Stringent mitigation pathways frame the deployment of second-generation bioenergy crops combined with carbon capture and storage (CCS) to generate negative CO2 emissions. This bioenergy with CCS (BECCS) technology facilitates the achievement of the long-term temperature goal of the Paris Agreement. Here, we use five state-of-the-art Earth system models (ESMs) to explore the consequences of large-scale BECCS deployment on the climate–carbon cycle feedbacks under the CMIP6 SSP5-3.4-OS overshoot scenario keeping in mind that all these models use generic crop vegetation
to simulate BECCS. First, we evaluate the land cover representation by ESMs
and highlight the inconsistencies that emerge during translation of the data from integrated assessment models (IAMs) that are used to develop the
scenario. Second, we evaluate the land-use change (LUC) emissions of ESMs
against bookkeeping models. Finally, we show that an extensive cropland
expansion for BECCS causes ecosystem carbon loss that drives the acceleration of carbon turnover and affects the CO2 fertilization
effect- and climate-change-driven land carbon uptake. Over the 2000–2100
period, the LUC for BECCS leads to an offset of the CO2 fertilization effect-driven carbon uptake by 12.2 % and amplifies the climate-change-driven carbon loss by 14.6 %. A human choice on land area
allocation for energy crops should take into account not only the potential
amount of the bioenergy yield but also the LUC emissions, and the associated loss of future potential change in the carbon uptake. The dependency of the land carbon uptake on LUC is strong in the SSP5-3.4-OS scenario, but it also affects other Shared Socioeconomic Pathway (SSP) scenarios and should be taken into account by the IAM teams. Future studies should further investigate the trade-offs between the carbon gains from the bioenergy yield and losses from the reduced CO2 fertilization effect-driven carbon uptake where BECCS is applied.
Introduction
All stringent future socio-economic mitigation scenarios have negative
emissions that rely on carbon dioxide removal (CDR) technologies (Fuss et al., 2021; Rogelj et al., 2018). CDR is important especially in overshoot
scenarios, in which temperature temporarily exceeds the given target, e.g.,
the Paris Agreement temperature target, before ramping down as CO2 is withdrawn artificially from the atmosphere (Jones et al., 2016a; Keller et al., 2018; Tanaka et al., 2021).
Bioenergy with carbon capture and storage (BECCS) is one of the most
cost-effective CDR technologies
(Jones and Albanito, 2020; Babin
et al., 2021). In BECCS, atmospheric CO2 is captured from biomass
growth, and the harvested biomass is then converted into bioenergy or
directly combusted and a fraction of the carbon contained in the CO2
produced is recuperated and is stored in geological reservoirs without being
released back to the atmosphere (Canadell and Schulze, 2014).
BECCS is a nascent CDR technology that has not been proven at large spatial
scales. Its potential advantages include technical feasibility and a
relatively low discounted cost in future decades that allows spreading
mitigation efforts over a longer period (Anderson
and Peters, 2016; Dooley et al., 2018).
The limitations of BECCS are the requirement of potentially large land
areas, a loss of biodiversity, and the need for extra water and nutrients
(Heck et
al., 2018; Séférian et al., 2018; Li et al., 2021). Besides, BECCS
may lead to a large amount of carbon emissions from land-use change (LUC),
when bioenergy crops are grown over high-carbon content ecosystems such as
grassland and forest
(Clair
et al., 2008; Gibbs et al., 2008; Schueler et al., 2013; Smith et al., 2016;
Harper et al., 2018; Whitaker et al., 2018). The LUC emissions released due
to land conversion to bioenergy crops include immediate (direct) greenhouse
gas (GHG) emissions associated with the destruction of biomass and slash
during LUC but also delayed (indirect) emissions from the decay of stumps
and soil carbon. These emissions are termed as “carbon debt”
(Clair
et al., 2008; Fargione et al., 2008; Gibbs et al., 2008; Krause et al.,
2018) because for BECCS to be carbon neutral, this loss of carbon must be
paid back by several cycles of BECCS harvest followed by carbon geological
storage, assumed to substitute with fossil carbon emissions. Using
low-productivity marginal or degraded lands for the deployment of
second-generation bioenergy crops (such as Miscanthus or switchgrass)
reduces the carbon debt because such lands have less carbon to lose.
Further, soil carbon sequestration, in the long run, may even be achieved
with BECCS if non-harvested residues of BECCS crops exceed the carbon input
to the soil of the native ecosystems they substitute
(Campbell
et al., 2008; Gibbs et al., 2008; Mohr and Raman, 2013; Whitaker et al.,
2018).
The issue with putting second-generation bioenergy crops in low-productivity
lands is a need to invest large areas of land
(Jones et al., 2016a; Smith et al., 2016).
Currently, some land ecosystems act as a carbon sink primarily driven by the
CO2 fertilization effect on photosynthesis and the carbon turnover in
ecosystems. As croplands, unlike other ecosystems, have limited potential to
store additional carbon because the biomass is harvested regularly, and as
the new croplands have a lower soil carbon stock with a short turnover time
for soil carbon, the large-scale BECCS deployment must affect the land
carbon uptake, although this has not been specifically looked at in Earth
system model (ESM) simulation results. No study to date has estimated the
effects of BECCS deployment on the terrestrial carbon cycle under an
overshoot scenario.
In this study, we estimate the impact of large-scale BECCS deployment on the
carbon–climate feedbacks under the Shared Socioeconomic Pathway (SSP)
overshoot scenario named SSP5-3.4-OS that includes mitigation policies via
an increase in the land area covered by second-generation bioenergy crops
for CDR (Hurtt et
al., 2020). We use simulations from five Coupled Model Intercomparison
Project 6 (CMIP6) ESMs to estimate LUC impacts on the changes in land carbon
uptake and carbon–climate feedbacks.
Data and methodsSSP5-3.4-OS scenario
The SSP5-3.4-OS follows the high-emission SSP5-8.5 scenario and branches
from it in 2040 when aggressive mitigation policies are implemented
(O'Neill
et al., 2016; Meinshausen et al., 2020). The delayed mitigation leads to an
overshoot of the Paris Agreement 2 ∘C temperature limit. In
addition to a decline in fossil fuel emissions, mitigation efforts after
2040 include the expansion of second-generation bioenergy crops (for BECCS)
at the cost mainly of pasture lands (Hurtt et al., 2020). There is no
deforestation assumed after 2010, in order to preserve the areas with high
carbon content. Second-generation bioenergy crops account for most of the
new cropland areas deployed after 2040.
CMIP6 ESMs
We use five CMIP6 ESMs that simulate the SSP5-3.4-OS (Table 1). In addition
to fully coupled simulations (COU), biogeochemically (BGC) coupled
simulations, where only changes in the atmospheric CO2 concentration,
and not the temperature, affect the carbon–cycle processes, are also
provided as part of the Coupled Climate–Carbon Cycle Model Intercomparison
Project (C4MIP) (Jones et al.,
2016b). The combination of COU and BGC simulations allows us to study
carbon–climate feedbacks. The BGC simulation outputs indicate the changes in
the carbon fluxes driven by the CO2 fertilization effect; the
difference between COU and BGC simulations indicates the changes in the
carbon fluxes driven by climate change.
Major characteristics of the Earth system models.
ESM∗ReferenceLand carbon model and resolutionInclusion of “fLuc”Processes included to “fLuc”Treatment of LUH2 pastures and rangelandsIPSL-CM6A-LRBoucher et al. (2020)ORCHIDEE, br.2.0 144×143YesDeforestationPastures correspond to grass PFTs, rangelands – natural PFTsCNRM-ESM2-1Séférian et al. (2019)ISBA-CTRIP 256×128YesDeforestation decompositionPastures correspond to grasslands, rangelands – to shrubsCanESM5Swart et al. (2019)CLASS-CTEM 128×64NoNot treated. Can be grasslands or shrubsUKESM1-0-LLSellar et al. (2019)JULES-ES-1.0 192×144Yes (excluded)Deforestation wood harvest decompositionPastures are managed grasslands; rangelands correspond to natural PFTsMIROC-ES2LHajima et al. (2020)VISIT-e 128×64NoThe “closed pasture” and “rangeland” – natural vegetation, can be grasses or shrubs that get impact from grazing pressure
∗ DOIs of simulations by each ESM are provided in Table S1.
The LUC emissions in the ESMs can be estimated as the difference in net
biome production (NBP) between simulations with and without land-use change
that is between the “historical” and “hist-noLu” simulations for the
historical period. However, simulation pairs for future scenarios such as
SSP5-3.4-OS are not usually available. The “fLuc” (net carbon mass flux
into the atmosphere due to LUC) variable provided by some ESMs enables an
alternative way to incompletely quantify direct LUC emissions that include
deforestation (biomass loss during deforestation), wood harvest, and the
release of CO2 by harvested wood products, but they exclude forest regrowth
and legacy soil carbon decay or gains. Three models, IPSL-CM6A-LR,
CNRM-ESM2-1, and UKESM1-0-LL under consideration, provide the variable
“fLuc” (Table 1).
Gridded CMIP6 data, with the exception of the “fLuc” variable, were
adjusted by subtracting the long-term pre-industrial linear trend from the
control (piControl) experiment at a grid level. We used the anomalies
relative to the branching year values (indicated in Table S1 in the Supplement) for changes in
carbon pools and long-term mean piControl values for changes in carbon
fluxes.
Methodology
ESMs do not provide necessary outputs to diagnose the specific carbon fluxes
generated from the transitions to bioenergy crops: (1) they do not treat
energy crops explicitly but rather use a generic “crop” vegetation type,
itself being a grass with a higher photosynthesis rate in some models, (2)
crops only cover a fraction (tile) of a model grid box, and (3) the soil
carbon pool is usually not split into tiles for each vegetation type in land
surface models. Hence there is no perfect way to diagnose such fluxes. We
pragmatically decompose the global changes in land carbon uptake to the
contributions that are LUC- and noLUC-induced by using three different
approaches described below.
In the “fLuc” approach (1), we exploit the “fLuc” variable provided by most
models in CMIP6.
The global carbon flux, NBP that includes changes in ecosystems both with
LUC and noLUC effects, cumulated over time, approximates the changes in the
land carbon pool. Thus, cumulative NBP + fLuc (because NBP and fLuc have
opposite sign conventions with NBP positive sink to land) approximates the
changes in the land carbon pool of noLUC ecosystems.
In the “cropland threshold” approach (2), we divide
the global land area into energy-crop-concentrated and no-energy-crop (not
energy-crop-concentrated) grid cells by taking into account their evolution
after 2015. Hurtt et al. (2020) reported that after 2040, cropland areas expanded “mainly due to large-scale deployment of second-generation bioenergy crops”. We carry out a sensitivity study (Appendix A) to label the given grid cell as crop-concentrated if the cropland fraction of the grid cell is larger than a given threshold. In the sensitivity analysis, we examine a range of post-2015 cropland fraction thresholds of the grid box area and select the (ESM-specific) thresholds that best approximate the total cropland area change in 2015–2100 diagnosed by each ESM.
Under this approach, the treatment of LUC and noLUC lands and the
attribution of the LUC effects on the carbon uptake that are relevant to
BECCS are both spatially explicit. The disadvantage of this approach is that
by sampling an arbitrary fraction of crop-concentrated grid cells, we
inevitably omit some carbon changes in cropland or encroach carbon belonging
to non-crop vegetation.
In the “two simulations” approach (3), we performed
additional SSP5-3.4-OS scenario simulations by IPSL-CM6A-LR and MIROC-ES2L.
In addition to standard SSP5-3.4-OS and SSP5-3.4-OS-BGC simulations, we
performed simulations in which land use is held constant corresponding to
the 1850 usage (SSP5-3.4-OS-noLUC1850 and SSP5-3.4-OS-noLUC1850-BGC). In
addition, using IPSL-CM6A-LR, we performed simulations with 2040 land cover
usage (SSP5-3.4-OS-noLUC2040 and SSP5-3.4-OS-noLUC2040-BGC). The difference
in NBP between simulations with and without LUC indicates LUC emissions,
which are dominated by bioenergy crops area expansion after 2040. Unlike in
approaches (1) and (2), the term LUC here incorporates a carbon source
called the “loss of additional sink capacity” (LASC) relative to the
reference years 1850 and 2040
(Gasser and Ciais, 2013;
Pongratz et al., 2014). LASC is a change in carbon flux, or a foregone sink,
in response to environmental changes on managed land compared to potential
natural vegetation. The approach (3) accounts for the indirect LUC emissions
while the approaches (1) and (2) do not.
Evaluation and data consistency
The SSP5-3.4-OS is a concentration-driven scenario based on the
implementation of SSP5 in the REMIND-MAgPIE integrated assessment model
(IAM)
(Kriegler
et al., 2017; Meinshausen et al., 2020). Bauer et al.
(2017), Popp et al. (2017), and Riahi et al.
(2017) provided additional details on the changes in
energy and land use. Hurtt et al.
(2020) provided the
changes in land use in a coherent gridded format required for ESMs in the
Harmonization of Global Land-Use Change and Management version 2 (LUH2)
project. In LUH2, the historical data (up to the year 2014) based on the
History of the Global Environment database (HYDE) and future scenarios
(2015–2300) based on IAM are harmonized to minimize the differences between
the end of historical reconstruction and IAM initial conditions
(Hurtt et al.,
2020). The harmonization process, however, is expected to result in some
mismatches between LUH2 and the IAM during the early stage of the post-2014
period. First, we check the consistency of the global and regional cropland
and other land-state areas reported by REMIND-MAgPIE, LUH2, and CMIP6 ESMs.
Second, we evaluate global and regional historical LUC estimates by CMIP6
ESMs against three bookkeeping approaches.
Consistency of cropland area between REMIND-MAgPIE, LUH2, and ESMs
Under the SSP5-3.4-OS pathway, the cropland area increases by 8.1×106km2 (∼50 %) from the 2010 level in the 21st century to 2100 (Hurtt et al., 2020). The global cropland area modeled by REMIND-MAgPIE and downscaled by LUH2 increases due to the expansion of second-generation bioenergy crops. The global cropland areas by REMIND-MAgPIE and LUH2 are largely consistent with a slightly larger area of crops by REMIND-MAgPIE till the 2050s (reaching 0.6×106km2 in the year 2050) and a larger area of crops by LUH2 in 2060–2090s (Fig. 1a). Unlike the REMIND-MAgPIE, LUH2 simulates a slight reduction of forest area (by 1.3×106km2 in 2100 from 2010 level). The global cropland area in LUH2 is less than in REMIND-MAgPIE by 0.3×106km2 in 2015, and larger by 2.9×106km2 in 2060 that is 14 % of the total cropland area of 20.7×106km2 by LUH2 in 2060 (and corresponds to a 43.4 % increase from the 2015 level) and may cause additional uncertainty in estimates of the BECCS area and LUC. Further, ESMs implement the global and regional gridded cropland fractions following LUH2 and using their own land cover map (Fig. 1b), with an exception of UKESM1-0-LL that reports an evolution of the global cropland area smaller than those of other ESMs. This deviation of UKESM1-0-LL may occur because of its specifications in the treatment of croplands and the model's dry bias (precipitation deficit) in India and the Sahel (Sellar et al., 2019). While the model uses the LUH2 data to prescribe an area available for crops to grow in, this area is covered by the crop plant functional types (PFTs) only if the model's climate is suitable for the grass PFTs, otherwise, the area remains bare soil.
Time series of (a) the changes in the area of croplands, pastures,
and forests according to REMIND-MAgPIE and LUH2, and (b) the area of
croplands in LUH2, REMIND-MAgPIE, and five CMIP6 ESMs under the SSP5-3.4-OS
pathway. In panel (a), pastures and rangelands of LUH2 are treated together
as pastures.
Aside from the deviations in total areas of land cover types between
REMIND-MAgPIE, LUH2, and ESMs listed above, a discrepancy arises from the
implementation of LUH2's land cover types to the ESM's plant functional
types (PFTs). Nevertheless, most CMIP6 ESMs produce cropland areas
consistent with LUH2. However, the other vegetation classes of LUH2 (e.g.,
forested lands, non-forested lands, pastures) do not match the PFTs of ESMs
because most ESMs decided to use their own land cover map rather than using
the LUH2 one for these ecosystems. First, spatial distributions of
vegetation classes are tightly associated with climate and biogeochemical
processes, and thus, the replacement of the vegetation covers in ESMs would
lead to large changes in the model performances. Second, some models that
include dynamic vegetation, like UKESM1-0-LL, predict the vegetation
distribution change, and sometimes the predicted distribution does not
coincide with the one prescribed by LUH2. Besides, the pastures of
REMIND-MAgPIE are translated to two land-use states in LUH2: pastures and
rangelands. While they are treated predominantly as low-productivity areas
in REMIND-MAgPIE, this may not be a case in ESMs, where pastures and
rangelands may correspond to grasslands and perhaps to shrublands (if this
land cover exists in an ESM). Some ESMs do not distinguish pastures and
rangelands because of the ambiguity in their definitions. Likewise, the
SSP5-3.4-OS scenario involves large-scale second-generation bioenergy crops
whose benefit is the capability to grow in so-called “marginal” lands
(Krause et al., 2018). The ambiguity and inconsistency in the definition of
land-use and land-cover tiles between IAM, LUH2, and ESMs may have
implications for the interpretation of the scenario.
We shed light on an issue of inconsistency when translating LUC from IAMs
into LUH2 and, then, into ESMs. Overall, implementation of the LUC scenario
of REMIND-MAgPIE to first LUH2 and then ESMs leads to a consistency loss
of simulated scenario during the harmonization process. Further, the land
cover representation in ESMs is subjective and different from the IAM and
LUH2 mainly because of ambiguity in the correspondence between land-use and
vegetation type definitions. This problem requires thorough attention,
especially in ESM and IAM intercomparison studies.
Evaluation of land-use change emissions
The global and regional LUC emissions estimated by ESMs were evaluated
against three bookkeeping models for the historical period, namely BLUE
(Hansis et al., 2015), HN2017
(Houghton and Nassikas, 2017),
and OSCAR (Gasser et al., 2020a). The models differ in the
spatial units (spatially explicit, country level, region level),
parametrization, and process representations
(Friedlingstein
et al., 2020; Gasser et al., 2020a). Unlike other bookkeeping models, OSCAR
also reported LASC in LUC estimates but the utilized version did not include
peat emissions.
Unlike the difference in NBP between simulations with and without LUC, the
“fLuc” variable accounts only for the direct LUC emissions and does not
account for all the fluxes reported by bookkeeping models, e.g., forest
regrowth and slash and soil organic matter decay, as well as for shifting
cultivation and degradation
(Houghton and Nassikas, 2017).
Thus, its values are expected to be lower. We use an average of multiple
realizations when provided by the model teams (details in Table S1). The
evaluation targets estimating LUC emissions in “fLuc” and “two
simulations” approaches.
We found that ESMs tend to estimate lower global LUC emissions than
bookkeeping models by both “fLuc” variable and “two simulations”
approaches (Fig. 2). This is remarkable in the three tropical regions that
have dominated global LUC emissions since the 1960s, and particularly South and
Southeast Asia (Fig. S1 in the Supplement). In 1960–2014, on average, bookkeeping models
estimate that three tropical regions account for 56.8%±2.3 % of
global LUC emissions, while ESMs estimate that they account for 35%±10 % based on simulations with and without LUC and 40%±15 % based
on the “fLUC” variable.
Evaluation of cumulative global LUC emissions by ESMs against
three bookkeeping models. LUC emissions are defined by two methods: (1) the
difference in NBP between simulations with and without LUC (solid lines) and
(2) the “fLuc” variable provided in CMIP6 (dashed lines). The estimates of
the bookkeeping approach using OSCAR are shown for cases with (noLUC-LUC)
and without LASC. The range of bookkeeping models is shaded green.
LUC emission estimates by MIROC-ES2L (for which only LUC emissions derived
from simulations with and without LUC were available) are the most
consistent with the estimates of bookkeeping models among considered ESMs
(see also Liddicoat et al., 2021). We excluded the estimates of
LUC emissions by CNRM-ESM2-1 based on simulations with and without LUC and
by UKESM1-0-LL based on “fLuc” from the analysis. CNRM-ESM2-1 estimates
much lower LUC emissions derived from simulations with and without LUC than
other ESMs, possibly because the CMIP6 version of the model does not include
a harvest module, i.e., croplands are modeled as natural grasslands
(Séférian et al., 2019), and cropland
soils continue to be loaded by harvest inputs. UKESM1-0-LL estimates
implausibly low LUC emissions derived from the “fLuc” variable.
The LUC emissions estimated by the two approaches differ remarkably due to
inconsistent “fLuc” definitions among models (Gasser and Ciais, 2013). We call for a clearer and more rigorous definition of this variable in future CMIPs so that model outputs can be compared on the same basis. As some examples for improvement, we suggest that model teams provide explicit detail of processes that contribute to “fLuc”, e.g., direct deforestation and wood harvest emissions, decomposition flux, as well as indirect emissions, e.g., per each PFT.
Evaluation of land-use change emissions from BECCS deployment
The increased LUC emissions to account for BECCS are a part of total carbon
budget calculations in the IAM scenario. We compared LUC emissions by
different approaches using ESMs with LUC of REMIND-MAgPIE (Fig. S2). While
the IAMs design the scenario in a way that the benefits of BECCS exceed the
carbon losses from LUC, the ability of IAM to accurately estimate LUC
emissions including legacy emissions is questionable. In the SSP5-3.4-OS
scenario, the REMIND-MAgPIE estimates lower LUC emission compared ESMs.
BECCS dominates negative emissions in the SSP5-3.4-OS pathway. We confirmed
that BECCS is predominantly deployed in low-carbon uptake areas by comparing
the changes in carbon pools and NBP globally and crop-concentrated areas
(Fig. S3). Because bioenergy crops are deployed in low-carbon uptake areas
and they dominate LUC emissions in the 21st century, the NBP over
crop-concentrated areas derived by the “cropland threshold” approach
approximates global LUC emissions. The comparison of NBP in
crop-concentrated grids with the original LUC emissions of the REMIND-MAgPIE
IAM scenario confirms a similar trend between IAM-based global LUC emissions
and ESMs-based global temporal NBP changes in the crop-concentrated areas
after 2040. The strong correlation is evident in three ESMs, namely CanESM5,
UKESM1-0-LL, and MIROC-ES2L (correlation coefficient is 0.72 for the
2015–2100 period). The carbon loss in the crop-concentrated areas over the
21st century period averaged over these three ESMs reaches 37.8±30.3 Gt C. Two models, IPSL-CM6A-LR and CNRM-ESM2-1, however, do not capture the
increased carbon loss after 2040 perhaps due to low estimates of LUC
emissions from crop expansion (especially CNRM-ESM2-1) or overestimated
uptake by no-LUC areas (Figs. 2 and S1). Besides, IPSL-CM6A-LR simulates the
lowest ecosystem carbon pool, especially in soils (Arora et al., 2020) that
may lead to relatively small LUC-induced carbon losses when cropland areas
expand. Thus, the estimates of LUC impact on carbon–climate feedbacks from
IPSL-CM6A-LR and CNRM-ESM2-1 need to be considered with the above-mentioned
caveats.
The impact of LUC from bioenergy crop expansion on the carbon uptakeDifferences in LUC impact on carbon uptake estimated by three approaches
We use the estimates of the LUC impacts on global carbon uptake by
IPSL-CM6A-LR and MIROC-ES2L to compare the three approaches described in
Sect. 2.3. The estimates of both models and three approaches show that the
LUC impacts lead to a loss of carbon fluxes (Fig. 3). The losses from LUC
surpass the benefits from the CO2 fertilization effect, so that the LUC
ecosystems become a carbon source to the atmosphere. The “cropland
threshold”, unlike the other two approaches, separates
cropland-concentrated and no-crop contributions spatially. Thus, the
estimated changes in carbon uptake are areal cumulative under the “cropland
threshold” approach. In the other two approaches, in contrast, the changes
in carbon fluxes are calculated in each grid cell for both LUC-dominated and
noLUC ecosystems, so that carbon change of these two land-use categories may
partly offset each other.
Cumulative land carbon uptake from the year 2000 in
LUC-concentrated (solid lines) and noLUC (dashed lines) ecosystems estimated
by three approaches by (a) IPSL-CM6A-LR and (b) MIROC-ES2L.
Interannual variation of global (a, b) land carbon uptake and (c, d) cumulative carbon uptake in LUC-concentrated and noLUC ecosystems given
as mean and standard deviation (shaded area) of five ESMs and three
approaches. Panels (a) and (c) show BGC simulation
outputs, and panels (b) and (d)
show the difference in COU and BGC simulation outputs.
A larger loss is seen in “two simulations since 1850” because these
simulations include LASC and legacy soil emissions (Fig. 3a). Intermediate
loss is from “fLUC” because this approach includes only immediate (direct)
carbon loss. Lower carbon losses correspond to the “cropland threshold”
approach that also includes a carbon sink in natural ecosystems over
selected grid cells and misses initial carbon loss, and to “two simulations
since 2040” that miss legacy emissions of activities before 2040. The
larger carbon losses in the “two simulations since 1850” than in the “two
simulations since 2040” estimates also reveal the long-term effects of LUC.
In the case of IPSL-CM6A-LR, the “cropland threshold” and “two
simulations since 2040” approaches produce similar estimates of LUC impact
on cumulative land carbon uptake because these two methods target the
changes in the carbon fluxes, particularly due to cropland expansion for
BECCS in the 21st century. MIROC-ES2L that accounts for gross LUC emissions
(Liddicoat et al., 2021) produces similar estimates of LUC
impact by “cropland threshold” and “two simulations since 1850”
approaches.
Temporal impacts of LUC on global carbon uptake
Figure 4 illustrates the attribution of global carbon fluxes to LUC (or
crop-concentrated) and no-LUC (no-crop) ecosystems by five ESMs and three
approaches (see Fig. S4 for the results, specific for each ESM and
approach). The large-scale deployment of bioenergy crops even on low
carbon-uptake areas causes a carbon loss from the ecosystem. The negative
values of the carbon flux in the CO2 concentration-only simulation
indicate the domination of the LUC losses over the CO2 fertilization
effect-driven carbon gains in the ecosystems.
For the “cropland threshold” approach, the majority of ESM simulations,
excluding IPSL-CM6A-LR and CNRM-ESM2-1 (see Sect. 3.3), agree that
cropland expansion causes a decrease in global CO2 fertilization
effect-driven carbon uptake, especially in crop-concentrated grids which
lose carbon from LUC. Cropland expansion for BECCS may also contribute to
the global climate change-driven carbon loss. However, these changes are
small in the “cropland threshold” and absent in “fLUC” estimates. We
speculate this occurs because the “fLuc” variable involves only direct LUC
changes such as deforestation, wood harvest, and soil carbon decay. On top
of it, earlier findings show that the ESMs do not realistically represent
the dynamics of soil and litter carbon after LUC (Boysen et al.,
2021). The LUC carbon losses for BECCS deployment cannot be overridden by
the increased CO2 effects, but they contribute to the carbon losses
driven by climate change. Overall, the three approaches and five ESMs
demonstrate that the BECCS expansion under the SSP5-3.4-OS pathway results
in 42.55±41.08 Gt C loss that corresponds to 12.2 % of noLUC
CO2 fertilization-driven uptake and to an additional 13.00±12.27 Gt C loss that corresponds to 14.6 % of noLUC climate change-driven
loss over the 2000–2100 period (Table S2).
Spatial variation of impacts of LUC on global carbon uptake
We investigated the spatial variation of LUC impact on the land carbon cycle
using simulations with and without LUC by MIROC-ES2L and IPSL-CM6A-LR
(Fig. 5). Two models show that the carbon uptake decreases in the BECCS
areas due to LUC emissions. Even though the SSP5-3.4-OS scenario is designed
so that BECCS utilizes low carbon areas to cause the least possible impact
on the carbon sink in unmanaged lands, these BECCS areas lose their CO2
fertilization-driven carbon uptake potential but do not escape climate
change-driven carbon losses. In the SSP5-3.4-OS scenario, second-generation
biofuel cropland areas estimated by LUH2 reach nearly 6 % of global land
(potentially vegetated) area in 2100. Assigning such vast areas to bioenergy
crops – even if they correspond to low-carbon content ecosystems – affects
the land carbon uptake and the global carbon cycle feedbacks. The decision
on the assignment of these areas for energy crops requires assessment of
both the current state of the ecosystem, e.g., the carbon content in
vegetation and soil, and the future potential increase in the carbon uptake.
The impact of LUC on the carbon cycle should be accounted for in developing
future mitigation pathways so that the benefits of BECCS are not minimized
by the carbon losses.
Spatial variations of the cumulative over 2040–2100 period
carbon uptake by (a) IPSL-CM6A-LR and (b) MIROC-ES2L given for the fully
coupled simulations with and without LUC. The negative values indicate less
sink/larger source from land to atmosphere. (c) The bioenergy crop area in
2100 from LUH2.
The carbon cycle feedback framework perspective
The CO2 fertilization effect- and climate-change-driven changes in the
carbon fluxes and storages may be expressed as β and γ
feedback parameters per unit changes in the global atmospheric CO2
concentration (ΔCO2) and surface air temperature
(ΔT),
respectively
(Jones
et al., 2016b; Friedlingstein et al., 2020; Zhang et al., 2021).
Here the temperature change is taken as a proxy for the response of the
ecosystem carbon storage to climate change. The carbon–concentration β (GtCppm-1) and carbon–climate γ (GtC∘C-1) feedback parameters can be estimated using BGC and COU simulation outputs (Friedlingstein et al., 2006; Gregory et al., 2009; Jones et al., 2016a; Melnikova et al., 2021; Zhang et al., 2021):
1β=ΔCBGCΔCO2,2γ=ΔCCOU-ΔCBGCΔT,
where ΔCBGC and ΔCCOU indicate the changes in the land carbon pool (or cumulative uptake) in BGC and COU simulations,
respectively, and ΔCO2 and ΔT (from COU runs) indicate the changes in the global CO2 concentration and mean surface air temperature, respectively, all reported changes being relative to
pre-industrial level (piControl).
The carbon cycle feedback framework is often compared between ESMs in
idealized scenarios (such as 1 % CO2 increase), and the β and
γ feedback parameters/metrics are assumed to be a pure response to
the CO2 concentration and temperature changes. Applying this framework
to non-idealized and more socially relevant scenarios provides another
perspective for understanding the changes in the carbon fluxes under more
realistic evolutions. Previously, Melnikova et al. (2021) applied the β and γ framework to the SSP5-3.4-OS scenario and showed an
amplification of the feedback parameters after the CO2 concentration
and temperature peaks due to inertia of the Earth system. Here we performed
an estimation of the β and γ feedback parameters to
investigate the impacts of the LUC on the behavior of the feedback
parameters.
Note, in the case of the overshoot scenarios, if the CO2 concentration
and temperature changes during the ramp-down period went to zero, the
definitions described in Eqs. (1) and (2) would become invalid. Although
because in this study, the change in CO2 concentration and the
temperature never goes to zero (in the SSP5-3.4-OS before 2300), and the
feedbacks parameters can safely be calculated, the limitation should be
taken into account.
The land carbon uptake and the β and γ feedback parameters
are affected by LUC, so that they are lower in the simulations with LUC
(Fig. 6). Moreover, the difference in the β parameter estimated by
IPSL-CM6A-LR in simulations with LUC and without LUC after the year 2040
suggests that even only LUC for bioenergy crop expansion affects the
hysteresis behavior of the carbon cycle feedback parameters under declining
CO2 concentration and temperature.
The variation of (a) global βland (GtCppm-1) and
γland (GtC∘C-1), and (b) cumulative over
2000–2300 (for IPSL-CM6A-LR) and over 2000–2100 (for MIROC-ES2L) β-
and γ-driven land carbon uptakes with and without LUC. The changes
in LUC are given as 9-year moving averages; negative value corresponds to a
land sink.
To date, the LUC impacts on the carbon cycle have not been included into the
β and γ feedback framework, and the LUC emissions are
discussed as an anthropogenic forcing separately from the feedbacks of land
ecosystems to the changed CO2 and climate. However, the β and
γ parameters cannot be decoupled either from the state of the land
use, or from the pre-industrial state of land cover, or from other model
structural parts, leading to a value for equilibrium carbon stock. There is
an interplay between land cover and the model's response to CO2 (and
climate) that has been demonstrated mathematically in Gasser and Ciais
(2013) and defined as LASC. Gasser et al. (2020a) quantified it as a foregone
sink of about 30 Gt C over the historical period. But this value can only
increase as future CO2 will be much higher than in the past.
In a broader sense, the land-cover- and land-use-associated differences in
the initial conditions of ESMs simulations influence the estimates of global
carbon cycle feedback parameters even under idealized pathways. The
divergences in the pre-industrial land covers among ESMs lead to spatial
differences in the ecosystem carbon stocks (e.g., ESM with larger forest
cover has larger land carbon pool size). Furthermore, the pre-industrial
levels of ecosystem carbon stock vary among models even for identical
land-cover types. The estimated global β and γ feedback
parameters involve these land-cover-related uncertainties. Future studies
should address the issue by benchmarking the sets of idealized experiments
with different types of land-cover and land-use changes.
Conclusions
In this study, we investigated the impacts of bioenergy crop deployment on
the carbon cycle under an overshoot pathway. In the evaluation part of this
study, we highlighted some inconsistencies in the land-use states and their
temporal transitions between the REMIND-MAgPIE, LUH2, and ESMs. These
differences arise from differences in process representations and initial
conditions, as well as land-use and land-cover tiles definitions across
models. The inconsistencies should be taken into account in comparative
studies of IAMs and ESMs. Further work will be required to address the issue
of the level of inconsistency between the IAMs, LUH2, and ESMs that should
be tolerated to have confidence that ESMs and IAMs describe the same
scenario.
We exploit five ESMs and three approaches to show that cropland expansion
for BECCS causes a carbon loss even in low-carbon uptake lands and reduces
the future potential increase in the global carbon uptake via LUC impact on
the carbon stock, and the carbon–concentration and carbon–climate feedbacks.
Under the SSP5-3.4-OS, the LUC emissions from BECCS deployment cause a
decrease in global CO2 fertilization effect-driven carbon uptake and
increase the climate change-driven carbon loss.
Our results are consistent with the IPCC special report on climate change
and land (Shukla et al., 2019) and highlight the need
for considering trade-offs in BECCS deployment and other land-uses but, to
some extent, they go beyond this assessment by considering the implication
of carbon cycle feedbacks. Our work shows that areas best suited for BECCS
should also be assessed both in terms of their potential amount of the
bioenergy yield and potential future impact on the carbon–climate feedbacks.
Future studies need to further investigate the potential of BECCS to provide
negative carbon emissions with little loss of storage from the LUC.
Sensitivity study for deriving the crop-concentrated grid
thresholds
Neither IAMs nor ESMs provide BECCS-related LUC emissions. Separating
BECCS-related emissions from all other LUC emissions is virtually impossible
due to spatial heterogeneity and many complex factors that affect the
bioenergy crop deployment.
ESMs do not distinguish second-generation bioenergy crops from other crops
in CMIP6. Moreover, the cropland area in ESMs is defined at a sub-grid scale
(i.e., on a fraction or tile of a grid box). Because land-use states (e.g.,
forest, crops, pastures) vary in productivity and, thus, carbon uptakes and
because modeling teams do not provide NBP estimates at the sub-grid level,
to estimate the area and carbon fluxes of the biofuel crops in ESMs, we
assume that all croplands deployed after the 2040s are for second-generation
biofuel crops (Fig. A1). We label the given grid of CMIP6 simulation
outputs as crop-concentrated if the cropland fraction of the grid is larger
than a given threshold derived via a sensitivity analysis (Fig. A1).
A schematic presentation of the sensitivity study for estimating
the carbon–climate feedback parameters over the energy-crop-concentrated and
no-energy-crop grids.
(a) The cropland-fraction thresholds ranging from 25 % to
45 % of the grid box area analyzed in the sensitivity study and (b) the
selected (resultant) range of thresholds for identifying the
energy-crop-concentrated area with the selected range for each ESM indicated
in the table. Panel (c) shows the cumulative NBP of the areas corresponding
to the range of cropland thresholds from 1 % to 100 % (left dark to right
light color) in three periods.
We examined time-invariant cropland fraction thresholds ranging from 25 %
to 45 % of the grid box area and selected a range of thresholds that best
approximate the change in the total cropland area of each ESM in 2015–2100
(Fig. A2). Here we choose the fitting period of 2015–2100 because a
shorter period (2040–2100) would result in a lower threshold during the
2050–2060 period with a large global cropland increase. More specifically,
we selected a range of thresholds with a 1 % step so that they intersect
at least once either the global cropland area estimated by ESM itself or
LUH2 data set from 2015 to 2100. Although the selected ensembles of
thresholds are time-invariant, the resultant cropland area increases. We
find that for a later period (end of the 21st century), a higher
threshold is required because both the spatial coverage (the number of grid
boxes that have crops) and cropland concentration (a grid fraction of
cropland) increase (Fig. A2).
We confirmed the spatial distribution of the minimum and maximum selected
thresholds of energy-crop-concentrated grids against sub-grid-scale ESM and
the LUH2 estimates of cropland area (Fig. A3).
Spatial variation of (a) grid cropland fraction (b) and
second-generation bioenergy cropland fraction by LUH2. Panel (c) shows the
spatial variation of grid cropland fraction estimated by CMIP6 ESMs. The
spatial variation of the selected (d) minimum and (e) maximum thresholds
(which intersect at least once either the global cropland area estimated by
ESM itself or LUH2 data set from 2015 to 2100 as shown in Fig. A1) for
estimating crop-concentrated grids in 2100.
Data availability
The data from the CMIP6 simulations are available from the CMIP6 archive: https://esgf-node.llnl.gov/search/cmip6 (WCRP, 2022), the LUH2 data from https://luh.umd.edu/data.shtml (UoM, 2022), and the IIASA database via https://tntcat.iiasa.ac.at/SspDb/dsd?Action=htmlpage&page=20 (IIASA, 2022). We obtained LUC emission data of bookkeeping approaches from the modeling teams and http://pure.iiasa.ac.at/id/eprint/17551/ (Gasser et al., 2020b) for OSCAR.
The supplement related to this article is available online at: https://doi.org/10.5194/esd-13-779-2022-supplement.
Author contributions
OB, PCi, KTan, and IM initiated the study, and all co-authors
provided input into developing the study ideas. IM performed data analysis
and wrote the initial draft. TH (MIROC-ES2L) and PCa (IPSL-CM6A-LR)
performed additional ESM simulations. All authors contributed to writing and
commenting on the paper.
Competing interests
At least one of the (co-)authors is a member of the editorial board of Earth System Dynamics. The peer-review process was guided by an independent editor, and the authors also have no other competing interests to declare.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
We acknowledge the World Climate Research Programme, which, through its
Working Group on Coupled Modelling, coordinated and promoted CMIP6. We thank
the climate modeling groups for producing and making available their model
output, the Earth System Grid Federation (ESGF) for archiving the data and
providing access, and the multiple funding agencies who support CMIP6 and
ESGF. We thank Richard Houghton of Woodwell Climate Research Center for
providing the regional annual fluxes for LUC from HN2017, Eddy Robertson of
Met Office, and Vivek Arora of Canadian Centre for Climate Modelling and
Analysis for providing additional information on the LUC implementation in
ESMs. The IPSL-CM6 experiments were performed using the HPC resources of
TGCC under the allocation 2020-A0080107732 (project gencmip6) provided by
GENCI (Grand Equipement National de Calcul Intensif). This study benefited
from state assistance managed by the National Research Agency in France
under the Programme d'Investissements d'Avenir under the reference ANR-19-MPGA-0008. Our study was also supported by the European Union's Horizon 2020 research and innovation programme under grant agreement number 820829 for the “Constraining uncertainty of multi-decadal climate
projections (CONSTRAIN)” project, by a grant from the French Ministry of the
Ecological Transition as part of the Convention on financial support for
climate services. Roland Séférian and Thomas Gasser acknowledge innovation programme under grant agreement no. 101003536 (ESM2025 – Earth System Models for the Future). Roland Séférian acknowledges the European Union's Horizon 2020 research and the support of the team in charge of the CNRM-CM climate model. Supercomputing time was provided by the Météo-France/DSI supercomputing center.
Financial support
This research has been supported by the Agence Nationale de la Recherche (grant no. ANR-19-MPGA-0008), Horizon 2020 (CONSTRAIN (grant no. 820829)), the Agence de la transition écologique (convention on financial support for climate services), the Ministry of Education, Culture, Sports, Science and Technology (grant no. JPMXD0717935715), and the Environmental Restoration and Conservation Agency (grant no. JPMEERF20192004).
Review statement
This paper was edited by Ning Zeng and reviewed by Vivek Arora and Wang Xiaobo.
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