Editing IPCC:AR6/WGIII/Chapter-7 (section)

=== 7.2.2 Flux of CO 2 from AFOLU, and the Non-anthropogenic Land Sink ===

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==== 7.2.2.1 Global Net AFOLU CO 2 Flux ====

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Comparison of estimates of the global net AFOLU flux of CO 2 from diverse approaches (Figure 7.4) show differences on the order of several GtCO 2 yr –1 . When considering the reasons for the differences, and an approach to reconcile them ( [[#Grassi--2021|Grassi et al. 2021]] ) ( [[#7.2.2.3|Section 7.2.2.3]] ), there is ''medium confidence'' in the magnitude of the net AFOLU CO 2 flux. There is a discrepancy in the reported CO 2 AFOLU emissions magnitude because alternative methodological approaches that incorporate different assumptions are used ( [[#7.2.2.2|Section 7.2.2.2]] ). While the mean of the bookkeeping and DGVM model’s show a small increase in global CO 2 net emissions since year 2000, individual models suggest opposite trends ( [[#Friedlingstein--2020|Friedlingstein et al. 2020]] ). The latest FAOSTAT and NGHGI estimates show a small reduction in net emission. Overall, the trends are unclear.

Regionally (Figure 7.5), there is ''high confidence'' of net emissions linked to deforestation in Latin America, Africa and South-East Asia from 1990 to 2019. There is ''medium confidence'' in trends indicating a decrease in net emissions in Latin America since 2005 linked to reduced gross deforestation emissions, and a small increase in net emissions related to increased gross deforestation emissions in Africa since 2000 (Figure 7.5). There is ''high confidence'' regarding the net AFOLU CO 2 sink in Europe due to forest regrowth and known other sinks in managed forests, and ''medium confidence'' of a net sink in North America and Eurasia since 2010.

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'''Figure 7.4 |''' '''Global net CO''' 2 '''flux due to AFOLU estimated using different methods for the period 1960 to 2019 (GtCO''' 2 '''y''' '''r''' –1 ''').''' Positive numbers represent emissions. '''Light-blue line:''' The mean from 17 DGVMs all using the same driving data under TrendyV9 used within the Global Carbon Budget 2020 and including different degrees of management ( [[#Bastos--2020|Bastos et al. 2020]] ; [[#Friedlingstein--2020|Friedlingstein et al. 2020]] ). '''Brown line:''' Data downloaded 6 June 2021 from FAOSTAT ( [[#FAO--2021b|FAO 2021b]] ; http://www.fao.org/faostat/ ) comprising: net emissions from (i) forest land converted to other land, (ii) net emissions from organic soils in cropland, grassland and from biomass burning, including peat fires and peat draining ( [[#Prosperi--2020|Prosperi et al. 2020]] ) and (iii) net emissions from forest land remaining forest land, which includes managed forest lands ( [[#Tubiello--2020|Tubiello et al. 2020]] ). '''Yellow''' '''line:''' Net flux estimate from National Greenhouse Gas Inventories (NGHGI) based on country reports to the UNFCCC for LULUCF ( [[#Grassi--2021|Grassi et al. 2021]] ) which include land-use change, and flux in managed lands. '''Red EO line:''' The 2001–2019 average net CO 2 flux from non-intact forest-related emissions and removals based on ground and Earth Observation data (EO) ( [[#Harris--2021|Harris et al. 2021]] ). Data to mask non-intact forest were used in the tropics ( [[#Turubanova--2018|Turubanova et al. 2018]] ) and extra-tropics ( [[#Potapov--2017|Potapov et al. 2017]] ). '''Dark''' '''blue line:''' the mean estimate and minimum and maximum (dark-blue shading) from three bookkeeping models ( [[#Hansis--2015|Hansis et al. 2015]] ; [[#Houghton--2017|Houghton and Nassikas 2017]] ; [[#Gasser--2020|Gasser et al. 2020]] ). These include land cover change (e.g., deforestation, afforestation), forest management including wood harvest and land degradation, shifting cultivation, regrowth of forests following wood harvest or abandonment of agriculture, grassland management, agricultural management. Emissions from peat burning and draining are added from external datasets (see text). Both the DGVM and bookkeeping global data is available at: https://www.icos-cp.eu/science-and-impact/global-carbon-budget/2020 (accessed on 4 October 2021). Data consistent with IPCC AR6 WGI Chapter 5. Dotted lines denote the linear regression from 2000 to 2019. Trends are statistically significant (P &lt;0.05) with exception for the NGHGI trend (P &lt;0.01).

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'''Figure 7.5 | Regional net flux of CO''' 2 '''due to AFOLU estimated using different methods for the period''' '''1990–2019''' '''(GtCO''' 2 '''y''' '''r''' –1 ''').''' Positive numbers represent emissions. The upper-central panel depicts the world map shaded according to the IPCC AR6 regions corresponding to the individual graphs. For each regional panel; '''brown''' '''line:''' Total net flux data from FAOSTAT ( [[#Tubiello--2020|Tubiello et al. 2020]] ); '''yellow line:''' Net emissions estimates from National Greenhouse Gas Inventories based on country reports to the UNFCCC for LULUCF ( [[#Grassi--2021|Grassi et al. 2021]] ); '''dark-blue line:''' The mean estimate and minimum and maximum (dark-blue shading) from three bookkeeping models. ( [[#Hansis--2015|Hansis et al. 2015]] ; [[#Houghton--2017|Houghton and Nassikas 2017]] ; [[#Gasser--2020|Gasser et al. 2020]] ). Regional estimates from bookkeeping models are available at: https://zenodo.org/record/5548333#.YVwJB2LMJPY ( [[#Minx--2021|Minx et al. 2021]] ). See the legend in Figure 7.4 for a detailed explanation of flux components for each dataset.

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==== 7.2.2.2 Why Do Various Methods Deliver Difference in Results? ====

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The processes responsible for fluxes from land have been divided into three categories ( [[#IPCC--2006|IPCC 2006]] , 2010): (i) the ''direct human-induced effects'' due to changing land cover and land management; (ii) the ''indirect human-induced effects'' due to anthropogenic environmental change, such as climate change, CO 2 fertilisation, nitrogen deposition, and so on; and (iii) ''natural effects,'' including climate variability and a background natural disturbance regime (e.g., wildfires, windthrows, diseases or insect outbreaks).

Global models estimate the anthropogenic land CO 2 flux considering only the impact of direct effects, and only those areas that were subject to intense and direct management such as clear-cut harvest. It is important to note, that DGVMs also estimate the non-anthropogenic land CO 2 flux (Land Sink) that results from indirect and natural effects (Table 7.1). In contrast, estimates of the anthropogenic land CO 2 flux in NGHGIs (LULUCF) include the impact of direct effects and, in most cases, of indirect effects on a much greater area considered ‘managed’ than global models ( [[#Grassi--2021|Grassi et al. 2021]] ).

The approach used by countries follows the IPCC methodological guidance for NGHGIs ( [[#IPCC--2006|IPCC 2006]] , 2019). Since separating direct, indirect and natural effects on the land CO 2 sink is impossible with direct observation such as national forest inventories ( [[#IPCC--2010|IPCC 2010]] ), upon which most NGHGIs are based, the IPCC adopted the ‘managed land’ concept as a pragmatic proxy to facilitate NGHGI reporting. Anthropogenic land GHG fluxes (direct and indirect effects) are defined as all those occurring on managed land, that is, where human interventions and practices have been applied to perform production, ecological or social functions ( [[#IPCC--2006|IPCC 2006]] , 2019). GHG fluxes from unmanaged land are not reported in NGHGIs because they are assumed to be non-anthropogenic. Countries report NGHGI data with a range of methodologies, resolution and completeness, dependent on capacity and available data, consistent with IPCC guidelines ( [[#IPCC--2006|IPCC 2006]] , 2019) and subject to an international review or assessment processes.

The FAOSTAT approach is conceptually similar to NGHGIs. FAOSTAT data on forests are based on country reports to FAO-FRA 2020 ( [[#FAO--2020a|FAO 2020a]] ), and include changes in biomass carbon stock in ‘forest land’ and ‘net forest conversions’ in five-year intervals. ‘Forest land’ may include unmanaged natural forest, leading to possible overall overestimation of anthropogenic fluxes for both sources and sinks, though emissions from deforestation are likely underestimated ( [[#Tubiello--2020|Tubiello et al. 2020]] ). FAOSTAT also estimate emissions from forest fires and other land uses (organic soils), following IPCC methods ( [[#Prosperi--2020|Prosperi et al. 2020]] ). The FAO-FRA 2020 ( [[#FAO--2020b|FAO 2020b]] ) update leads to estimates of larger sinks in Russia since 1991, and in China and the USA from 2011, and larger deforestation emissions in Brazil and smaller in Indonesia than FRA 2015 ( [[#FAO--2015|FAO 2015]] ; [[#Tubiello--2020|Tubiello et al. 2020]] ).

The bookkeeping models by [[#Houghton--2017|Houghton and Nassikas (2017)]] , [[#Hansis--2015|Hansis et al. (2015)]] , and [[#Gasser--2020|Gasser et al. (2020)]] and the DGVMs used in the Global Carbon Budget ( [[#Friedlingstein--2020|Friedlingstein et al. 2020]] ) use either the LUH2 dataset ( [[#Hurtt--2020|Hurtt et al. 2020]] ), HYDE ( [[#Goldewijk--2017|Goldewijk et al. 2017]] ), FRA 2015 ( [[#FAO--2015|FAO 2015]] ) or a combination. The LUH2 dataset includes a new wood harvest reconstruction, new representation of shifting cultivation, crop rotations, and management information including irrigation and fertiliser application. The area of forest subject to harvest in LUH2 is much less than the area of forest considered ‘managed’ in the NGHGIs ( [[#Grassi--2018|Grassi et al. 2018]] ). The model datasets do not yet include the FAO FRA 2020 update ( [[#FAO--2020a|FAO 2020a]] ). The DGVMs consider CO 2 fertilisation effects on forest growth that are sometimes confirmed from the ground-based forest inventory networks ( [[#Nabuurs--2013|Nabuurs et al. 2013]] ) and sometimes not at all ( [[#van%20der%20Sleen--2015|van der Sleen et al. 2015]] ).

Further, the DGVMs and bookkeeping models do not include a wide range of practices which are implicitly covered by the inventories; for example: forest dynamics ( [[#Pugh--2019|Pugh et al. 2019]] ; [[#Le%20Noë--2020|Le Noë et al. 2020]] ), forest management including wood harvest (Nabuurs, et al. 2013; [[#Arneth--2017|Arneth et al. 2017]] ), agricultural and grassland practices ( [[#Pugh--2015|Pugh et al. 2015]] ; [[#Sanderman--2017|Sanderman et al. 2017]] ; [[#Pongratz--2018|Pongratz et al. 2018]] ); or, for example, fire management ( [[#Andela--2017|Andela et al. 2017]] ; [[#Arora--2018|Arora and Melton 2018]] ).

Increasingly, higher emissions estimates are expected from DGVMs compared to bookkeeping models, because DGVMs include a loss of additional sink capacity of 3.3 ± 1.1 GtCO 2 yr –1 on average over 2009–2018, which is increasing with larger climate and CO 2 impacts ( [[#Friedlingstein--2020|Friedlingstein et al. 2020]] ). This arises because the DGVM methodological setup requires a reference simulation including climate and environmental changes but without any land-use change such as deforestation, so DGVMs implicitly include the sink capacity forests would have developed in response to environmental changes on areas that in reality have been cleared ( [[#Gitz--2003|Gitz and Ciais 2003]] ; [[#Pongratz--2014|Pongratz et al. 2014]] ) (IPCC AR6 WGI Chapter 5).

Carbon emissions from peat burning have been estimated based on the Global Fire Emission Database (GFED4s; [[#van%20der%20Werf--2017|van der Werf et al. 2017]] ). These were included in the bookkeeping model estimates and added 2.0 GtC over 1960–2019 (e.g., causing the peak in South-East Asia in 1998) (Figure 7.5). Within the Global Carbon Budget ( [[#Friedlingstein--2020|Friedlingstein et al. 2020]] ), peat drainage from agriculture accounted for an additional 8.6 GtC from 1960–2019 according to FAOSTAT ( [[#Conchedda--2020|Conchedda and Tubiello, 2020]] ) used by two of the bookkeeping models ( [[#Hansis--2015|Hansis et al. 2015]] ; [[#Gasser--2020|Gasser et al. 2020]] ).

Remote-sensing products provide valuable spatial and temporal land-use and biomass data globally (including in remote areas), at potentially high spatial and temporal resolutions, that can be used to calculate CO 2 fluxes, but have mostly been applied only to forests at the global or even regional scale. While such data can strongly support monitoring reporting and verification, estimates of forest carbon fluxes directly from Earth Observation (EO) data vary considerably in both their magnitude and sign (i.e., whether forests are a net source or sink of carbon). For the period 2005–2017, net tropical forest carbon fluxes were estimated as –0.4 GtCO 2 yr –1 ( [[#Fan--2019|Fan et al. 2019]] ); 0.58 GtCO 2 yr –1 ( [[#Grace--2014|Grace et al. 2014]] ); 1.6 GtCO 2 yr –1 ( [[#Baccini--2017|Baccini et al. 2017]] ) and 2.87 GtCO 2 yr –1 ( [[#Achard--2014|Achard et al. 2014]] ). Differences can in part be explained by spatial resolution of the datasets, the definition of ‘forest’ and the inclusion of processes and methods used to determine degradation and growth in intact and secondary forests, or the changes in algorithm over time ( [[#Palahí--2021|Palahí et al. 2021]] ). A recent global study integrated ground observations and remote sensing data to map forest-related GHG emissions and removals at a high spatial resolution (30 m spatial scale), although it only provides an average estimate of annual carbon loss over 2001–2019 ( [[#Harris--2021|Harris et al. 2021]] ). The estimated net global forest carbon sink globally was –7.66 GtCO 2 yr −1 , being –1.7 GtCO 2 yr −1 in the tropics only.

Remote sensing products can help to attribute changes to anthropogenic activity or natural inter-annual climate variability ( [[#Fan--2019|Fan et al. 2019]] ; [[#Wigneron--2020|Wigneron et al. 2020]] ). Products with higher spatial resolution make it easier to determine forest and carbon dynamics in relatively small-sized managed forests (e.g., Y. [[#Wang--2020|Wang et al. 2020]] ; [[#Heinrich--2021|Heinrich et al. 2021]] ; [[#Reiche--2021|Reiche et al. 2021]] ). For example, secondary forest regrowth in the Brazilian Amazon offset 9 to 14% of gross emissions due to deforestation 1 ( [[#Aragão--2018|Aragão et al. 2018]] ; [[#Silva%20Junior--2021|Silva Junior et al. 2021]] ). Yet disturbances such as fire and repeated deforestation cycles due to shifting cultivation over the period 1985 to 2017, were found to reduce the regrowth rates of secondary forests by 8 to 55% depending on the climate region of regrowth ( [[#Heinrich--2021|Heinrich et al. 2021]] ).

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==== 7.2.2.3 Implications of Differences in AFOLU CO 2 Fluxes Between Global Models and National Greenhouse Gas Inventories (NGHGIs), and Reconciliation ====

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There is about 5.5 GtCO 2 yr –1 difference in the anthropogenic AFOLU estimates between NGHGIs and global models (this number relates to an IAMs comparison for the period 2005–2015 – see Cross-Chapter Box 6 in this chapter; for comparison with other models see Figure 7.4). Reconciling the differences, in other words, making estimates comparable, can build confidence in land-related CO 2 estimates, for example for the purpose of assessing collective progress in the context of the Global Stocktake (Cross-Chapter Box 6 in this chapter). The difference largely results from greater estimated CO 2 in NGHGIs, mostly occurring in forests ( [[#Grassi--2021|Grassi et al. 2021]] ). This difference is potentially a consequence of: (i) simplified and/or incomplete representation of management in global models ( [[#Popp--2017|Popp et al. 2017]] ; [[#Pongratz--2018|Pongratz et al. 2018]] ), for example, concerning impacts of forest management in biomass expansion and thickening ( [[#Nabuurs--2013|Nabuurs et al. 2013]] ; [[#Grassi--2017|Grassi et al. 2017]] ), (ii) inaccurate and/or incomplete estimation of LULUCF fluxes in NGHGIs ( [[#Grassi--2017|Grassi et al. 2017]] ), especially in developing countries, primarily in non-forest land uses and in soils, and (iii) conceptual differences in how global models and NGHGIs define ‘anthropogenic’ CO 2 flux from land ( [[#Grassi--2018|Grassi et al. 2018]] ). The impacts of (i) and (ii) are difficult to quantify and result in uncertainties that will decrease slowly over time through improvements of both models and NGHGIs. By contrast, the inconsistencies in (iii) and its resulting biases were assessed as explained below.

Since changing the NGHGIs’ approach is impractical, an interim method to translate and adjust the output of global models was outlined for reconciling a bookkeeping model and NGHGIs ( [[#Grassi--2018|Grassi et al. 2018]] ). More recently, an improved version of this approach has been applied to the future mitigation pathways estimated by IAMs ( [[#Grassi--2021|Grassi et al. 2021]] ), with the implications for the Global Stocktake discussed in Cross-Chapter Box 6 in this chapter. This method implies a post-processing of current global models’ results that addresses two components of the conceptual differences in the ‘anthropogenic’ CO 2 flux; (i) how the impact of human-induced environmental changes (indirect effects) are considered, and (ii) the extent of forest area considered ‘managed’. Essentially, this approach adds DGVM estimates of CO 2 fluxes due to indirect effects from countries’ managed forest area (using non-intact forest area maps as a proxy) to the original global models’ anthropogenic land CO 2 fluxes (Figure 7.6).

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'''Figure 7.6 | Main conceptual differences between global models (bookkeeping models, IAMs and DGVMs) and NGHGIs definitions of what is considered the ‘anthropogenic’ land CO''' 2 '''flux, and proposed solution (from''' '''Grassi''' '''et al.''' '''2021). (a)''' Differences in defining the anthropogenic land CO 2 flux by global models (‘land use’) and NGHGIs (‘LULUCF’), including the attribution of processes responsible for land fluxes ( [[#IPCC--2006|IPCC 2006]] ; 2010) in managed and unmanaged lands. The anthropogenic land CO 2 flux by global models typically includes only the CO 2 flux due to ‘direct effects’ (land-use change, harvest, regrowth). By contrast, most NGHGIs consider anthropogenic all fluxes occurring in areas defined as ‘managed’, including also the sink due to ‘indirect effects’ (climate change, atmospheric CO 2 increase, N deposition etc.) and due to ‘natural effects’ (climate variability, background natural disturbances). '''(b)''' Proposed solution to the inconsistency, via disaggregation of the ‘Land Sink’ flux from DGVMs into CO 2 fluxes occurring in managed and in unmanaged lands. The sum of ‘land use’ flux (direct effects from bookkeeping models or IAMs) and the ‘Land Sink’ (indirect effects from DGVMs) in managed lands produces an adjusted global model CO 2 flux which is conceptually more comparable with LULUCF fluxes from NGHGIs. Note that the figure may in some cases be an oversimplification, in other words, not all NGHGIs include all recent indirect effects.

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