9 count per person s−1) and population density (Dp; person) (Venevsky et al. 2002): In = ψ 5.16+ 2.16cos[3,min(60,λ)].

Ia = αDp × (6.8D−0.6p ). (4)

  • Following Li et al. (2012), the authors use Blow = 155 g C m−2 as the lower fuel threshold, below which fire does not occur and Bup = 1050 g C m−2 as the upper fuel threshold, above which fuel load is not a constraint for fire occurrence.
  • Fuel combustibility fm (fraction, 0–1) is given as follow: fm = fRHfθ , (6) where fRH and fθ represent the dependence of fuel combustibility on relative humidity (RH; %) and the root zone soil moisture (θ ), respectively (Li and Lawrence, 2017).
  • (8) Relative humidity suppresses fire occurrence when it is larger than RHup = 70 %, and relative humidity does not con- https://doi.org/10.5194/gmd-13-6029-2020.
  • The PFTdependent θup and θlow are used as the upper and lower thresholds of soil moisture in a similar way to the thresholds of relative humidity (Table 1).
  • Βlow = 0.85 and βup = 0.98 are used as the lower and upper thresholds for all PFTs, yet the narrow range of β in CLM5 has led to fire model too sensitive to drought.

2.2.2 Average spread area after fire ignition

  • The average spread area of a fire is assumed to be elliptical in shape, with the ignition point located at one of the foci and the fastest spread occurring along the major axis.
  • The human suppression factor, Fse, reflects the human impact on fire spread through firefighting activities and is parameterized following Li et al. (2013).

2.2.3 Carbon emissions, post-fire mortality, and emissions of aerosols and trace gases

  • As the carbon cycle in current SSiB4/TRIFFID does not explicitly represent the litter carbon storage and decomposition, the authors assume the litter carbon and woody debris account for about 25 % of aboveground biomass for global forest and about 30 % for savanna and grassland based on previous studies (Pan et al., 2011; de Oliveira et al., 2019).
  • CCj is the corresponding combustion completeness for leaf, stem, root, and litter of the j th PFT (Table S2).
  • Meanwhile, fire-induced mortality transfers carbon from uncombusted leaf, stem, and root to litter: ψj = AbCj · (1−CCj ) ·Mj , (14) where Mj = (Mleaf,Mstem,Mroot)j is the corresponding mortality factor for the j th PFT(Table S2).
  • Finally, the emissions of trace gases and aerosols species x for the j th PFT (EMx,j , g s−1) can be calculated from carbon emissions (ϕj ) using the PFT-dependent emission factor (EFx,j , g species (kg dm)−1): EMx,j = EFx,j ϕj [C] , (15) where [C] (= 0.5 g C (kg dm)−1) is a unit conversion factor from dry matter to carbon (Li et al., 2019).
  • The emissions of trace gases and aerosols can be applied in the atmospheric chemistry model to calculate the production of secondary aerosols, transport of pollutants, and the resultant aerosol direct and indirect effects on climate.

2.2.4 Including the fire effect on the carbon pool

  • When the Li et al. (2012) fire model is coupled with CLM, the vegetation distribution is prescribed using satellite-based Geosci.
  • Model Dev., 13, 6029–6050, 2020 https://doi.org/10.5194/gmd-13-6029-2020 land cover, and therefore the fire impact on vegetation cover is not simulated.
  • In SSiB4/TRIFFID-Fire, the fire-related carbon loss due to combustion and post-fire mortality is transferred to changes of PFT fraction based on carbon balance.
  • Cvj is the carbon density (g C km−2); cij is the competition coefficient between the ith and j th PFTs; and γj (s−1) is the constant disturbance representing the loss of PFT fraction due to fires, pests, windthrow, and many other processes.
  • The fire-caused PFT fraction loss results in bare soil for vegetation spreading decided by the competition strategy in TRIFFID.

2.3 Implementing the fire model in SSiB4/TRIFFID

  • In SSiB4/TRIFFID, SSiB4 provides GPP, autotrophic respiration, and other physical variables such as canopy temperature and soil moisture every 3 h for TRIFFID (Fig. 1).
  • TRIFFID accumulates the 3-hourly GPP and respiration and provides biotic carbon, PFT fractional coverage, vegetation height, and LAI every 10 d, which are used to update surface properties (albedo, canopy height, roughness length, and aerodynamic/canopy resistances) in SSiB4.
  • When the fire model is included, it uses the meteorological forcings and physical variables provided by SSiB4 every 3 h and the biophysical properties (PFT fraction and biotic carbon) provided by TRIFFID every 10 d. TRIFFID updates the vegetation dynamics based on carbon balance on day 10, using the net primary production, fireinduced carbon loss, and PFT competition strategy.
  • The updated vegetation dynamics are transferred to SSiB4 to reflect fire effects on surface properties.

3.1 Experimental design

  • Two sets of offline experiments have been conducted using SSiB4/TRIFFID-Fire, which consist of FIRE-ON (SSiB4/TRIFFID-Fire with fire model switched on) and FIRE-OFF (SSiB4/TRIFFID-Fire with fire model switched off).
  • To obtain the initial conditions for these two experiments, similar to their previous SSiB4/TRIFFID experiments (Zhang et al., 2015; Liu et al., 2019), the authors conducted spinup simulations (SPFIRE-ON and SPFIRE-OFF) for 100 years to reach a quasi-equilibrium PFT distribution with and without fire disturbance.
  • These spin-up simulations were initialized using the quasi-equilibrium state from Liu et al. (2019) (SPINIT in Fig. 2) and were driven by climatology forcing averaged over 1948–1972 and atmospheric CO2 concentration, population density, and GDP in 1948 (Fig. 2).
  • FIRE-ON produced the fire regime, ecosystem, and surface conditions during 1948–2014.
  • A FIRE-OFF run, based on SPFIRE-OFF, was carried out with the fire model switched off during 1948–2014.

3.2 Model input and validation data

  • The meteorological forcings used to drive SSiB4/TRIFFIDFire for the period of 1948–2014 are from the Princeton global meteorological dataset for land surface modeling (Sheffield et al., 2006), including surface air temperature, surface pressure, specific humidity, wind speed, downward shortwave radiation flux, downward longwave radiation flux, and precipitation (Table 3).
  • The dataset is constructed by combining global observation-based datasets with the NCEP/NCAR reanalysis.
  • The 2-hourly climatology lightning flashes data from NASA LIS/OTD v2.2 at 2.5◦× 2.5◦ resolution are used to calculate the number of natural ignitions.
  • GDP per capita in 2000 is from van Vuuren et al. (2007).
  • The GFED fire product provides the burned area and fire emissions on the global scale and has been widely used for fire model validation and calibration (van Marle et al., 2017; Li et al., 2019).

4 Results

  • This section evaluates the model simulation of burned area, carbon emissions, PFT fraction, and GPP by comparing FIRE-ON results with GFED4s, GLC2000, and FLUXNET-MTE data.
  • Specifically, the authors will focus on the model description of fire seasonality.
  • -Fire is applied to assess the long-term fire effect on the ecosystem and surface energy budget using the differences between the FIRE-ON and FIRE-OFF.

4.1 Burned area

  • The simulations of burned area are evaluated using satellitebased product GFED4s for the period of 2000–2014.
  • In 8 out of the 14 sub-regions, SSiB4/TRIFFID-Fire reproduces the observed interannual variability (IAV) of burned area well, with the correlation between simulations and observations significant at p < 0.05 (Fig. 4c).
  • The simulated IAV of SHAF burned area is not as good as other savanna fire regions (e.g., NHAF, SHSA, and AUST), although the IAV is small there (Fig. 4c).
  • As lightning flash is the predominant ignition source in the Northern Hemisphere high latitudes, the application of climatology lightning has a greater impact in boreal regions than in other parts of the globe.
  • -Fire generates two fire seasons in June–July–August and December–January–February, capturing the peak month in August but underestimating the burned area in December.

4.2 Fire emissions

  • Biomass burning emissions are determined by burned area, fuel combustion rate per unit area, and emission factors per unit mass of fuel burned (van der Werf et al., 2017).
  • -Fire captures the high carbon emissions in tropical savannas, the intermediate emissions in Northern Hemisphere boreal forests, and the low emissions in hu- https://doi.org/10.5194/gmd-13-6029-2020.
  • The simulated global CO emission is 433.7.
  • Tg yr−1 in 2000–2014, very close to the observational estimates (434.0 Tg yr−1) from Zheng et al. (2019) with a spatial correlation of 0.74.
  • In NHAF, SSiB4/TRIFFID-Fire reproduces the large CO emission in DJF, although the model slightly underestimates CO emis- sion in December and overestimates it in February.

4.3 PFT distribution and GPP

  • The simulation of vegetation coverage, which represents model description of biomass allocation and influences the fuel availability and flammability in fire modeling, is evaluated against GLC2000 (Bartholome and Belward, 2005).
  • Compared with the observations (Fig. 10a), SSiB4/TRIFFID-Fire captures the fractional coverage of trees in the Amazon rainforest, tropical Africa, equatorial Asia, Southeast Asia, southeastern North America, and the Northern Hemisphere Boreal regions (Fig. 10c).
  • Shrubs are primarily located in the semiarid regions and the pan-Arctic area and tundra is limited to the pan-Arctic area and Tibetan Plateau (Fig. S3f–g).
  • The highest GPP occurs in the tropical evergreen forest and decreases with latitude in both observations and model.
  • In addition, the correlation of IAV of global GPP is 0.68 (p < 0.05) between SSiB4/TRIFFID-Fire and FLUXNET-MTE, indicating that the model has reasonably captured the terrestrial ecosystem variability during the historical period.

4.4 Fire effects on ecosystem characteristics and surface properties

  • The authors investigate long-term fire effects on ecosystem characteristics, surface properties, and surface energy budget using the differences between FIREON and FIRE-OFF (FIRE-ON minus FIRE-OFF).
  • In Africa, the simulated fire effects on vegetation structure (tree and grass cover, LAI, and vegetation height) peak in the tropical savanna surrounding the forests and gradually decrease towards the deserts (Fig. 11).
  • Geosci.
  • Model Dev., 13, 6029–6050, 2020 Geosci.
  • The authors results are in agreement with the observational studies on different fire types (e.g., forest fire and savanna fire) showing that surface properties changes after fire results in an increase in albedo (Gholz and Clark, 2002; Amiro et al., 2006b; Sun et al., 2010) and a decrease in sensible heat (Chambers and Chapin, 2002; Liu et al., 2005; Amiro et al., 2006a, b; Rogers et al., 2013).
  • Therefore, the latent heat can be increased due to enhanced soil evaporation.

5 Conclusions and discussions

  • The authors have implemented a process-based fire model of intermediate complexity into a DGVM, SSiB4/TRIFFID, which is based on the surface water, carbon, and energy balances, as well as the PFT competition.
  • The SSiB4/TRIFFID-Fire is then applied to study the long-term fire effects on ecosystem characteristics and surface energy.
  • As Li et al. (2017) is the only modeling study investigating the long-term fire effects on the land energy budget, their simulation provides another approach that quantifies fire effects using a different land surface model with different approaches in parameterizing some land surface processes and vegetation dynamics.
  • All authors (HH, YX, FL, and YL) have contributed to the analysis methods and to the text.
  • The authors acknowledge the use of the Cheyenne supercomputer (https://doi.org/10.5065/D6RX99HX, Computational and Information Systems Laboratory, 2019), provided by NCAR CISL, for providing HPC resources.

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Geosci. Model Dev., 13, 6029–6050, 2020
https://doi.org/10.5194/gmd-13-6029-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Modeling long-term fire impact on ecosystem characteristics and
surface energy using a process-based vegetation–fire
model SSiB4/TRIFFID-Fire v1.0
Huilin Huang
1
, Yongkang Xue
1,2
, Fang Li
3
, and Ye Liu
1
1
Department of Geography, University of California, Los Angeles, CA 90095, USA
2
Department of Atmospheric & Oceanic Sciences, University of California, Los Angeles, CA 90095, USA
3
International Center for Climate and Environmental Sciences, Institute of Atmospheric Physics,
Chinese Academy of Sciences, Beijing, China
Correspondence: Yongkang Xue (yxue@geog.ucla.edu)
Received: 26 April 2020 Discussion started: 15 June 2020
Revised: 19 September 2020 Accepted: 16 October 2020 Published: 2 December 2020
Abstract. Fire is one of the primary disturbances to the
distribution and ecological properties of the world’s ma-
jor biomes and can influence the surface fluxes and climate
through vegetation–climate interactions. This study incorpo-
rates a fire model of intermediate complexity to a biophys-
ical model with dynamic vegetation, SSiB4/TRIFFID (The
Simplified Simple Biosphere Model coupled with the Top-
down Representation of Interactive Foliage and Flora Includ-
ing Dynamics Model). This new model, SSiB4/TRIFFID-
Fire, updating fire impact on the terrestrial carbon cycle ev-
ery 10 d, is then used to simulate the burned area during
1948–2014. The simulated global burned area in 2000–2014
is 471.9 Mha yr
1
, close to the estimate of 478.1 Mha yr
1
in Global Fire Emission Database v4s (GFED4s), with a spa-
tial correlation of 0.8. The SSiB4/TRIFFID-Fire reproduces
temporal variations of the burned area at monthly to inter-
annual scales. Specifically, it captures the observed decline
trend in northern African savanna fire and accurately simu-
lates the fire seasonality in most major fire regions. The sim-
ulated fire carbon emission is 2.19 Pg yr
1
, slightly higher
than the GFED4s (2.07 Pg yr
1
).
The SSiB4/TRIFFID-Fire is applied to assess the long-
term fire impact on ecosystem characteristics and surface en-
ergy budget by comparing model runs with and without fire
(FIRE-ON minus FIRE-OFF). The FIRE-ON simulation re-
duces tree cover over 4.5 % of the global land surface, ac-
companied by a decrease in leaf area index and vegetation
height by 0.10 m
2
m
2
and 1.24 m, respectively. The surface
albedo and sensible heat are reduced throughout the year,
while latent heat flux decreases in the fire season but in-
creases in the rainy season. Fire results in an increase in sur-
face temperature over most fire regions.
1 Introduction
Wildfire, whether natural or human-made, is one of the pri-
mary ecosystem disturbances and it plays a major role in the
terrestrial biogeochemical cycles and ecological succession
across spatial and temporal scales (Sousa, 1984; Bowman et
al., 2009). Every year in the dry season, wildfires burn about
400 Mha of land vegetated areas, leaving behind numerous
scars in the landscape (Giglio et al., 2013; Chuvieco et al.,
2016). Fires affect the climate through modification of wa-
ter, energy, and momentum exchange between land and at-
mosphere (Chambers and Chapin, 2002; Bond-Lamberty et
al., 2009) and can interact with monsoons by affecting at-
mospheric circulations (De Sales et al., 2016; Saha et al.,
2016). Fires are also important sources of global carbon,
aerosols, and trace gas emissions. Based on the latest satel-
lite estimates, global fires emit 1.5–4.2 Pg C yr
1
carbon, 7–
8.2 Pg C yr
1
CO
2
, and 1.9–6.0 Tg C yr
1
black carbon to
the atmosphere (Chuvieco et al., 2016; van der Werf et al.,
2017; Li et al., 2019). Fire emissions contribute to increases
in greenhouse gases and cloud condensation nuclei through
geochemistry processes (Scholes et al., 1996), affecting ra-
diative forcing, the hydrology cycle (Ward et al., 2012; Jiang
Published by Copernicus Publications on behalf of the European Geosciences Union.

6030 H. Huang et al.: Modeling long-term fire impact on ecosystem characteristics
et al., 2016; Hamilton et al., 2018), and air quality (van der
Werf et al., 2010; Johnston et al., 2012).
Since the early 2000s, fire models have been developed
within Dynamic Global Vegetation Models (DGVMs) to ex-
plicitly describe the burned area, fire emissions, and fire dis-
turbance on terrestrial ecosystems (Thonicke et al., 2001;
Venevsky et al., 2002; Arora and Boer, 2005; Thonicke et
al., 2010; Li et al., 2012; Pfeiffer et al., 2013; Lasslop et al.,
2014; Yue et al., 2014; Rabin et al., 2018; Burton et al., 2019;
Venevsky et al., 2019). These fire models have various lev-
els of complexity, from simple statistical models (SIMFIRE;
Knorr et al., 2016) to complicated process-based ones such
as SPITFIRE (Thonicke et al., 2010) and MC2 (Bachelet et
al., 2015). With increasing complexity, more fire processes
and fire characteristics are considered in fire models. In gen-
eral, current fire models broadly capture the global amounts
and spatial distribution of burned area and carbon emissions,
as compared to different observations. However, many em-
pirically determined parameters are included in the compli-
cated process-based models, which leads to large uncertain-
ties. There is no model that outperforms other models across
all fire variables (Hantson et al., 2020). Moreover, current fire
models have deficiencies in simulating the peak fire month,
fire season length, and interannual variability, as reported by
the Fire Model Intercomparison Project (FireMIP; Hantson
et al., 2020; Li et al., 2019). Most fire models show a 1–2
month shift in peak burned area and simulate a longer fire
season compared to observations.
Fire models have been used to reconstruct fire history be-
fore the satellite era (Yang et al., 2015; van Marle et al., 2017;
Li et al., 2019). In addition, they are widely used to attribute
historical variability of burned area to various climate and
anthropogenic driving factors (Kloster et al., 2012; Andela
et al., 2017; Forkel et al., 2019; Teckentrup et al., 2019).
Some fire models have been used to assess long-term fire im-
pact on the terrestrial carbon cycle by comparing a reference
simulation with fire and a sensitivity simulation representing
“a world without fire”. However, the simulated responses of
vegetation and carbon cycle are divergent. Bond et al. (2005)
reported that forest cover would double in a world without
fire, while in the recent fire-coupled DGVMs, a much smaller
tree cover reduction by 10 % (ranges between 3 % and 25 %)
is simulated when fire is taken into account (Lasslop et al.,
2020). Earlier model-based studies reported that fire reduced
terrestrial carbon uptake. However, the range of the quan-
tified reduction was fairly broad (0.05–3.60 Pg C yr
1
), and
most studies did not consider the fire effects on vegetation
distribution and related mechanisms (Li et al., 2014; Yue et
al., 2015; Poulter et al., 2015; Yang et al., 2015; Seo and Kim,
2019; Zou et al. 2020).
Thus far, only the fire model developed by Li et al. (2012,
2013) has been used to investigate the long-term fire effects
on surface energy. By comparing the simulated climate with
and without fire, Li et al. (2017) concluded that fire caused
a significant decrease in surface radiation, latent heat, and
a slight decrease in sensible heat fluxes through changes in
biophysical properties such as albedo, Bowen ratio, and aero-
dynamic resistance. An increase in surface temperature was
found over most fire regions. However, the long-term fire im-
pact on vegetation distribution was not taken into account in
Li et al. (2017), which has been widely observed on site-level
studies (Higgins et al., 2007; Smit et al., 2010) and can cause
substantial changes in aerodynamic resistance due to conver-
sions of dominant plant functional type (PFT) (Huang et al.,
2020b). Moreover, Li et al. (2017) focused on the annual fire
impact on energy fluxes. However, fire’s effects on energy
budget can have large seasonal variations associated with the
vegetation loss during the fire seasons and vegetation recov-
ery during post-fire rainy seasons. The seasonal variations in
fire effects have not been investigated in any fire studies.
In the original SSiB4/TRIFFID, the carbon disturbance
caused by fire and insects was assumed to be a constant,
which depended solely on PFT without spatial and tempo-
ral changes (Cox et al., 2001; Liu et al., 2019). However,
the fire disturbance is varies greatly with climate, vegeta-
tion productivity, and socioeconomic conditions, which has
a strong influence on vegetation dynamics, carbon cycling,
and soil processes. In this study, we develop the fire mod-
eling by incorporating the fire scheme of Li et al. (2012,
2013) to SSiB4/TRIFFID (hereafter, SSiB4/TRIFFID-Fire).
The SSiB4/TRIFFID-Fire model updates fire-induced carbon
loss every 10 d, which has been rarely employed in current
process-based fire models, and is used to provide a quanti-
tative assessment of fire impact on ecosystem characteristics
and surface energy at subseasonal, seasonal, interannual, and
long-term scales. Specifically, our objectives are (1) to evalu-
ate the climatology and interannual variability of burned area
and carbon emissions simulated by offline SSiB4/TRIFFID-
Fire, (2) to assess the ability of SSiB4/TRIFFID-Fire in cap-
turing the fire seasonality in major fire regions, and (3) to
assess the long-term fire impact on PFT distribution and veg-
etation properties and the resultant changes in seasonal sur-
face energy budget and temperature. In Sect. 2, we provide
a brief description of the DGVM, SSiB4/TRIFFID; the fire
model, taken from Li et al. (2012, 2013); and the coupling
procedures. The experimental design and data for model in-
put and validation are introduced in Sect. 3. The fire model
evaluation on a global scale and the application of long-term
fire impact on ecosystem characteristics and surface proper-
ties are presented in Sect. 4. Discussions and conclusions are
given in Sect. 5.
2 Method
2.1 Land and vegetation model
The Simplified Simple Biosphere Model (SSiB, Xue et al.,
1991; Zhan et al., 2003) is a biophysical model which sim-
ulates fluxes of surface radiation, momentum, sensible and
Geosci. Model Dev., 13, 6029–6050, 2020 https://doi.org/10.5194/gmd-13-6029-2020

H. Huang et al.: Modeling long-term fire impact on ecosystem characteristics 6031
latent heat, runoff, soil moisture, surface temperature, and
vegetation gross and net primary productivity (GPP and
NPP) based on energy and water balance. The SSiB was
coupled with a dynamic vegetation model, the Top-down
Representation of Interactive Foliage and Flora Including
Dynamics Model (TRIFFID), to calculate leaf area index
(LAI), canopy height, and PFT fractional coverage accord-
ing to the carbon balance (Cox, 2001; Zhang et al., 2015;
Harper et al., 2016; Liu et al., 2019). We have improved
the PFT competition strategy and plant physiology processes
to make the SSiB4/TRIFFID suitable for seasonal, interan-
nual, and decadal studies (Zhang et al., 2015; Liu et al.,
2019). SSiB4/TRIFFID includes seven PFTs: (1) broadleaf
evergreen trees (BET), (2) needleleaf evergreen trees (NET),
(3) broadleaf deciduous trees (BDT), (4) C
3
grasses, (5) C
4
plants, (6) shrubs, and (7) tundra. The coverage of a PFT is
determined by net carbon availability, competition between
species, and disturbance, which implicitly includes mortal-
ity due to fires, pests, and windthrow. A detailed description
and validation of SSiB4/TRIFFID can be found in Zhang et
al. (2015) and Liu et al. (2019).
2.2 Fire model and modifications
In this study, a process-based fire model of intermediate
complexity has been implemented in the SSiB4/TRIFFID,
called SSiB4/TRIFFID-Fire. The fire model developed by
Li et al. (2012, 2013) was first built on the model platform
of CLM-DGVM and has been incorporated in IAP-DGVM
(Zeng et al., 2014), CLM4.5 (Oleson et al., 2013), CLM5
(Lawrence et al., 2019), LM3 in Earth system model GFDL-
ESM (Rabin et al., 2018; Ward et al., 2018), AVIM in Cli-
mate System Model BCC-CSM (Weiping Li, personal com-
munication, 2016), E3SM Land Model (ELM; Ricciuto et al.,
2018), NASA GEOS catchment-CN4.5 model (Zeng et al.,
2019), and DLEM (Yang et al., 2014), and it has been partly
used in GLASS-CTEM (Melton and Arora, 2016). The fol-
lowing briefly describes the fire schemes adapted from Li et
al. (2012, 2013), Li and Lawrence (2017), and our own mod-
ifications.
The fire model is comprised of three parts: fire occurrence,
fire spread, and fire impact. The basic equation is that the
burned area in a grid cell (A
b
, km
2
s
1
) is determined by the
number of fires per time step (N
f
, count s
1
) and the average
spread area per fire (a, km
2
count
1
):
A
b
= N
f
a. (1)
2.2.1 Fire occurrence
N
f
is the product of the number of potential ignition counts
due to both natural causes (I
n
, count s
1
km
2
) and human
ignitions (I
a
, count s
1
km
2
), fuel availability (f
b
), fuel
combustibility (f
m
), and human suppression factors (f
eo
). In
this paper, we only consider non-crop fire by excluding the
cropland fraction (f
crop
) from burning:
N
f
= (I
n
+ I
a
)f
b
f
m
f
eo
(1 f
crop
)A
g
, (2)
where A
g
is the land area of the grid cell (km
2
). Fires in the
croplands are excluded here due to their small extent within
the major fire regions and their relatively low intensity (Bisti-
nas et al., 2014). Cropland fire is still a major uncertainty in
remote sensing datasets (Randerson et al., 2012), and more
data and investigation are needed.
The number of natural ignitions is related to lightning
flashes (I
l
, count s
1
); cloud-to-ground lightning fraction,
1
5.16+2.16 cos[3,min(60,λ)]
, which depends on latitude λ (Pren-
tice and Mackerras, 1977); and ignition efficiency (ψ =
0.22). The anthropogenic ignition, I
a
, is parameterized using
the number of potential anthropogenic ignitions by a person
(α = 1.35 × 10
9
count per person s
1
) and population den-
sity (D
p
; person) (Venevsky et al. 2002):
I
n
=
ψ
5.16 + 2.16cos[3,min(60,λ)]
I
l
, (3)
I
a
= αD
p
× (6.8D
0.6
p
). (4)
The fuel availability f
b
(fraction, range 01) is given as fol-
lows:
f
b
=
0 B
ag
B
low
B
ag
B
low
B
up
B
low
B
low
< B
ag
< B
up
1 B
ag
B
up
, (5)
where B
ag
(g C m
2
) is the aboveground biomass (leaf and
stem in SSiB4/TRIFFID-Fire) of all PFTs. Following Li et
al. (2012), we use B
low
= 155 g C m
2
as the lower fuel
threshold, below which fire does not occur and B
up
=
1050 g C m
2
as the upper fuel threshold, above which fuel
load is not a constraint for fire occurrence.
Fuel combustibility f
m
(fraction, 0–1) is given as follow:
f
m
= f
RH
f
θ
, (6)
where f
RH
and f
θ
represent the dependence of fuel com-
bustibility on relative humidity (RH; %) and the root zone
soil moisture (θ), respectively (Li and Lawrence, 2017). Fol-
lowing Li et al. (2013), we assume f
m
= 0 when surface air
temperature T is below 10
C. f
RH
reflects the impact of
real-time climate conditions on fuel combustibility, while f
θ
reflects the response of fuel combustibility to the preceding
climate conditions (Shinoda and Yamaguchi, 2003):
f
RH
=
0 RH RH
up
(
RH
up
RH
RH
up
RH
low
)
1.3
RH
low
< RH < RH
up
1 RH RH
low
, (7)
f
θ
=
0 θ θ
up
(
θ
up
θ
θ
up
θ
low
)
0.7
θ
low
< θ < θ
up
1 θ θ
low
. (8)
Relative humidity suppresses fire occurrence when it is larger
than RH
up
= 70 %, and relative humidity does not con-
https://doi.org/10.5194/gmd-13-6029-2020 Geosci. Model Dev., 13, 6029–6050, 2020

6032 H. Huang et al.: Modeling long-term fire impact on ecosystem characteristics
Table 1. The upper (θ
up
) and lower (θ
low
) thresholds of root zone
soil moisture for PFTs in SSiB4/TRIFFID-Fire.
Vegetation types θ
up
θ
low
BET 0.80 0.30
NET 0.80 0.30
BDT 0.80 0.30
C
3
grasses 0.75 0.30
C
4
plants 0.75 0.30
Shrubs 0.60 0.30
Tundra 0.60 0.30
strain fire when it is smaller than RH
low
= 30 %. The PFT-
dependent θ
up
and θ
low
are used as the upper and lower
thresholds of soil moisture in a similar way to the thresh-
olds of relative humidity (Table 1). In the Li et al. (2012)
fire scheme, this factor (f
θ
) is parameterized using root zone
soil moisture potential factor β (0–1.0), a model-dependent
variable used to calculate transpiration in CLM (Li and
Lawrence, 2017). β
low
= 0.85 and β
up
= 0.98 are used as
the lower and upper thresholds for all PFTs, yet the narrow
range of β in CLM5 has led to fire model too sensitive to
drought. In SSiB4/TRIFFID-Fire, the root zone soil moisture
θ is found to be the best variable to describe the dependence
of fuel combustibility on the preceding climate.
The human suppression factor (f
eo
; 0–1) reflects the de-
mographic (f
d
) and economic (f
e
) impact on fire occurrence
in populated areas (population density D
p
> 0.1 per person
km
2
):
f
seo
= f
d
f
e
. (9)
The human suppression is assumed to be negligible (f
seo
=
1) when D
p
0.1 person km
2
. A detailed description of
f
d
and f
e
parameterization can be found in Li et al. (2012,
2013).
2.2.2 Average spread area after fire ignition
The average spread area of a fire is assumed to be elliptical
in shape, with the ignition point located at one of the foci
and the fastest spread occurring along the major axis. The
average burned area of a fire a (km
2
per count) is represented
as follows (Li et al., 2012):
a = π L
B
(u
max
g
0
F
m
τ/1000)
2
F
se
, (10)
where L
B
is the length-to-breadth ratio of the ellipse shape
and is related to the wind speed, W (m s
1
):
L
B
= 1.0 + 10.0
1 exp
(
0.06W
)
. (11)
u
max
is the PFT-dependent maximum fire spread rate (m s
1
;
Table S1). g
0
= 0.05 is the dependence of fire spread rate
perpendicular to the wind direction. F
m
is the influence of
fuel wetness on fire spread and is assumed to be related to
f
m
in the fire occurrence Eq. (6):
F
m
= f
0.5
m
. (12)
τ (= 3600 × 24 s) is the global average fire duration, which
is simply taken to be 1 d, as reported by Giglio et al. (2006).
The human suppression factor, F
se
, reflects the human impact
on fire spread through firefighting activities and is parameter-
ized following Li et al. (2013).
2.2.3 Carbon emissions, post-fire mortality, and
emissions of aerosols and trace gases
In post-fire regions, the fire carbon emission, ϕ
j
(g C s
1
),
from the jth PFT is calculated based on the burned area (A
b
;
km
2
s
1
), Carbon density C
j
, and carbon combustion com-
pleteness CC
j
:
ϕ
j
= A
b
C
j
· CC
j
. (13)
C
j
= (C
leaf
, C
stem
, C
root
, C
litter
)
j
is carbon density vector
(g C km
2
) for leaf, stem, root, and litter of the j th PFT
calculated in TRIFFID. As the carbon cycle in current
SSiB4/TRIFFID does not explicitly represent the litter car-
bon storage and decomposition, we assume the litter carbon
and woody debris account for about 25 % of aboveground
biomass for global forest and about 30 % for savanna and
grassland based on previous studies (Pan et al., 2011; de
Oliveira et al., 2019). CC
j
is the corresponding combustion
completeness for leaf, stem, root, and litter of the jth PFT
(Table S2). Meanwhile, fire-induced mortality transfers car-
bon from uncombusted leaf, stem, and root to litter:
ψ
j
= A
b
C
j
· (1 CC
j
) · M
j
, (14)
where M
j
= (M
leaf
, M
stem
, M
root
)
j
is the corresponding
mortality factor for the jth PFT(Table S2).
Finally, the emissions of trace gases and aerosols species
x for the j th PFT (EM
x,j
, g s
1
) can be calculated from car-
bon emissions (ϕ
j
) using the PFT-dependent emission factor
(EF
x,j
, g species (kg dm)
1
):
EM
x,j
= EF
x,j
ϕ
j
[C]
, (15)
where
[
C
]
(= 0.5 g C (kg dm)
1
) is a unit conversion factor
from dry matter to carbon (Li et al., 2019). The emission
factors, EF
x,j
, of trace gases and aerosols in Table S3 are
based on Andreae (2019). The emissions of trace gases and
aerosols can be applied in the atmospheric chemistry model
to calculate the production of secondary aerosols, transport
of pollutants, and the resultant aerosol direct and indirect ef-
fects on climate.
2.2.4 Including the fire effect on the carbon pool
When the Li et al. (2012) fire model is coupled with CLM,
the vegetation distribution is prescribed using satellite-based
Geosci. Model Dev., 13, 6029–6050, 2020 https://doi.org/10.5194/gmd-13-6029-2020

H. Huang et al.: Modeling long-term fire impact on ecosystem characteristics 6033
land cover, and therefore the fire impact on vegetation cover
is not simulated. In SSiB4/TRIFFID-Fire, the fire-related
carbon loss due to combustion and post-fire mortality is
transferred to changes of PFT fraction based on carbon bal-
ance.
In TRIFFID (Cox, 2001), the fractional change of the jth
PFT (
df
j
dt
) is governed by the Lotka–Volterra equation:
df
j
dt
=
λ
j
NPP
j
f
j
Cv
j
h
1
X
j
c
ij
f
j
i
γ
j
f
j
, (16)
where f
j
is the fractional coverage of the j th PFT; λ
j
NPP
j
is
the carbon available for spreading; Cv
j
is the carbon density
(g C km
2
); c
ij
is the competition coefficient between the ith
and jth PFTs; and γ
j
(s
1
) is the constant disturbance repre-
senting the loss of PFT fraction due to fires, pests, windthrow,
and many other processes.
When the fire model is coupled to SSiB4/TRIFFID, the
loss of PFT fraction due to fires (β
j
) can be explicitly derived
from the fire-induced carbon loss:
β
j
=
j
+ ψ
j
) · f
j
Cv
j
, (17)
where ϕ
j
and ψ
j
are PFT-dependent carbon loss due to com-
bustion and post-fire mortality, respectively. The fire-caused
PFT fraction loss results in bare soil for vegetation spreading
decided by the competition strategy in TRIFFID. As such,
fire disturbance is explicitly represented and varies in space
and time, and the original γ
j
is adjusted to γ
0
j
to exclude fire
disturbance (Table 2):
df
j
dt
=
λ
j
· NPP
j
· f
j
C
j
h
1
X
j
c
ij
f
j
i
γ
0
j
+ β
i
f
j
. (18)
2.3 Implementing the fire model in SSiB4/TRIFFID
In SSiB4/TRIFFID, SSiB4 provides GPP, autotrophic res-
piration, and other physical variables such as canopy tem-
perature and soil moisture every 3 h for TRIFFID (Fig. 1).
TRIFFID accumulates the 3-hourly GPP and respiration and
provides biotic carbon, PFT fractional coverage, vegetation
height, and LAI every 10 d, which are used to update sur-
face properties (albedo, canopy height, roughness length, and
aerodynamic/canopy resistances) in SSiB4. When the fire
model is included, it uses the meteorological forcings and
physical variables provided by SSiB4 every 3 h and the bio-
physical properties (PFT fraction and biotic carbon) provided
by TRIFFID every 10 d. The fire model calculates the burned
area, carbon combustion, post-fire mortality, and emissions
every 3 h, and the fire-induced carbon loss is subtracted from
fuel load. The carbon loss is accumulated within 10 d in
the fire model and is transferred to TRIFFID on day 10.
TRIFFID updates the vegetation dynamics based on carbon
balance on day 10, using the net primary production, fire-
induced carbon loss, and PFT competition strategy. The up-
dated vegetation dynamics are transferred to SSiB4 to reflect
fire effects on surface properties.
3 Experimental setup and data
3.1 Experimental design
Two sets of offline experiments have been conducted
using SSiB4/TRIFFID-Fire, which consist of FIRE-ON
(SSiB4/TRIFFID-Fire with fire model switched on) and
FIRE-OFF (SSiB4/TRIFFID-Fire with fire model switched
off). To obtain the initial conditions for these two experi-
ments, similar to our previous SSiB4/TRIFFID experiments
(Zhang et al., 2015; Liu et al., 2019), we conducted spin-
up simulations (SP
FIRE-ON
and SP
FIRE-OFF
) for 100 years to
reach a quasi-equilibrium PFT distribution with and with-
out fire disturbance. These spin-up simulations were initial-
ized using the quasi-equilibrium state from Liu et al. (2019)
(SP
INIT
in Fig. 2) and were driven by climatology forcing av-
eraged over 1948–1972 and atmospheric CO
2
concentration,
population density, and GDP in 1948 (Fig. 2). Following Liu
et al. (2019), the quasi-equilibrium status is defined as the
rate of relative change in fractional coverage of all PFTs is
less than 2 % over the last 10 years of simulation.
Based on the quasi-equilibrium status with fire disturbance
(SP
FIRE-ON
), a transient run was performed (FIRE-ON) with
the fire model turned on from 1948 to 2014 (Fig. 2). The
model was forced by 3-hourly meteorological forcings and
yearly updated atmospheric CO
2
concentration, population
density, and GDP data. FIRE-ON produced the fire regime,
ecosystem, and surface conditions during 1948–2014. A
FIRE-OFF run, based on SP
FIRE-OFF
, was carried out with
the fire model switched off during 1948–2014. The vegeta-
tion distribution was allowed to respond to climate variations
in both FIRE-ON and FIRE-OFF simulations and to fire dis-
turbances only in the FIRE-ON.
3.2 Model input and validation data
The meteorological forcings used to drive SSiB4/TRIFFID-
Fire for the period of 1948–2014 are from the Prince-
ton global meteorological dataset for land surface model-
ing (Sheffield et al., 2006), including surface air tempera-
ture, surface pressure, specific humidity, wind speed, down-
ward shortwave radiation flux, downward longwave radia-
tion flux, and precipitation (Table 3). The dataset is con-
structed by combining global observation-based datasets
with the NCEP/NCAR reanalysis. The spatial resolution is
1.0
× 1.0
, and the temporal interval is 3 h.
The required inputs for driving the fire model are listed
in Table 3. The 2-hourly climatology lightning flashes data
from NASA LIS/OTD v2.2 at 2.5
× 2.5
resolution are used
to calculate the number of natural ignitions. The population
https://doi.org/10.5194/gmd-13-6029-2020 Geosci. Model Dev., 13, 6029–6050, 2020

Figures (13)
Citations
More filters

01 Dec 2012-
Abstract: We upscaled FLUXNET observations of carbon dioxide, water, and energy fluxes to the global scale using the machine learning technique, model tree ensembles (MTE). We trained MTE to predict site-level gross primary productivity (GPP), terrestrial ecosystem respiration (TER), net ecosystem exchange (NEE), latent energy (LE), and sensible heat (H) based on remote sensing indices, climate and meteorological data, and information on land use. We applied the trained MTEs to generate global flux fields at a 0.5 degrees x 0.5 degrees spatial resolution and a monthly temporal resolution from 1982 to 2008. Cross-validation analyses revealed good performance of MTE in predicting among-site flux variability with modeling efficiencies (MEf) between 0.64 and 0.84, except for NEE (MEf = 0.32). Performance was also good for predicting seasonal patterns (MEf between 0.84 and 0.89, except for NEE (0.64)). By comparison, predictions of monthly anomalies were not as strong (MEf between 0.29 and 0.52). Improved accounting of disturbance and lagged environmental effects, along with improved characterization of errors in the training data set, would contribute most to further reducing uncertainties. Our global estimates of LE (158 +/- 7 J x 10(18) yr(-1)), H (164 +/- 15 J x 10(18) yr(-1)), and GPP (119 +/- 6 Pg C yr(-1)) were similar to independent estimates. Our global TER estimate (96 +/- 6 Pg C yr(-1)) was likely underestimated by 5-10%. Hot spot regions of interannual variability in carbon fluxes occurred in semiarid to semihumid regions and were controlled by moisture supply. Overall, GPP was more important to interannual variability in NEE than TER. Our empirically derived fluxes may be used for calibration and evaluation of land surface process models and for exploratory and diagnostic assessments of the biosphere.

857 citations


15 Dec 2014-
Abstract: This study investigates the impact of burned areas on the surface energy balance and monthly precipitation in northern Africa as simulated by a state-of-the-art regional model. Mean burned area fraction derived from MODIS date of burning product was implemented in a set of 1-year long WRF-NMM/SSiB2 model simulations. Vegetation cover fraction and LAI were degraded daily based on mean burned area fraction and on the survival rate for each vegetation land cover type. Additionally, ground darkening associated with wildfire-induced ash and charcoal deposition was imposed through lower ground albedo for a period after burning. In general, wildfire-induced vegetation and ground condition deterioration increased mean surface albedo by exposing the brighter bare ground, which in turn caused a decrease in monthly surface net radiation. On average, the wildfire-season albedo increase was approximately 6.3 % over the Sahel. The associated decrease in surface available energy caused a drop in surface sensible heat flux to the atmosphere during the dry months of winter and early spring, which gradually transitioned to a more substantial decrease in surface evapotranspiration in April and May that lessened throughout the rainy season. Overall, post-fire land condition deterioration resulted in a decrease in precipitation over sub-Saharan Africa, associated with the weakening of the West African monsoon progression through the region. A decrease in atmospheric moisture flux convergence was observed in the burned area simulations, which played a dominant role in reducing precipitation in the area, especially in the months preceding the monsoon onset. The areas with the largest precipitation impact were those covered by savannas and rainforests, where annual precipitation decreased by 3.8 and 3.3 %, respectively. The resulting precipitation decrease and vegetation deterioration caused a drop in gross primary productivity in the region, which was strongest in late winter and early spring. This study suggests the cooling and drying of atmosphere induced by burned areas caused the strengthening of subsidence during pre-onset and weakening of upward atmospheric motion during onset and mature stages of the monsoon leading to a waning of convective instability and precipitation. Monthly mid-tropospheric vertical wind showed a strengthening of downward motion in winter and spring seasons, and weakening of upward movement during the rainy months. Furthermore, precipitation energy analysis revealed that most of precipitation decrease originated from convective events, which supports the hypothesis of reduced convective instability due to wildfires.

17 citations


Journal ArticleDOI
20 May 2021-PLOS ONE
Abstract: There is a debate concerning the definition and extent of tropical dry forest biome and vegetation type at a global spatial scale. We identify the potential extent of the tropical dry forest biome based on bioclimatic definitions and climatic data sets to improve global estimates of distribution, cover, and change. We compared four bioclimatic definitions of the tropical dry forest biome–Murphy and Lugo, Food and Agriculture Organization (FAO), DryFlor, aridity index–using two climatic data sets: WorldClim and Climatologies at High-resolution for the Earth’s Land Surface Areas (CHELSA). We then compared each of the eight unique combinations of bioclimatic definitions and climatic data sets using 540 field plots identified as tropical dry forest from a literature search and evaluated the accuracy of World Wildlife Fund tropical and subtropical dry broadleaf forest ecoregions. We used the definition and climate data that most closely matched field data to calculate forest cover in 2000 and change from 2001 to 2020. Globally, there was low agreement (< 58%) between bioclimatic definitions and WWF ecoregions and only 40% of field plots fell within these ecoregions. FAO using CHELSA had the highest agreement with field plots (81%) and was not correlated with the biome extent. Using the FAO definition with CHELSA climatic data set, we estimate 4,931,414 km 2 of closed canopy (≥ 40% forest cover) tropical dry forest in 2000 and 4,369,695 km 2 in 2020 with a gross loss of 561,719 km 2 (11.4%) from 2001 to 2020. Tropical dry forest biome extent varies significantly based on bioclimatic definition used, with nearly half of all tropical dry forest vegetation missed when using ecoregion boundaries alone, especially in Africa. Using site-specific field validation, we find that the FAO definition using CHELSA provides an accurate, standard, and repeatable way to assess tropical dry forest cover and change at a global scale.

3 citations


Journal ArticleDOI
Ye Liu1, Weidong Guo2, Huilin Huang3, Jun Ge2  +1 moreInstitutions (3)
Abstract: The bulk surface properties, including canopy height (h), zero-plane displacement height (d), roughness length (z0), and aerodynamic resistance (rb and rd), are crucial biophysical parameters that influence momentum, energy and mass exchanges at the land-atmosphere interface. The variabilities of these parameters are important for understanding possible impacts of the ecosystem on climate change, yet they have not been systematically evaluated due to the lack of large-scale, long-term observations. Here we provide global estimates of these bulk aerodynamic parameters, including d, z0, rb, and rd, for the period 1982–2017 based on remote-sensed leaf area index (LAI), h, and plant functional type dependent canopy morphological characteristics. The global h estimate is acquired from LAI using a semi-empirical relation in which the coefficients have been optimized based on the canopy height product from the Geoscience Laser Altimeter System (GLAS) onboard the Ice, Cloud, and land Elevation Satellite (ICESat). Two LAI products, Global Land Surface Satellite (GLASS) and Global Inventory Modeling and Mapping Studies (GIMMS), are used to calculate canopy height and parameters separately. The products derived from the above two LAI datasets agree very well (spatial correlation coefficient, SCC = 0.99, relative root-mean-square-error, rRMSE = 8.28% for h, SCC = 0.99, rRMSE = 12.15% for d, and SCC = 0.98, rRMSE = 13.78% for z0). Verification of the products against in-situ canopy height records from the FLUXNET and eddy-covariance (EC)-based d and z0 estimates shows that the estimates can reproduce the annual means and seasonal variations of the bulk surface properties. We found significant positive trends in global h (0.04–0.05% year−1), d (0.07% year−1) and z0 (0.07% year−1) associated with the Earth greening, but they are different from the trend in LAI (0.14–0.25% year−1). The differences between the trends in LAI and the trends in derived parameters are also found in different latitudes. For instance, in the Northern Hemisphere Polar region, the GLASS LAI increases by 0.48% year−1, leading to positive trends of 0.03% year−1 in h, 0.18% in d, and 0.20% year−1 in z0. The above results highlight the importance of comprehensively considering changes in LAI, canopy height, and aerodynamic parameters when evaluating the effects of ecosystem change on climate. This long-term dataset can be used in climate models to assess the response of bulk aerodynamic parameters to climate changes and the impact of vegetation dynamics on regional and global climate.

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01 Apr 2003-
Abstract: Accurate assessments of the CO2 fluxes between the terrestrial ecosystems and the atmosphere are pressingly needed for the climate change and carbon cycle studies. The Collatz et al. parameterization of leaf photosynthesis-stomatal conductance has been widely applied in land surface parameterization schemes for simulating the land surface CO2 fluxes. The study in this paper developed an analytical solution approach for the Collatz et al.'s parameterization for stable solution and computational efficiency. This analytical approach is then applied to the simplified biosphere model (SSiB), enhancing its capability of simulating land surface CO2 fluxes. The enhanced SSiB model is tested with field observation data sets from two Amazonian field experiments (ABRACOS missions and Manaus Eddy Covariance Study). Simulations of the land surface fluxes of latent heat, sensible heat and soil heat by the enhanced SSiB agree very well with observations with correlation coefficients being larger than 0.80. However, the correlation coefficient for the daily means Of CO2 fluxes is only 0.42 for the Manaus data set. A day-time, square wave in the simulated CO2 flux diurnal curves is found. The discrepancies between simulation and observation were found to be the results of incorrect parameter setup or improper leaf to canopy scaling strategy. A modification to the scaling strategy improves significantly the accuracy of the photosynthesis-stomatal conductance model. (C) 2002 Elsevier Science B.V. All rights reserved.

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References
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Journal ArticleDOI
Yude Pan1, Richard Birdsey1, Jingyun Fang2, Jingyun Fang3  +15 moreInstitutions (13)
19 Aug 2011-Science
TL;DR: The total forest sink estimate is equivalent in magnitude to the terrestrial sink deduced from fossil fuel emissions and land-use change sources minus ocean and atmospheric sinks, with tropical estimates having the largest uncertainties.
Abstract: The terrestrial carbon sink has been large in recent decades, but its size and location remain uncertain. Using forest inventory data and long-term ecosystem carbon studies, we estimate a total forest sink of 2.4 ± 0.4 petagrams of carbon per year (Pg C year–1) globally for 1990 to 2007. We also estimate a source of 1.3 ± 0.7 Pg C year–1 from tropical land-use change, consisting of a gross tropical deforestation emission of 2.9 ± 0.5 Pg C year–1 partially compensated by a carbon sink in tropical forest regrowth of 1.6 ± 0.5 Pg C year–1. Together, the fluxes comprise a net global forest sink of 1.1 ± 0.8 Pg C year–1, with tropical estimates having the largest uncertainties. Our total forest sink estimate is equivalent in magnitude to the terrestrial sink deduced from fossil fuel emissions and land-use change sources minus ocean and atmospheric sinks.

3,846 citations


Journal ArticleDOI
Abstract: . New burned area datasets and top-down constraints from atmospheric concentration measurements of pyrogenic gases have decreased the large uncertainty in fire emissions estimates. However, significant gaps remain in our understanding of the contribution of deforestation, savanna, forest, agricultural waste, and peat fires to total global fire emissions. Here we used a revised version of the Carnegie-Ames-Stanford-Approach (CASA) biogeochemical model and improved satellite-derived estimates of area burned, fire activity, and plant productivity to calculate fire emissions for the 1997–2009 period on a 0.5° spatial resolution with a monthly time step. For November 2000 onwards, estimates were based on burned area, active fire detections, and plant productivity from the MODerate resolution Imaging Spectroradiometer (MODIS) sensor. For the partitioning we focused on the MODIS era. We used maps of burned area derived from the Tropical Rainfall Measuring Mission (TRMM) Visible and Infrared Scanner (VIRS) and Along-Track Scanning Radiometer (ATSR) active fire data prior to MODIS (1997–2000) and estimates of plant productivity derived from Advanced Very High Resolution Radiometer (AVHRR) observations during the same period. Average global fire carbon emissions according to this version 3 of the Global Fire Emissions Database (GFED3) were 2.0 Pg C year−1 with significant interannual variability during 1997–2001 (2.8 Pg C year−1 in 1998 and 1.6 Pg C year−1 in 2001). Globally, emissions during 2002–2007 were relatively constant (around 2.1 Pg C year−1) before declining in 2008 (1.7 Pg C year−1) and 2009 (1.5 Pg C year−1) partly due to lower deforestation fire emissions in South America and tropical Asia. On a regional basis, emissions were highly variable during 2002–2007 (e.g., boreal Asia, South America, and Indonesia), but these regional differences canceled out at a global level. During the MODIS era (2001–2009), most carbon emissions were from fires in grasslands and savannas (44%) with smaller contributions from tropical deforestation and degradation fires (20%), woodland fires (mostly confined to the tropics, 16%), forest fires (mostly in the extratropics, 15%), agricultural waste burning (3%), and tropical peat fires (3%). The contribution from agricultural waste fires was likely a lower bound because our approach for measuring burned area could not detect all of these relatively small fires. Total carbon emissions were on average 13% lower than in our previous (GFED2) work. For reduced trace gases such as CO and CH4, deforestation, degradation, and peat fires were more important contributors because of higher emissions of reduced trace gases per unit carbon combusted compared to savanna fires. Carbon emissions from tropical deforestation, degradation, and peatland fires were on average 0.5 Pg C year−1. The carbon emissions from these fires may not be balanced by regrowth following fire. Our results provide the first global assessment of the contribution of different sources to total global fire emissions for the past decade, and supply the community with an improved 13-year fire emissions time series.

2,277 citations


Journal ArticleDOI
TL;DR: For many communities, a self-reproducing climax state may only exist as an average condition on a relatively large spatial scale, and even that has yet to be rigorously demonstrated.
Abstract: Two features characterize all natural communities. First, they are dynamic systems. The densities and age-structures of populations change with time, as do the relative abundances of species; local extinctions are commonplace (37). For many communities, a self-reproducing climax state may only exist as an average condition on a relatively large spatial scale, and even that has yet to be rigorously demonstrated (36). The idea that equilibrium is rarely achieved on the local scale was expressed decades ago by a number of forest ecologists (e.g. 10 1, 168). One might even argue that continued application of the concept of climax to natural systems is simply an exercise in metaphysics (41). While this view may seem extreme, major climatic shifts often recur at time intervals shorter than that required for a community to reach competitive equilibrium or alter the geographical distributions of species (6, 21, 43, 76, 92). Climatic variation of this kind influences ecological patterns over large areas, sometimes encompassing entire continents. Other agents of temporal change in natural communities operate over a wide range of smaller spatial scales (47, 242). Second, natural communities are spatially heterogeneous. This statement is true at any scale of resolution (242), but it is especially apparent on what is commonly referred to as the regional scale. (By region I mean an area that potentially encompasses more than one colonizable patch.) Across any land or seascape, one observes a mosaic of patches identified by spatial discontinuities in the distributions of populations (153, 159, 161, 231, 239, 240). Closer examination often reveals a smaller-scale patchwork of same-aged individuals (e.g. 85-87, 101, 146, 199,204,217-220,235,246). Discrete patch boundaries sometimes reflect species-specific responses to

2,224 citations


Journal ArticleDOI
24 Apr 2009-Science
TL;DR: What is known and what is needed to develop a holistic understanding of the role of fire in the Earth system are reviewed, particularly in view of the pervasive impact of fires and the likelihood that they will become increasingly difficult to control as climate changes.
Abstract: Fire is a worldwide phenomenon that appears in the geological record soon after the appearance of terrestrial plants. Fire influences global ecosystem patterns and processes, including vegetation distribution and structure, the carbon cycle, and climate. Although humans and fire have always coexisted, our capacity to manage fire remains imperfect and may become more difficult in the future as climate change alters fire regimes. This risk is difficult to assess, however, because fires are still poorly represented in global models. Here, we discuss some of the most important issues involved in developing a better understanding of the role of fire in the Earth system.

1,941 citations


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