Achieving the long-term temperature goal of the Paris Agreement (PA) requires forest-based mitigation. Collective progress towards this goal will be assessed by the PA's Global Stocktake. Currently, there is about a 4 GtCO 2 /y discrepancy in global anthropogenic net land use emissions between global models (reflected in IPCC Assessment Reports) and aggregated national greenhouse gas (GHG) inventories (under the UNFCCC). We show that this discrepancy is largely explained (about 3.2 GtCO 2 /y) by conceptual differences in anthropogenic forest sink estimation, related to representation of environmental change impacts and the areas considered managed. For a more credible tracking of collective progress under the Global Stocktake, these conceptual differences between models and inventories need to be reconciled. We implement a new method of disaggregation of global land model results that allows greater comparability with GHG inventories. This deepens understanding of model-inventory differences, allowing more transparent analysis of forestbased mitigation and facilitating a more meaningful Global Stocktake.

Mitigation pathways by Integrated Assessment Models (IAMs) describe future emissions that keep global warming below specific temperature limits and are compared with countries' collective greenhouse gas (GHG) emission reduction pledges. This is needed to assess mitigation progress and inform emission targets under the Paris Agreement. Currently, however, a mismatch of ~5.5 GtCO 2 yr −1 exists between the global land-use fluxes estimated with IAMs and from countries' GHG inventories. Here we present a 'Rosetta stone' adjustment to translate IAMs' land-use mitigation pathways to estimates more comparable with GHG inventories. This does not change the original decarbonization pathways, but reallocates part of the land sink to be consistent with GHG inventories. Adjusted cumulative emissions over the period until net zero for 1.5 or 2 °C limits are reduced by 120-192 GtCO 2 relative to the original IAM pathways. These differences should be taken into account to ensure an accurate assessment of progress towards the Paris Agreement.

BackgroundAccording to the post-2012 rules under the Kyoto protocol, developed countries that are signatories to the protocol have to estimate and report the greenhouse gas (GHG) emissions and removals from forest management (FM), with the option to exclude the emissions associated to natural disturbances, following the Intergovernmental Panel on Climate Change (IPCC) guidelines. To increase confidence in GHG estimates, the IPCC recommends performing verification activities, i.e. comparing country data with independent estimates. However, countries currently conduct relatively few verification efforts. The aim of this study is to implement a consistent methodological approach using the Carbon Budget Model (CBM) to estimate the net CO2 emissions from FM in 26 European Union (EU) countries for the period 2000–2012, including the impacts of natural disturbances. We validated our results against a totally independent case study and then we compared the CBM results with the data reported by countries in their 2014 Greenhouse Gas Inventories (GHGIs) submitted to the United Nations Framework Convention on Climate Change (UNFCCC).ResultsThe match between the CBM results and the GHGIs was good in nine countries (i.e. the average of our results is within ±25 % compared to the GHGI and the correlation between CBM and GHGI is significant at P < 0.05) and partially good in ten countries. When the comparison was not satisfactory, in most cases we were able to identify possible reasons for these discrepancies, including: (1) a different representation of the interannual variability, e.g. where the GHGIs used the stock-change approach; (2) different assumptions for non-biomass pools, and for CO2 emissions from fires and harvest residues. In few cases, further analysis will be needed to identify any possible inappropriate data used by the CBM or problems in the GHGI. Finally, the frequent updates to data and methods used by countries to prepare GHGI makes the implementation of a consistent modeling methodology challenging.ConclusionsThis study indicates opportunities to use the CBM as tool to assist countries in estimating forest carbon dynamics, including the impact of natural disturbances, and to verify the country GHGIs at the EU level, consistent with the IPCC guidelines. A systematic comparison of the CBM with the GHGIs will certainly require additional efforts—including close cooperation between modelers and country experts. This approach should be seen as a necessary step in the process of continuous improvement of GHGIs, because it may help in identifying possible errors and ultimately in building confidence in the estimates reported by the countries.Electronic supplementary materialThe online version of this article (doi:10.1186/s13021-016-0047-8) contains supplementary material, which is available to authorized users.

BackgroundForests and the forest sector may play an important role in mitigating climate change. The Paris Agreement and the recent legislative proposal to include the land use sector in the EU 2030 climate targets reflect this expectation. However, greater confidence on estimates from national greenhouse gas inventories (GHGI) and more comprehensive analyses of mitigation options are needed to seize this mitigation potential. The aim of this paper is to provide a tool at EU level for verifying the EU GHGI and for simulating specific policy and forest management scenarios. Therefore, the Carbon Budget Model (CBM) was applied for an integrated assessment of the EU forest carbon (C) balance from 2000 to 2012, including: (i) estimates of the C stock and net CO2 emissions for forest management (FM), afforestation/reforestation (AR) and deforestation (D), covering carbon in both the forest and the harvest wood product (HWP) pools; (ii) an overall analysis of the C dynamics associated with harvest and natural disturbances (mainly storms and fires); (iii) a comparison of our estimates with the data reported in the EU GHGI.ResultsOverall, the average annual FM sink (−365 Mt CO2 year−1) estimated by the CBM in the period 2000–2012 corresponds to about 7 % of total GHG emissions at the EU level for the same period (excluding land use, land-use change and forestry). The HWP pool sink (−44 Mt CO2 year−1) contributes an additional 1 %. Emissions from D (about 33 Mt CO2 year−1) are more than compensated by the sink in AR (about 43 Mt CO2 year−1 over the period). For FM, the estimates from the CBM were about 8 % lower than the EU GHGI, a value well within the typical uncertainty range of the EU forest sink estimates. For AR and D the match with the EU GHGI was nearly perfect (difference <±2 % in the period 2008–2012). Our analysis on harvest and natural disturbances shows that: (i) the impact of harvest is much greater than natural disturbances but, because of salvage logging (often very relevant), the impact of natural disturbances is often not easily distinguishable from the impact of harvest, and (ii) the impact of storms on the biomass C stock is 5–10 times greater than fires, but while storms cause only indirect emissions (i.e., a transfer of C from living biomass to dead organic matter), fires cause both direct and indirect emissions.ConclusionsThis study presents the application of a consistent methodological approach, based on an inventory-based model, adapted to the forest management conditions of EU countries. The approach captures, with satisfactory detail, the C sink reported in the EU GHGI and the country-specific variability due to harvest, natural disturbances and land-use changes. To our knowledge, this is the most comprehensive study of its kind at EU level, i.e., including all the forest pools, HWP and natural disturbances, and a comparison with the EU GHGI. The results provide the basis for possible future policy-relevant applications of this model, e.g., as a tool to support GHGIs (e.g., on accounting for na...

Abstract. Despite an increasing attention on the role of land in meeting countries' climate pledges under the Paris Agreement, the range of estimates of carbon fluxes from land use, land-use change, and forestry (LULUCF) in available databases is very large. A good understanding of the LULUCF data reported by countries under the United Nations Framework Convention on Climate Change (UNFCCC) – and of the differences with other datasets based on country-reported data – is crucial to increase confidence in land-based climate change mitigation efforts. Here we present a new data compilation of LULUCF fluxes of carbon dioxide (CO2) on managed land, aiming at providing a consolidated view on the subject. Our database builds on a detailed analysis of data from national greenhouse gas inventories (NGHGIs) communicated via a range of country reports to the UNFCCC, which report anthropogenic emissions and removals based on the IPCC (Intergovernmental Panel on Climate Change) methodology. Specifically, for Annex I countries, data are sourced from annual GHG inventories. For non-Annex I countries, we compiled the most recent and complete information from different sources, including national communications, biennial update reports, submissions to the REDD+ (reducing emissions from deforestation and forest degradation) framework, and nationally determined contributions. The data are disaggregated into fluxes from forest land, deforestation, organic soils, and other sources (including non-forest land uses). The CO2 flux database is complemented by information on managed and unmanaged forest area as available in NGHGIs. To ensure completeness of time series, we filled the gaps without altering the levels and trends of the country reported data. Expert judgement was applied in a few cases when data inconsistencies existed. Results indicate a mean net global sink of −1.6 Gt CO2 yr−1 over the period 2000–2020, largely determined by a sink on forest land (−6.4 Gt CO2 yr−1), followed by source from deforestation (+4.4 Gt CO2 yr−1), with smaller fluxes from organic soils (+0.9 Gt CO2 yr−1) and other land uses (−0.6 Gt CO2 yr−1). Furthermore, we compare our NGHGI database with two other sets of country-based data: those included in the UNFCCC GHG data interface, and those based on forest resources data reported by countries to the Food and Agriculture Organization of the United Nations (FAO) and used as inputs into estimates of GHG emissions in FAOSTAT. The first dataset, once gap filled as in our study, results in a net global LULUCF sink of −5.4 Gt CO2 yr−1. The difference with the NGHGI database is in this case mostly explained by more updated and comprehensive data in our compilation for non-Annex I countries. The FAOSTAT GHG dataset instead estimates a net global LULUCF source of +1.1 Gt CO2 yr−1. In this case, most of the difference to our results is due to a much greater forest sink for non-Annex I countries in the NGHGI database than in FAOSTAT. The difference between these datasets can be mostly explained by a more complete coverage in the NGHGI database, including for non-biomass carbon pools and non-forest land uses, and by different underlying data on forest land. The latter reflects the different scopes of the country reporting to FAO, which focuses on area and biomass, and to UNFCCC, which explicitly focuses on carbon fluxes. Bearing in mind the respective strengths and weaknesses, both our NGHGI database and FAO offer a fundamental, yet incomplete, source of information on carbon-related variables for the scientific and policy communities, including under the Global stocktake. Overall, while the quality and quantity of the LULUCF data submitted by countries to the UNFCCC significantly improved in recent years, important gaps still remain. Most developing countries still do not explicitly separate managed vs. unmanaged forest land, a few report implausibly high forest sinks, and several report incomplete estimates. With these limits in mind, the NGHGI database presented here represents the most up-to-date and complete compilation of LULUCF data based on country submissions to UNFCCC. Data from this study are openly available via the Zenodo portal (Grassi et al., 2022), at https://doi.org/10.5281/zenodo.7190601.