GMD - Definitions and methods to estimate regional land carbon fluxes for the second phase of the REgional Carbon Cycle Assessme

GMD - Definitions and methods to estimate regional land carbon fluxes for the second phase of the REgional Carbon Cycle Assessment and Processes Project (RECCAP-2)
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Methods for assessment of models
16 Feb 2022
Methods for assessment of models |
16 Feb 2022
Definitions and methods to estimate regional land carbon fluxes for the second phase of the REgional Carbon Cycle Assessment and Processes Project (RECCAP-2)
Definitions and methods to estimate regional land carbon fluxes for the second phase of the REgional Carbon Cycle Assessment and Processes Project (RECCAP-2)
Definitions and methods to estimate regional land carbon fluxes for the second phase of the...
Philippe Ciais et al.
Philippe Ciais
Ana Bastos
Frédéric Chevallier
Ronny Lauerwald
Ben Poulter
Josep G. Canadell
Gustaf Hugelius
Robert B. Jackson
Atul Jain
Matthew Jones
Masayuki Kondo
Ingrid T. Luijkx
Prabir K. Patra
Wouter Peters
Julia Pongratz
Ana Maria Roxana Petrescu
Shilong Piao
Chunjing Qiu
Celso Von Randow
Pierre Regnier
Marielle Saunois
Robert Scholes
Anatoly Shvidenko
Hanqin Tian
Hui Yang
Xuhui Wang
and
Bo Zheng
Philippe Ciais
CORRESPONDING AUTHOR
philippe.ciais@lsce.ipsl.fr
Laboratoire des Sciences du Climat et de l'Environnement,
CEA-CNRS-UVSQ-U.P.Saclay, Gif sur Yvette, France
Ana Bastos
Department Biogeochemical Integratio, Max-Planck-Institut für Biogeochemie, Hans-Knöll-Str. 10,
Jena, Germany​​​​​​​
Frédéric Chevallier
Laboratoire des Sciences du Climat et de l'Environnement,
CEA-CNRS-UVSQ-U.P.Saclay, Gif sur Yvette, France
Ronny Lauerwald
Laboratoire des Sciences du Climat et de l'Environnement,
CEA-CNRS-UVSQ-U.P.Saclay, Gif sur Yvette, France
Department Geoscience, Environment & Society, Université Libre de Bruxelles, Brussels, Belgium​​​​​​​
Ben Poulter
NASA Goddard Space Flight Center, Biospheric Sciences Lab.,
Greenbelt, USA
Josep G. Canadell
Global Carbon Project, CSIRO Oceans and Atmosphere, GPO Box 1700,
Canberra, Australia
Gustaf Hugelius
Department of Physical Geography, Stockholm University, Stockholm, Sweden
Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden
Robert B. Jackson
Department of Earth System Science, Woods Institute for the
Environment, Stanford University, Stanford, CA 94305, USA​​​​​​​
Atul Jain
Department of Atmospheric Sciences, University of Illinois, Urbana, USA
Matthew Jones
Tyndall Centre for Climate Change Research, School of
Environmental Sciences, University of East Anglia, Norwich Research Park,
Norwich NR4 7TJ, UK
Masayuki Kondo
Center for Global Environmental Research, National Institute for
Environmental Studies, Tsukuba, Japan
Ingrid T. Luijkx
Meteorology and Air Quality, Wageningen University, Wageningen,
the Netherlands
Prabir K. Patra
Japan Agency for Marine-Earth Science and Technology (JAMSTEC),
Yokohama, Japan
Wouter Peters
Meteorology and Air Quality, Wageningen University, Wageningen,
the Netherlands
Centre for Isotope Research, University of Groningen, Groningen,
the Netherlands
Julia Pongratz
Department für Geographie,
Ludwig-Maximilians-Universität München, Luisenstr. 37, Munich, Germany
Ana Maria Roxana Petrescu
Department of Earth Sciences, Vrije Universiteit Amsterdam,
Amsterdam, the Netherlands
Shilong Piao
Sino-French Institute for Earth System Science, College of Urban
and Environmental Sciences, Peking University, Beijing, China
Institute of Tibetan Plateau Research, Chinese Academy of
Sciences, Beijing, China
Chunjing Qiu
Laboratoire des Sciences du Climat et de l'Environnement,
CEA-CNRS-UVSQ-U.P.Saclay, Gif sur Yvette, France
Celso Von Randow
Earth System Science Center, National Institute of Space Research, São José dos Campos,
Brazil​​​​​​​
Pierre Regnier
Department Geoscience, Environment & Society, Université Libre de Bruxelles, Brussels, Belgium​​​​​​​
Marielle Saunois
Laboratoire des Sciences du Climat et de l'Environnement,
CEA-CNRS-UVSQ-U.P.Saclay, Gif sur Yvette, France
Robert Scholes
Global Change Institute, University of the Witwatersrand,
Johannesburg, South Africa
deceased
Anatoly Shvidenko
Ecosystem Services and Management (ESM) Program, International Institute for Applied Systems Analysis, 2361
Laxenburg, Austria​​​​​​​
Center of Productivity of Forests Russian Academy of Sciences,
Moscow, Russia
Hanqin Tian
International Center for Climate and Global Change Research,
School of Forestry and Wildlife Sciences, Auburn University, Auburn, USA
Hui Yang
Laboratoire des Sciences du Climat et de l'Environnement,
CEA-CNRS-UVSQ-U.P.Saclay, Gif sur Yvette, France
Xuhui Wang
Sino-French Institute for Earth System Science, College of Urban
and Environmental Sciences, Peking University, Beijing, China
Bo Zheng
Laboratoire des Sciences du Climat et de l'Environnement,
CEA-CNRS-UVSQ-U.P.Saclay, Gif sur Yvette, France
Abstract
Regional land carbon budgets provide insights into the spatial distribution of
the land uptake of atmospheric carbon dioxide and can be used to evaluate
carbon cycle models and to define baselines for land-based additional
mitigation efforts. The scientific community has been involved in providing
observation-based estimates of regional carbon budgets either by downscaling
atmospheric CO
observations into surface fluxes with atmospheric
inversions, by using inventories of carbon stock changes in terrestrial
ecosystems, by upscaling local field observations such as flux towers with
gridded climate and remote sensing fields, or by integrating data-driven or
process-oriented terrestrial carbon cycle models. The first coordinated
attempt to collect regional carbon budgets for nine regions covering the
entire globe in the RECCAP-1 project has delivered estimates for the decade
2000–2009, but these budgets were not comparable between regions due to
different definitions and component fluxes being reported or omitted. The recent
recognition of lateral fluxes of carbon by human activities and rivers that
connect CO
uptake in one area with its release in another also requires
better definitions and protocols to reach harmonized regional budgets that
can be summed up to a globe scale and compared with the atmospheric CO
growth
rate and inversion results. In this study, using the international initiative
RECCAP-2 coordinated by the Global Carbon Project, which aims to be an update
to regional carbon budgets over the last 2 decades based on observations
for 10 regions covering the globe with a better harmonization than the
precursor project, we provide recommendations for using atmospheric
inversion results to match bottom-up carbon accounting and models, and we
define the different component fluxes of the net land atmosphere carbon
exchange that should be reported by each research group in charge of each
region. Special attention is given to lateral fluxes, inland water fluxes,
and land use fluxes.
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Ciais, P., Bastos, A., Chevallier, F., Lauerwald, R., Poulter, B., Canadell, J. G., Hugelius, G., Jackson, R. B., Jain, A., Jones, M., Kondo, M., Luijkx, I. T., Patra, P. K., Peters, W., Pongratz, J., Petrescu, A. M. R., Piao, S., Qiu, C., Von Randow, C., Regnier, P., Saunois, M., Scholes, R., Shvidenko, A., Tian, H., Yang, H., Wang, X., and Zheng, B.: Definitions and methods to estimate regional land carbon fluxes for the second phase of the REgional Carbon Cycle Assessment and Processes Project (RECCAP-2), Geosci. Model Dev., 15, 1289–1316, https://doi.org/10.5194/gmd-15-1289-2022, 2022.
Received: 03 Aug 2020
Discussion started: 28 Sep 2020
Revised: 29 Nov 2021
Accepted: 07 Dec 2021
Published: 16 Feb 2022
Introduction
The objective of this paper is to define the land–atmosphere CO
or
total carbon (C) fluxes to be used in the REgional Carbon Cycle Assessment
and Processes-2 (RECCAP2) project. Accurate and consistent observation-based
estimates of terrestrial carbon budgets at regional scales are needed to
understand the global land carbon sink, to evaluate land carbon models used
for carbon budget assessments and future climate projections, and to define
baselines for land-based mitigation efforts. In the previous synthesis,
RECCAP1, regional data from inventories were compared with global
models output from atmospheric inversions and process-based land models, with the
results for 9 land regions in the period 2000–2009 being synthesized in a special issue
, last access: November 2021​​​​​​​). The definition of fluxes was not
harmonized, and inland waters and trade-induced CO
fluxes were not
considered for most regions. The RECCAP1 synthesis spurred efforts to
provide new global analysis of inland water CO
fluxes (Raymond et al., 2013). Recently, Ciais et al. (2020) collected bottom-up inventory estimates
from RECCAP1 papers and completed them with other components to derive the
first global bottom-up estimate of the net land atmosphere C exchange, which
compared well with the independent top-down estimate obtained from the CO
growth rate minus fossil fuel emissions and ocean uptake.
The aims of RECCAP2 are to collect and synthesize regional CO
CH
, and N
O budgets for 10 continental-scale regions (including
one “cross-cutting” region consisting of all permafrost-covered boreal
areas) that together cover the globe (Fig. 1). There is thus a requirement
for sufficient harmonization and consistency to be able to scale regional
budgets to the globe and to compare different regions with each other for
all component fluxes and each greenhouse gas. In RECCAP-2, the results of
top-down atmospheric inversions will also be compared with bottom-up
accounting approaches. Since research groups working on the synthesis of
greenhouse gas budgets in different regions or using different approaches
use different datasets and definitions, it is important to provide a set of
shared and agreed definitions that are as precise as possible for each flux
to be reported. We focus here on land C and CO
budgets, defined from
two approaches: “top-down” estimates from atmospheric inversions and
“bottom-up” carbon accounting approaches based on C stock inventories and
process- and data-oriented models.
Figure 1
Map of the RECCAP2 regions. The “region” in red corresponds
to permafrost-covered areas. Map plot courtesy of Naveen Chandra
(NIES/JAMSTEC).
Additionally, we propose guidelines to separate and quantify the different
gross fluxes that compose the net budget. Such attribution can be done by
process (e.g., photosynthesis, soil respiration, fires, etc) or by cause
(natural vs. anthropogenic). Each approach responds to specific objectives,
e.g., attribution by cause being crucial for national greenhouse gas (GHG) accounting, but
also reflects practical considerations
on how to measure or quantify certain
fluxes. For example, biomass combustion fluxes can be a result of climate
variations or of land use change and management, but separating these causes
is challenging since they co-vary and current scientific methods cannot
separate the two. Attribution by cause is usually based in national
inventories on the definition of “managed” and “unmanaged land” proxies,
which can lead to inconsistencies between different estimates (Grassi et
al., 2018). In RECCAP-2, we propose a process-based approach whenever
possible and separation by cause only when required by the existing methods
for flux estimation.
Estimates of land–atmosphere CO
fluxes by atmospheric inversions
inherently differs from bottom-up C budgets for two reasons. The first reason
is the existence of lateral fluxes at the land surface and from the land to
the ocean, which displace carbon initially fixed as CO
from the
atmosphere in one region and release it outside that region. Consequently,
the CO
flux diagnosed by an inversion is not equal to the change of
stock in a region. The second reason is that carbon enters from the atmosphere
in the land reservoirs almost uniquely as CO
fixed by photosynthesis,
while it is released both as CO
and as reduced carbon compounds
encompassing CO, CH
, and biogenic volatile organic compounds (BVOCs).
This process again makes CO
fluxes different from total carbon fluxes
across the land–atmosphere surface.
To address these issues, Sect. 1 of this paper covers atmospheric CO
inversions and the treatment of reduced C compound emissions, with the goal
of making inversion results comparable with total C flux estimates from
bottom-up approaches. Section 2 deals with bottom-up estimates and provides
definitions of the main component land–atmosphere C fluxes that should be
estimated individually to provide a full assessment of the C balance of each
region and to enable consistent comparisons between regions and upscaling of
regional budgets to the globe. Section 3 provides a description of different
approaches used to derive regional component C fluxes in different bottom-up
approaches, outlining which fluxes are included or ignored by each different
approach. Section 4 gives recommendations regarding the estimation of carbon
emissions resulting from land use change, with systematic errors and
omission errors associated to different approaches. We conclude by providing
recommendations for a multiple-tier approach to develop regional C budgets
in RECCAP2.
Top-down land–atmosphere C fluxes from atmospheric inversions
2.1
Land CO
fluxes covered by inversions
The​​​​​​​ approaches known as top-down atmospheric inversions estimate the net
CO
flux exchanged between the surface and the atmosphere by using
atmospheric transport models and CO
mole fraction measurements at
various locations. The mole fraction data come from surface stations, which
have been available in increasing numbers since 1957. More recently, total
column mole fraction of CO
have been observed with global coverage by
satellites, e.g., GOSAT since 2009 and OCO-2 since 2014 (Liu et al., 2021).
Because the sampling of the atmosphere is sparse even with the recent
global satellite observations, there is an infinite number of flux
combinations that can fit atmospheric CO
observations within their
errors. Most inversions therefore use a Bayesian statistical approach where
an optimal CO
flux is found as a maximum likelihood estimate in the
statistical distribution of possible fluxes, given the prior value and its
uncertainty distribution, and observations, which also have an uncertainty
distribution. The effect of fossil fuel and cement production CO
emissions (hereafter collectively called “fossil fuel” for simplicity) on
mixing ratio gradients is accounted for by prescribing transport models
with an assumed fixed map of fossil CO
emissions. The signal from
these emissions in the space of concentrations is removed at the pre- or
post-processing stage from inversions to solve for residual non-fossil
CO
fluxes. Over land, output fluxes from inversions are thus the sum
of all non-fossil CO
fluxes. This includes gross primary production
CO
uptake, plant and soil respiration, litter photo-oxidation,
biomass burning emissions both from wildfires and for the purposes of energy
provision, inland water fluxes, the oxidative release of CO
from
biomass consumed by animals and humans and decaying in waste pools, CO
emitted by insect grazing, geological CO
emissions from volcanoes and
seepage from belowground sources, CO
uptake from weathering reactions,
and geological CO
release from microbial oxidation of petrogenic
carbon (Hemingway et al., 2018). Inversions have very limited capability to
separate those different fluxes unless they use additional information,
which is not the case for inversions used in global budgets. An example of
additional information is the use of CO as a tracer to separate emissions
from vegetation fires from those from fossil fuels and respiration.
Atmospheric inversion models provide CO
fluxes over all land (and
ocean) grid cells, whereas national inventories estimate carbon stock changes
over managed land only. Managed land is used by countries as a proxy to
separate “direct human-induced effects” from “indirect effects” leading to
carbon stock changes. If the purpose is to compare inversions with national
inventories (e.g., Deng et al. 2021), we recommend using spatially explicit
managed land masks and applying them to gridded inversions fluxes. Some (but not all)
countries provided such datasets (Ogle et al., 2018). One
approximation for defining managed lands in the absence of national gridded
areas could be to use masks of intact forests (Potapov et al., 2017) with some
adjustment to match reported national totals.
2.2
Prescribing fossil CO
emission fields that include bunker fuels
Within RECCAP1 (Canadell et al., 2015) the same fossil fuel emission
estimate was subtracted from the total posterior fluxes of participating
inversions, even when those inversions had used different fossil fuel
inventories (Peylin et al., 2013). This inconsistency between the inversion
process and the inversion post-processing induced artifacts (see discussion
in Thompson et al., 2016) but is of lesser importance for the
intercomparison than the use of different fossil fuel inventories within
the inversion ensemble. We thus recommend here that a standard gridded a
priori fossil fuel CO
emission estimate is used by all regions in
RECCAP2, such as the one recently prepared by Jones et al. (2021). Another important
issue is that about 10 % of CO
emissions come from mobile sources,
from ships on the ocean surface, and aircraft in the volume of the
atmosphere. We recommend that these “bunker fuel” emissions are prescribed
to RECCAP2 inversions by using three-dimensional maps of fossil fuel
CO
emissions. Each grid box should thus include the emissions within
its borders, along ship routes on the surface, and flight paths at the
appropriate altitude in the atmosphere. This option is increasingly viable
due to the emerging availability of sectoral emissions grids for recent
years (Choulga et al., 2021; Jones et al., 2021).
2.3
Reduced C compound emissions
Reduced C compounds are emitted by the land surface as biogenic and
anthropogenic CH
, BVOCs, and CO. Globally, emissions of reduced C
compounds from land ecosystems and fossil fuel use are a large and
overlooked component of the C budget, with CO carbon emissions from
incomplete fuel combustion equaling
≈0.3
PgC yr
−1
(Zheng et
al., 2019), CH
carbon emissions equaling 0.43 PgC yr
−1
(Saunois et al.,
2020), and non-methane biogenic compounds emissions that total up to 0.75 PgC yr
−1
(Sindelarova et al., 2014). Given that inversions only assimilate
atmospheric observations of CO
, they omit regional emissions of
reduced C compounds. However, reduced C compounds all oxidize to CO
in
the atmosphere, with lifetimes of hours to days for BVOCs, months for CO, and
nearly 10 years for CH
. The global CO
growth rate thus includes
the signal of the global reduced C emissions being oxidized into CO
in
the volume of the atmosphere, though not necessarily in the year of their
emission. By fitting the global CO
growth rate, inversions thus
include global emission of reduced C compounds, which is diagnosed as a
diffuse natural CO
emission over the whole surface of the globe in
that year. This implies that inversions place an incorrect ocean CO
emission in the place of reduced C compounds emitted only over land (Enting
and Mansbridge, 1991). Further, current inversions assume that all the
fossil C is emitted as CO
, ignoring incomplete fuel combustion
emitted as CO. The signal from fossil fuel CO emissions on the CO
concentration field is therefore incorrectly treated as a surface emission
of fossil CO
. Such an overestimation of fossil CO
emissions at
the surface, mainly over Northern Hemisphere large fossil-fuel-emitting
regions, leads to an overestimation of the surface CO
sink in order to
match the interhemispheric CO
gradient.
A mathematical formulation of the effect of CO emissions and oxidation on
the latitudinal gradient of atmospheric CO
and its impact on natural
CO
fluxes in a 2D inversion ignoring incomplete fuel combustion
emitted as CO that amounts to
≈0.3
PgC (latitude-vertical) was
given by Enting and Mansbridge (1991). They showed that an inversion that
includes an atmospheric CO loop of the carbon cycle placed a larger surface
CO
sink in the northern tropics and a smaller surface CO
sink
north of 50
N compared to an inversion without this process.
Using a 3D inversion, Suntharalingam et al. (2005) confirmed the impact of
CO oxidation in the atmosphere, albeit with modest effects on diagnosed
land CO
fluxes. We describe an approach to correct for the
effect of BVOCs, CO, and CH
in inversions for RECCAP2 below. This approach
allows the translation of current inversions CO
fluxes into total C
fluxes that can then be consistently compared with total C fluxes given by
bottom-up approaches.
2.4
Correcting net CO
ecosystem exchange from inversions for reduced compounds
Separate corrections to inversions should be made for BVOCs, CO, and CH
because they have very different lifetimes and thus affect
the CO
mole fraction gradients measured by surface networks or
satellites in different ways. Most BVOCs have a short lifetime and are oxidized to CO
in
the boundary layer. This means that inversions using CO
concentration
observations interpret BVOC emissions as local surface CO
emissions.
Globally, carbon emissions from VOCs amount to 0.8 PgC yr
−1
; are mostly
biogenic (Guenther et al., 2012); and are dominated by isoprene, methanol, and
terpenes (Folberth et al., 2005). If the purpose is to compare inversions to
net ecosystem exchange (NEE) of total C derived from bottom-up methods (see
Sect. 2​​​​​​​), we recommend including BVOC carbon emissions in bottom-up
regional estimates of NEE, rather than making BVOC correction of inversion
CO
fluxes.
Regarding the effect of the fossil CO loop of the atmospheric CO
cycle
mentioned above, we propose treating fossil CO as a “bunker fuel”. First, we
have to reduce the prescribed prior gridded fossil CO
emissions by the
gridded amount emitted as CO using the space–time distribution of this CO
source from inventories or from fossil CO emission inversion results. Following this,
we have to prescribe a compensatory prior 3D atmospheric CO
source
originating from fossil CO oxidized by OH in the atmosphere. Knowledge of
this prior 3D source of CO
from the fossil origin is now available from
the atmospheric chemistry models used by global fossil CO emissions inversions
since 2000 (Zheng et al., 2019). Other chemistry transport models simulating
the atmospheric oxidation chain of reduced C compounds unconstrained by
observations may not be accurate enough for that purpose (Stein et al.,
2014). We thus recommend developing new fossil CO
emission
prior fields for RECCAP2 that include the fossil CO loop. The impact of such new priors
will be to reduce inversion estimates of natural CO
sinks in the
Northern Hemisphere over regions where fossil fuels are burned and to
enhance sinks in the tropics and subtropics where CO is oxidized into
CO
Regarding the effect of CO emissions from wildfires, which ranges globally
from 0.15 to 0.3 PgC yr
−1
(Zheng et al., 2019; van der Werf et al.,
2017), the action to be taken for inversions depends on the configuration of
each system, since inversions do not all use a prior fire emission map, in
which case CO from fires could be treated like CO from fossil fuels as
explained above. Looking into the three global inversions used in previous
global carbon budget assessments, the Jena CarboScope inversion
(Rödenbeck et al., 2003) does not have biomass burning a priori CO
emissions. The CarbonTracker Europe (CTE) inversion (Peters et al., 2010;
van der Laan-Luijkx et al., 2017​​​​​​​) prescribes temporal and spatial prior fire emissions,
which means that any CO
uptake by vegetation regrowth after fire will
be spread as a diffuse CO
sink within and outside burned regions. The
CAMS inversion (Chevallier, 2019) prescribes temporal and spatial prior fire
emissions and an annual CO
uptake equal to annual emissions over each
grid cell affected by fires. This setting of CAMS forces an annual regrowth
of forests after burning but allows the inversion to temporally allocate
this regrowth uptake. CTE and CAMS consider all prior fire emissions
as CO
emissions, ignoring incomplete combustion emissions of CO.
Thus, just as in fossil CO
emissions, CTE and CAMS inversions will
overestimate the prior values of CO
mixing ratios over burned areas
during the fire season. Given the lifetime of CO and the fact that
most biomass burning takes place in the tropics, prescribing all prior fire
emissions as CO
in CTE and CAMS will cause only a small positive bias
in prior CO
mixing ratio at tropical stations. The situation may be
different for satellite inversions assimilating column CO
data. These
inversions sample CO
plumes resulting from biomass burning but not
co-emitted CO. In this case, it is expected that inversions based on
satellite observations will capture biomass burning CO
emissions but
underestimate fire C emissions by the amount of CO emitted by fires. Carbon
emitted as CO by fires will contribute after its oxidation to the global
CO
growth rate. This signal will thus be wrongly interpreted by
inversions as a diffuse CO
source spread uniformly over land and
ocean. For RECCAP2, we recommend pursuing research to include CO
fluxes from the fire CO loop as a prior field to be tested by the
inversions that already have fire prior emissions in their settings.
Regarding the effect of CH
carbon emitted over land and oxidized into
CO
with a lifetime of 9.6 years, which thus impacts the interpretation of
inversion results, we conceptually separate the effects of fossil vs.
biogenic CH
emissions. Fossil CH
fugitive anthropogenic
emissions from oil, coal, and gas contribute after atmospheric oxidation to
the CO
growth rate of 0.08 PgC yr
−1
(Saunois et al., 2020; their
top-down estimate) for some years after the emission has occurred. This signal
is interpreted by inversions as a uniform surface natural CO
source
over land and ocean. We thus recommend removing the source when it is uniformly
distributed over each grid cell and each month from inversion posterior
gridded fluxes to obtain gridded natural land and ocean CO
fluxes. A
more complex treatment of this fossil CH
loop of the atmospheric
CO
cycle, as proposed above for the fossil CO loop, is not a priority
in RECCAP2 because of the small magnitude of fossil CH
carbon
compared to the fossil CO one. Biogenic CH
emissions from agriculture,
inland waters, waste, and wetlands amount to 0.3 PgC yr
−1
globally
(Saunois et al., 2020; their top-down estimate) and get oxidized by OH to
create a global CO
source of the same magnitude. This source will be
included in inversion's gridded fluxes as a spatially uniform emission over
land and ocean. Nevertheless, unlike for fossil CH
emissions, this
source is compensated by CO
sinks from photosynthesis over ecosystems
releasing CH
(rice paddy areas, grazed lands, and wetlands). Inversions
will capture the global effect of these CO
sinks but not their
spatial patterns given the low density of the surface network over CH
-emitting areas. Thus, we will not recommend a correction of gridded
inversions CO
fluxes for the effect of biogenic CH
carbon
emissions.
2.5
Adjustment for “lateral fluxes” in CO
inversions to compare them with bottom-up C budgets
With the above-recommended treatment of reduced C emissions, inversions in
RECCAP2 will provide gridded and regional means of land atmosphere C fluxes.
Inversions form a complete approach, but to compare their regional C fluxes
with bottom C stock changes, attention needs to be paid to lateral C fluxes,
as was done partially by Kondo et al. (2020) and Piao et al. (2018) and
comprehensively by Ciais et al. (2020) for RECCAP1 regions. For conversion
of C storage change to land–atmosphere C fluxes using lateral fluxes, we
recommend using the same methodology as in Ciais et al. (2020). The section
below defines bottom-up C budgets in a way that makes it possible to match
them with inversion results.
Bottom-up carbon budgets
Bottom-up approaches encompass various methods to quantify regional C
budgets and their component fluxes. There
is no single observation-based
bottom-up method that comprehensively gives all terrestrial CO
or C
fluxes. The currently incomplete scope of existing bottom-up estimates is a
source of uncertainty when trying to combine top-down with bottom-up approaches or
when using one of these approaches to verify the results of the other (Kondo
et al., 2020; Ciais et al., 2020). For improving the completeness of regional
bottom-up C budgets in RECCAP2, below we define a reasonable number of
component C fluxes that can all be estimated from observations. In most
cases, full observation-based estimates of component C fluxes are not
feasible, but limited observations can be generally extrapolated using
empirical models to the scale of RECCAP2 regions.
Figure 2 displays the required set of component C fluxes between the land
and the atmosphere to be estimated for each region. No unique dataset or
method is imposed to estimate each individual C flux, but we give references to existing datasets that already quantified those
fluxes wherever
possible. Two criteria informed the selection of C fluxes that we recommend
for reporting in the RECCAP2 budgets: (1) there exists at least one estimate
of each flux available at regional scale that can be used as a default tier
in the case where no regional new estimate can be obtained, and (2) each flux is
a non-negligible component of the global land C budget, typically an annual
flux larger than 0.1 PgC yr
−1
, and thus cannot be ignored. If more
detailed C fluxes are available for some RECCAP2 regions, we recommend these
to be regrouped into the categories shown in Fig. 2 and for this grouping to be
described.
Figure 2
Summary of C fluxes to be reported in each RECCAP2 region (top)
and the name of each flux (bottom).
The general recommendation is to provide, where possible, several estimates
for each C flux based on different approaches. This could take the form of
ensemble medians and ranges from different models. In the case where one
estimate is thought to be more realistic than others, for instance a model
with a better score when benchmarked against observations or a higher
spatial resolution dataset with better ground validation, the underlying
reasons for preferring that estimate need to be explained based on peer-reviewed literature or evaluation. Uncertainty can be calculated from the
spread of different estimates in those cases where the state of knowledge
cannot establish that one estimate is better than another. The use of IPCC
methods (Mastrandrea et al., 2011) and uncertainty language
, last access: November 2021​​​​​​​) is recommended when different estimates
of the same component C flux are available. If different estimates report
their own uncertainty, either based on data or an evaluation of the method
used, e.g., by performing sensitivity analysis through changing model
parameters, input datasets, or randomly varying input data, this information
should be used to evaluate consistency between estimates, given their
uncertainties. It is recommended to use the word “uncertainty” when
comparing different estimates and “error” for the difference between an
estimate and true values. Because “truth” is unknown for component C fluxes
at the scale of large regions, errors cannot be estimated in RECCAP2.
3.1
Net carbon stock change
The net carbon stock change of terrestrial ecosystems C pools in a region
C in Fig. 2) can be obtained by repeated inventories of live
biomass, litter (including dead biomass), soil carbon, and carbon stock
change in wood and crop products. None of the RECCAP2 region has a complete
gridded inventory of all carbon stocks and their change over time. Some
regions, like North America, China, Europe, and Russia have forest biomass
inventories that were established long ago by forest resource agencies (Goodale et
al., 2002; Pan et al., 2011). A few countries, e.g., England and Wales
(Bellamy et al., 2005) and France (Martin et al., 2011), have repeated soil C
inventories that allow trends to be quantified. Other countries have
one-off soil carbon inventories (e.g., US, Australia, Germany). Many
regions are able to make estimates of carbon stocks in products from
forestry, wood use, and crop production statistics.
For RECCAP2, we recommend that each region reports carbon stock changes in
all the listed terrestrial ecosystem aggregated pools in Fig. 2, namely
forest
croplands
grasslands
, and
others
, and specify which sub-pools are include in each case.
The sub-pools can include, but are not limited to, the following sources: biomass,
litter and woody debris, and soil mineral and organic carbon. Where
attribution of these pools or sub-pools to biomes, land cover types, or
political units is made by a regional synthesis group, the corresponding
areas involved must be systematically reported. This includes the definition
of the reporting depth for soil C stocks (0–30 and 0–100 cm are
recommended). The choice of how many biomes are reported needs to balance
data availability with the importance of carbon stock and carbon stock
changes within particular biomes (typically a reported biome should
contribute at least 10 % of the regional C changes). Regions with
significant wetland C or permafrost C stocks may report this C stock
separately, especially in cases where the areas involved occur in
different biomes, but this must be done in a way that allows the C stocks to
be subtracted from the biome total or added back into it without double
counting. The area of biomes for which no carbon storage or carbon storage
change is available needs to be reported, and a default value of
−9999
should
be given to such stocks and their stock change value. The biomes with no
data can be specified (preferable if the area and stock involved is
potentially large, since this identifies gaps needing future work) or
simply lumped under “others” if they are minor.
The net C stock change of biological product pools also needs to be
reported for crops, wood, and other carbon-containing products (see Fig. 2).
The depletion of peat C stocks for use as a fuel (
peat use
in
Fig. 2), thus causing C emissions to the atmosphere, was significant in
the early
20th century in some northern countries and still is today in few
countries (Conchedda and Tubiello, 2020). It should be reported where
relevant using regional data if available (Joosten, 2009). In the case of C
stock change in wood products (
wood products
), if possible
the change in those wood products in use (e.g., construction, paper) should
be reported separately from those in waste undergoing decay (e.g.,
landfills). The names and definitions of the wood product pools considered
should be specified. The C stock change of crop product pools (
crop products
) is usually small on an annual timescale. It can be
reported if data are available, otherwise a value of zero can be assumed.
The net carbon stock change of organic carbon accumulation in lakes and
reservoirs (known as burial
burial
) should be reported based
on regional data or global estimates (Mendonça et al., 2017; Maavara et
al., 2017).
3.2
Lateral displacement fluxes within and between regions
One of the reasons why net land–atmosphere C exchange that excludes fossil fuel
emissions, hereafter called net ecosystem exchange (NEE), of a region is not
equal to the net carbon stock change in the same region is because of
lateral C fluxes, as alluded to in Sect. 1.5. Carbon is lost by each
region to the adjacent estuaries through river export and is lost or gained
through the trade of crop, wood, and animal products and through the
atmospheric transport and deposition of C particles emitted with dust in dry
regions. In order to allow the net C stock change estimates to be corrected,
we recommend that lateral fluxes in and out of each RECCAP2 region be
reported. The main ones are river C export and those from wood and crop
trade, as denoted by the red arrows in Fig. 2. A strong point of the RECCAP2
project is an attempt at mass balance closure between pools and fluxes.
Therefore, lateral displacement fluxes of C within each region but between pools
denoted by the brown arrows in Fig.  should also be reported or calculated
by mass balance. More details on these fluxes is given below.
3.2.1
Riverine carbon export to estuaries and the coastal ocean
Lateral C export fluxes in rivers (
rivers
in Fig. 2) should be reported
at the interface between rivers and estuaries. We recommend to top the
“land” at the mouth of rivers and to take estuaries being coupled to the
coastal ocean by dynamical and biogeochemical processes as “blue carbon” in
RECCAP2. Mangroves and salt marshes export large fluxes of dissolved and
particulate C produced in upland systems or within riverine systems to
estuaries and the coastal ocean (Bauer et al., 2013). These fluxes determine
the carbon budget of the aquatic coastal margin ecosystems, and we recommend
that they should also be considered “blue carbon”. River C fluxes at the
river mouth into estuaries can be estimated from dissolved organic carbon
(DOC), dissolved inorganic carbon (DIC), and particulate organic carbon (POC)
concentration data for the rivers involved and the associated river flow
rates (Ludwig et al., 1998; Mayorga et al., 2010; Dai et al., 2012). Few
RECCAP2 regions (Fig. 1) receive C from rivers entering their territory. If
this is the case, this input of flux of fluvial carbon from rivers should be
reported,
even though it is not represented in Fig. 2 for simplicity.
Evasion from aquatic systems to the atmosphere is treated in Sect. 2.2.7.
3.2.2
Inputs of carbon to riverine from soils and weathered rocks
The inland water carbon cycle receives C leached or eroded from soils as an
input. This carbon can be redeposited and buried in the freshwater
ecosystems, outgassed to the atmosphere, or exported to estuaries and the
coastal ocean. This flux is called
bio river input
in Fig. 2. It
cannot be measured directly at large spatial scales. We therefore recommend
calculating it by using mass balance as the sum of burial, outgassing, and export.
Similarly, weathering processes consume atmospheric CO
(see Sect. 2.7). This C is subsequently delivered as dissolved bicarbonate ions to
rivers. At the global scale and over long timescales, two-thirds of the average proportion
of bicarbonate in waters is derived from atmospheric C with the final third being from lithogenic C. We recommend calculating this weathering-related
DIC flux, called
litho river input
in Fig. 2, by using geological maps
and global weathering rates (Hartmann et al., 2009).
3.2.3
Carbon fluxes in and out each region due to trade
Net trade-related C fluxes for wood and crop products exchanged by each
region with other regions need to be reported in C units using statistical
economic data about the trade volume and the carbon content of each product. These
are available from regional datasets (or using FAOSTAT and GTAP data) or the
global dataset of Peters et al. (2012). This net trade flux should be
reported separately for crop products and wood products (
crop trade
and
wood trade
in Fig. 2). If the amount is relevant, it can be reported for animal
products as well, but this flux is much smaller than that in crops and
wood and is therefore not shown in Fig. 2. Our best-practice
recommendation is to separate the net trade C flux into gross fluxes of
imports and exports. The list of commodities included and ignored should be
specified where they are material; commodities making a small contribution
can be lumped under “other”. Quantification of carbon fluxes due to trade of
unburned fossil fuels can be reported if data are available.
3.2.4
Crop and wood product transfers within in each region
Figure 2 links the C stock change of terrestrial ecosystem pools to the
change of C storage in biological wood products by the harvest and lateral
displacement of crop and wood. The harvest of grass for foraging can be
assumed to be given to animals locally and can be included in
grazing
(see details in Sect. 2.4). We recommend reporting the total
amount of C harvested as wood and crops in each region as
wood
harvest
and
crop harvest
(Fig. 2), respectively. Subtracting trade fluxes from
the harvest fluxes will provide the C flux displaced within each region for
domestic activities. Note that non-harvested and non-burned residues for
crops and forest harvesting, such as slash and felling losses, should not be
part of the harvest flux and should instead be counted as part of
LUC
and
land management
. We note that this locally decomposing
flux is globally large, in the year 2000 it amounted to 1.5 PgC yr
−1
for crop
residues and 0.7 PgC yr
−1
for felling losses in forests (Krausmann et
al., 2013).
3.3
Net ecosystem exchange
More than a decade ago there were a number of papers trying to reconcile
different definitions of land carbon fluxes, including the papers by Schulze et al. (2000), Randerson et al. (2002),
and Chapin et al. (2006). Schulze et al. (2000) focused on the importance of
accounting for disturbance C losses at site scale when considering an ecosystem
over a long time period and hence separating net ecosystem production (NEP, i.e., gross primary productivity minus ecosystem respiration) from net biome
production (NBP or net biome productivity, i.e., NEP minus disturbance
emissions). Randerson et al. (2002) argued that the net carbon balance should be
described by a single name, NEP, provided that this flux includes all carbon
gains and losses at the spatial scale considered. Finally, Chapin et al. (2006) in a
“reconciliation” paper proposed the use of net ecosystem carbon balance (NECB)
for the net C balance of ecosystems at any given spatial or temporal scale
and the restriction of the use of NEP to the difference between gross primary
productivity minus ecosystem respiration. These three definitions consider
the C balance from the point of view of ecosystems. Here we seek to estimate
the atmospheric C balance of ecosystems at the spatial scale of large
regions and the temporal scale of 1 decade, and we call this net ecosystem
exchange (NEE). NEE is defined as the exchange of all C atoms between a land
region and the atmosphere over it, excluding fossil fuels and cement
production emissions. We use a similar definition to Hayes and Turner (2012), but
extend it to include natural geological emissions and sinks, acknowledging
that geological fluxes are not from ecosystems per se. NEE includes biogenic
atmospheric emissions of CO, CH
, and VOCs, all expressed in C units.
This definition of NEE matches the land–atmosphere flux of total C that
inversions estimate, provided they account for CO
, CH
, CO, and
VOC fluxes. NEE cannot be derived using the bottom-up approach from a single
observation-based approach.
We acknowledge that the geological fluxes are not strictly speaking from
ecosystems, and we could therefore have called this flux net terrestrial
carbon exchange rather than NEE, but the former terminology could be
ambiguous since some might assume that it includes fossil fuels and cement.
NEE also includes biogenic emissions of CO, CH
, and VOCs, all expressed
in C units. This definition of NEE matches the land–atmosphere flux of total
C that inversions estimate, provided they account for CO
, CH
, CO,
and VOC fluxes. NEE cannot be derived using the bottom-up approach from a
single observation-based approach. Various bottom-up datasets and methods
must be combined to obtain each component flux, and those fluxes can be
summed up to NEE.
We recommend that when a component C flux of NEE contains meaningful amounts
of C emitted as CO, CH
, and VOCs, the type and fraction of reduced
carbon compound emitted should be reported. For instance,
grazing
emits
carbon partly as CH
fires
emits CO (and a smaller component of
CH
), VOCs, and CH
wood products
emits CO when burned and
CH
when the products decay in landfills (see Sect. 2.5);
rivers outgas
lakes outgas
, and
estuaries outgas
emit
CH
(see Sect. 2.6); and
geological emissions
emit CH
and CO
(see Sect. 2.7). The CO
and reduced C
composition of each flux should be reported separately for clarity, with both
expressed in C units. This level of detail in the reporting will allow a
precise comparison with inversion fluxes (see Sect. 1).
In Fig. 2, the component fluxes that sum to NEE are subdivided for four
sub-systems: terrestrial ecosystems, biological products, inland waters, and
geological pools (excluding those mined for fossil fuel and cement
production). The section below describes the C flux components of NEE in
each sub-system.
3.4
Component fluxes of net ecosystem exchange for terrestrial ecosystems
3.4.1
Net primary productivity
Net primary productivity (NPP) is the flux of carbon transformed into
biomass tissues after fixation by GPP. In RECCAP-2 we recommend reporting
GPP but focus on NPP as the relevant input flux of carbon to terrestrial
systems. NPP can be measured in the field using biometric methods, but this
method does not measure non-structural carbohydrates or NPP-acquired
carbon lost to exudates, herbivores, leaf DOC leaching, biogenic VOC
emissions, and CH
emission by plants (Barba et al., 2019). Field
measurements thus estimate the biomass production (BPE is the sum of carbon in
leaves, wood, and roots), which is lower than NPP. Different satellite
products provide global maps of NPP for the past decades, but the conversion
of GPP to NPP is usually made by an empirical carbon use efficiency model
(ratio of GPP to NPP) like the BIOME-BGC model for the GIMMS-NPP (Smith et
al., 2016) and for MODIS-NPP (Running et al., 2004) or the BETHY-DLR
(Wißkirchen et al., 2013​​​​​​​​​​​​​​) global products. Field estimates of BPE can
also be combined with satellite products of GPP to derive NPP (Carvalhais et
al., 2014). Discussing uncertainties of satellite NPP and GPP products is
not in the scope of this report, but light use efficiency formulations used
in many datasets tend to ignore the effect of CO
fertilization and
soil moisture deficits, which has motivated attempts to use data-driven
models or hybrid models combining process-based leaf-scale photosynthesis
models with satellite data, e.g., FAPAR, like in the P-MODEL (Stocker et al.,
2019) or the BESS model for GPP (Jiang and Ryu, 2016). Those models
assimilate satellite observations but include the effects of CO
diffuse light, or water stress on photosynthesis.
Additional methods can be used to estimate regional NPP. For crop NPP,
aggregated estimates can be obtained from yield statistics and allometric
expansion factors (Wolf et al., 2011), the spatial scale being the one at
which yield data can be collected (e.g., farm, county, province, country).
For forest NPP, woody NPP can be obtained from forest inventories, with some of
the sites having several decades of measurements to enable studies of trends.
The recommendation for RECCAP2 is to document the
definition of NPP in the datasets that will be used for each region and the
ecosystems covered in the case of NPP estimates limited to specific ecosystems as precisely as possible .
It also needs to be made explicit how NPP datasets were obtained and what their possible
limitations are. We recommend that NPP (rather than GPP) should be reported for
each region, given that C from NPP links directly to biomass and soil C
inputs and to partial appropriation by humans and animals in managed
ecosystems, due to the fact that harvested C is further displaced laterally and turned into
emissions of C to the atmosphere where it is used.
3.4.2
Carbon emissions from soil heterotrophic respiration (SHR)
Soil heterotrophic respiration (SHR) is the C emitted by decomposers in
soils and released to the atmosphere. Up until recently, this flux could not be
estimated directly, but the availability of point-scale measurements from
6000 sites for total soil respiration and
≈500
sites for heterotrophic
respiration in the peer-reviewed literature used by the SRDB 4.0 database
(Bond-Lamberty, 2018) allows for regional and global upscaling of this flux for
averages over a given period (Hashimoto et al., 2015; Konings et al., 2019;
Warner et al., 2019) or with annual variations (Yao et al., 2021) that can be
used for RECCAP2.
3.4.3
Carbon fluxes from land use change and land management
The net land use change flux, called
LUC
, includes C gross fluxes
exchanged with the atmosphere from gross deforestation, legacy and
instantaneous soil CO
emissions, forest degradation emissions, and
sinks from post-abandonment regrowth and afforestation and reforestation
activities (Houghton et al., 2012). This flux can be positive or negative
depending on the region considered and the balance of gross fluxes. The net
land use change flux results from changes in NPP, SHR, and deforestation
fires over areas affected by land use change in the past. Attribution by
cause is, in this case, relevant to evaluate the human impact on terrestrial
CO
exchange and to inform policy making. In
absence of local NPP and
SHR measurements over areas subject to land use change, especially at the
scale of RECCAP-2 regions,
LUC
should be treated as a separate flux
component of NEE in each region.
LUC
is widespread in all RECCAP2
regions and highly uncertain, and its estimates depend on the approach used.
The different terms and definitions used to estimate
LUC
need to be
clearly defined to avoid counting the fluxes twice within the regional budgets.
More details about the calculation of
LUC
are given in Sect. 4 since
estimates depend on the method used.
The carbon flux exchanged with the atmosphere from management processes,
called
management
, includes a wide range of forest, crop, and
rangeland management practices. It is extremely difficult to separate
management
from
LUC
as it would require the quantification of
C fluxes from land use change, followed by no management of the new land use
in
LUC
and C fluxes from additional management activities on
top of land use change. In practice, bookkeeping models of
LUC
include management of new land use types in the empirical data they use.
Fire is also commonly used in land management and for deforestation, but it
is only implicitly included in
LUC
estimates. For instance,
forest to cropland land use emissions are based on empirical observations of
soil C changes in croplands from multiple sites, which implicitly include
tillage, fertilization, cultivars, and biomass burning effects but do not
separate each of these practices explicitly in each region due to a lack of
data.
Likewise,
management
is not simulated separately in global
studies based on dynamic global vegetation models (DGVMs), and the effects of management are included in
LUC
instead, based on the idealized parameterizations of management
practices (Arneth et al., 2017). For croplands, DGVMs include crop
harvest preventing the return of residues to soils, and some models
represent tillage (Lutz et al., 2019) and changes in fertilization (Olin et
al., 2015). To our knowledge, there is no DGVM simulating the effect of
irrigation, changes of cultivars and rotations (cover crops), and
conservation agriculture on C fluxes. For fires, management activities such
as deforestation fires or fire prevention are usually not represented,
although population density maps are used to modulate ignitions. For managed
forests, several global models that include wood harvest (Arneth et al., 2017;
Yue et al., 2018) as a forcing do not have a detailed representation of
practices, mainly due to the lack of forcing data, although management is
represented in some regions (Luyssaert et al., 2018). For pastures, few
models include variable grazing intensity, fertilization, and forage cutting
(Chang et al., 2015). In addition to structural DGVM limitations and a lack of
representation of management precluding an estimate of
management
, there is also no framework to perform factorial simulations with and without
land use change and management that would allow us to separate
management
and
LUC
LUC
and
management
are accounted for by UNFCCC national
communications of C fluxes in the
land use, land use change, and forestry (LULUCF) sector for managed lands. UNFCCC
national communications report land use change emissions in their Common
Reporting Format (CRF) communications for different bidirectional land use
transitions. These estimates of
LUC
have a different system
boundary from those simulated by bookkeeping models (Grassi et al., 2018;
Hansis et al., 2015; Houghton and Nassikas, 2017). National communications
following the IPCC guidelines (Dong et al., 2006) usually do not consider
LUC
from land use that occurred more than 20 years before the
reporting period, whereas bookkeeping models and DGVMs consider all land use
transitions that occurred since 1700 CE. On the other hand, national
communications include
LUC
from the expansion of urban areas,
which is ignored in bookkeeping models and DGVMs. In national
communications,
management
as defined here is not separately
estimated. Its effect is implicitly included in the LULUCF sector based on
empirical emission factors that include management practices of the new land
use types in reports of C fluxes of stable land use types (e.g., cropland
remaining croplands). Since 75 % of the global land ecosystems are managed
(Ellis et al., 2010; Liang et al., 2016), it will be a major challenge for
RECCAP2 to comprehensively account for
LUC
and
management
and even more challenging to reach a harmonized method for comparing
estimates between regions. We thus recommend for each synthesis chapter to
describe the components of
LUC
and
management
as precisely as possible and to explain in which cases they are combined together.
Note that it is recommended that the emissions of wood products, crop products, and grazing are reported as separate fluxes. If they are provided as part of
LUC
and
management
they should thus be identified
separately.
3.4.4
Carbon emissions from fires
This flux, called
fires
, represents the emission of all carbon species to
the atmosphere from wildfires, prescribed fires, biomass burning, and
biofuel burning, including CO
, CO, CH
, and black carbon, separated
if possible into crop residue burning and other fires. The burning of crop
residues occurs though small-scale fires, which continue to be
underestimated by global satellite burned area products. Further, some
residues are burned out of the field, and those emissions are not measurable
with satellites. Burning emissions from crop residues can be calculated from
fuel consumption and carbon emission factors. Emissions from other fires can
be estimated by ground-based and aerial surveys (several countries perform such
surveys) or from satellite-based datasets based on burned areas, such as GFED
(van der Werf et al., 2010) (
, last access: November 2021​​​​​​​), or based
on fire radiative power, such as GFAS (Di Giuseppe et al., 2018). The Global Fire Emission Database version 4.1s (GFED4.1s) is
an update of the GFED3 product
with an updated burned area and is complemented by
an active fire detection algorithm that improves detection of small fires
(van der Werf et al., 2017). In tropical regions, deforestation causes fires
(including peat fires in Southeast Asia). It is important here to avoid
double accounting by checking in each region if C emissions from
deforestation fires were already included in land use change emissions
LUC
), and if this is the case these must be subtracted from
fires
. A possible approach here is to separate fire emissions over
intact, transitional, and managed lands if spatially explicit datasets for
managed lands are available (Table 1).
Table 1
Cross-cutting processes in carbon budgets and their separation by
cause in different approaches.
If simulated by individual DGVMs.
In Houghton and Nassikas (2017) and BLUE (Hansis et al., 2015) models.
Download Print Version
Download XLSX
3.4.5
Carbon emissions from insects grazing and disturbances
This flux, called
insects
, represents C emissions to the atmosphere
associated with background grazing and sporadic outbreak of insects. It is a
significant C emission in regional budgets, though it is usually ignored,
but it may be estimated as a fraction of NPP or leaf biomass if data is
available and provided there is no double counting. Insect outbreaks
(Kautz et al., 2017) cause direct and committed emissions to the atmosphere
beyond the background grazing of a fraction of biomass as they partly
destroy foliage or cause tree morality (e.g., bark beetles in Canada, Kurz et
al., 2008) that induces legacy emissions that can last for several decades.
To our knowledge, only a few regions have estimates of insects-disturbance-induced C emissions at a regional scale, e.g., the US (Williams et al., 2016),
Canada, and some countries in Europe, and this component flux may not be
possible to estimate for each RECCAP2 region (particularly in tropical
regions).
3.4.6
Carbon emissions from reduced carbon species
This flux, called
reduced
, is the sum of emissions to the atmosphere of
reduced C compounds, including biogenic CH
, non-methane
biogenic volatile organic compounds (BVOCs), and biogenic CO (excluding
fires). Carbon emitted as CH
by wetlands, termites, and rice paddy
agriculture sources and removed by soils can be estimated by bottom-up
approaches, e.g., synthesized in the global CH
budget or from
atmospheric CH
inversions in the case where those inversions report
those flux components separately (Saunois et al., 2020). In the framework
proposed here, CH
emissions from crop and wood products in landfills
are counted as
crop products
and
wood products
, and
CH
carbon from animals and manure is counted in
grazing
. Emissions of carbon
from BVOCs and CO by the vegetation can be obtained from models used to
simulate those fluxes for atmospheric chemistry after conversion into units
of carbon mass. For instance, the CLM-MEGAN2.1 model (Guenther et al., 2012)
estimates biogenic emissions of CO and of
∼150
BVOC compounds,
with the main contributions being from terpenes, isoprene, methanol,
ethanol, acetaldehyde, acetone,
-pinene,
-pinene, t-
-ocimene, limonene, ethene, and propene.
3.4.7
Carbon emissions from biomass grazed by animals
This flux, called
grazing
, represents the C emission that incurs from the
consumption of herbage by grazing animals, including the decomposition of
animal products used in the bio-economy, the decomposition of manure, and
direct animal emissions from digestion. Only the fraction of manure from
animals grazing on grass should be accounted for because C emitted from
manure originating from crop products given to animals is already included
in
crop products
. Grass requirements by animals can be derived from
grass biomass use datasets (Herrero et al., 2013). Grass biomass use per
grazing animal head in a region can be calculated based on data of total
metabolizable energy (ME) of ruminants in each region. Actual grass intake
can be derived from empirical models or from vegetation models that include
management of pasture (Chang et al., 2016). Carbon emitted from grazed grass
biomass includes CH
emissions from manure C (excreta) and from
enteric fermentation, animal CO
respiration from grass intake, and C
emissions from the consumption and decay of meat and milk products derived
from grass grazing. The C in milk, animal, and manure products can be assumed
to decay in 1 year and to be emitted as C to the atmosphere. Here
“animals” are domestic or wild mammals but not insects.
3.5
Component fluxes of net ecosystem exchange from biological products
3.5.1
Carbon emissions from crop biomass consumed by animals and humans
This flux, called
crop products
, represents the carbon emissions to the
atmosphere from the consumption of harvested crop products. It can be
calculated from agricultural statistics as the sum of domestically harvested
products minus net export minus storage in each region. Crop products are
consumed both by animals (including wild animals) and humans, and a
distinction may be made between these two groups of consumers if additional
data on consumption type are available in each region. The digestion of crop
products by ruminants emits CH
carbon and double-counting must be
avoided in case this CH
and C flux is included in another C flux like
ruminant methane emissions. A fraction of C in consumed crop products is
also channeled to sewage systems and lost to rivers as DOC instead of being
emitted to the atmosphere (globally 0.1 PgC yr
−1
; Regnier et al.,
2013). Although it is a small flux, we recommend including it in regional
budgets if data are available. River CO
outgassing flux estimates
should contain the fraction of this sewage C flux returned back to the
atmosphere.
3.5.2
Carbon emissions from harvested wood products used by humans
This flux, called
wood products decay
, represents a net
carbon emission to the atmosphere from the decay and burning of harvested
wood products used for paper, furniture, and construction. The emission from
decay,
wood products decay
, can be calculated
with models of the fate of wood products in the economy, e.g., Eggers (2002),
Mason Earles et al. (2012), forced by input to product pools from domestic
harvest of non-fuel wood and net export of wood products. The small fraction of
wood product waste going to sewage waters and rivers can also be estimated
if relevant data are available. If
wood products decay
is calculated in carbon units, e.g., from a model of wood
product pools, it also includes carbon lost to the atmosphere as CH
in
landfills, thus double-counting must be avoided in case CH
and C
emissions from wood in landfills are also reported separately in a region.
The flux from burning of wood products,
wood product burning
, can be
estimated from statistics of fuel wood consumption and carbon emission
factors during combustion (including CO
, CO, and CH
). This flux
should include emissions from commercial fuel wood burned to produce
electricity, non-commercial fuel wood gathered locally and burned in
households, and fuel wood burned as a fuel by industry. It is important to
note that we recommend to report
wood products decay
here for each RECCAP2 region as a separate flux. This term is usually included
in
LUC
in C budget studies based on DGVMs and bookkeeping models
(Friedlingstein et al., 2019). It should then be removed from currently
reported estimates of
LUC
in order to avoid double-counting.
3.6
Component fluxes of net ecosystem exchange for inland waters
Carbon emissions from rivers, lakes, and reservoirs
The​​​​​​​ fluxes, called
rivers outgas
and flakes plus reservoirs outgas in
Fig. 2, correspond to those from the outgassing of C from lakes and rivers,
respectively. There are two global observation-based estimates of this flux
calculated using the same GLORICH river
CO
database but with
different data selection criteria and upscaling techniques. That of
Raymond et al. (2013) was produced using the COSCAT regions that represent
groups of watersheds and can be re-interpolated to the RECCAP2 regions. That of Lauerwald et al. (2015) was produced on a 0.5
0.5
global grid and does not include lakes. Gridded CO
emissions of boreal lakes have been estimated separately by Hastie et al. (2018) using an empirical model trained on
CO
data from mainly
Swedish and Canadian lakes. The riverine CO
evasion outgassing flux
from Lauerwald et al. (2015) is about half that of Raymond et al. (2013) due to
lower estimates of average river
CO
for the tropics and Siberia
resulting from a more restrictive data selection process and additional
averaging effects from the statistical model applied. In addition, the
estimates by Lauerwald et al. (2015) do not account for CO
emissions
from headwater streams, which may be substantial. For instance, Horgby et
al. (2019) estimated that mountain streams alone emit about 0.15 PgC yr
−1
globally. Some land models have been developed to include the land
to ocean loop of the carbon cycle, and their output may be used to assess
river and lake CO
evasion fluxes for selected regions (Hastie et al.,
2019) or the globe. These models have also confirmed previous observational
findings (e.g., Borges et al., 2015) that river floodplains are a potentially
significant yet overlooked component of the inland water C budget. Up until
now, however, only CO
outgassing from rivers, lakes, and reservoirs has
been considered in regional C budgets. New synthesis estimates of CH
emissions from those inland waters are now available from the CH
budget synthesis (Saunois et al., 2020), and we recommend that this source in
C units should be added to
rivers outgas
and
lakes+reservoirs outgas
3.7
Component fluxes of net ecosystem exchange from geological pools
3.7.1
Geological carbon emissions
This flux, called
geological emissions
, corresponds to natural emissions
of CO
and CH
from geological pools. The Earth's degassing of
geological carbon consists of geogenic CO
emissions of 0.16 PgC yr
−1
(Mörner and Etiope, 2002), microbial oxidation of rock carbon
(Hemingway et al., 2018), and CH
emission estimated to be 0.027 PgC yr
−1
(Etiope et al., 2019), which has recently been revised (Hmiel et al., 2020) to
a smaller value of 0.0012 PgC yr
−1
. Geogenic CH
and C land emissions
are from volcanoes, mud volcanoes, geothermal sources, seeps, and
micro-seepage, and if the gridded dataset of Etiope et al. (2019) is used,
we recommend removing the marine coastal seepage CH
and C emissions
reported separately in this dataset. Geogenic CO
and C emissions are
almost exclusively related to geothermal and volcanic areas
(high-temperature fluid–rock interactions, crustal magma, and mantle
degassing). We suggest here to report these fluxes if there is a published
estimate in the region considered.
3.7.2
Weathering uptake of atmospheric CO
This flux, called
weathering uptake
, corresponds to the weathering of
carbonate and silicate rocks, which is a net sink of atmospheric CO
and corresponds to C then transferred by rivers to the ocean. We recommend
that these fluxes should be reported for each region as they are needed to
rigorously compare the output of CO
inversions (which cover all
CO
fluxes) with bottom-up NEE estimates (Fig. 2). This can be achieved
using the global dataset from Hartmann et al. (2009) and the
gridded product of Lacroix et al. (2020) for instance. Weathering of cement is
represented in Fig. 2 and
should be reported as part of fossil fuel
emissions, which is not within the scope of this paper.
Methods to estimate bottom-up components of NEE
The methods described here are as follows:
C stock changes from ground-based estimates (forest biomass and soil
carbon inventories),
CO
fluxes measured by eddy covariance,
other ground-based measurements (e.g.,
CO
in rivers, site NPP, soil
respiration data),
models driven by statistical data (e.g., wood and crop products and grazing
emissions),
models driven by satellite data (e.g., fire emissions models, NPP models),
process-based terrestrial carbon cycle models (e.g., TRENDY models).
The general approach of RECCAP2 is to use more than one of these approaches
for each flux to gain further insights into the carbon budget of a region
by exploring the full range of data available. The purpose of this section
is to describe what each method does and does not estimate in terms of NEE
component C fluxes, as defined in Sect. 2 and illustrated in Fig. 2, and
therefore what valid comparisons can be made.
4.1
Inventory-based measurements of carbon stock changes
This approach generally uses biomass determined from repeated forest
inventories. The stock changes for the LULUCF sector in UNFCCC
communications reports are usually based on inventories. In some countries
these have been done for many years, but in many countries they are not
available. The sampling density and sampling schemes vary greatly between
countries and regions (Pan et al., 2011). The Global Forest Biomass
Biodiversity Initiative (
, last access: November 2021​​​​​​​) contains 1.2 million
forest plots, mainly in countries in the Northern Hemisphere, although the data are
currently not publicly available. The forest inventory data for tropical
regions typically comes from research plots, rather than production forests.
Forest inventories measure aboveground biomass, from which C stocks can be
derived (and stock changes in case of repeated census), but do not quantify
soil carbon changes. Repeated inventories of soil carbon only exist in very
few countries or regions; where they do, they are often focused on
agricultural soils alone. If site history information is available, the
repeated inventories of biomass and soil C can be used to
LUC
over
time for various land practices.
Point-scale data from inventories can be upscaled (by simple averaging,
by including spatial trends and covariates using geo-statistics, or more
recently by using machine learning) to provide regional budgets of C stock
changes in biomass and soils. Forest biomass inventory estimates of tree
mortality can further be used to estimate C stock changes for pools that
are not directly measured, like litter and soil C, given assumptions
regarding their mean residence times. For instance, in their global
synthesis of forest C stock changes, Pan et al. (2011) used simple fractions
of growing stocks to estimate soil carbon changes. In national inventories,
more detailed models of soil C change can be used.
C stock changes are assumed to be the sum of NEE and lateral C fluxes
exported from or imported into the territory considered. For RECCAP2, this
territory is the area of each region, where the lateral fluxes consist of C
exported to the ocean via inland waters and exported or imported from trade
routes, as it is impractical to have observation-based gridded datasets of
lateral fluxes at sub-regional resolution. Therefore, when comparing
observation-based C stock change estimates with independent NEE estimates,
e.g., from inversions or other sources, it is strongly recommended to first
correct the stock change from each region by the net import or export of C
in trade and by the export in rivers. In RECCAP2, there is potential to use
smaller sub-regions than in RECCAP1, and thus some regions may also receive
incoming C from rivers entering their territory.
4.2
Eddy covariance networks
Eddy covariance flux tower networks measure the net CO
flux of
terrestrial ecosystems (NPP-SHR) across a global network with a typical
footprint of about 1 km
. The networks currently consist of about 600 sites (Jung et al., 2020). Given the small footprint, flux tower sites do
not adequately measure the fluxes of
geological
fires
reduced
rivers+lakes outgas
(except for a very few towers
in wetlands or flooded systems),
crop products
, and
wood products
. For
grazing
, only the fraction emitted as CO
by
livestock in the field (not in the barn) in the footprint of a tower is
measured. Too few towers are installed over ecosystems in transition at
different times after a land use change, and the network is potentially
biased toward younger, more productive forest stands, and thus regional
estimates of
LUC
cannot be directly obtained from eddy covariance flux
towers measurements. The small spatial footprint of eddy flux towers can be
upscaled into gridded maps of NPP-SHR (NEE at ecosystem level) using the
relationship between the continuous measurements from flux towers and
simultaneously recorded climate and vegetation parameters. The fluxes are
upscaled using gridded predictors from remote sensing (such as FAPAR or
NDVI) and climate fields using machine learning or data assimilation
techniques (Jung et al., 2020; Tramontana et al., 2016).
Both inventories and eddy covariance networks provide point sampling with
many gaps between points. These gaps
are filled using upscaling models like
FLUXCOM (Jung et al., 2020; Tramontana et al., 2016). The FLUXCOM data show
fair agreement with inversions and TRENDY models for the seasonal cycle of
NEE and for the phase of inter-annual NEE anomalies (Jung et al., 2017), but
the absolute magnitude of interannual anomalies is strongly underestimated.
One attempt to close the global NEE budget by combining FLUXCOM estimates of
GPP and total ecosystem respiration (TER) with other fluxes not measured by flux towers (Zscheischler et
al., 2017) obtained a net sink of CO
that was 10 PgC yr
−1
larger than
the net land CO
sink deduced from the global budget. One possible
reason for this mismatch could be biases introduced during the processing of
micro-meteorological observations, for instance
filtering, or the
sampling bias in the tower network. The tower sites are not randomly
distributed, and therefore they measure fewer recently disturbed ecosystems
(typically C sources) than recovering ones (C sinks), thus overestimating
CO
uptake given the available network. Since we do not know the true
distribution of land fluxes, upscaling models of flux towers data could
miss important ecosystems not sampled by the training data or
representative landscape elements with intense sources (peatlands,
permafrost, disturbed ecosystems) or sinks (peatlands, plantations) that
might contribute significantly to the carbon balance of a region.
We recommend that RECCAP2 teams use eddy covariance estimates of net
ecosystem CO
fluxes, but since they consist only of NPP and SHR, these
fluxes should add C fluxes not measured by this approach. This can be done
using aggregated estimates of the non-measured C fluxes in each region or
using gridded estimates. For instance, Zscheischler et al. (2017) used
gridded estimates of
fires
rivers+lakes outgas
LUC
crop products
, and
wood products
. They did not add
reduced
, but gridded monthly estimates of this flux could be included in
RECCAP2 based, e.g., on Guenther et al. (2012). We should remain cautious,
noting that NPP and SHR upscaled from eddy flux towers so far gives
unrealistically high global CO
sinks.
4.3
Other ground-based measurements
The list provided here is not exhaustive. It includes “ecological”
measurements of NPP (e.g., Olson et al., 2001), biometric C stock changes at
site level (e.g., Campioli et al., 2015; Luyssaert et al., 2007); soil
respiration, e.g., the SRDB database (Bond-Lamberty and Thomson, 2010); and
CO
data in rivers and lakes (GLORICH). These measurements are sparse
and local in nature. In a similar fashion to the flux tower measurements
described above, it is possible to derive empirical relationships linking
point data with local climate and other predictor variables; these
relationships can then be used for spatial or temporal extrapolation using
gridded fields of the same predictors. In recent years, gridded estimates
have been provided for soil respiration (Hashimoto et al., 2015), soil
heterotrophic respiration (Konings et al., 2019; Tang et al., 2020), and
river+lakes outgas
(Lauerwald et al., 2015; Raymond et al., 2014),
which can be used to create regional totals.
4.4
Models driven by statistical data
Here we refer to a variety of models that do not use physical measurements
at selected locations but instead use statistical data about harvested C, C in
product pools, and C traded or consumed. These data are usually sourced from
national or international statistical agencies or sector bodies. Examples
are the study of Wolf et al. (2015), who estimated crop NPP,
grazing
and
crop products
; Krausmann et al. (2013), who estimated crop NPP
from statistical data on yield; Ciais et al. (2007), who estimated
crop products
and the corresponding CO
uptake by growing crops
and horizontal displacement of harvested crop biomass; and Zscheischler et
al. (2017), who provided gridded estimates of
wood products
(albeit
ignoring trade).
4.5
Models driven by satellite data
Satellite data are also used in upscaling forest inventory, eddy covariance,
and other ground-based measurements, although giving a full list of this
category of models is not the purpose of this paper. Here we refer to
satellite-driven NPP models (Bloom et al., 2016; Smith et al., 2016;
Running et al., 2004; Tum et al., 2016; Wißkirchen et al., 2013) based
on light use efficiency formulations or hybrid land carbon cycle models
that explicitly represent photosynthesis (and NPP) driven by
directly assimilated satellite data. Similarly, fire emission models like
GFED and GFAS rely on satellite input data like burned area and fire
radiative power (FRP) but estimate emissions using fields from models or
other datasets (information on the fuel load, the burning completeness, and
emission factors for different gaseous species). Remotely sensed models of
aboveground biomass, derived from optical sensors, i.e., MODIS (Baccini et
al., 2017), lidar from ICESAT-1 GLAS (Saatchi et al., 2011), synthetic
aperture radar (SAR, Santoro, 2018), and L-band vegetation optical
depth (VOD, Liu et al., 2015), have been produced globally and regionally
(i.e., for mangroves using X-band radar, Simard et al., 2019). When they are
repeated over time they allow estimates of biomass stock change, such as those
presented by Brandt et al. (2018) over Africa and Fan et al. (2019) over the
tropics. These datasets differ not only in their methodology and training
datasets but also in their spatial (300 m to 25 km) and temporal
(annual, or epoch) resolutions, and thus an ensemble-based approach is
preferable for assessing uncertainty. Belowground carbon stock estimates
are more challenging to access, and for live root biomass often a scaling
assumption is made, but for mineral and organic carbon estimates are
derived from the empirical upscaling or inventory approaches or process-based
models described in Sect. 3.6.
4.6
Process-based terrestrial carbon cycle models
Dynamic global vegetation models (DGVM) simulate bottom-up NEE and a number
of ecosystem carbon pools and fluxes, and their change over time on a
gridded basis worldwide. The grid resolution ranges from 0.5
for
global applications, e.g., TRENDY (Sitch et al., 2015) or MstMIP (Wei et al., 2014), to fine resolutions (300 m or less) regionally. These models are not
tightly driven by observations (unlike those in Sect. 3.5), but some observations
are used by modelers to calibrate parameters. TRENDY models are now
benchmarked following ILAMB (Friedlingstein et al., 2019). Dense observation
datasets are not assimilated systematically, although some carbon cycle data
assimilation systems exist that make use of DGVMs (Kaminski et al., 2013;
MacBean et al., 2016) or simpler models like CARDAMOM (Bloom et al., 2016).
The advantages of DGVMs for carbon budgeting are that (1) they provide an
ensemble of gridded NEE and NEE component estimates as part of TRENDY and that (2)
these models should in principle conserve mass and simulate consistent C
fluxes and C stock changes for all regions. A limitation of DGVMs (apart from
the fact that they can differ substantially from observations) is that they
do not explicitly represent some of the fluxes in Fig. 2.
fires
is
available from 10 out of 16 DGVMs in TRENDY and FIREMIP (Hantson et
al., 2020).
LUC
from DGVMs includes a foregone sink of CO
called
the loss of additional sink capacity (Gasser et al., 2020; Pongratz et al.,
2014), which is not included in data-driven methods, to quantify this flux
(see Sect. 4). DGVMs partly include
wood products
and
crop products
but assume that all harvest is released locally as
CO
to the atmosphere, ignoring lateral displacement of harvested C
within and across regions. DGVMs ignore
reduced
and only one or
two include
rivers+lakes outgas
. Hence, care should be taken when
combining DGVM outputs with observation-based estimates of C fluxes
because of double-counting or undercounting. For instance, C outgassing from
rivers and lakes derives from C exported by soils, but if this export is not
represented in a DGVM, C will be otherwise released as SHR, and thus adding
to DGVM output an outgassing flux would lead to an erroneous double-counting.
In general, for RECCAP2 we recommend describing exactly what each
estimation approach includes or excludes for each C flux of Fig. 2 in
order to minimize the risk of missing some fluxes or double-counting others.
Mass conservation should be the key underlying principle when combining
bottom-up C fluxes originating from different approaches.
Fluxes from land use change
Fluxes from land use change and management (abbreviated to
LUC
and
defined as having a positive sign for net fluxes from the atmosphere to the
land C) are defined as changes in C stocks due to deforestation, forest
degradation and afforestation or reforestation, wood harvest, subsequent
regrowth of forest following harvest or agriculture abandonment, conversion
between croplands and grasslands (also sometimes called pastures or, more
generally, rangelands), and management practices such as shifting
cultivation (land cyclically rotating between forest and agriculture). Where
applicable, peat burning and drainage should also be considered, as well as
carbon fluxes related to management practices such as fire management,
particularly if those practices have changed within the relevant period (for
instance, when historically burning ecosystems are subject to fire
suppression or where fire-protected ecosystems become fire-susceptible ecosystems)
(Alvarado et al., 2020; Forkel et al., 2017; Kelley et al., 2019). Where
possible,
LUC
should be separated into the component fluxes
corresponding to the different processes and adding up to the net regional
LUC
. Typical components of
LUC
, as reported by bookkeeping models,
include immediate biomass losses during deforestation, delayed emissions
from soil carbon and litter decomposition for all subsequent years
following land use change (legacy emissions), emissions from wood products
harvested as a result of deforestation or derived from secondary forests,
and recovery gains due to secondary forest regrowth or afforestation (Hansis
et al., 2015; Houghton et al., 2012). Previous versions of the Houghton et
al. (2012) bookkeeping model (up until 2017) reported emissions from
shifting cultivation as part of
LUC
, but this term has been dropped in
the most recent version of this model (Houghton and Nassikas, 2017).
Houghton and Nassikas (2017) also provide emissions from forest degradation
(i.e., biomass-reducing activities that do not result in the land parcel
being reclassified as a non-forest) and subsequent recovery as part of
LUC
The various methods available to quantify
LUC
(Table 2) rely on
different input datasets and models with different abilities to represent
land use practices. They further use different terminology and assumptions
of which component fluxes to include, leading to inconsistencies between one
another. For RECCAP2, the best data available in each region should be used.
However, it is crucial to clearly define the methods and assumptions made
and which
LUC
fluxes are included in the corresponding results. If
possible, regional
LUC
fluxes estimated by the “best method” should be
compared with those estimated by the global datasets from the most
up-to-date Global Carbon Budget coordinated by the Global Carbon Project (GCP) Global Carbon Budget in order to ensure consistency and
comparability between regions. The methods used to estimate
LUC
include: (i) bookkeeping models (BKs), (ii) dynamic global vegetation models
(DGVMs), (iii) remote-sensing based methods, and (iv) national inventories, as
detailed below.
Table 2
Main differences between the methods used to estimate
LUC
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5.1
Bookkeeping models
Bookkeeping models rely on present-day vegetation and soil C densities
(aggregated or spatially explicit) and different response curves (i.e., time
courses of change) to estimate changes in C stocks following a given
transition.
LUC
in bookkeeping models includes respiration fluxes from
crop and wood harvest slash and from legacy fluxes due to land use change.
Based on the underlying forcing data, these can be separated by cause (e.g.,
management, deforestation, land abandonment, shifting cultivation).
The two BK models used in the Global Carbon Budget (GCB) (Friedlingstein et
al., 2019) are those from Houghton and Nassikas (2017) and Hansis et al. (2015), referred to as H&N and BLUE respectively. Both BK models are able
to provide
LUC
at country level but differ in a number of
characteristics, such as the input data, the C densities and response curves
used, the spatial resolution, and period covered, as summarized in Table 2.
Spatially explicit BK models such as BLUE can be adapted to run at regional
scales with finer spatial resolution of land use change, derived from either
national inventories or from remote-sensing (RS)-based transitions (e.g.,
ESA-CCI land cover). If very good data on C densities and ideally response
curves is available regionally and no superior regionally BK model is
available, BLUE can also be adapted to run with that information at country
or regional level. One term that is not considered by bookkeeping models is
the indirect effect of climate and environmental variability and change on
sinks and sources (including respiration fluxes) resulting from land use change (LUC)
(Obermeier et al., 2021; Yue et al., 2018). This effect is, however,
implicitly included in stock-change-based estimates used in national
inventories and in the most commonly used method to estimate
LUC
by
dynamic global vegetation models. Below, we discuss how to reconcile these
different estimates.
5.2
Dynamic global vegetation models (DGVMs)
DGVMs explicitly simulate the processes controlling photosynthesis, growth,
decomposition, and mortality of vegetation and the processes involved in
biomass and soil C changes. They also simulate the fluxes resulting from
forest clearing, pasture and crop conversion, abandonment and regrowth, and
crop harvest, although the implementation varies between models, as do the
assumptions about the areas being converted (e.g., gross vs. net
conversion; see Sect. 4.5), the management practices included, and the
fate of C following transitions. DGVMs in RECCAP2 can be used to estimate
LUC
in two ways: (i) the global simulations from TRENDY for GCB2019 can
be analyzed at country or region level, and (ii) any DGVM including the
aforementioned processes can be forced with better or finer data at country
or regional level. If a DGVM with an improved representation of regional
processes is available, it is recommended to use it rather than more generic
global models. However, it is important for regional models to follow the
simulation protocols of TRENDY (Friedlingstein et al., 2019) to facilitate
comparison between regions. In order to estimate
LUC
with DGVMs,
factorial simulations with and without LUC from the preindustrial period
until present are generally used. The year 1700 CE should be used as the
reference data for the preindustrial state in RECCAP2 in order to be
consistent with the TRENDY protocol in depicting legacy fluxes.
There are different ways to estimate
LUC
, which partly explains
differences between DGVMs and BK models. The DGVM simulations used to
evaluate
LUC
under different assumptions are listed in Table 3. Up to
now,
LUC
from DGVMs have been estimated from the difference between two
simulations, one forced with changing CO
, climate, and LUC and another
forced with changing CO
and climate but a fixed preindustrial
land cover map (corresponding to S2
S3; see Table and the TRENDY protocol
, last access: 8 February 2022​​​​​​​). The
potential natural vegetation in the simulation with fixed land cover (S2) is
affected by CO
fertilization and therefore provides an additional sink
that is lost, e.g., when deforestation occurs. This foregone sink is loss of
additional sink capacity (LASC) (Gasser et al., 2020; Pongratz et al.,
2014). For consistency with BK models,
LUC
estimates with no LASC and
based on present-day C densities should be delivered instead based on
differences between two simulations under time-invariant present-day
environmental conditions of climate, CO
, N deposition, and
N fertilization: one with LUC (S5 in the TRENDY protocol and Fig. 3) and one
with fixed preindustrial (1700 CE) land cover (S6 in the TRENDY protocol). In
that case,
LUC
can be estimated as follows:
(1)
LUC S5
Because
LUC
from both S5 and BK models are forced with present-day
C densities, which have on average increased during the perturbation of the
carbon cycle since preindustrial times, they may overestimate LUC emission
fluxes in the first part of the last century. Therefore, an additional
simulation (S4) can be performed where models are forced with
time-invariant preindustrial environmental conditions and annual
time-varying land use 1700–2018 CE. In that case the following equation can be used:
(2)
LUC S4
where S0 is a control simulation with time-invariant preindustrial (1700 CE)
CO
, climate, and land use. In this case,
LUC
is calculated based
on preindustrial potential C densities and does not include LASC. For
consistency, the natural land sink over areas not affected by LUC can then
be estimated with DGVMs as follows:
(3)
LAND
An additional feature of the simulations proposed here (
LUC S5
and
LUC S6
) is that since
LUC
are calculated as
the difference of two simulations with fixed CO
and climate,
LUC
does
not include effects of elevated CO
and climate change on fire regimes,
which should reduce the risk of double-counting of emissions. For RECCAP2,
we recommend that
LUC
from DGVMs is estimated following Eq. (1)
LUC S5
) so that results can best be compared with BK results in the
recent decades.
Table 3
DGVM simulations to calculate
LUC
from the TRENDY v8 protocol
(Friedlingstein et al., 2019).
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Figure 3
Terrestrial cumulative C stocks
(a)
and corresponding
LUC
(b)
as simulated by the JSBACH dynamic vegetation model for the
different simulations discussed. Shown is
LUC
derived as S5 minus S6,
S3 minus S2, and S4 minus S0 (see text).​​​​​​​​​​​​​​
5.3
Remote-sensing data related to LUC
Several global remote-sensing products can be useful in estimating
LUC
in RECCAP2. They can be applied in various ways. The first is by estimating
land cover change in the recent decades to produce regional transition maps
at finer spatial scales and with better accuracy than is currently
available. These maps can then be used to force BK or DGVMs. The second is by providing
finer-resolution and globally consistent maps of vegetation C densities (for
undisturbed locations) that can be used in BK models. The third is by directly estimating
changes in biomass C stocks, for instance using optical data (Harris et al.,
2012) vegetation optical depth (Fan et al., 2019), or lidar data, and report
these values for deforestation areas only (to exclude environmentally induced
fluxes).
Examples of already available remote-sensing-based datasets than can be used
for land cover and land cover change mapping are the ESA-CCI land cover
product, based on five different satellite missions at 300 m spatial
resolution and annual time steps between 1992 and 2018 (ESA,
2017), and the Landsat 30 m spatial-resolution forest cover change product,
covering 2000 to 2018 (Hansen et al., 2013) and extended to land cover change
for forest, short vegetation, and bare soil (Song et al., 2018). For
vegetation C densities, the ESA GlobBiomass dataset provides aboveground
biomass data for a period centered on the year 2010 at 100 m spatial
resolution (Santoro, 2018). Because of its fine spatial resolution, this
dataset could, in principle, be used to evaluate undisturbed C densities (Erb
et al., 2018; Luyssaert et al., 2012). Other datasets currently under
development include the ESA-CCI high-resolution land-cover product, expected to
provide a long-term record since the 1990s of regional high-resolution land
cover maps at 30 m spatial resolution every 5 years in regions of interest, and
the ESA-CCI biomass dataset, which will provide aboveground biomass data
for four epochs, i.e., the mid-1990s, 2007–2010, 2017/2018, and 2018/2019, at 100 m
spatial resolution with a relative error of less than 20 %. The NASA
Carbon Monitoring System program is also supporting the development of
regional- to global-scale biomass products based on optical reflectance data
from MODIS (as well as active lidar-based approaches using ICESAT-1 and now
ICESAT-2 GLAS-retrievals) and the Global Ecosystem Dynamics Instrument or
GEDI aboard the International Space Station. The lidar approaches require
integration with wall-to-wall optical measurements as lidar is a “shot”
retrieval with a fairly small footprint size but has high accuracy in
terms of measurement ability when retrieving canopy height and thus biomass
(Dubayah et al., 2020). Satellite-based products have important advantages
for estimating contemporary direct emissions from changes in aboveground
biomass, such as global coverage, consistency, reliability, and
increasingly higher spatial resolution. However, they cannot estimate legacy
soil fluxes from land use change prior to the satellite era, and they are also unable to separate the contribution of environmental changes to
LUC
. The
comparison of FLUC derived from RS-based methods with DGVMs or BK estimates
should therefore be made with care.
5.4
National inventories
National Greenhouse Gas Inventories (NGHGIs) report anthropogenic emissions
and sinks to the UNFCCC and are the official numbers used to take stock of
the nationally determined contributions (NDCs). NGHGI use different
definitions and assumptions than those used by the carbon
cycle research
community, as detailed in Grassi et al. (2018). NGHGI, in their agriculture
forestry and land use (AFOLU) sector, report CO
fluxes of managed
land, as defined by each country. Such managed land can include areas under
nature conservation management. The C balance of established cropland,
grassland, and forests are reported by national inventories under the LULUCF
sub-sectors. The C fluxes of land use change transitions involving managed
lands are reported separately based on national data on the area of
different land use types. The carbon fluxes associated with transitions
older than 20 years old are ignored. A variety of approaches are used by
NGHGIs, mostly based on general emission factors following IPCC guidelines.
Only lands converted within the past 20 years are included under LULUCF
fluxes, unlike BK and DGVMs that calculate land use change fluxes
since 1700 or 1850 CE. On the other hand, NGHGIs include land use change fluxes
for transitions that are usually not implemented in BK and DGVMs, such
as from peatland converted to agriculture and from land converted to human
settlements.
5.5
Land use change transitions, definitions, and assumptions
The land use change transitions and land management fields used in the
latest version of the GCP Global Carbon Budget (Friedlingstein et al., 2019)
to calculate the net land use change flux, called
LUC latest
, are from
the harmonized land use change data (LUH2v2.1h) dataset (Hurtt et al.,
2011), which is based on HYDE3.1 (Klein Goldewijk et al., 2011). These data
have the advantage of being globally consistent and covering a long period
(850 CE–present) but have relatively coarse spatial resolution (0.25
0.25
), and due to a globally consistent methodology they may
not account for regional specificities (Bastos et al., 2018; Li et al.,
2018). For each region, the best available information (in terms of
spatiotemporal resolution or detail of processes covered) on land use change
should be used. This can be from national statistics, inventories, or
remote sensing. In RECCAP2, each regional team will decide the land cover
classification scheme that best fits a given region, but it is recommended
that the LUH2v2h forest and non-forest distinction be used when classifying
rangelands.
Concluding remarks
We present a way forward for developing consistent top-down and bottom-up
estimates for regional carbon dioxide budgets. The methodology focuses on
reconciling the treatment of non-CO
emissions from CH
, CO, and
BVOCs and their contribution to CO
via atmospheric chemistry and the
treatment of lateral fluxes of carbon. Given the complexity of this task,
the approaches toward implementation can be considered using the tiered
approach of the IPCC, whereby higher tiers use progressively more complex
regionally and locally calibrated sources of information. For example, a Tier 1 approach combines global emission factors with activity data to estimate
fluxes, Tier 2 uses regionally calibrated emission factors, whereas
Tier 3 uses locally calibrated emission factors to estimate fluxes from
activity information. The Global Carbon Project now conducts greenhouse gas
budget accounting for the three major greenhouse gases, i.e., carbon dioxide,
methane, and nitrous oxide, where each budget provides detailed sectoral
information for sources and sinks using a system that is more closely aligned with
Tier 1 approaches. Beginning with Tier 1 data can help initiate regional
budgets and identify areas of uncertainty or opportunities for regionally
and locally calibrated approaches to be used to reduce uncertainty.
Code and data availability
There is no code associated with this paper. Many datasets we work with
are publicly available (e.g., CMIP6 [
, WCRP, 2019], ocean models [
, last access: 8 February 2022​​​​​​​, Hauck and Gruber, 2022], ICOS FLUXNET [
, ICOS, 2022], FLUX-COM [
, FLUX-COM, 2022],
LUH2 [
, Chini et al., 2022], …)​​​​​​​. Most of the global datasets that are not public yet are already
available to the RECCAP2 teams through the MPI-data portal:
(RECCAP-2, 2022). RECCAP2 studies will
be hosted as a special collection at the American Geophysical Union (AGU). Following AGU data policy, the
datasets used in each paper will be made available upon publication
(last access: 8 February 2022​​​​​​​).
We will encourage teams to do this through the ICOS carbon portal
, last access: 8 February 2022​​​​​​​)​​​​​​​.
Author contributions
PC designed and wrote the manuscript, with input from FC for the inversion section and AB and JP for the land use section. HY additionally helped with references. All other contributors helped to improve the text in their field of expertise. PC and AB revised the manuscript and prepared the response to reviewers. RL, BP, JGC, GH, RBJ, AJ, MJ, MK, ITL, PKP, WP, AMRP, SP, CQ, CVR, PR, MS, RS, AS, HT, XW, and BZ provided scientific support and helped to improve the text in their field of expertise.
Competing interests
The contact author has declared that neither they nor their co-authors have any competing interests.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
Philippe Ciais acknowledges funding from the ANR CLAND Convergence Institute.
Ana Bastos, Frédéric Chevallier, and Philippe Ciais acknowledge support from the VERIFY
H2020 project and the RECCAP2 ESA Climate Change Initiative (CCI) project. The authors are very grateful to the many data providers (measurements, models, inventories,
atmospheric inversions, hybrid products, etc.) that are directly or indirectly used in this study.
Financial support
This study has been co-funded by the European Space Agency Climate Change Initiative ESA-CCI RECCAP2 project 1190 (ESRIN/grant no. 4000123002/18/I-NB) and by the Observation-based system for monitoring and verification of greenhouse gases (VERIFY, grant no. 725546).
Review statement
This paper was edited by Carlos Sierra and reviewed by Richard Houghton and one anonymous referee.
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Articles
Abstract
Introduction
Top-down land–atmosphere C fluxes from atmospheric inversions
Bottom-up carbon budgets
Methods to estimate bottom-up components of NEE
Fluxes from land use change
Concluding remarks
Code and data availability
Author contributions
Competing interests
Disclaimer
Acknowledgements
Financial support
Review statement
References
Article
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Short summary
The second phase of the Regional Carbon Cycle Assessment and Processes (RECCAP) will provide updated quantification and process understanding of CO
, CH
, and N
O emissions and sinks for ten regions of the globe. In this paper, we give definitions, review different methods, and make recommendations for estimating different components of the total land–atmosphere carbon exchange for each region in a consistent and complete approach.
The second phase of the Regional Carbon Cycle Assessment and Processes (RECCAP) will provide...
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Sections
Abstract
Introduction
Top-down land–atmosphere C fluxes from atmospheric inversions
Bottom-up carbon budgets
Methods to estimate bottom-up components of NEE
Fluxes from land use change
Concluding remarks
Code and data availability
Author contributions
Competing interests
Disclaimer
Acknowledgements
Financial support
Review statement
References