Comment on essd-2022-245

<strong class="journal-contentHeaderColor">Abstract.</strong> With the focus of climate policy shifting from pledges to implementation, there is an increasing need to track progress on climate change mitigation at country level, especially for the land-use sector. Despite new tools and models offering unprecedented monitoring opportunities, striking differences remain in estimations of anthropogenic land-use CO₂ fluxes between the national greenhouse gas inventories (NGHGIs) used to assess compliance with the Paris Agreement, and the Global Carbon Budget and IPCC assessment reports, both based on global bookkeeping models (BMs). Recent evidence showed that these differences are mainly due to inconsistent definitions of anthropogenic forest CO₂ fluxes. In particular, the part of the land sink that is caused by the indirect effects of human-induced environmental change (e.g., fertilization effect on vegetation growth due to increase atmospheric CO₂ concentration, climate change) on managed lands is treated as non-anthropogenic by BMs, while in most cases is considered anthropogenic in NGHGIs. In addition, countries use a broader definition of managed land than BMs. Building on previous studies, we implement an approach that adds the CO₂ sink due to environmental change from countries&rsquo; managed forest area (estimated by Dynamic Global Vegetation Models, DGVMs) to the original land-use flux from BMs. This sum is expected to be conceptually more comparable to NGHGIs. Our analysis uses updated and more comprehensive data from NGHGIs than previous studies and provides model results at a greater level of disaggregation in terms of land categories (i.e., forest land, deforestation, organic soils, other land uses) and countries. Our results confirm a large difference in land use CO₂ fluxes between the ensemble mean of the BMs, estimating a source of 4.3 GtCO₂ yr^-1 globally for the period 2001&ndash;2020, and NGHGIs, which estimate a sink of -1.7 GtCO₂ yr^-1. Most of this 6.0 GtCO₂ yr^-1 gap is found on forest land (3.8 GtCO₂ yr^-1), with differences also for deforestation (1.1 GtCO₂ yr^-1), other land uses (1.0 GtCO₂ yr^-1), and to a lesser extent for organic soils (0.1 GtCO₂ yr^-1). By adding the DGVM ensemble mean sink arising from environmental change in managed forests (-5.1 GtCO₂ yr^-1) to BMs estimates, the gap between BMs and NGHGIs becomes significantly smaller both globally (residual gap: 0.9 GtCO₂ yr^-1) and in most regions and countries. The remaining differences mostly reflect smaller net emissions from deforestation and agricultural land in the NGHGIs of developing countries than in the BMs. By reconciling most of the differences between NGHGIs and global models (BMs and DGVMs), offering a blueprint for operationalizing future comparisons, and identifying areas to be further investigated, this study represents an important step forward for increasing transparency and confidence in land-use CO₂ flux estimates at the country level. This is crucial to support land-based mitigation investments and assess the countries&rsquo; collective progress under the Paris Agreement&rsquo;s Global Stocktake.

<strong class="journal-contentHeaderColor">Abstract.</strong> With the focus of climate policy shifting from pledges to implementation, there is an increasing need to track progress on climate change mitigation at country level, especially for the land-use sector. Despite new tools and models offering unprecedented monitoring opportunities, striking differences remain in estimations of anthropogenic land-use CO₂ fluxes between the national greenhouse gas inventories (NGHGIs) used to assess compliance with the Paris Agreement, and the Global Carbon Budget and IPCC assessment reports, both based on global bookkeeping models (BMs). Recent evidence showed that these differences are mainly due to inconsistent definitions of anthropogenic forest CO₂ fluxes. In particular, the part of the land sink that is caused by the indirect effects of human-induced environmental change (e.g., fertilization effect on vegetation growth due to increase atmospheric CO₂ concentration, climate change) on managed lands is treated as non-anthropogenic by BMs, while in most cases is considered anthropogenic in NGHGIs. In addition, countries use a broader definition of managed land than BMs. Building on previous studies, we implement an approach that adds the CO₂ sink due to environmental change from countries&rsquo; managed forest area (estimated by Dynamic Global Vegetation Models, DGVMs) to the original land-use flux from BMs. This sum is expected to be conceptually more comparable to NGHGIs. Our analysis uses updated and more comprehensive data from NGHGIs than previous studies and provides model results at a greater level of disaggregation in terms of land categories (i.e., forest land, deforestation, organic soils, other land uses) and countries. Our results confirm a large difference in land use CO₂ fluxes between the ensemble mean of the BMs, estimating a source of 4.3 GtCO₂ yr^-1 globally for the period 2001&ndash;2020, and NGHGIs, which estimate a sink of -1.7 GtCO₂ yr^-1. Most of this 6.0 GtCO₂ yr^-1 gap is found on forest land (3.8 GtCO₂ yr^-1), with differences also for deforestation (1.1 GtCO₂ yr^-1), other land uses (1.0 GtCO₂ yr^-1), and to a lesser extent for organic soils (0.1 GtCO₂ yr^-1). By adding the DGVM ensemble mean sink arising from environmental change in managed forests (-5.1 GtCO₂ yr^-1) to BMs estimates, the gap between BMs and NGHGIs becomes significantly smaller both globally (residual gap: 0.9 GtCO₂ yr^-1) and in most regions and countries. The remaining differences mostly reflect smaller net emissions from deforestation and agricultural land in the NGHGIs of developing countries than in the BMs. By reconciling most of the differences between NGHGIs and global models (BMs and DGVMs), offering a blueprint for operationalizing future comparisons, and identifying areas to be further investigated, this study represents an important step forward for increasing transparency and confidence in land-use CO₂ flux estimates at the country level. This is crucial to support land-based mitigation investments and assess the countries&rsquo; collective progress under the Paris Agreement&rsquo;s Global Stocktake.

A transparency framework has been enhanced under the Paris Agreement. Developing countries need to develop the capacity to regularly update national greenhouse gas (GHG) inventories, thereby tracking progress toward climate change mitigation goals under their nationally determined contributions (NDCs). Although much study in recent years has been focused on the challenges faced by developing countries in building the long-term institutional capabilities required for more frequent inventory reporting, little work has been done to analyze the institutional linkage between the national GHG inventories and NDCs in the respective countries. Against this backdrop, we examined the cases of Vietnam and the Philippines. Through desktop reviews and a series of interviews conducted in 2018, the present study characterized their institutional designs for the national GHG inventory. It also evaluated observed outcomes of institutional performance in the following dimensions: (1) the frequency and quality of national GHG inventory reporting and (2) the effectiveness of the national GHG inventory at informing the process to formulate mitigation actions under NDCs. The study found that there are differences in performance outcomes between the two countries, which may be linked to the differences in institutional designs. It also found potential trade-offs: A good performance outcome in one dimension may not necessarily translate into a similar result in the other. The findings have implications for transparency-related capacity building assistance: A sound understanding of institutional arrangements and their potential consequences is important for development partners to facilitate the interplay between the national GHG inventory and NDCs in developing countries.

The magnitude of net carbon dioxide emissions resulting from global forest carbon change, and hence the contribution of forests to global climate change, is highly uncertain, owing to the lack of direct measurement by Earth observation and ground data collection. This paper uses a new method to evaluate this uncertainty with greater precision than before. Sources of uncertainty are divided into conceptualization and measurement categories and distributed between the spatial, vertical and temporal dimensions of Earth observation. The method is applied to Forest Reference Emission Level (FREL) reports and National Greenhouse Gas Inventories (NGGIs) submitted to the UN Framework Convention on Climate Change (UNFCCC) by 12 countries containing half of tropical forest area. The two sets of estimates are typical of those to be submitted to the Reducing Emissions from Deforestation and Degradation (REDD+) mechanism of the UNFCCC and the 2023 Global Stocktake of its Paris Agreement, respectively. Assembling the Uncertainty Fingerprint of each estimate shows that Uncertainty Scores are between 10 and 14 for the NGGIs and 5 and 10 for the FREL reports, and so both exceed the threshold of 2 when it is advisable to evaluate uncertainty by standard statistical methods. Conceptualization uncertainties account for 60% of all uncertainties in the NGGIs and 47% in the FREL reports, e.g., there is incomplete coverage of forest carbon fluxes, and limited disaggregation of fluxes between different ecosystem types and forest carbon pools. Of the measurement uncertainties, all FREL reports base forest area estimates on at least medium resolution satellite data, compared with only 3 NGGIs; after REDD+ Readiness schemes, mean area mapping frequency has fallen to 2.3 years in Latin America and 3.0 years in Asia, but only 8.3 years in Africa; and carbon density estimates are based on national forest inventory data in all FREL reports but only 4 NGGIs. The effectiveness of the Global Stocktake and REDD+ monitoring will therefore be constrained by considerable uncertainties, and to reduce these requires a new phase of REDD+ Readiness to ensure more frequent national forest inventories and forest carbon mapping.

BackgroundThe application of different approaches calculating the anthropogenic carbon net flux from land, leads to estimates that vary considerably. One reason for these variations is the extent to which approaches consider forest land to be “managed” by humans, and thus contributing to the net anthropogenic flux. Global Earth Observation (EO) datasets characterising spatio-temporal changes in land cover and carbon stocks provide an independent and consistent approach to estimate forest carbon fluxes. These can be compared against results reported in National Greenhouse Gas Inventories (NGHGIs) to support accurate and timely measuring, reporting and verification (MRV). Using Brazil as a primary case study, with additional analysis in Indonesia and Malaysia, we compare a Global EO-based dataset of forest carbon fluxes to results reported in NGHGIs.ResultsBetween 2001 and 2020, the EO-derived estimates of all forest-related emissions and removals indicate that Brazil was a net sink of carbon (− 0.2 GtCO2yr−1), while Brazil’s NGHGI reported a net carbon source (+ 0.8 GtCO2yr−1). After adjusting the EO estimate to use the Brazilian NGHGI definition of managed forest and other assumptions used in the inventory’s methodology, the EO net flux became a source of + 0.6 GtCO2yr−1, comparable to the NGHGI. Remaining discrepancies are due largely to differing carbon removal factors and forest types applied in the two datasets. In Indonesia, the EO and NGHGI net flux estimates were similar (+ 0.6 GtCO2 yr−1), but in Malaysia, they differed in both magnitude and sign (NGHGI: -0.2 GtCO2 yr−1; Global EO: + 0.2 GtCO2 yr−1). Spatially explicit datasets on forest types were not publicly available for analysis from either NGHGI, limiting the possibility of detailed adjustments.ConclusionsBy adjusting the EO dataset to improve comparability with carbon fluxes estimated for managed forests in the Brazilian NGHGI, initially diverging estimates were largely reconciled and remaining differences can be explained. Despite limited spatial data available for Indonesia and Malaysia, our comparison indicated specific aspects where differing approaches may explain divergence, including uncertainties and inaccuracies. Our study highlights the importance of enhanced transparency, as set out by the Paris Agreement, to enable alignment between different approaches for independent measuring and verification.

The Global Stocktake (GST), implemented by the Paris Agreement, requires rapid developments in the capabilities to quantify annual greenhouse gas (GHG) emissions and removals consistently from the global to the national scale and improvements to national GHG inventories. In particular, new capabilities are needed for accurate attribution of sources and sinks and their trends to natural and anthropogenic processes. On the one hand, this is still a major challenge as national GHG inventories follow globally harmonized methodologies based on the guidelines established by the Intergovernmental Panel on Climate Change, but these can be implemented differently for individual countries. Moreover, in many countries the capability to systematically produce detailed and annually updated GHG inventories is still lacking. On the other hand, spatially-explicit datasets quantifying sources and sinks of carbon dioxide, methane and nitrous oxide emissions from Earth Observations (EO) are still limited by many sources of uncertainty. While national GHG inventories follow diverse methodologies depending on the availability of activity data in the different countries, the proposed comparison with EO-based estimates can help improve our understanding of the comparability of the estimates published by the different countries. Indeed, EO networks and satellite platforms have seen a massive expansion in the past decade, now covering a wide range of essential climate variables and offering high potential to improve the quantification of global and regional GHG budgets and advance process understanding. Yet, there is no EO data that quantifies greenhouse gas fluxes directly, rather there are observations of variables or proxies that can be transformed into fluxes using models. Here, we report results and lessons from the ESA-CCI RECCAP2 project, whose goal was to engage with National Inventory Agencies to improve understanding about the methods used by each community to estimate sources and sinks of GHGs and to evaluate the potential for satellite and in-situ EO to improve national GHG estimates. Based on this dialogue and recent studies, we discuss the potential of EO approaches to provide estimates of GHG budgets that can be compared with those of national GHG inventories. We outline a roadmap for implementation of an EO carbon-monitoring program that can contribute to the Paris Agreement.

Abstract. Reliable quantification of the sources and sinks of greenhouse gases, together with trends and uncertainties, is essential to monitoring the progress in mitigating anthropogenic emissions under the Paris Agreement. This study provides a consolidated synthesis of CH4 and N2O emissions with consistently derived state-of-the-art bottom-up (BU) and top-down (TD) data sources for the European Union and UK (EU27 + UK). We integrate recent emission inventory data, ecosystem process-based model results and inverse modeling estimates over the period 1990–2017. BU and TD products are compared with European national greenhouse gas inventories (NGHGIs) reported to the UN climate convention UNFCCC secretariat in 2019. For uncertainties, we used for NGHGIs the standard deviation obtained by varying parameters of inventory calculations, reported by the member states (MSs) following the recommendations of the IPCC Guidelines. For atmospheric inversion models (TD) or other inventory datasets (BU), we defined uncertainties from the spread between different model estimates or model-specific uncertainties when reported. In comparing NGHGIs with other approaches, a key source of bias is the activities included, e.g., anthropogenic versus anthropogenic plus natural fluxes. In inversions, the separation between anthropogenic and natural emissions is sensitive to the geospatial prior distribution of emissions. Over the 2011–2015 period, which is the common denominator of data availability between all sources, the anthropogenic BU approaches are directly comparable, reporting mean emissions of 20.8 Tg CH4 yr−1 (EDGAR v5.0) and 19.0 Tg CH4 yr−1 (GAINS), consistent with the NGHGI estimates of 18.9 ± 1.7 Tg CH4 yr−1. The estimates of TD total inversions give higher emission estimates, as they also include natural emissions. Over the same period regional TD inversions with higher-resolution atmospheric transport models give a mean emission of 28.8 Tg CH4 yr−1. Coarser-resolution global TD inversions are consistent with regional TD inversions, for global inversions with GOSAT satellite data (23.3 Tg CH4 yr−1) and surface network (24.4 Tg CH4 yr−1). The magnitude of natural peatland emissions from the JSBACH–HIMMELI model, natural rivers and lakes emissions, and geological sources together account for the gap between NGHGIs and inversions and account for 5.2 Tg CH4 yr−1. For N2O emissions, over the 2011–2015 period, both BU approaches (EDGAR v5.0 and GAINS) give a mean value of anthropogenic emissions of 0.8 and 0.9 Tg N2O yr−1, respectively, agreeing with the NGHGI data (0.9 ± 0.6 Tg N2O yr−1). Over the same period, the average of the three total TD global and regional inversions was 1.3 ± 0.4 and 1.3 ± 0.1 Tg N2O yr−1, respectively. The TD and BU comparison method defined in this study can be operationalized for future yearly updates for the calculation of CH4 and N2O budgets both at the EU+UK scale and at the national scale. The referenced datasets related to figures are visualized at https://doi.org/10.5281/zenodo.4590875 (Petrescu et al., 2020b).

In this study, we provide an update on the methodology and data to compare the national greenhouse gas inventories (NGHGIs) and atmospheric inversion model ensembles contributed by international research teams coordinated by the Global Carbon Project. The comparison framework uses transparent processing of the net ecosystem exchange fluxes of carbon dioxide (CO2) from inversions to provide estimates of terrestrial carbon stock changes over managed land that can be used to evaluate NGHGIs. For methane (CH4), and nitrous oxide (N2O), we separate anthropogenic emissions from natural sources based directly on the inversion results to make them compatible with NGHGIs. Our global harmonized NGHGI database was updated with inventory data by compiling data from the first biennial transparency reports (BTRs) under the Paris Agreement, providing the first annual time-series official reported values in most non-Annex I countries. For the inversion data, we used an ensemble of 30 global inversions produced for the most recent assessments of the global CO2 and CH4 budgets coordinated by the Global Carbon Project with ancillary data, and 6 inversion results of CH4 budgets from Ciais et al, 2026. The inversion ensemble in this study goes through 2024, building on our previous report from 1990 to 2021. The methodology proposed here to compare inversion results with NGHGIs can be applied regularly for monitoring the effectiveness of mitigation policy and progress by countries to meet the objectives of their pledges.

Northern forests are an important carbon sink, but our understanding of the driving factors is limited due to discrepancies between dynamic global vegetation models (DGVMs) and atmospheric inversions. We show that DGVMs simulate a 50% lower sink (1.1 ± 0.5 PgC yr−1 over 2001–2021) across North America, Europe, Russia, and China compared to atmospheric inversions (2.2 ± 0.6 PgC yr−1). We explain why DGVMs underestimate the carbon sink by considering how they represent disturbance processes, specifically the overestimation of fire emissions, and the lack of robust forest demography resulting in lower forest regrowth rates than observed. We reconcile net sink estimates by using alternative disturbance-related fluxes. We estimate carbon uptake through forest regrowth by combining satellite-derived forest age and biomass maps. We calculate a regrowth flux of 1.1 ± 0.1 PgC yr−1, and combine this with satellite-derived estimates of fire emissions (0.4 ± 0.1 PgC yr−1), land-use change emissions from bookkeeping models (0.9 ± 0.2 PgC yr−1), and the DGVM-estimated sink from CO2 fertilisation, nitrogen deposition, and climate change (2.2 ± 0.9 PgC yr−1). The resulting ‘bottom-up’ net flux of 2.1 ± 0.9 PgC yr−1 agrees with atmospheric inversions. The reconciliation holds at regional scales, increasing confidence in our results.

Better information on greenhouse gas (GHG) emissions and mitigation potential in the agricultural sector is necessary to manage these emissions and identify responses that are consistent with the food security and economic development priorities of countries. Critical activity data (what crops or livestock are managed in what way) are poor or lacking for many agricultural systems, especially in developing countries. In addition, the currently available methods for quantifying emissions and mitigation are often too expensive or complex or not sufficiently user friendly for widespread use.The purpose of this focus issue is to capture the state of the art in quantifying greenhouse gases from agricultural systems, with the goal of better understanding our current capabilities and near-term potential for improvement, with particular attention to quantification issues relevant to smallholders in developing countries. This work is timely in light of international discussions and negotiations around how agriculture should be included in efforts to reduce and adapt to climate change impacts, and considering that significant climate financing to developing countries in post-2012 agreements may be linked to their increased ability to identify and report GHG emissions (Murphy et al 2010, CCAFS 2011, FAO 2011).

&amp;lt;p&amp;gt;Brazil is responsible for about one third of the global land use and land cover change (LULCC) carbon dioxide emissions. However, there is a disagreement among different methodologies on the magnitude and trends in emissions and their geographic distribution. One of the main uncertainties is associated with different LULCC datatasets used as input in the different approaches. In this work we perform an evaluation of LULCC datasets for Brazil, including the global dataset (HYDE 3.2) used in the annual Global Carbon Budget (GCB), and national Brazilian dataset (MapBiomas) over the period 2000-2018. We also analyze the latest global HYDE 3.3 dataset based on new FAO inventory estimates and multi-annual ESA CCI satellite-based land cover maps. Results show that the new HYDE 3.3 can represent well the observed spatial variation in cropland and pastures areas over the last decades compared to national data (MapBiomas) and shows an improvement compared to HYDE 3.2 used in GCB. However, the magnitude of LULCC assessed with HYDE 3.3 is lower than national estimates from MapBiomas. Finally, we used HYDE 3.3 as input to two different approaches included in GCB, a global bookkeeping model (BLUE) and a process-based Dynamic Global Vegetation Model (JULES-ES) to determine the impact of the new version of HYDE dataset on Brazil&amp;amp;#8217;s land-use emissions trends over the period 2000-2017. Both JULES-ES and BLUE now simulate a negative land-use emissions trend for the last two decades. This negative trend is in agreement with Brazilian INPE-EM, global H&amp;amp;N bookkeeping models, FAO and as reported in National GHG inventories (NGHGI), although magnitudes differ among approaches. Overall, the inclusion of the multi-annual ESA CCI Land Cover dataset to allocate spatially the FAO statistical data has improved spatial representation of agricultural area change in Brazil in the last two decades, contributing to improve global model capability to simulate Brazil&amp;amp;#8217;s LULCC emissions in agreement with national trends estimates and spatial distribution.&amp;lt;/p&amp;gt;

Accurate estimation of forest biomass and carbon stocks at regional to national scales is a key requirement in determining terrestrial carbon sources and sinks on United States (US) forest lands. To that end, comprehensive assessment and testing of alternative volume and biomass models were conducted for individual tree models employed in the component ratio method (CRM) currently used in the US' National Greenhouse Gas Inventory. The CRM applies species-specific stem volume equations along with specific gravity conversions and component expansion factors to ensure consistency between predicted stem volumes and weights, and additivity of predicted live tree component weights to match aboveground biomass (AGB). Data from over 76 600 stem volumes and 6600 AGB observations were compiled from individual studies conducted in the past 115 years – what we refer to as legacy data – to perform the assessment. Scenarios formulated to incrementally replace constituent equations in the CRM with models fitted to legacy data were tested using cross-validation methods, and estimates of AGB were scaled using forest inventory data to compare across 33 states in the eastern US. Modifications all indicated that the CRM in its present formulation underestimates AGB in eastern forests, with the range of underestimation ranging from 6.2 to 17 per cent. Cross-validation results indicated the greatest reductions in estimation bias and root-mean squared error could be achieved by scenarios that replaced stem volume, sapling AGB, and component ratio equations in the CRM. A change in the definitions used in apportioning biomass to aboveground components was also shown to increase prediction accuracy. Adopting modifications tested here would increase AGB estimates for the eastern US by 15 per cent, accounting for 1.5 Pg of C currently unaccounted for in live tree aboveground forest C stock assessments. Expansion of the legacy data set currently underway should be useful for further testing, such as whether similar gains in accuracy can be achieved in estimates of regional or national-scale C sequestration rates.

&amp;lt;div&amp;gt; &amp;lt;p&amp;gt;It is now commonly-understood that improved understanding of global fire regimes demands better representation of anthropogenic fire in dynamic global vegetation models (DGVMs). However, currently there is no clear agreement on how human activity should be incorporated into fire-enabled DGVMs and existing models exhibit large differences in the sensitivities of socio-economic variables. Furthermore, existing approaches are limited to empirical statistical relations between fire regime variables and globally available socio-economic indicators such as population density or GDP. Although there has been some limited representation in global models of the contrasting ways in which different classes of actors use or manage fires, we argue that fruitful progress in advancing representation of anthropogenic fire in DGVMs will come by building on agent-based modelling approaches. Here, we report on our progress developing a global agent-based representation of anthropogenic fire and its coupling with the JULES-INFERNO fire-enabled DGVM.&amp;amp;#160;&amp;amp;#160;&amp;lt;/p&amp;gt; &amp;lt;/div&amp;gt;&amp;lt;div&amp;gt; &amp;lt;p&amp;gt;Our modelling of anthropogenic fire adopts an approach that classifies &amp;amp;#8216;agent functional types&amp;amp;#8217; (AFTs) to represent human fire activity based on land use/cover and Stephen Pyne&amp;amp;#8217;s fire development stages. For example, the &amp;amp;#8216;swidden&amp;amp;#8217; AFT represents shifting cultivation farmers managing cropland and secondary vegetation in a pre-industrial development setting. This approach is based on the assumption that anthropogenic fire use and management is primarily a function of land use but influenced by socio-economic context, leading different AFTs to produce qualitatively distinct fire regimes. The literature empirically supports this assumption, however data on human fire interactions are fragmented across many academic fields (including anthropology, geography, land economics). Therefore, we developed a Database of Anthropogenic Fire Impacts (DAFI) containing 1798 case studies of fire use/management from 519 publications, covering more than 100 countries and all major biomes (except Arctic/Antarctic). We discuss DAFI development, patterns in the resulting data, and possible applications. Specifically, DAFI is used with ancillary data (e.g. biophysical, socio-economic indicators), classification and regression methods to test and refine our initial AFT classification, characterise AFT fire variables, and distribute AFTs spatially. Our model will then simulate AFT distributions for alternative scenarios of change (e.g. specified by the Shared Socioeconomic Pathways).&amp;amp;#160;&amp;lt;/p&amp;gt; &amp;lt;/div&amp;gt;&amp;lt;div&amp;gt; &amp;lt;p&amp;gt;Coupling distinct models can be achieved in a variety of ways, but broadly we can distinguish between &amp;amp;#8216;loose&amp;amp;#8217; coupling in which information flow is uni-directional, and &amp;amp;#8216;tight&amp;amp;#8217; coupling in which information flows are integrated with feedbacks and dynamic updating. Our intention is to tightly couple our AFT model with JULES-INFERNO, such that fire use and suppression behaviours from the former influence simulated fire ignitions and burned area in the latter. Reciprocally, total burned area simulated by JULES-INFERNO will feedback to influence spatial distribution of AFTs in the next time step, modifying anthropogenic fire patterns for the next step of DGVM simulation. We discuss the potential for this tight model coupling to capture socio-ecological feedbacks in fire regimes, as well as&amp;amp;#160; possible pitfalls and steps needed to test and verify model outputs. These are early steps in an important journey to improve representation of anthropogenic fire in DGVMs.&amp;lt;/p&amp;gt; &amp;lt;/div&amp;gt;

Agriculture and its land use are associated with 22% of global annual anthropogenic greenhouse gas (GHG) emissions [1]. Reducing these emissions requires insight into how much emissions are caused by specific agricultural commodities and where they occur. Commodity-specific GHG footprints are a useful tool in this regard as they enable producers to determine the emission intensity and environmental impact of their products [2]-[3]. Further, they can help identify emission reduction strategies and region-specific mitigation efforts [4]-[6]. Spatially-explicit GHG footprints are particularly useful since they show the geographic distribution of commodities&amp;#8217; emission intensity and allow for the comparison across countries [7]. Several past studies have produced crop-specific footprints but considered emissions solely from land use change and did not include emissions from agricultural practices [8]-[11]. Attribution was mostly conducted at aggregate level such as country and region level [16],[17]. Those studies that employed spatially-explicit attribution methods are characterized by limited geographical coverage [14] and a limited selection of crops [15]. Studies also applied largely different methods for attributing emissions to crops, making comparison across studies not possible.&amp;#160; The current study aims at filling in this research gap by improving data resolution and the methodology for attributing dynamic land-use emissions to specific crops. We derive global spatially-explicit GHG emission footprints for 161 agricultural crops over the period of 1970 to 2021 at 15 arcmin resolution. We do so by quantifying spatially-explicit land-use emissions related to agriculture and then attributing them to specific agricultural commodities (Fig1). The analysis is conducted using the LUH2 dataset on land use dynamics over time [16] and IMAGE-LPJmL 3.2 for carbon stock data [17]. IMAGE-LPJmL is a dynamic global vegetation model that simulates vegetation dynamics and distribution based on carbon cycle and crop growth model [18], [19]. This allows leveraging advanced data in terms of dynamic, annual, and spatially specific carbon stocks (Tier-3), rather than constant and/or national level carbon stock data (Tier-1) as generally used in the literature. This study also compares three different emission attribution methods (AM) (Fig2). AM1 uses an annual accounting period, attributing emissions to the land use change committed in the same year. AM2 uses a larger time step and attributes the emissions only to the land use type at the end of the accounting period. These two methods are the two most employed methods in carbon accounting studies. We also propose an alternative approach that reflects the dynamics of land use (AM3); we attribute the emissions based on occupation year of each land use type in the accounting period. The expected results of this study are crop-specific GHG footprints in terms of land use emissions per production area (tCO2eq/ha) and per crop yield (tCO2eq/ton) at the grid level as well as means and variations per crop and country. Also, variations as a result of different AMs will be presented and its implications for research and application in e.g. corporate emission reporting and target setting will be discussed.&amp;#160;

Combating climate change requires reducing greenhouse gas (GHG) emissions from human activities and enhancing carbon dioxide removal (CDR) through blue carbon ecosystems (BCEs). Progress is measured by international reporting of national GHG inventories under the UNFCCC, following IPCC guidelines. The EU Green Deal, particularly the EU Biodiversity Strategy to 2030, recognizes BCEs’ potential by advocating for the restoration of carbon-rich habitats. However, the IPCC Wetlands Supplement is underutilized in national GHG reporting, and few countries, especially in Europe, include blue carbon in their Nationally Determined Contributions (NDCs) under the Paris Agreement. This is due to limited awareness, the non-mandatory nature of the IPCC Wetlands Supplement, and data gaps. Our research aims to advance blue carbon knowledge, promoting BCEs as nature-based solutions, and addressing key gaps and scientific uncertainties to improve quantification and reporting of blue carbon under the UNFCCC in GHG inventory reporting under the Paris Agreement. The C-BLUES project, part of the Joint EU-China Flagship Initiative on Climate Change &amp; Biodiversity, seeks to enhance the IPCC Wetlands Supplement, increase BCE inclusion in national GHG inventories, and explore additional BCEs like natural and farmed kelp systems. We document best practices for monitoring and verifying BCE actions, model BCE sequestration capacity, and upscale GHG budgets to estimate BCE contributions to EU and Chinese inventories. We assess carbon stock changes, GHG emissions, and removals from various management interventions, and evaluate the integration of BCEs into national reporting mechanisms. We present a roadmap and invite participation with the scientific community, policy makers and the wider civil society to generate knowledge, raise awareness of BCEs and build capacity for BC research, as well as provide guidance on voluntary reporting on BCE.