Climate change increases the risk of disruptions across European food systems, including the productivity of crops, livestock, and fisheries, as well as the stability of global trade and markets. These risks propagate through food storage, processing, distribution, and ultimately access to safe and nutritious food, with socio-economic factors determining household vulnerability. The risk of climate impacts to European food systems is subject to uncertainties due to the complex interactions between climatic, ecological, and economic drivers, and the potential for simultaneous stressors both within Europe and globally. Strengthening resilience requires integrated adaptation strategies across production, supply chains, trade, and social protection, coupled with governance approaches that address systemic risks, promote sustainability, and ensure equitable access. Current EU policies address many relevant aspects, but lack coherence and ambition. While adaptation options exist for various climate risks, gaps remain in managing climate risks to food processing, transport, retail, and social vulnerability, highlighting the need for coordinated, forward-looking risk management and for developing strategies for transformative adaptation across the food system.

Resilience is a cornerstone of the European Green Deal, essential for ensuring food security, economic stability, and environmental sustainability amid challenges such as climate change, market volatility, and socio-political disruptions. To tackle these challenges, the European Commission developed the EU Food System Sustainability Model (https://datam.jrc.ec.europa.eu/datam/mashup/EU_FOOD_SYSTEM_MONITORING/), a fit for purpose framework that integrates environmental, social, and economic dimensions. With 37 headline and supporting indicators, the framework monitors the transition toward sustainable food systems within planetary boundaries. Resilience is emphasized as a key horizontal thematic area, reflecting the system's capacity to absorb, adapt to, and recover from shocks while maintaining functionality and ensuring long-term sustainability.However, existing frameworks for assessing food system resilience often rely on indicators that fail to fully capture the unique aspects of resilience within the European context and lack integration across key dimensions. To address this limitation, we propose an innovative methodology that integrates the four key aspects of resilience - preparedness, shock resistance, adaptation, and transformation - each linked to specific capacities and vulnerabilities. Indicators are carefully selected, categorized as either capacities (positive trends like crop diversity) or vulnerabilities (negative trends like soil erosion), and scored relative to the EU median. These scores are aggregated and normalized to produce a composite resilience score ranging from 0 to 1, offering a robust metric to evaluate resilience across Member States and the EU27. This score supports targeted strategies and interventions, enabling policymakers to strengthen the adaptability and sustainability of the food system.While the proposed methodology marks significant progress, its full potential relies on addressing critical data gaps, particularly in underrepresented areas of the food supply chain and sustainability criteria. We will discuss the need for improved data availability and greater collaboration among public and private stakeholders at national and regional levels. Addressing these challenges is key for refining the framework and strengthening evidence-based policymaking.

In the context of climate change, high expectations have been put on the agricultural sector for reducing greenhouse gas (GHG) emissions and enhance carbon sequestration. Consequently, a large and growing number of studies have evaluated the efficacy of various agricultural practices for climate change mitigation. However, the scientific evidence is often heterogeneous and frequently contradictory, making it difficult to use to support policy decisions. Meta-analyses synthesise large data sets and have become the gold standard for providing scientific evidence to inform environmental and agricultural policies. However, a growing number of meta-analyses are now available on a specific topic, occasionally with conflicting conclusions, requiring a further level of synthesis to consolidate the findings. We present the results of a systematic review of 693 published meta-analyses on the effect of farming practices on climate change mitigation. After a systematic search and review of the literature, we extracted data assessing the climate impacts of 34 farming practices and 123 comparisons of sub-practices with corresponding control practices, for a wide range of cropping and livestock systems around the world. From this dataset, we selected the farming practices that showed overall significantly positive effects on the reduction of GHG emission and/or on the increase of carbon sequestration. For cropland and grassland, we were able to identify a set of 35 mitigation sub-practices , including cover and catch crops, intercropping, leguminous crops, the use of enhanced efficiency fertilisers, soil amendment with lime and gypsum, different crop residue management techniques, water management practices, different conservation, restoration and management measures in grasslands, conservation and restoration of peatlands and wetlands, the conservation and creation of landscape features, as well as organic farming systems. For livestock, we identified seven effective mitigation practices, including livestock feeding techniques, manure land application techniques, manure storage techniques. A limited number meta-analyses reported the effect of a given practice on more than one GHG or on GHGs coupled with carbon sequestration together, limiting the exploration of interacting effects. The systematic evidence map provides robust and encompassing literature based evidence on farming practices with established positive effect on climate change mitigation to support a wide community of inventory compilers, modellers and policymakers. Our review also identifies farming practices with remaining knowledge gaps and research priorities.

Comprehensive, wall-to-wall, evaluation of cropping systems and therefore crop diversity using Earth Observation (EO) data is becoming reality. Agricultural policies such as the Common Agricultural Policy (CAP) in the European Union (EU) stimulate more diverse crop-mixes. We compute a new EU-wide overview of crop diversification for 2018 using the Shannon diversity index based on 10 m resolution EO crop map. For the EU as a whole, the effective number of species broadly ranges from 2.6 (p10) to 5.3 (p90) with a median of 3.9. The EO-based map allows identifying how crop diversity varies at an informative spatial resolution, e.g. in areas dominated by mono-cropping or with extensive forest cover. Here we compare the crop diversity calculated from top-down EO-data and bottom-up farmers' declarations in the Netherlands using the EO-based map (more than 84 millions 10 m pixels) and 2018 Dutch farmers declarations (more than 323 thousands parcels). After accounting for differences in thematic and spatial detail we show agreement in spatial patterns and calculated crop diversification (R2 = 0.62). Furthermore, we track changes in crop diversity over time using the Dutch farmers' declarations from 2009 to 2021, which suggest a positive response to the 2014 CAP greening policy. Combining the forthcoming Copernicus High Resolution Layers on Crop Types with increasingly available parcel data will enhance spatial targeting of agricultural policies and facilitate the monitoring of policy impact at farm to regional level.

Understanding the spatial patterns and temporal dynamics of nitrogen (N) deposition is a prerequisite to evaluate its ecological impacts on global forests that cover nearly one third of the land area and provide fundamental ecosystem services. Although regional monitoring networks together with independent and short-term studies have provided important insights into the characteristics of N deposition in regional forests, the assessments and mapping of rates, components and seasonality of N deposition have rarely been conducted for global forests. This chapter summarizes the approaches in modeling N deposition to forests, reviews the modeling studies of N deposition to regional and global forested areas, and presents modeling estimates of N deposition for base year 2015 across global forests using the Meteorological Synthesizing Center—West of the European Monitoring and Evaluation Program (EMEP MSC-W) model. Particularly, modeling results are presented to show the seasonality of N deposition on global forests.

In the last two decades, an exponentially growing number of meta-analyses (MAs) synthesize thousands of peer-reviewed studies on the environmental impacts of farming practices (FPs). This paper describes the iMAP-FP evidence library, a comprehensive dataset on the effects of 34 categories of FPs (such as agronomic practices, cropping and livestock systems, land management options and mitigation techniques) on 34 impacts including climate mitigation, soil health, environmental pollution, water use, nutrients cycling, biodiversity, and agricultural productivity. Through systematic screening, 570 MAs published since 2000 were selected and categorized according to the type of FP. We assessed their impacts, the geographic regions covered, and their quality. We extracted 3,811 effects and their statistical significance associated with sustainable FPs (intervention) compared to a control (typically conventional agriculture) across 223 different intervention-control pairs. Our dataset is accompanied with an online free-access library, which includes a catalogue of synthetic reports summarizing the available evidence on each evaluated FP.

Characterizing and quantifying crop diversity ("effective number of crops") across scales is needed to understand a wide range of issues related to resilience of farms and the agricultural sector, the provision of ecosystem services, and ultimately to provide a scientific basis for effective agro-environmental policies. We use a novel European Union (EU) wide satellite-derived product at 10 m spatial resolution to produce datasets of crop diversity across spatial (1-100 km) and administrative scales for the year 2018. We focus on the 27 EU countries and the United Kingdom. We define local crop diversity (alpha-diversity) at the 1 km scale corresponding to large farms or clusters of small-to-medium sized farms. Across countries, the alpha crop diversity ranges from 2.3 to 4.4 with the highest levels achieved by systems dominated by a high number of small farms (less than 10 ha on average). Computed at grid level aggregation, gamma-diversity (the number and area of crops that are grown independently from the precise location, for landscape region, and country levels) increases rapidly from 2.85 at 1 km to 3.86 at 10 km and levels off 4.27 at 100 km. Such diversity levels are higher than that reported for the U.S.A., likely related to differences in farm structure and practices. beta-diversity, the ratio of gamma and alpha diversity, provides a measure of the diversity between agroecosystems and ranges from 1.2 to 2.3 across EU countries. Based on the magnitude and change of gamma-diversity across scales, we classify countries' diversity in four groups with possible consequences for regional to national agro-environmental policy recommendations, in particular the monitoring activities and indicator development of interventions for the implementation of the Common Agricultural Policy (CAP) in the EU. Forthcoming annual high-resolution continental Copernicus crop type maps will facilitate temporal comparisons. Various ecosystem co-variates are to be explored for deeper understanding of the link of crop diversity to agro-ecosystem services.

Contemporary crop production in Europe relies on nitrogen (N) fertilization. Fertilizer prices soared in 2021-2022, and remained at historical high levels in 2023. These high prices invoked an immediate concern on the possible consequences for Europe's food production. In this study, we use a biogeochemical model framework to estimate the impact of reducing mineral N fertilization on crop yields in the European Union (EU). First, crop yields simulated with the biogeochemical DayCent model are evaluated against subnational yield data averaged for 2015-2018 reported by Eurostat and National Statistical Institutes in the EU for soft wheat, barley, grain maize and rapeseed. Then, we simulate three different scenarios where mineral N fertilization across the EU is abruptly reduced by respectively 5, 15 and 25 %, and compare yields to the projected baseline for contemporary conditions (2019-2022). The model evaluation gives r2 values ranging from 0.28 (rapeseed) to 0.61 (soft wheat) and root mean square errors (RMSE) ranging from 0.6 (rapeseed) to 1.95 t ha-1 (maize). The model shows a reduction in yield per crop at the EU level up to 2.1, 6.4 and 11.2 % with the 5, 15 and 25 % reduction scenario, respectively. Different crops show different percentage reduction in yield following a reduction in mineral N fertilization, showing a legacy effect over the years and depending on the availability of organic fertilizer. The strongest relative yield reduction occurs for soft wheat for all three scenarios. Even with 25 % drop in mineral N fertilization, maize yield in the Netherlands, Belgium and Denmark is not significantly reduced, because of the high N surplus and large share of organic fertilization in these countries. This process-based modelling study provides spatially explicit, high resolution information on the response of crop yields to N fertilizer input reductions, helping policy-makers in decision-making on food security and environmentally-friendly food systems.

CONTEXT: Crop models are essential tools for assessing the impact of climate change on national or regional agricultural production. Starting from meteorology, soil and crop management, fertilization and irrigation practices, they predict the yield of specific crop varieties. For long term assessments, climate models are the source of primary information. To make climate model results usable in a specific time frame context, bias adjustment (BA) is required. In fact, climate models tend to deviate from day-to-day values of the physical parameters while conserving the climate variability signal. BA brings the climatic signal to the actual values observed in a specific location and period, and to be representative of a specific period in absolute terms. BA techniques come in different flavours. The broadest categorization is univariate and multivariate methods. Multivariate methods adjust the variables considering possible cross -correlations while univariate methods treat the variables one by one without accounting for possible dependence on one another. OBJECTIVE: The hypothesis tested in this paper is that since crop models require as input climate variables that are in most of the cases cross -correlated, the multi-variate bias adjustment of the latter is likely to improve performance compared to univariate bias adjusted climate model results. METHODS: To verify this hypothesis, 14 BA methods were applied to 9 variables from 8 climate models at 21 locations across Europe and Northern Africa for a period of 5 years. Twelve crop models, from the AgMIP Wheat community, were run using the climate model results. All crop models, except one, were restarted at every growing season. The crop models were also run using the AgMERRA re -analysis. The latter were used as reference to compare the results when using the other climate models treated with the various sets of biasadjustment methods. RESULTS AND CONCLUSIONS: The results show that multivariate BA treatment should be preferred to univariate ones. The error obtained by comparing crop simulation obtained with AgMERRA with those obtained with multivariate bias -adjusted climate prediction is systematically lower. The error reduction varies as a function of the variable, the location, the crop model, and the climate model though the tendency is for smaller errors when multivariate methods are used to treat the latter. The results are attributed to the nature of crop models and the fact that multivariate methods consider more adequately the correlation existing between the meteorological variables. SIGNIFICANCE: The study shows the importance of considering the nature of a model and the selection of input data that best suited to the former. In this case the improvements produced when using multivariate data appears to be significant especially in the light of the variety of crop models used and the similar response obtained and it is therefore recommended.

Abstract. Quantification of land surface-atmosphere fluxes of carbon dioxide (CO2) fluxes and their trends and uncertainties is essential for monitoring progress of the EU27+UK bloc as it strives to meet ambitious targets determined by both international agreements and internal regulation. This study provides a consolidated synthesis of fossil sources (CO2 fossil) and natural sources and sinks over land (CO2 land) using bottom-up (BU) and top-down (TD) approaches for the European Union and United Kingdom (EU27+UK), updating earlier syntheses (Petrescu et al., 2020, 2021b). Given the wide scope of the work and the variety of approaches involved, this study aims to answer essential questions identified in the previous syntheses and understand the differences between datasets, particularly for poorly characterized fluxes from managed ecosystems. The work integrates updated emission inventory data, process-based model results, data-driven sectoral model results, and inverse modeling estimates, extending the previous period 1990–2018 to the year 2020 to the extent possible. BU and TD products are compared with European National Greenhouse Gas Inventories (NGHGIs) reported by Parties including the year 2019 under the United Nations Framework Convention on Climate Change (UNFCCC). The uncertainties of the EU27+UK NGHGI were evaluated using the standard deviation reported by the EU Member States following the guidelines of the Intergovernmental Panel on Climate Change (IPCC) and harmonized by gap-filling procedures. Variation in estimates produced with other methods, such as atmospheric inversion models (TD) or spatially disaggregated inventory datasets (BU), originate from within-model uncertainty related to parameterization as well as structural differences between models. By comparing NGHGIs with other approaches, key sources of differences between estimates arise primarily in activities. System boundaries and emission categories create differences in CO2 fossil datasets, while different land use definitions for reporting emissions from Land Use, Land Use Change and Forestry (LULUCF) activities result in differences for CO2 land. The latter has important consequences for atmospheric inversions, leading to inversions reporting stronger sinks in vegetation and soils than are reported by the NGHGI. For CO2 fossil emissions, after harmonizing estimates based on common activities and selecting the most recent year available for all datasets, the UNFCCC NGHGI for the EU27+UK accounts for 3392 ± 49 Tg CO2 yr-1 (926 ± 13 Tg C yr-1), while eight other BU sources report a mean value of 3340 [3238,3401] [25th,75th percentile] Tg CO2 yr-1 (948 [937,961] Tg C yr-1). The sole top-down inversion of fossil emissions currently available accounts for 3800 Tg CO2 yr-1 (1038 Tg C yr-1), a value close to that of the NGHGI, but for which uncertainty estimates are not yet available. For the net CO2 land fluxes, during the most recent five-year period including the NGHGI estimates, the NGHGI accounted for -91 ± 32 Tg C yr-1 while six other BU approaches reported a mean sink of -62 [-117,-49] Tg C yr-1 and a 15-member ensemble of dynamic global vegetation models (DGVMs) reported -69 [-152,-5] Tg C yr-1. The five-year mean of three TD regional ensembles combined with one non-ensemble inversion of -73 Tg C yr-1 has a slightly smaller spread (0th–100th percentile of [-135,45] Tg C yr-1), and was calculated after removing land-atmosphere CO2 fluxes caused by lateral transport of carbon (crops, wood trade and inland waters) resulting in increased agreement with the the NGHGI and bottom-up approaches. Results at the sub-sector level (Forestland, Cropland, Grassland) show generally good agreement between the NGHGI and sub-sector-specific models, but results for a DGVM are mixed. Overall, for both CO2 fossil and net CO2 land fluxes, we find current independent approaches are consistent with the NGHGI at the scale of the EU27+UK. We conclude that CO2 emissions from fossil sources have decreased over the past 30 years in the EU27+UK, while large uncertainties on net uptake of CO2 by the land surface prevent trend identification. In addition, a gap on the order of 1000 Tg C yr-1 between CO2 fossil emissions and net CO2 uptake by the land exists regardless of the type of approach (NGHGI, TD, BU), falling well outside all available estimates of uncertainties. However, uncertainties in top-down approaches to estimate CO2 fossil emissions remain uncharacterized and are likely substantial. The data used to plot the figures are available at https://doi.org/10.5281/zenodo.7365863.

Global reactive nitrogen (N) deposition has more than tripled since 1860 and is expected to remain high due to food production and fossil fuel consumption. Global sulfur emissions have been decreasing worldwide over the last 30 years, but many regions are still experiencing unhealthily high levels of deposition. We update the 2010 global deposition budget for reactive nitrogen and sulfur components with new regional wet deposition measurements from Asia, improving the ensemble results of 11 global chemistry transport models from the second phase of the United Nations Economic Commission for Europe's Task Force on Hemispheric Transport of Air Pollution (HTAP II). The observationally adjusted global N deposition budget is 114.5 TgN, representing a minor increase of 1% from the model-only derived values, and the adjusted global sulfur deposition budget is 88.9 TgS, representing a 6.5% increase from the modeled values, using an interpolation distance of 2.5 degrees. Regionally, deposition adjustments can be up to similar to 73% for nitrogen and 112% for sulfur. Our study demonstrates that a global measurement-model fusion approach can improve N and S deposition model estimates at a regional scale, with sufficient availability of observations; however, in large parts of the world, alternative approaches need to be explored. The analysis presented here represents a step forward toward the World Meteorological Organization's goal of global fusion products for accurately mapping harmful air pollution deposition.

Knowledge of the spatial distribution of the fluxes of greenhouse gases (GHGs) and their temporal variability as well as flux attribution to natural and anthropogenic processes is essential to monitoring the progress in mitigating anthropogenic emissions under the Paris Agreement and to inform its global stocktake. This study provides a consolidated synthesis of CH4 and N2O emissions using bottom-up (BU) and top-down (TD) approaches for the European Union and UK (EU27 + UK) and updates earlier syntheses (Petrescu et al., 2020, 2021). The work integrates updated emission inventory data, process-based model results, data-driven sector model results and inverse modeling estimates, and it extends the previous period of 1990–2017 to 2019. BU and TD products are compared with European national greenhouse gas inventories (NGHGIs) reported by parties under the United Nations Framework Convention on Climate Change (UNFCCC) in 2021. Uncertainties in NGHGIs, as reported to the UNFCCC by the EU and its member states, are also included in the synthesis. Variations in estimates produced with other methods, such as atmospheric inversion models (TD) or spatially disaggregated inventory datasets (BU), arise from diverse sources including within-model uncertainty related to parameterization as well as structural differences between models. By comparing NGHGIs with other approaches, the activities included are a key source of bias between estimates, e.g., anthropogenic and natural fluxes, which in atmospheric inversions are sensitive to the prior geospatial distribution of emissions. For CH4 emissions, over the updated 2015–2019 period, which covers a sufficiently robust number of overlapping estimates, and most importantly the NGHGIs, the anthropogenic BU approaches are directly comparable, accounting for mean emissions of 20.5 Tg CH4 yr−1 (EDGARv6.0, last year 2018) and 18.4 Tg CH4 yr−1 (GAINS, last year 2015), close to the NGHGI estimates of 17.5±2.1 Tg CH4 yr−1. TD inversion estimates give higher emission estimates, as they also detect natural emissions. Over the same period, high-resolution regional TD inversions report a mean emission of 34 Tg CH4 yr−1. Coarser-resolution global-scale TD inversions result in emission estimates of 23 and 24 Tg CH4 yr−1 inferred from GOSAT and surface (SURF) network atmospheric measurements, respectively. The magnitude of natural peatland and mineral soil emissions from the JSBACH–HIMMELI model, natural rivers, lake and reservoir emissions, geological sources, and biomass burning together could account for the gap between NGHGI and inversions and account for 8 Tg CH4 yr−1. For N2O emissions, over the 2015–2019 period, both BU products (EDGARv6.0 and GAINS) report a mean value of anthropogenic emissions of 0.9 Tg N2O yr−1, close to the NGHGI data (0.8±55 % Tg N2O yr−1). Over the same period, the mean of TD global and regional inversions was 1.4 Tg N2O yr−1 (excluding TOMCAT, which reported no data). The TD and BU comparison method defined in this study can be operationalized for future annual updates for the calculation of CH4 and N2O budgets at the national and EU27 + UK scales. Future comparability will be enhanced with further steps involving analysis at finer temporal resolutions and estimation of emissions over intra-annual timescales, which is of great importance for CH4 and N2O, and may help identify sector contributions to divergence between prior and posterior estimates at the annual and/or inter-annual scale. Even if currently comparison between CH4 and N2O inversion estimates and NGHGIs is highly uncertain because of the large spread in the inversion results, TD inversions inferred from atmospheric observations represent the most independent data against which inventory totals can be compared. With anticipated improvements in atmospheric modeling and observations, as well as modeling of natural fluxes, TD inversions may arguably emerge as the most powerful tool for verifying emission inventories for CH4, N2O and other GHGs. The referenced datasets related to figures are visualized at https://doi.org/10.5281/zenodo.7553800 (Petrescu et al., 2023).

This chapter provides some insights on how climate change can affect agricultural commodity markets in the medium to long term. It includes some methodological aspects on how different quantitative tools, ranging from earth system models to farm-level production decisions, can be combined in the analysis of climate change impacts on agricultural markets. The analysis presented here is supported by several agro-economic studies carried out by the Joint Research Centre of the European Commission.

Understanding crop diversity is crucial for resilience in farming, ecosystem services, and effective agro-environmental policies. We utilize a novel EU-wide satellite product (2018, 10 m resolution) to assess crop diversity across different scales. We define local crop diversity ($\alpha$-diversity) at 1 km scale, which in the EU is proportional to the area covered by large farms or clusters of small-to-medium sized farms. We also compute $\gamma$-diversity, covering landscape, regional, and national levels crop diversity. $\beta$-diversity ($\gamma$/$\alpha$) provides a measure of between agroecosystems diversity. National $\alpha$, $\gamma$, and $\beta$ diversity varies greatly ($\alpha$: 2.1-3.9, $\gamma$: 3.5-7.5, $\beta$: 1.22-2.27). EU-wide $\gamma$-diversity increases logarithmically with spatial aggregation (1 km: 2.85, 100 km: 4.27). We categorize EU Member States (MS) into four groups for crop diversification policy recommendations. Compared to the USA, the EU exhibits higher diversity related to differences in farm structure and practices. High local $\alpha$-diversity is only found for MS with small farms (<25 ha), but their presence doesn't always guarantee high local diversity. This study aids CAP implementation in the EU, with potential for annual continental Copernicus crop type maps and ecosystem co-variates exploration for a deeper understanding of agro-ecosystem services.

Intergovernmental Panel on Climate Change (IPCC) assessments are the trusted source of scientific evidence for climate negotiations taking place under the United Nations Framework Convention on Climate Change (UNFCCC), including the first global stocktake under the Paris Agreement that will conclude at COP28 in December 2023. Evidence-based decision-making needs to be informed by up-to-date and timely information on key indicators of the state of the climate system and of the human influence on the global climate system. However, successive IPCC reports are published at intervals of 5–10 years, creating potential for an information gap between report cycles. We follow methods as close as possible to those used in the IPCC Sixth Assessment Report (AR6) Working Group One (WGI) report. We compile monitoring datasets to produce estimates for key climate indicators related to forcing of the climate system: emissions of greenhouse gases and short-lived climate forcers, greenhouse gas concentrations, radiative forcing, surface temperature changes, the Earth's energy imbalance, warming attributed to human activities, the remaining carbon budget, and estimates of global temperature extremes. The purpose of this effort, grounded in an open data, open science approach, is to make annually updated reliable global climate indicators available in the public domain (https://doi.org/10.5281/zenodo.8000192, Smith et al., 2023a). As they are traceable to IPCC report methods, they can be trusted by all parties involved in UNFCCC negotiations and help convey wider understanding of the latest knowledge of the climate system and its direction of travel. The indicators show that human-induced warming reached 1.14 [0.9 to 1.4] ∘C averaged over the 2013–2022 decade and 1.26 [1.0 to 1.6] ∘C in 2022. Over the 2013–2022 period, human-induced warming has been increasing at an unprecedented rate of over 0.2 ∘C per decade. This high rate of warming is caused by a combination of greenhouse gas emissions being at an all-time high of 54 ± 5.3 GtCO2e over the last decade, as well as reductions in the strength of aerosol cooling. Despite this, there is evidence that increases in greenhouse gas emissions have slowed, and depending on societal choices, a continued series of these annual updates over the critical 2020s decade could track a change of direction for human influence on climate.

The Glossary of the Synthesis Report (SYR) of the Sixth Assessment Report (AR6) of the Intergovernmental Panel on Climate Change (IPCC), is an annex of the SYR which provides explanations on relevant terms used in the SYR. The Glossary is consistent with the glossaries of the three reports of the Working Groups of the IPCC during the AR6, and the three AR6 Special Reports.

The Synthesis Report (SYR) is a stand-alone synthesis of the most policy-relevant evidence from the scientific, technical, and socio-economic literature assessed in the Sixth Assessment Report (AR6) of the Intergovernmental Panel on Climate Change (IPCC). The SYR distils and integrates the main findings of the three reports of the Working Groups of the IPCC during the AR6, and the three AR6 Special Reports into a concise document. It consists of a Summary for Policymakers and a longer report.

A summary is not available for this content so a preview has been provided. As you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

Quantification of land surface–atmosphere fluxes of carbon dioxide (CO2) and their trends and uncertainties is essential for monitoring progress of the EU27+UK bloc as it strives to meet ambitious targets determined by both international agreements and internal regulation. This study provides a consolidated synthesis of fossil sources (CO2 fossil) and natural (including formally managed ecosystems) sources and sinks over land (CO2 land) using bottom-up (BU) and top-down (TD) approaches for the European Union and United Kingdom (EU27+UK), updating earlier syntheses (Petrescu et al., 2020, 2021). Given the wide scope of the work and the variety of approaches involved, this study aims to answer essential questions identified in the previous syntheses and understand the differences between datasets, particularly for poorly characterized fluxes from managed and unmanaged ecosystems. The work integrates updated emission inventory data, process-based model results, data-driven categorical model results, and inverse modeling estimates, extending the previous period 1990–2018 to the year 2020 to the extent possible. BU and TD products are compared with the European national greenhouse gas inventory (NGHGI) reported by parties including the year 2019 under the United Nations Framework Convention on Climate Change (UNFCCC). The uncertainties of the EU27+UK NGHGI were evaluated using the standard deviation reported by the EU member states following the guidelines of the Intergovernmental Panel on Climate Change (IPCC) and harmonized by gap-filling procedures. Variation in estimates produced with other methods, such as atmospheric inversion models (TD) or spatially disaggregated inventory datasets (BU), originate from within-model uncertainty related to parameterization as well as structural differences between models. By comparing the NGHGI with other approaches, key sources of differences between estimates arise primarily in activities. System boundaries and emission categories create differences in CO2 fossil datasets, while different land use definitions for reporting emissions from land use, land use change, and forestry (LULUCF) activities result in differences for CO2 land. The latter has important consequences for atmospheric inversions, leading to inversions reporting stronger sinks in vegetation and soils than are reported by the NGHGI. For CO2 fossil emissions, after harmonizing estimates based on common activities and selecting the most recent year available for all datasets, the UNFCCC NGHGI for the EU27+UK accounts for 926 ± 13 Tg C yr−1, while eight other BU sources report a mean value of 948 [937,961] Tg C yr−1 (25th, 75th percentiles). The sole top-down inversion of fossil emissions currently available accounts for 875 Tg C in this same year, a value outside the uncertainty of both the NGHGI and bottom-up ensemble estimates and for which uncertainty estimates are not currently available. For the net CO2 land fluxes, during the most recent 5-year period including the NGHGI estimates, the NGHGI accounted for −91 ± 32 Tg C yr−1, while six other BU approaches reported a mean sink of −62 [-117,-49] Tg C yr−1, and a 15-member ensemble of dynamic global vegetation models (DGVMs) reported −69 [-152,-5] Tg C yr−1. The 5-year mean of three TD regional ensembles combined with one non-ensemble inversion of −73 Tg C yr−1 has a slightly smaller spread (0th–100th percentiles of [-135,+45] Tg C yr−1), and it was calculated after removing net land–atmosphere CO2 fluxes caused by lateral transport of carbon (crop trade, wood trade, river transport, and net uptake from inland water bodies), resulting in increased agreement with the NGHGI and bottom-up approaches. Results at the category level (Forest Land, Cropland, Grassland) generally show good agreement between the NGHGI and category-specific models, but results for DGVMs are mixed. Overall, for both CO2 fossil and net CO2 land fluxes, we find that current independent approaches are consistent with the NGHGI at the scale of the EU27+UK. We conclude that CO2 emissions from fossil sources have decreased over the past 30 years in the EU27+UK, while land fluxes are relatively stable: positive or negative trends larger (smaller) than 0.07 (−0.61) Tg C yr−2 can be ruled out for the NGHGI. In addition, a gap on the order of 1000 Tg C yr−1 between CO2 fossil emissions and net CO2 uptake by the land exists regardless of the type of approach (NGHGI, TD, BU), falling well outside all available estimates of uncertainties. However, uncertainties in top-down approaches to estimate CO2 fossil emissions remain uncharacterized and are likely substantial, in addition to known uncertainties in top-down estimates of the land fluxes. The data used to plot the figures are available at https://doi.org/10.5281/zenodo.8148461 (McGrath et al., 2023).