Introduction
Carbon emissions from land-use and land-cover change (LULCC) are part of the
human perturbation to the global carbon cycle (Houghton et al., 2012; Le
Quéré et al., 2015) and started before the industrial era
when fossil fuel CO2 emissions appeared. Since 1850, estimated
cumulative LULCC emissions, ELUCc, have represented one-third of total cumulative
anthropogenic CO2 emissions (Boden et al., 2013; Houghton et al., 2012;
Le Quéré et al., 2015). Annual LULCC emissions have been higher than
those from fossil fuel burning until the 1930s (Boden et al., 2013; Houghton
et al., 2012; Le Quéré et al., 2015) and today represent a smaller
but persistent perturbation in the global carbon cycle. Unlike fossil fuel
emissions, relative uncertainties in LULCC emissions are high due to the
difficulty of assessing this flux from measurements. Some progress has been
made to better quantify gross tropical deforestation emissions by combining
spatial biomass data with satellite-derived maps delineating forest cover
loss (Harris et al., 2012). However, such spatially resolved data are not
available beyond the last decade and provide only gross deforestation
emissions, i.e., do not track the regrowth of secondary ecosystems or legacy
soil carbon losses that can persist long after deforestation.
Bookkeeping models (Hansis et al., 2015; Houghton, 1999) based on historical
LULCC area data and tabulated functions of carbon losses and gains are one
approach to estimating ELUCc, but they do not include the effects of
environmental changes on carbon stocks before and after LULCC happens (Gasser
and Ciais, 2013; Pongratz et al., 2014). The bookkeeping model of
Houghton (1999) used for the annual update of the global carbon budget (Le
Quéré et al., 2015) is based on regionally aggregated data and does
not consider spatial differences in LULCC fluxes within a region.
Alternatively, the estimated LULCC fluxes by dynamic global vegetation models
(DGVMs) account for spatial and temporal variations in carbon stock densities
and land-cover change, as well as for delayed (“legacy”) carbon fluxes. In
DGVMs, LULCC fluxes are related to environmental conditions through simulated
carbon cycle processes, i.e., net primary production (NPP) and respiration,
resulting in changes in biomass and soil carbon stocks simulated with
variable atmospheric CO2 concentration and climate. Yet, LULCC emissions
from DGVMs differ greatly, even when these models are prescribed with the
same inputs of land-cover change data (such as time-variable areas of pasture
and crops; Pitman et al., 2009). Several factors are responsible for
differences in ELUCc among DGVMs, including (1) different representations of
processes that determine the carbon densities of vegetation and soils subject
to land-use change; (2) using dynamic vegetation or prescribing a fixed
vegetation distribution; and 3) the use of different rules assigning how natural
vegetation types change to agricultural areas (Peng et al., 2017; Pitman et
al., 2009; Reick et al., 2013).
Carbon initially stored in forest biomass contributes the predominant portion
of the LULCC emissions after deforestation (Hansis et al., 2015). Thus, an
accurate representation of the biomass carbon density exposed to LULCC is
crucial to reduce uncertainties in DGVM-based ELUCc estimates. Global
biomass datasets based on inventories and satellites recently became
available. These datasets (Table 1) provide the spatially distributed biomass
carbon density on regional or global scales (Avitabile et al., 2016; Baccini
et al., 2012; Carvalhais et al., 2014; Liu et al., 2015; Pan et al., 2011;
Saatchi et al., 2011; Santoro et al., 2015; Thurner et al., 2014), but differ
in terms of their coverage of aboveground or belowground biomass and whether
they provide only forest biomass or biomass for all vegetation types.
The different biomass datasets based on observations. The
biomass information from the TRENDY-v2 project is also listed for comparison.
Dataset
Coverage
Resolution
Biome type
Aboveground/belowground
Note
Thurner et al. (2014)
30∘ N–80∘ N
0.01∘
forest
aboveground + belowground
Growing stock volume from Santoro et al. (2015)
Saatchi et al. (2011)
30∘ N–40∘ S
1 km
forest
aboveground
Carvalhais et al. (2014)
global (without South Australia)
0.5∘
forest + herbaceous
aboveground + belowground
Merged map of Thurner et al. (2014) and Saatchi et al. (2011)
Baccini et al. (2012)
23∘ N–23∘ S
500 m
forest
aboveground
Liu et al. (2015)
global
0.25 degree
all
aboveground
Calibration based onSaatchi et al. (2011)
Avitabile et al. (2016)
30∘ N–40∘ S
1 km
forest
aboveground
Fusion of Saatchi et al. (2011)and Baccini et al. (2012)
Santoro et al. (2015)
30∘ N+
0.01∘
forest
aboveground
Sharing growing stock volumewith Thurner et al. (2014)
Pan et al. (2011)
global
regional
forest
aboveground + belowground
Based on FAO data
TRENDY-v2
global
1∘
all
aboveground + belowground
The framework of this study.
In this study, we propose a new method to combine recent satellite- and
inventory-based biomass datasets to constrain ELUCc simulated by DGVMs
(Fig. 1). We analyzed the outputs from nine DGVMs (Table 2) of the Trends in
Net Land–Atmosphere Exchange (TRENDY-v2) project (Sitch et al., 2015; http://dgvm.ceh.ac.uk/node/9) and developed global and regional
regressions between initial biomass in 1901 and present-day biomass (average
of 2000–2012) and between ELUCc during 1901–2012 and initial
biomass across the DGVMs. The former set of regressions is used to
extrapolate present-day observation-based biomass (Table 1) to initial
biomass in the year 1901. The latter set of regressions is applied to provide
an emerging constraint on ELUCc as a function of initial biomass (Fig. 1).
Using the Gaussian uncertainties associated with the observation-based
biomass datasets and the uncertainties in the two regressions, the Gaussian
errors in ELUCc can be derived after applying the biomass constraint.
Description of TRENDY model setups used in this study.
Model
PFT number
Allocation rules of changesin agriculture area
Spatialresolution
dynamicvegetationactivated
Wood harvest
Shiftingagriculture
Explicit grossLULCCtransitions
Start oftransientsimulation
Reference
CLM4.5
17
pasture; crop model exists butnot used in these simulations
0.9∘ × 1.25∘
no
yes
no
no
1860
Oleson et al. (2013)
JSBACH
12
proportional allocation of cropland;preferential allocation of pastureon natural grassland
T63a
no
yes
yes
yes
1850
Reick et al. (2013)
JULES3.2
5
crop and pasture added together tocreate a single agricultural mask in which trees and shrubs are excluded fromgrowing; there is no assumption forwhich PFTs the agriculture replaces
N96b
yes
yes
no
no
1860
Clark et al. (2011),Best et al. (2011)
LPJ
9
crop and pasture were added together to create a single managed land fraction
0.5∘ × 0.5∘
yes
no
no
no
1860
Sitch et al. (2003)
LPJ-GUESS
11
proportional allocation ofcropland and pasture
0.5∘ × 0.5∘
yes
no
no
no
1860
Smith et al. (2001)
LPX-Bern
9
proportional allocation ofcropland and pasture
1.0∘ × 1.0∘
yes
no
no
no
1860
Stocker et al. (2014)
ORCHIDEE
13
proportional allocation ofcropland and pasture
2∘ × 2∘
no
no
no
no
1860
Krinner et al. (2005)
VISIT
16
no specific rule applied because only one natural PFT exists for primaryand secondary land in a grid cell
0.5∘ × 0.5∘
no
yes
yes
yes
1860
Kato et al. (2013),Ito and Inatomi (2012)
OCN
12
proportional allocation ofcropland and pasture
2.5∘ × 3.75∘
no
no
no
no
1860
Zaehle and Friend (2010)
a T63 grid has an approximate resolution of 1.9∘ × 1.9∘.
b N96 resolution is equivalent to 1.25∘ latitude × 1.875∘
longitude.
Materials and methods
LULCC emissions and biomass from the DGVMs
The DGVMs in TRENDY-v2 was used to conduct two simulations (labeled S2 and S3) between
1860 (except JSBACH from 1850, Table 2) and 2012, with outputs quantifying
LULCC emissions over the period 1901–2012 (Sitch et al., 2015). Both
simulations are performed with changing climate and CO2 concentration,
but one (called S3) has variable LULCC maps based on Land-Use Harmonization
(LUH) dataset (Hurtt et al., 2011; with an extension until 2012), and the
other (called S2) has a time-invariant land-cover map representing the state
in 1860. The difference in net biome production (NBP, the net carbon exchange
between the biosphere and the atmosphere) between these two simulations (S3
and S2) defines modeled LULCC emissions. This calculation of LULCC emissions
by DGVMs includes the “lost sink capacity” (called “altered sink
capacity” in Gasser and Ciais, 2013, and “the loss of additional sink
capacity” in Pongratz et al., 2014) because simulated NBP in the S2
simulation without LULCC is a net sink over areas affected by LULCC in S3.
For example, forests have larger carbon storage and a slower turnover time than
croplands and are thus expected to be carbon sinks when the atmospheric CO2
level increases. After deforestation to croplands, this sink capacity due to
CO2 fertilization is lost. Modeled LULCC emissions include the legacy
emissions from soil carbon losses and emissions from wood and other products
produced by LULCC, as far as the latter are included in the TRENDY-v2 models
(Table 2). The DGVMs used in this study are CLM4.5 (Oleson et al., 2013),
JSBACH (Reick et al., 2013), JULES3.2 (Best et al., 2011; Clark et al.,
2011), LPJ (Sitch et al., 2003), LPJ-GUESS (Smith et al., 2001), LPX-Bern
(Stocker et al., 2014), ORCHIDEE (Krinner et al., 2005), VISIT (Ito and
Inatomi, 2012; Kato et al., 2013) and OCN (Zaehle and Friend, 2010). Each
DGVM is described briefly in Table 2.
LULCC can either reduce or increase the biomass amount over time depending on
the LULCC types. For example, forest clearing turns forest biomass into
atmospheric CO2 eventually, while secondary forest regrowth can increase
biomass. The overall effect of LULCC on biomass during the historical period
is a net loss of carbon (Houghton, 1999) due to converting natural vegetation
into cultivated lands by humans (Klein Goldewijk et al., 2011). Identifying
the LULCC-affected grid cells in each model is thus critical because only
biomass in these grid cells should be used to constrain LULCC emissions. Grid
cells affected by LULCC differ among models. Although all models share the
same pasture and cropland areas from the LUH dataset (Hurtt et al., 2011), the
models have different numbers of PFT, use different PFT definitions and have
different allocation rules for translating the shared agricultural data into
the new vegetation cover (Peng et al., 2017; Pitman et al., 2009; Reick et
al., 2013). As a result, there is no unified map to determine the
LULCC-affected grid cells in all models. For the same reasons, the forest
areas and the LULCC types are also different among models.
Temporal change in forest area from TRENDY-v2 models in each of the
nine regions. Differences between models arise from their specific vegetation
maps and rules through which natural PFTs are chosen to give land to
agriculture.
In this study, we adopted the “deforestation grid cells” in their
corresponding PFT maps as a criterion to locate the LULCC-affected grid cells
from DGVM outputs. Thus we used the PFT maps from each model to first
calculate the temporal change in forest area (total area of all forest PFTs)
during 1901–2012 and then selected the grid cells that experienced
deforestation by comparing the forest area maps between 1901 and
2012 (net deforestation). This procedure produces a good approximation given
the continuously decreasing trend of forest area in LULCC hotspot regions
like South and Central America (Fig. 2). We also tested an alternative method
to determine the LULCC-affected grid cells in TRENDY model outputs; i.e., PFT
maps were compared year by year during 1901–2012, and grid cells with
deforestation were selected (gross deforestation). This method tends to give
a greater number of LULCC-affected grid cells, reducing the goodness of
fit in the regression between the biomass in 1901 and ELUCc during
1901–2012 (Figs. S1 and S2 in the Supplement). Therefore, the method of
gross deforestation is not used for further analyses.
We verified that deforestation grid cells are responsible for most of the
total net LULCC flux. In fact, the average of the different model simulations
of LULCC emissions from deforestation grid cells between 1901 and 2012 is
approximately 90 % of the total LULCC emissions from all grid cells
(Fig. S1). The LULCC emissions in this study are thus taken to equal the sum
of LULCC emissions from the selected deforestation grid cells using our
criterion. It should be noted that although only deforestation is used as a
single criterion to define grid cells affected by LULCC in DGVMs, modeled
LULCC emissions also include other types of land-use transitions involving
pairs of non-forest PFTs in the selected grid cells.
In each model, only biomass in deforestation grid cells is considered.
Biomass in the year 1901 is thereby defined as initial biomass, and
biomass averaged during 2000–2012 is defined as present biomass. An
ordinary least squares linear regression is performed with the outputs of all
models between initial biomass and ELUCc from 1901 to 2012 and between the
initial and the present biomass on both global and regional scales. Our
division of nine regions in the world (Fig. 2) for estimating LULCC fluxes is
the same as in Houghton et al. (1999).
Observation-based biomass datasets
Several biomass datasets (Avitabile et al., 2016; Baccini et al., 2012;
Carvalhais et al., 2014; Liu et al., 2015; Pan et al., 2011; Saatchi et al.,
2011; Santoro et al., 2015; Thurner et al., 2014) based on inventories and
remote sensing can potentially be used to constrain ELUCc through the set of
regressions from DGVMs. However, these biomass datasets cover different parts
of biomass (aboveground, belowground or total) and different regions
(tropics, Northern Hemisphere or the globe) at different spatial resolutions
(Table 1). We choose the global grid-based biomass dataset from Carvalhais et
al. (2014) to derive an observational constraint that results in a best estimate
of ELUCc. This map merges the Northern Hemisphere biomass dataset from
Thurner et al. (2014) and the tropical biomass dataset from Saatchi et
al. (2011). An advantage of this map is its consistency in biomass terms with
the outputs of TRENDY models because it documents
aboveground + belowground and forest + herbaceous biomass (Tables 1,
2). Three other biomass maps are used as alternative datasets for sensitivity
tests: (1) the global biomass map from the GEOCARBON project, a merged
product of the biomass datasets in the Northern Hemisphere (Santoro et al.,
2015) and tropics (Avitabile et al., 2016); (2) regional biomass estimates
from Pan et al. (2011) based on forest inventory data; and (3) the biomass
map from Liu et al. (2015) derived from satellite vegetation optical depth.
The GEOCARBON (Avitabile et al., 2016; Santoro et al., 2015) and Liu et al.
(2015) datasets that only provide aboveground biomass were extended to total
forest biomass using the conversion factors for the nine regions (Liu et al.,
2015). The global biomass maps from GEOCARBON (Avitabile et al., 2016;
Santoro et al., 2015) and Pan et al. (2011) are only for forest (Table 1),
and we do not add the herbaceous biomass to these two datasets because the
global herbaceous biomass only accounts for about 3 % of the global total
biomass (Carvalhais et al., 2014). Note that the uncertainties in the
corresponding constrained results using these three alternative datasets do
not include (1) the uncertainties in converting aboveground biomass to the
total of aboveground and belowground biomass for the datasets from Liu et
al. (2015) and GEOCARBON (Avitabile et al., 2016; Santoro et al., 2015) or
(2) the uncertainties in ignoring non-woody biomass in the datasets from
GEOCARBON (Avitabile et al., 2016; Santoro et al., 2015) and Pan et
al. (2011). The biomass maps of Carvalhais et al. (2014), GEOCARBON
(Avitabile et al., 2016; Santoro et al., 2015) and Liu et al. (2015) with
different spatial resolutions were aggregated to a
1∘ × 1∘ resolution before selecting the
deforestation grid cells.
Methods to identify grid cells subject to past deforestation in biomass
datasets
It is not practical to use PFT maps from DGVMs to define deforestation grid
cells in the observation-based biomass datasets because PFT maps and forest
area change since 1901 differ across DGVMs. Instead, we diagnosed
deforestation grid cells in the biomass maps using three harmonized methods
(Method A, Method B and Method C). All the methods are based on the
reconstructed historical agricultural area from the History Database of the
Global Environment (HYDE v3.1; Klein Goldewijk et al., 2011) but with
different hypotheses regarding how agricultural expansion has affected
forests. These harmonized methods are representative of the different rules
for assigning LULCC data to natural vegetation types in DGVMs. Method-A
assumes that the increase in cropland area in a grid cell between 1901 and
2012 is taken from forest; Method B assumes that the increase in cropland and
pasture is taken proportionally from all natural vegetation types; and
Method C (like the “BM3” scenario in Peng et al., 2017) assumes that the
increase in cropland and pasture is first taken from forest and then from
natural grassland if no more forest area is available and that the regional
forest area change is set to match the historical forest reconstruction from
Houghton (2003). Because the biomass distribution in Pan et al. (2011) is
given as regional mean values and not resolved on a grid cell basis, it is
impossible to select deforestation grid cells directly from this dataset
using the above methods. Therefore, for each region, we calculated the ratios
of biomass in deforestation grid cells according to Method A, Method B and
Method C to the total biomass in all grid cells in each of the other three
biomass datasets (Carvalhais et al., 2014; GEOCARBON, Avitabile et al., 2016;
Santoro et al., 2015; Liu et al., 2015). For each method (Method A, B and
C), the three ratios corresponding to the three biomass datasets were
further averaged in each region. The total biomass amount from Pan et
al. (2011) in each region was multiplied by the average ratio to derive the
biomass equivalent to using Method A, Method B and Method C for the dataset
from Pan et al. (2011).
These three methods applied to the above-listed biomass datasets are also
applied as sensitivity tests to select the deforestation grid cells since
1901 in the TRENDY model outputs. Identically, regressions are performed
using the initial biomass amount and ELUCc from these selected grid cells. Due
to the inconsistencies among the three methods and the historical PFT maps
of each DGVM, the biomass amount in 1901 in the selected grid cells using
these three methods is higher than using PFT maps, but the ELUCc are lower,
reflecting a lower representativeness of the deforestation grid cells using
these three methods for DGVM outputs (Fig. S1). As a consequence, a weaker
goodness of regression fit was found between ELUCc and initial biomass
(Fig. S2).
Uncertainties in constrained LULCC emissions
The biomass from Method A, Method B and Method C obtained from each dataset
is extrapolated into biomass for the year 1901 using the regression between
initial biomass and present biomass modeled by the DGVMs. This biomass in
1901 is then applied in the regression between modeled ELUCc and modeled
initial biomass among different DGVMs to calculate constrained ELUCc. In
this emerging constraint approach (Fig. 1), the uncertainties in constrained
ELUCc are a function of the uncertainties in the observed biomass datasets,
the linear regression goodness of fit for the two regressions (regressions
between ELUCc and the initial biomass and between the initial and present
biomass) and the slopes of the regressions. The uncertainty in
constrained LULCC emissions is calculated as in Stegehuis et al. (2013):
σLULCC=α2σinitial_biomass2+σres_LULCC2,σinitial_biomass=β2σpresent_biomass2+σres_biomass2,
where σLULCC, σinitial_biomass and
σpresent_biomass are the uncertainties in constrained ELUCc, the uncertainty in initial biomass and the uncertainty in present
biomass; α and σres_LULCC represent the slope and
the standard deviation of the residuals from the linear regression fit
between ELUCc and initial biomass, and β and
σres_biomass represent the slope and standard deviation of
the residuals from the linear regression between initial biomass and present
biomass.
Two supplementary methods to constrain ELUCc using biomass
observations
We also tested two supplementary methods to constrain ELUCc: first, Method S1
using the regression between ELUCc and present-day biomass from TRENDY
models rather than extrapolating present biomass to biomass in 1901,
and then Method S2 using ΔB (biomass difference between present biomass
and biomass in 1901 derived from the model simulations) instead of a
regression between biomass in 1901 and present-day biomass to extrapolate
the observation-based biomass in 1901. In Method S1, the uncertainties in
the biomass observations and in the regression between ELUCc and present
biomass from the models are used to calculate the uncertainties in the
constrained ELUCc. In Method S2, the uncertainties in the biomass
observations and the standard deviation of ΔB among the models are used.
The global and regional cumulative land-use and land-cover change
(LULCC) emissions (PgC) during 1901–2012 from original TRENDY models and
from the estimates constrained by different biomass datasets with different
methods to define deforestation grid cells. The interquantile ranges are
shown in Table S1.
TRENDY
Carvalhais et al. (2014)
Liu et al. (2015)
GEOCARBON
Pan et al. (2011)
(Avitabile et al., 2016;
Santoro et al., 2015)
median
min
max
Method A
Method B
Method C
Method A
Method B
Method C
Method A
Method B
Method C
Method A
Method B
Method C
China region
10.7
6.0
19.1
13.8 ± 4.0
16.0 ± 4.3
16.3 ± 4.3
10.5 ± 3.1
11.1 ± 3.1
11.1 ± 3.1
10.0 ± 4.0
10.5 ± 4.5
10.5 ± 4.6
7.3 ± 2.9
7.6 ± 2.9
7.7 ± 2.9
North America
19.9
8.6
40.8
10.8 ± 7.1
9.6 ± 7.0
7.8 ± 6.7
14.7 ± 6.9
13.6 ± 6.8
9.3 ± 6.6
17.8 ± 8.3
15.4 ± 7.7
13.0 ± 7.6
9.5 ± 6.4
8.5 ± 6.4
6.7 ± 6.4
South and Central America
42.6
33.5
81.4
44.4 ± 17.8
46.4 ± 18.1
46.8 ± 18.2
48.3 ± 17.0
50.1 ± 17.0
50.6 ± 17.0
44.5 ± 16.6
46.4 ± 16.7
46.8 ± 16.8
43.1 ± 17.0
44.8 ± 17.1
45.1 ± 17.2
Western Europe
3.8
1.2
5.3
3.6 ± 0.8
3.2 ± 0.8
3.0 ± 0.8
4.1 ± 0.8
3.4 ± 0.8
3.2 ± 0.8
5.0 ± 1.2
3.8 ± 0.9
3.4 ± 0.8
3.6 ± 0.8
3.2 ± 0.8
3.0 ± 0.8
Tropical Africa
21.8
15.8
57.8
24.6 ± 10.3
28.2 ± 11.4
28.6 ± 11.5
31.4 ± 8.8
36.3 ± 9.4
36.9 ± 9.4
22.7 ± 12.8
26.2 ± 14.6
26.4 ± 16.1
23.8 ± 10.3
27.5 ± 11.4
27.8 ± 11.5
The former Soviet Union
10.5
7.2
33.0
10.9 ± 6.7
10.7 ± 6.7
11.3 ± 6.8
14.2 ± 6.5
14.3 ± 6.5
14.9 ± 6.5
14.7 ± 6.6
13.0 ± 6.5
13.5 ± 6.5
10.1 ± 6.2
9.8 ± 6.2
10.2 ± 6.2
South and Southeast Asia
21.8
9.6
46.6
37.2 ± 14.4
33.6 ± 13.3
38.5 ± 14.8
27.8 ± 8.5
24.1 ± 7.9
27.9 ± 8.3
32.9 ± 10.1
29.0 ± 9.6
33.6 ± 10.4
15.1 ± 9.5
13.3 ± 8.9
15.5 ± 9.6
Pacific developed region
3.6
-6.0
18.6
6.0 ± 4.4
5.4 ± 4.2
6.0 ± 4.3
7.1 ± 3.5
6.0 ± 3.4
5.6 ± 3.3
18.0 ± 3.1
16.4 ± 2.9
14.3 ± 3.0
-1.7 ± 2.6
-1.9 ± 2.6
-2.0 ± 2.6
North Africa and the Middle East
1.7
-0.6
6.0
1.1 ± 0.6
0.6 ± 0.6
0.8 ± 0.6
4.3 ± 1.1
3.1 ± 0.8
3.5 ± 0.9
4.5 ± 4.5
3.0 ± 3.0
3.1 ± 3.7
-0.1 ± 0.5
-0.2 ± 0.5
-0.1 ± 0.5
Global
148
94
273
152 ± 49
154 ± 50
159 ± 51
161 ± 40
162 ± 39
163 ± 39
165 ± 46
160 ± 45
161 ± 47
119 ± 37
121 ± 38
122 ± 38
Relationship between biomass in 1901 and cumulative land-use and
land-cover change (LULCC) emissions during 1901–2012 across the nine TRENDY-v2 models. The black solid line is the linear regression line. The vertical
green solid line indicates the reconstructed biomass in 1901 from Carvalhais
et al. (2014) by applying Method A (the increase in cropland in HYDE v3.1
data from forest; see Figs. S4 and S5 for the results of Method B and Method C)
to define deforestation grid cells. The orange solid horizontal line
indicates the cumulative LULCC emissions constrained by reconstructed biomass
in 1901. Dashed lines represent 1σ uncertainties. The probability
density function of the constrained cumulative LULCC emissions is shown on
the right.
Results
Forest area change and cumulative LULCC emissions in DGVMs
As expected, a general decrease in forest area is found between 1901 and
2012, especially in regions subject to extensive deforestation over the last
decades, namely South and Central America, South and Southeast Asia and
tropical Africa (Fig. 2), which is in support of our methods of defining
deforestation grid cells, although the forest area in some regions differs
substantially across DGVMs. Differences in forest area are large in tropical
Africa, North America and the former Soviet Union, while they are smaller in
South and Central America and South and Southeast Asia (Fig. 2). There are
several reasons for these differences in forest area: (1) the models have
different initial distributions of PFTs (the TRENDY-v2 protocol only
prescribed the same initial area of natural vegetation, but did not specify
the PFTs that compose natural vegetation); (2) some models consider only net
LULCC, but others have gross LULCC including some sub-grid transitions
(Table 2; see a comparison using the JSBACH model; Wilkenskjeld et al.,
2014); (3) and the models have different treatments for changing pasture areas
(either proportional from natural vegetation or preferential from natural
grasslands). In North America, the China region and Western Europe, the forest
area decreased in the first half of the 20th century and then increased in
recent decades. Yet, the magnitude of the increase is smaller than that of
the previous decrease in these regions, and the global average is net forest
loss between 1901 and 2012 (ranging from 2.3 to 16.8 Mkm2 across the
nine models).
ELUCc from the nine DGVMs between 1901 and 2012 range from 1.7 PgC (-0.6 to
6.0; median and range are positive, indicating a net cumulative
flux to the atmosphere) in North Africa and the Middle East to 42.6 PgC (33.5 to
81.4) in South and Central America, resulting in a global total of 148 PgC
(94 to 273; Table 3). Tropical Africa and South and Southeast Asia
have the second-largest ELUCc of 21.8 (15.8 to 57.8) and 21.8 PgC (9.6 to
46.6), respectively. Although afforestation and reforestation occurred in
North America after around 1960 and in China after 2000 (Fig. 2), ELUCc in
these two regions have been positive since 1901, with median values of 19.9 and
10.7 PgC, respectively (Table 3).
Relationship between cumulative LULCC emissions and initial
biomass
We found a positive linear relationship between ELUCc and initial biomass
in the deforestation grid cells of each model on a global scale and in the
regions considered (Fig. 3). The coefficients of determination (r2) are
0.61, 0.58 and 0.76 in South and Central America, South and Southeast Asia
and tropical Africa, respectively. Due to stable or slightly increasing
forest area (Fig. 2), the correlation between initial biomass and ELUCc is
small in Western Europe (Fig. 3). The slopes of the relationships between ELUCc and initial biomass shown in Fig. 3 range from
0.13 PgC PgC-1 in Western Europe to 0.63 PgC PgC-1 in North Africa and
the Middle East. In tropical regions with intensive LULCC, the slope is similar
between South and Southeast Asia (0.36 PgC PgC-1) and tropical Africa
(0.37 PgC PgC-1), but lower in South and Central America (0.21 PgC PgC-1).
These slopes reflect the sensitivity of cumulative carbon loss to initial
biomass carbon stock. They are mainly influenced by the fraction of
deforested area relative to the initial forest area in each region, which
explains 46 % of the variations in the slopes across regions (Fig. S3).
Differences in biomass density across regions and in the use of gross or net
transitions among DGVMs (Table 2) also contribute to variations in slopes.
The relationship between initial biomass in 1901 and present biomass
(average of biomass from 2000 to 2012) across the TRENDY-v2 models for each
region. Note that both biomass in 1901 and present biomass are from TRENDY
models, not the observations. Dashed line is the 1:1 line.
Cumulative LULCC emissions constrained by present-day biomass
observations
There is also a strong positive relationship between initial biomass in 1901
and present-day biomass in grid cells that have experienced deforestation (Fig. 4). The
r2 of this regression is higher than 0.92 in most regions, except in
North America and the China region (0.89 and 0.76, respectively). The regression
between present-day and initial biomass was applied to extrapolate current
observation-based biomass back to the year 1901. The extrapolated biomass in
1901 is higher than that in the present day, mainly due to a larger forest
area, although it is difficult to discriminate other effects, such as
CO2 fertilization, that might have increased biomass between 1901 and
2012.
Using the chain of emerging constraints between present-day and initial
biomass (Fig. 4) and between ELUCc and initial biomass (Fig. 3), with all
uncertainties being propagated (Eqs. 1 and 2), we were able to constrain ELUCc during 1901–2012 by biomass observations (Figs. 3, S4, S5, Table 3).
The ELUCc value constrained by the biomass dataset of Carvalhais et al. (2014) is
155 ± 50 PgC (mean and 1σ Gaussian error) and this estimate
is robust to the choice of the methods to define deforestation grid cells in
biomass datasets (constrained ELUCc = 152 ± 49, 154 ± 50 and
159 ± 51 PgC for Method A, Method B and Method C, respectively). The
difference between the global constrained ELUCc and the median value of
original ELUCc (148 PgC) from TRENDY DGVMs is not significant, suggesting
that the median model estimate is independently verified by biomass
observations. Still, some models that are inconsistent with the observations
can be identified (Fig. 3).
Comparisons between the original TRENDY land-use and land-cover
change (LULCC) emissions and the cumulative LULCC emissions constrained by
the biomass dataset from Carvalhais et al. (2014). Panels (a), (b) and
(c) are the results from Method A, Method B and Method C, respectively.
The original TRENDY emissions are shown as the median value of all models.
Dashed line is the 1:1 line.
The uncertainties reported in our constrained estimate of ELUCc include
uncertainties in the biomass observations and in the scatter of the two
regressions (Figs. 3, 4) used to construct the emerging constraint. The
uncertainties in the constrained ELUCc are still relatively large, resulting
from the large uncertainties in the biomass observations. However, it should
be noted that we summed the biomass uncertainty in each deforestation grid
cell to give the regional biomass uncertainty, which gives a maximum
uncertainty with a potential assumption that the uncertainties in all grid
cells are fully correlated. In reality, the regional biomass uncertainty
should be lower, thus leading to lower uncertainty in constrained ELUCc.
However, it is difficult to estimate the error correlations of observation-based
biomass between different grid cells at this stage.
Although the constrained global ELUCc value is only 7 PgC higher than the
median of the original DGVM ensemble (Table 3), larger differences can be
found on a regional scale (Fig. 5). Constrained ELUCc estimates are higher
than the original modeled values in South and Southeast Asia, tropical Africa
and South and Central America (Table 3). For example, the constrained ELUCc
value is 37.2 ± 14.4 PgC in South and Southeast Asia compared to the
original TRENDY median value of 21.8 PgC (range of 9.6 to 46.6 PgC) for
that region. The constrained emissions are also higher in the China region and
the Pacific developed region compared to the prior median value (see
Table 3). A significantly large reduction in ELUCc through the emerging
constraint is found in North America because of the lower biomass amount from
observation-based datasets than from DGVMs. The original median ELUCc value of
that region is 19.9 PgC (range of 8.6 to 40.8 PgC), while the constrained
result is 10.8 ± 7.1 PgC. Constrained ELUCc are also lower than
original estimates in Western Europe, North Africa and the Middle East,
although their contributions to the global total emissions are very small
(Table 3).
Alternative estimates of ELUCc constrained by three other biomass datasets
(Liu et al., 2015; GEOCARBON, Avitabile et al., 2016; Santoro et al., 2015;
Pan et al., 2011) are provided in Fig. 6 and Table 3. In general, the
constrained ELUCc using biomass maps from Liu et al. (2015) and GEOCARBON
(Avitabile et al., 2016; Santoro et al., 2015) are rather consistent (on
average only 4.5 % higher) with those from Carvalhais et al. (2014),
implying the robustness of our estimates. The biomass dataset from Pan et
al. (2011) leads to lower LULCC emission estimates on a global scale, mainly
due to a lower estimate in South and Southeast Asia (Table 3) compared to the
other products. In the Pacific developed region, GEOCARBON-based estimates
(Avitabile et al., 2016; Santoro et al., 2015) are much higher than those
from Carvalhais et al. (2014) because the latter has a gap in the biomass
map in the southern part of Australia (Carvalhais et al., 2014). In Fig. 6,
we show the original ELUCc from TRENDY DGVMs as quantiles because we do not
know whether they follow a normal distribution; to be comparable, the
interquantiles of the constrained ELUCc are also shown. The interquantile
range of constrained ELUCc is larger than that of the original ELUCc
(Fig. 6). This, however, does not mean that our emerging constraint method is
not effective, but that the relatively large uncertainty in the constrained
ELUCc is propagated from the biomass observation uncertainty, which is about
one-third of the mean biomass at the global level (Carvalhais et al., 2014).
The global constrained ELUCc value obtained by using the two supplementary methods is almost
identical to that from our original method in Fig. 1 (see an example in
Fig. S6). The difference in ELUCc between the supplementary and original methods
at the global level is < 1 % for all biomass observation
datasets (Carvalhais et al., 2014; Liu et al., 2015; GEOCARBON, Avitabile et
al., 2016; Santoro et al., 2015; Pan et al., 2011) and all methods to select
LULCC grid cells (Method A, B and C). This suggests that our constrained
results are very robust. The change in the uncertainty in global constrained
ELUCc is also very small (< 2 %) because most of the
uncertainties are from the biomass observations (see Discussion) and the
regression between ELUCc and biomass (see r2 in Fig. 3), rather than
from
converting present-day biomass to biomass in 1901 (see r2 in Fig. 4).
The difference in regional ELUCc between different constraint methods is
relatively larger (12 % on average), but the difference remains very
small in tropical regions (∼ 1 %). However, we note that the
results from the two supplementary methods (Method S1 and S2) should be
cautiously treated. First, because ELUCc are related to the biomass that
has
been affected since the start of the land-use perturbation, only biomass in
1901 (rather than that left out of land use in the 2000s) in LULCC-affected
grid cells is logically related to historical ELUCc. Thus, converting
present-day biomass to biomass in 1901 (the original method; Fig. 1) is a
more direct and process-justified approach compared to regressing present-day
biomass versus ELUCc (Method S1), which is not justified by a logical
mechanism. Second, using ΔB in Method S2 is not a perfect solution to
extrapolate biomass in 1901 from present-day biomass because the change in
biomass is not solely impacted by land-use change. The interactions between
biomass and climate conditions, disturbances and nutrient limitation are also
very important in DGVMs. For example, historical LULCC may reduce biomass
over LULCC-affected regions by replacing forests with croplands. On the
contrary, the CO2 fertilization effects may increase biomass over LULCC
and non-LULCC regions. Therefore, ΔB reflects a mixed effect of
different factors, not a sole response to LULCC. In addition, as ΔB has a higher relative uncertainty among models (∼ 53 % at the
global level), using the regression (r2 > 0.92 in seven
regions; Fig. 4) to calculate biomass in 1901 could include relatively less
noisy information than using ΔB.
Discussion
Our approach to constraining ELUCc from an ensemble of DGVMs provides a best
estimate that is between those from two bookkeeping models (∼ 130 PgC
from Houghton et al., 2012, and 212 PgC for the default dataset from Hansis
et al., 2015). Although the bookkeeping model from Hansis et al. (2015) was
driven by the same agricultural land-use maps as the TRENDY models (the model
of Houghton et al., 2012, uses FRA/FAO data), the ELUCc value from Hansis et
al. (2015) is different from that constrained from the DGVMs. Differences in
estimates between DGVMs and bookkeeping models have been attributed to
different definitions of LULCC emissions (Pongratz et al., 2014; Stocker and
Joos, 2015). Indeed, LULCC emissions from DGVM simulations in TRENDY include
the “missed sink capacity in the deforested area” (Gasser and Ciais, 2013;
Pongratz et al., 2014), and so, all else being equal, should simulate higher
emissions than bookkeeping models, which do not include this term. However,
bookkeeping models take forest degradation into account, while this process
is ignored in DGVMs. Bookkeeping models also represent shifting cultivation
(resulting in larger sub-grid-scale gross land transitions as opposed to net
transitions) and wood harvest; these are processes that are accounted for in only a
subset of the TRENDY models (see Table 2). In addition to different driving
LULCC area data, differences between the two bookkeeping models were
discussed by Hansis et al. (2015); for example, Houghton et al. (2012) assumed
a preferential allocation of pastures on natural grasslands, while Hansis et
al. (2015) assumed a proportional allocation of both cropland and pasture on
all available natural vegetation types.
The global cumulative land-use and land-cover change (LULCC)
emissions during 1901–2012 from original TRENDY models and from the
estimates constrained by different biomass datasets with different methods to
define deforestation grid cells. “All methods” represents the ensemble mean
and uncertainty in the constrained results from Method A, Method B
and Method C for each biomass dataset. The whisker–box plot represents
the minimum and maximum values, 25th and 75th percentiles and the median
value of original TRENDY models. In the bar plot for the constrained
estimates, the red line represents the 1σ Gaussian errors; the black
ticks represent the 25th and 75th percentiles.
We are aware that our truncated diagnostic of a set of deforestation grid
cells, instead of grid cells affected by all LULCC types, is an
underestimate of the total area subject to LULCC because we ignore grid
cells that experienced land-use transitions between non-forest vegetation
only (e.g., only conversions from grasslands to cropland happening in a grid
cell). However, the conversion of forest to croplands and pasture dominates
the total net LULCC flux (Houghton, 2003, 2010), while the contribution of
transitions between non-forest vegetation and agriculture to ELUCc is
comparatively small (Fig. S1). In fact, the annual LULCC emission from
deforestation was estimated to be 2.2 PgC yr-1 during the 1990s, and the
total emissions from other activities (e.g., afforestation, reforestation,
non-forest transitions) are nearly neutral (Houghton, 2003).
The lack of direct biomass observations at the initial state forces us to
hindcast biomass in 1901 based on present-day observations; this is an extrapolation
that also comes with uncertainties. Some of the observed biomass datasets
only cover forests, and satellite measurements usually quantify aboveground
biomass carbon stocks and not total biomass stocks (Table 1). In addition,
the regression of modeled biomass between 1901 and 2000–2012 (average) to
extrapolate the biomass amount in 1901 is only a statistical approach. This
regression cannot be mechanistically explained because its slope and
intercept are impacted by multiple factors in the models like land clearing,
secondary vegetation regrowth, CO2 fertilization, climate, disturbances
and the nutrient limitation on biomass. Despite these uncertainties, the high
coefficient of determination in the regression increases our confidence in
the biomass extrapolation to 1901. For a given biomass dataset, the choice of
a method for defining deforestation grid cells (Method A, Method B and
Method C) has a very small influence on our results (Table 3).
LULCC carbon emissions are influenced not only by changes in biomass, but
also by how these are prescribed in the model to influence posterior changes
in detrital and soil organic carbon pools. However, LULCC emissions are
dominated by changes in biomass. For example, LULCC results in a net carbon
loss of 110 PgC in biomass during 1850–1990, accounting for 89 % of
the total ELUCc (Houghton, 1999). The soil carbon changes after LULCC is
also indirectly impacted by initial biomass, since the dead roots and
remaining aboveground debris turn into soil organic carbon after land
clearing, which takes longer to return into the atmosphere. In addition,
it is not necessary to account for all factors when applying an emergent
constraint approach (e.g., Cox et al., 2013; Kwiatkowski et al., 2017; Wenzel
et al., 2016). The regression between ELUCc and biomass in 1901 in the models in
our study is satisfying (e.g., r2=0.66 on a global scale; Fig. 3) to
constrain ELUCc through biomass observations.
The required model outputs for carbon stocks and fluxes in the TRENDY project
are not PFT specific; only the mean PFT-mixed variables in each grid cell are
required. Such an aggregation prevents a rigorous separation of biomass
between forest and other biomes in each grid cell. It was thus impossible for
us to calculate individual contributions of different LULCC types to the
overall LULCC emissions, which induces uncertainties when matching model
results with observed forest biomass distributions (e.g., only forest biomass
in datasets from GEOCARBON; Avitabile et al., 2016; Santoro et al., 2015; Pan
et al., 2011). Therefore, we suggest that the next generation of DGVM
comparisons report PFT-specific carbon stock and fluxes, and other model
intercomparison exercises should follow suit. The approach of using multiple
biomass observation datasets to constrain the LULCC emissions could also be
applied in other modeling projects, such as Coupled Model Intercomparison
Project Phase 5 (CMIP5) and CMIP6.
Currently, the uncertainties in the satellite-based biomass datasets are
relatively large (e.g., 38 % on average in the tropics at the pixel level (< 1 km); Saatchi et al., 2011).
This introduces uncertainties in the
constrained cumulative LULCC emissions, depending on the forest types and
biomass range. For example, on average on the global scale, the
uncertainty in the resolution of DGVM grid cells
(0.5∘ × 0.5∘) is about one-third of the mean biomass
(Carvalhais et al., 2014) and the relative uncertainty is smaller for high
biomass areas in the tropics (Avitabile et al., 2016; Saatchi et al., 2011).
The main sources of uncertainties in satellite-based biomass datasets depend
on the specific product, the spatial resolution of the datasets and the
methodology used to validate the data. For instance, in the case of radar
remote sensing used for biomass mapping in Northern Hemisphere boreal and
temperate forests, the uncertainty is largely due to the sensitivity of the
signal to properties other than vegetation structure (e.g., moisture), the
influence of non-forest vegetation on the signal (especially in fragmented
landscapes; Santoro et al., 2015) and uncertainties in the additional datasets
(allometric databases, land cover) used for the conversion of satellite
measurements to biomass estimates (Thurner et al., 2014). At the pixel level
and modeling grid cells, uncertainties may also be strongly influenced by the
quality and size of the inventory data used for validation and the significant
mismatch between pixel area and the plot data, as well as the difference between
the dates of satellite and ground observations (Saatchi et al., 2015, 2011;
Thurner et al., 2014).
Moreover, the satellite-derived biomass datasets used in this study
represent different dates. The tropical biomass products represent the circa
2000 status of forests, whereas the boreal and temperate biomass maps are
based on spaceborne radar data from the year 2010. These differences in the
date of observations introduce additional uncertainty in the biomass
estimates due to changes in forest cover from the disturbance, recovery and
land-use activities (Hurtt et
al., 2011) occurring annually and regionally.
However, in boreal, temperate and in tropical regions, the estimated
relative uncertainties were lowest in high biomass areas (Avitabile et al.,
2016; Thurner et al., 2014), which dominate the contribution to our results.
Moreover, the relatively high accuracy of biomass datasets when aggregated to
modeling grid cells from higher-resolution maps (< 1 km; Saatchiet
al., 2011; Thurner et al., 2014) suggests that the biomass datasets implemented in
our study provide a realistic representation of carbon stocks to constrain
the historical cumulative LULCC emissions from vegetation.