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In the Amazon, deforestation and climate change lead to increased vulnerability to forest degradation, threatening its existing carbon stocks and its capacity as a carbon sink. We use satellite L-Band Vegetation Optical Depth (L-VOD) data that provide an integrated (top-down) estimate of biomass carbon to track changes over 2011–2019. Because the spatial resolution of L-VOD is coarse (0.25°), it allows limited attribution of the observed changes. We therefore combined high-resolution annual maps of forest cover and disturbances with biomass maps to model carbon losses (bottom- up) from deforestation and degradation, and gains from regrowing secondary forests. We show an increase of deforestation and associated degradation losses since 2012 which greatly outweigh secondary forest gains. Degradation accounted for 40% of gross losses. After an increase in 2011, old-growth forests show a net loss of above-ground carbon between 2012 and 2019. The sum of component carbon fluxes in our model is consistent with the total biomass change from L-VOD of 1.3 Pg C over 2012-2019. Across nine Amazon countries, we found that while Brazil contains the majority of biomass stocks (64%), its losses from disturbances were disproportionately high (79% of gross losses). Our multi-source analysis provides a pessimistic assessment of the Amazon carbon balance and highlights the urgent need to stop the recent rise of deforestation and degradation, particularly in the Brazilian Amazon. [ABSTRACT FROM AUTHOR]

Uncovering the mechanisms that lead to Amazon forest resilience variations is crucial to predict the impact of future climatic and anthropogenic disturbances. Here, we apply a previously used empirical resilience metrics, lag‐1 month temporal autocorrelation (TAC), to vegetation optical depth data in C‐band (a good proxy of the whole canopy water content) in order to explore how forest resilience variations are impacted by human disturbances and environmental drivers in the Brazilian Amazon. We found that human disturbances significantly increase the risk of critical transitions, and that the median TAC value is ~2.4 times higher in human‐disturbed forests than that in intact forests, suggesting a much lower resilience in disturbed forests. Additionally, human‐disturbed forests are less resilient to land surface heat stress and atmospheric water stress than intact forests. Among human‐disturbed forests, forests with a more closed and thicker canopy structure, which is linked to a higher forest cover and a lower disturbance fraction, are comparably more resilient. These results further emphasize the urgent need to limit deforestation and degradation through policy intervention to maintain the resilience of the Amazon rainforests. [ABSTRACT FROM AUTHOR]

Strategies to mitigate climate change by reducing deforestation and forest degradation (e.g. REDD+) require country- or region-specific information on temporal changes in forest carbon (C) pools to develop accurate emission factors. The soil C pool is one of the most important C reservoirs, but is rarely included in national forest reference emission levels due to a lack of data. Here, we present the soil organic C ( SOC) dynamics along 20 years of forest-to-pasture conversion in two subregions with different management practices during pasture establishment in the Colombian Amazon: high-grazing intensity ( HG) and low-grazing intensity ( LG) subregions. We determined the pattern of SOC change resulting from the conversion from forest (C3 plants) to pasture (C4 plants) by analysing total SOC stocks and the natural abundance of the stable isotopes 13C along two 20-year chronosequences identified in each subregion. We also analysed soil N stocks and the natural abundance of 15N during pasture establishment. In general, total SOC stocks at 30 cm depth in the forest were similar for both subregions, with an average of 47.1 ± 1.8 Mg C ha−1 in HG and 48.7 ± 3.1 Mg C ha−1 in LG. However, 20 years after forest-to-pasture conversion SOC in HG decreased by 20%, whereas in LG SOC increased by 41%. This net SOC decrease in HG was due to a larger reduction in C3-derived input and to a comparatively smaller increase in C4-derived C input. In LG both C3- and C4-derived C input increased along the chronosequence. N stocks were generally similar in both subregions and soil N stock changes during pasture establishment were correlated with SOC changes. These results emphasize the importance of management practices involving low-grazing intensity in cattle activities to preserve SOC stocks and to reduce C emissions after land-cover change from forest to pasture in the Colombian Amazon. [ABSTRACT FROM AUTHOR]

Dead wood, composed of coarse standing and fallen woody debris (CWD), is an important carbon (C) pool in tropical forests and its accounting is needed to reduce uncertainties within the strategies to mitigate climate change by reducing deforestation and forest degradation (REDD+). To date, information on CWD stocks in tropical forests is scarce and effects of land-cover conversion and land management practices on CWD dynamics remain largely unexplored. Here we present estimates on CWD stocks in primary forests in the Colombian Amazon and their dynamics along 20 years of forest-to-pasture conversion in two sub-regions with different management practices during pasture establishment: high-grazing intensity (HG) and low-grazing intensity (LG) sub-regions. Two 20-year-old chronosequences describing the forest-to-pasture conversion were identified in both sub-regions. The line-intersect and the plot-based methods were used to estimate fallen and standing CWD stocks, respectively. Total necromass in primary forests was similar between both sub-regions (35.6 ± 5.8 Mg ha −1 in HG and 37.0 ± 7.4 Mg ha −1 in LG). An increase of ∼124% in CWD stocks followed by a reduction to values close to those at the intact forests were registered after slash-and-burn practice was implemented in both sub-regions during the first two years of forest-to-pasture conversion. Implementation of machinery after using fire in HG pastures led to a reduction of 82% in CWD stocks during the second and fifth years of pasture establishment, compared to a decrease of 41% during the same period in LG where mechanization is not implemented. Finally, average necromass 20 years after forest-to-pasture conversion decreased to 3.5 ± 1.4 Mg ha −1 in HG and 9.3 ± 3.5 Mg ha −1 in LG, representing a total reduction of between 90% and 75% in each sub-region, respectively. These results highlight the importance of low-grazing intensity management practices during ranching activities in the Colombian Amazon to reduce C emissions associated with land-cover change from forest to pasture. [ABSTRACT FROM AUTHOR]

A major limitation in modeling global ozone (O 3) vegetation damage has long been the reliance on empirical O 3 sensitivity parameters derived from a limited number of species and applied at the level of plant functional types (PFTs), which ignore the large interspecific variations within the same PFT. Here, we present a major advance in large-scale assessments of O 3 plant injury by linking the trait leaf mass per area (LMA) and plant O 3 sensitivity in a broad and global perspective. Application of the new approach and a global LMA map in a dynamic global vegetation model reasonably represents the observed interspecific responses to O 3 with a unified sensitivity parameter for all plant species. Simulations suggest a contemporary global mean reduction of 4.8 % in gross primary productivity by O 3 , with a range of 1.1 %–12.6 % for varied PFTs. Hotspots with damage >10% are found in agricultural areas in the eastern US, western Europe, eastern China, and India, accompanied by moderate to high levels of surface O 3. Furthermore, we simulate the distribution of plant sensitivity to O 3 , which is highly linked with the inherent leaf trait trade-off strategies of plants, revealing high risks for fast-growing species with low LMA, such as crops, grasses, and deciduous trees. [ABSTRACT FROM AUTHOR]

The purpose of this study was to evaluate 10 process-based terrestrial biosphere models that were used for the IPCC fifth Assessment Report. The simulated gross primary productivity ( GPP) is compared with flux-tower-based estimates by Jung et al. [ Journal of Geophysical Research 116 (2011) G00J07] ( JU11). The net primary productivity ( NPP) apparent sensitivity to climate variability and atmospheric CO2 trends is diagnosed from each model output, using statistical functions. The temperature sensitivity is compared against ecosystem field warming experiments results. The CO2 sensitivity of NPP is compared to the results from four Free-Air CO2 Enrichment ( FACE) experiments. The simulated global net biome productivity ( NBP) is compared with the residual land sink ( RLS) of the global carbon budget from Friedlingstein et al. [ Nature Geoscience 3 (2010) 811] ( FR10). We found that models produce a higher GPP (133 ± 15 Pg C yr−1) than JU11 (118 ± 6 Pg C yr−1). In response to rising atmospheric CO2 concentration, modeled NPP increases on average by 16% (5-20%) per 100 ppm, a slightly larger apparent sensitivity of NPP to CO2 than that measured at the FACE experiment locations (13% per 100 ppm). Global NBP differs markedly among individual models, although the mean value of 2.0 ± 0.8 Pg C yr−1 is remarkably close to the mean value of RLS (2.1 ± 1.2 Pg C yr−1). The interannual variability in modeled NBP is significantly correlated with that of RLS for the period 1980-2009. Both model-to-model and interannual variation in model GPP is larger than that in model NBP due to the strong coupling causing a positive correlation between ecosystem respiration and GPP in the model. The average linear regression slope of global NBP vs. temperature across the 10 models is −3.0 ± 1.5 Pg C yr−1 °C−1, within the uncertainty of what derived from RLS (−3.9 ± 1.1 Pg C yr−1 °C−1). However, 9 of 10 models overestimate the regression slope of NBP vs. precipitation, compared with the slope of the observed RLS vs. precipitation. With most models lacking processes that control GPP and NBP in addition to CO2 and climate, the agreement between modeled and observation-based GPP and NBP can be fortuitous. Carbon-nitrogen interactions (only separable in one model) significantly influence the simulated response of carbon cycle to temperature and atmospheric CO2 concentration, suggesting that nutrients limitations should be included in the next generation of terrestrial biosphere models. [ABSTRACT FROM AUTHOR]

We model future changes in land biogeochemistry and biogeography across East Africa. East Africa is one of few tropical regions where general circulation model (GCM) future climate projections exhibit a robust response of strong future warming and general annual-mean rainfall increases. Eighteen future climate projections from nine GCMs participating in the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment were used as input to the LPJ dynamic global vegetation model (DGVM), which predicted vegetation patterns and carbon storage in agreement with satellite observations and forest inventory data under the present-day climate. All simulations showed future increases in tropical woody vegetation over the region at the expense of grasslands. Regional increases in net primary productivity (NPP) (18–36%) and total carbon storage (3–13%) by 2080–2099 compared with the present-day were common to all simulations. Despite decreases in soil carbon after 2050, seven out of nine simulations continued to show an annual net land carbon sink in the final decades of the 21st century because vegetation biomass continued to increase. The seasonal cycles of rainfall and soil moisture show future increases in wet season rainfall across the GCMs with generally little change in dry season rainfall. Based on the simulated present-day climate and its future trends, the GCMs can be grouped into four broad categories. Overall, our model results suggest that East Africa, a populous and economically poor region, is likely to experience some ecosystem service benefits through increased precipitation, river runoff and fresh water availability. Resulting enhancements in NPP may lead to improved crop yields in some areas. Our results stand in partial contradiction to other studies that suggest possible negative consequences for agriculture, biodiversity and other ecosystem services caused by temperature increases. [ABSTRACT FROM AUTHOR]

We assess the role of changing natural (volcanic, aerosol, insolation) and anthropogenic (CO2 emissions, land cover) forcings on the global climate system over the last 150 years using an earth system model of intermediate complexity, CLIMBER-2. We apply several datasets of historical land-use reconstructions: the cropland dataset by (R&F), the HYDE land cover dataset of , and the land-use emissions data from . Comparison between the simulated and observed temporal evolution of atmospheric CO2 and δ13CO2 are used to evaluate these datasets. To check model uncertainty, CLIMBER-2 was coupled to the more complex Lund–Potsdam–Jena (LPJ) dynamic global vegetation model. In simulation with R&F dataset, biogeophysical mechanisms due to land cover changes tend to decrease global air temperature by 0.26°C, while biogeochemical mechanisms act to warm the climate by 0.18°C. The net effect on climate is negligible on a global scale, but pronounced over the land in the temperate and high northern latitudes where a cooling due to an increase in land surface albedo offsets the warming due to land-use CO2 emissions. Land cover changes led to estimated increases in atmospheric CO2 of between 22 and 43 ppmv. Over the entire period 1800–2000, simulated δ13CO2 with HYDE compares most favourably with ice core during 1850–1950 and Cape Grim data, indicating preference of earlier land clearance in HYDE over R&F. In relative terms, land cover forcing corresponds to 25–49% of the observed growth in atmospheric CO2. This contribution declined from 36–60% during 1850–1960 to 4–35% during 1960–2000. CLIMBER-2-LPJ simulates the land cover contribution to atmospheric CO2 growth to decrease from 68% during 1900–1960 to 12% in the 1980s. Overall, our simulations show a decline in the relative role of land cover changes for atmospheric CO2 increase during the last 150 years. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

A major limitation in modeling global ozone (O3) vegetation damage has long been the reliance on empirical O3 sensitivity parameters derived from a limited number of species and applied at the level of plant functional types (PFTs), which ignore the large interspecific variations within the same PFT. Here, we present a major advance in large-scale assessments of O3 plant injury by linking the trait leaf mass per area (LMA) and plant O3 sensitivity in a broad and global perspective. Application of the new approach and a global LMA map in a dynamic global vegetation model reasonably represents the observed interspecific responses to O3 with a unified sensitivity parameter for all plant species. Simulations suggest a contemporary global mean reduction of 4.8% in gross primary productivity by O3, with a range of 1.1%-12.6% for varied PFTs. Hotspots with damages > 10% are found in agricultural areas in the eastern U.S., western Europe, eastern China, and India, accompanied by moderate to high levels of surface O3. Furthermore, we simulate the distribution of plant sensitivity to O3, which is highly linked with the inherent leaf trait trade-off strategies of plants, revealing high risks for fast-growing species with low LMA, such as crops, grasses and deciduous trees. [ABSTRACT FROM AUTHOR]

Surface ozone (O3) is the primary air pollutant threatening global vegetation. It typically reduces photosynthetic rate and stomatal conductance, leading to changes in carbon, water, and energy cycles, vegetation structure and composition, and climate. Several parameterization schemes have been developed to integrate the photosynthetic and stomatal responses to O3 exposure in regional and global process-based models to simulate time- and space-varying O3 plant damage and its cascading dynamic influence. However, these schemes are calibrated based on limited observations and often fail to reproduce the response relationships in observations, impeding accurate assessments of the role of O3 plant damage in the Earth system. This study proposes a new parameterization scheme to utilize the extensive observations from O3 fumigation experiments to inform large-scale modeling. It is built on 4210 paired data points of photosynthetic and stomatal responses compiled from peer-reviewed literature, over six times larger than those employed in earlier schemes. Functions of phytotoxic O3 dose (POD) are found to accurately reproduce the statistically significant linear or nonlinear relationships observed between POD and either relative leaf photosynthetic rate or relative stomatal conductance for needleleaf trees, broadleaf trees, shrubs, grasses, and crops. These eliminate the practice in earlier schemes of setting response functions as constants and applying the response function from one vegetation type to another. It outperforms the old scheme in the Community Land Model (CLM) which skillfully reproduces the observed response for crop photosynthetic rate only. The nonlinear response functions we developed depict decreasing plant sensitivity with increases in POD, enabling models to implicitly capture the variability in plant ozone tolerance and the shift among plant species for both intra- and inter-PFT within a vegetation type observed in the real world. Then, the new scheme is incorporated into the Community Earth System Model version 2.2 (CESM2.2), specifically its land component CLM5, to quantify the global impacts of present-day O3 plant damage by comparing the simulations with and without O3 plant damage. Results show that O3 exposure reduces the global leaf photosynthetic rate by 8.5 % and stomatal conductance by 7.4 %, around half the estimates using the old scheme. Furthermore, the new scheme improves global GPP simulations, decreasing RMSE by 11.1 % relative to simulations without O3 plant damage and by 11.7 % compared against the old scheme. These results underscore the importance of including O3 plant damage in large-scale process-based models and the effectiveness of the new scheme in global assessing and projecting the role of O3 plant damage in the Earth system. [ABSTRACT FROM AUTHOR]

The reduction of CO 2 emissions and the enhancement of CO 2 removals related to land use are considered essential for future pathways towards net-zero emissions and mitigating climate change. With the growing pressure under global climate treaties, country-level land-use CO 2 flux data are becoming increasingly important. So far, country-level estimates are mainly available through official country reports, such as the greenhouse gas inventories reported to the United Nations Framework Convention on Climate Change (UNFCCC). Recently, different modelling approaches, namely dynamic global vegetation models (DGVMs) and bookkeeping models, have moved to higher spatial resolutions, which makes it possible to obtain model-based country-level estimates that are globally consistent in their methodology. To progress towards a largely independent assessment of country reports using models, we analyse the robustness of country-level CO 2 flux estimates from different modelling approaches in the period 1950–2021 and compare them with estimates from country reports. Our results highlight the general ability of modelling approaches to estimate land-use CO 2 fluxes at the country level and at higher spatial resolution. Modelled land-use CO 2 flux estimates generally agree well, but the investigation of multiple DGVMs and bookkeeping models reveals that the robustness of their estimates strongly varies across countries, and substantial uncertainties remain, even for top emitters. Similarly, modelled land-use CO 2 flux estimates and country-report-based estimates agree reasonably well in many countries once their differing definitions are accounted for, although differences remain in some other countries. A separate analysis of CO 2 emissions and removals from land use using bookkeeping models also shows that historical peaks in net fluxes stem from emission peaks in most countries, whereas the long-term trends are more connected to removal dynamics. The ratio of the net flux to the sum of CO 2 emissions and removals from land use (the net-to-gross flux ratio) underlines the spatio-temporal heterogeneity in the drivers of net land-use CO 2 flux trends. In many tropical regions, net-to-gross flux ratios of about 50 % are due to much larger emissions than removals; in many temperate countries, ratios close to zero show that emissions and removals largely offset each other. Considering only the net flux thus potentially masks large emissions and removals and the different timescales upon which they act, particularly if averaged over countries or larger regions, highlighting the need for future studies to focus more on the gross fluxes. Data from this study are openly available via the Zenodo portal at 10.5281/zenodo.8144174. [ABSTRACT FROM AUTHOR]

Tropospheric ozone (O3) is a global air pollutant that adversely affects plant growth. Whereas the impacts of O3 have previously been examined for some tropical commodity crops, no information is available for the pantropical crop, banana (Musa spp.). To address this, we exposed Australia's major banana cultivar, Williams, to a range of [O3] in open top chambers. In addition, we examined 46 diverse Musa lines growing in a common garden for variation in three traits that are hypothesised to shape responses to O3: (1) leaf mass per area; (2) intrinsic water use efficiency; and (3) total antioxidant capacity. We show that O3 exposure had a significant effect on the biomass of cv. Williams, with significant reductions in both pseudostem and sucker biomass with increasing [O3]. This was accompanied by a significant increase in total antioxidant capacity and phenolic concentrations in older, but not younger, leaves, indicating the importance of cumulative O3 exposure. Using the observed trait diversity, we projected O3 tolerance among the 46 Musa lines growing in the common garden. Of these, cv. Williams ranked as one of the most O3-tolerant cultivars. This suggests that other genetic lines could be even more susceptible, with implications for banana production and food security throughout the tropics. Tropospheric ozone (O3) is a global air pollutant that adversely affects plant growth and productivity. Whereas impacts of O3 have previously been examined in some commodity crops, no information is currently available for the pantropical staple crop, banana (Musa spp.). In this study, we have demonstrated the scale and possible prevalence of O3 susceptibility in bananas, with implications for production and food security throughout the tropics. [ABSTRACT FROM AUTHOR]

Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate is critical to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe and synthesize data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (EFOS) are based on energy statistics and cement production data, while emissions from land-use change (ELUC), mainly deforestation, are based on land-use and land-use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly, and its growth rate (GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) is estimated with global ocean biogeochemistry models and observation-based f CO2 products. The terrestrial CO2 sink (SLAND) is estimated with dynamic global vegetation models. Additional lines of evidence on land and ocean sinks are provided by atmospheric inversions, atmospheric oxygen measurements, and Earth system models. The resulting carbon budget imbalance (BIM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and incomplete understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the year 2022, EFOS increased by 0.9 % relative to 2021, with fossil emissions at 9.9±0.5 Gt C yr -1 (10.2±0.5 Gt C yr -1 when the cement carbonation sink is not included), and ELUC was 1.2±0.7 Gt C yr -1 , for a total anthropogenic CO2 emission (including the cement carbonation sink) of 11.1±0.8 Gt C yr -1 (40.7±3.2 Gt CO2 yr -1). Also, for 2022, GATM was 4.6±0.2 Gt C yr -1 (2.18±0.1 ppm yr -1 ; ppm denotes parts per million), SOCEAN was 2.8±0.4 Gt C yr -1 , and SLAND was 3.8±0.8 Gt C yr -1 , with a BIM of -0.1 Gt C yr -1 (i.e. total estimated sources marginally too low or sinks marginally too high). The global atmospheric CO2 concentration averaged over 2022 reached 417.1±0.1 ppm. Preliminary data for 2023 suggest an increase in EFOS relative to 2022 of +1.1 % (0.0 % to 2.1 %) globally and atmospheric CO2 concentration reaching 419.3 ppm, 51 % above the pre-industrial level (around 278 ppm in 1750). Overall, the mean of and trend in the components of the global carbon budget are consistently estimated over the period 1959–2022, with a near-zero overall budget imbalance, although discrepancies of up to around 1 Gt C yr -1 persist for the representation of annual to semi-decadal variability in CO2 fluxes. Comparison of estimates from multiple approaches and observations shows the following: (1) a persistent large uncertainty in the estimate of land-use changes emissions, (2) a low agreement between the different methods on the magnitude of the land CO2 flux in the northern extra-tropics, and (3) a discrepancy between the different methods on the strength of the ocean sink over the last decade. This living-data update documents changes in methods and data sets applied to this most recent global carbon budget as well as evolving community understanding of the global carbon cycle. The data presented in this work are available at 10.18160/GCP-2023 (Friedlingstein et al., 2023). [ABSTRACT FROM AUTHOR]

Background: The 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. Results: Between 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. Conclusions: By 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. [ABSTRACT FROM AUTHOR]

Tropospheric ozone (O 3) is the third most important anthropogenic greenhouse gas. O 3 is detrimental to plant productivity, and it has a significant impact on crop yield. Currently, the Joint UK Land Environment Simulator (JULES) land surface model includes a representation of global crops (JULES-crop) but does not have crop-specific O 3 damage parameters and applies default C3 grass O 3 parameters for soybean that underestimate O 3 damage. Physiological parameters for O 3 damage in soybean in JULES-crop were calibrated against leaf gas-exchange measurements from the Soybean Free Air Concentration Enrichment (SoyFACE) with O 3 experiment in Illinois, USA. Other plant parameters were calibrated using an extensive array of soybean observations such as crop height and leaf carbon and meteorological data from FLUXNET sites near Mead, Nebraska, USA. The yield, aboveground carbon, and leaf area index (LAI) of soybean from the SoyFACE experiment were used to evaluate the newly calibrated parameters. The result shows good performance for yield, with the modelled yield being within the spread of the SoyFACE observations. Although JULES-crop is able to reproduce observed LAI seasonality, its magnitude is underestimated. The newly calibrated version of JULES will be applied regionally and globally in future JULES simulations. This study helps to build a state-of-the-art impact assessment model and contribute to a more complete understanding of the impacts of climate change on food production. [ABSTRACT FROM AUTHOR]

Tropospheric ozone (O3) is the third most important anthropogenic greenhouse gas. O3 is detrimental to plant productivity, and it has a significant impact on crop yield. Currently, the Joint UK Land Environment Simulator (JULES) land surface model includes a representation of global crops (JULES-crop), but does not have crop-specific O3 damage parameters, and applies default C3 grass O3 parameters for soybean that underestimates O3 damage. Physiological parameters for O3 damage in soybean in JULES-crop were calibrated against leaf gas-exchange measurements from the Soybean Free-Air-Concentration-Enrichment (SoyFACE) with O3 experiment in Illinois, USA. Other plant parameters were calibrated using an extensive array of soybean observations such as crop height, leaf carbon, etc. and meteorological data from FLUXNET sites near Mead, Nebraska, USA. The yield, aboveground carbon and leaf area index (LAI) of soybean from the SoyFACE experiment were used to evaluate the newly calibrated parameters. The result shows good performance for yield, with the modelled yield being within the spread of the SoyFACE observations. Although JULES-crop is able to reproduce observed LAI seasonality, its magnitude is underestimated. The newly calibrated version of JULES will be applied regionally and globally in future JULES simulations. This study helps to build a state-of-the-art impact assessment model and contribute to a more complete understanding of the impacts of climate change on food production. [ABSTRACT FROM AUTHOR]

Robust estimates of CO2 budget, CO2 exchanged between the atmosphere and terrestrial biosphere, are necessary to better understand the role of the terrestrial biosphere in mitigating anthropogenic CO2 emissions. Over the past decade, this field of research has advanced through understanding of the differences and similarities of two fundamentally different approaches: "top‐down" atmospheric inversions and "bottom‐up" biosphere models. Since the first studies were undertaken, these approaches have shown an increasing level of agreement, but disagreements in some regions still persist, in part because they do not estimate the same quantity of atmosphere–biosphere CO2 exchange. Here, we conducted a thorough comparison of CO2 budgets at multiple scales and from multiple methods to assess the current state of the science in estimating CO2 budgets. Our set of atmospheric inversions and biosphere models, which were adjusted for a consistent flux definition, showed a high level of agreement for global and hemispheric CO2 budgets in the 2000s. Regionally, improved agreement in CO2 budgets was notable for North America and Southeast Asia. However, large gaps between the two methods remained in East Asia and South America. In other regions, Europe, boreal Asia, Africa, South Asia, and Oceania, it was difficult to determine whether those regions act as a net sink or source because of the large spread in estimates from atmospheric inversions. These results highlight two research directions to improve the robustness of CO2 budgets: (a) to increase representation of processes in biosphere models that could contribute to fill the budget gaps, such as forest regrowth and forest degradation; and (b) to reduce sink–source compensation between regions (dipoles) in atmospheric inversion so that their estimates become more comparable. Advancements on both research areas will increase the level of agreement between the top‐down and bottom‐up approaches and yield more robust knowledge of regional CO2 budgets. [ABSTRACT FROM AUTHOR]

Terrestrial carbon (C) sequestration is limited by nitrogen (N), an empirically established constraint that could intensify under CO 2 fertilization and future global change. The terrestrial C sink is estimated to currently sequester approximately a third of annual anthropogenic CO 2 emissions based on an ensemble of terrestrial biosphere models, which have been evaluated in their ability to reproduce observations of the C, water, and energy cycles. However, their ability to reproduce observations of N cycling and thus the regulation of terrestrial C sequestration by N have been largely unexplored. Here, we evaluate an ensemble of terrestrial biosphere models with coupled C–N cycling and their performance at simulating N cycling, outlining a framework for evaluating N cycling that can be applied across terrestrial biosphere models. We find that models exhibit significant variability across N pools and fluxes, simulating different magnitudes and trends over the historical period, despite their ability to generally reproduce the historical terrestrial C sink. Furthermore, there are no significant correlations between model performance in simulating N cycling and model performance in simulating C cycling, nor are there significant differences in model performance between models with different representations of fundamental N cycling processes. This suggests that the underlying N processes that regulate terrestrial C sequestration operate differently across models and appear to be disconnected from C cycling. Models tend to overestimate tropical biological N fixation, vegetation C : N ratio, and soil C : N ratio but underestimate temperate biological N fixation relative to observations. However, there is significant uncertainty associated with measurements of N cycling processes given their scarcity (especially relative to those of C cycling processes) and their high spatiotemporal variability. Overall, our results suggest that terrestrial biosphere models that represent coupled C–N cycling could be overestimating C storage per unit N, which could lead to biases in projections of the future terrestrial C sink under CO 2 fertilization and future global change (let alone those without a representation of N cycling). More extensive observations of N cycling processes and comparisons against experimental manipulations are crucial to evaluate N cycling and its impact on C cycling and guide its development in terrestrial biosphere models. [ABSTRACT FROM AUTHOR]

Tropical forests play a pivotal role in regulating the global carbon cycle. However, the response of these forests to changes in absorbed solar energy and water supply under the changing climate is highly uncertain. Three‐year (2018–2021) spaceborne high‐resolution measurements of solar‐induced chlorophyll fluorescence (SIF) from the TROPOspheric Monitoring Instrument (TROPOMI) provide a new opportunity to study the response of gross primary production (GPP) and more broadly tropical forest carbon dynamics to differences in climate. SIF has been shown to be a good proxy for GPP on monthly and regional scales. Combining tropical climate reanalysis records and other contemporary satellite products, we find that on the seasonal timescale, the dependence of GPP on climate variables is highly heterogeneous. Following the principal component analyses and correlation comparisons, two regimes are identified: water limited and energy limited. GPP variations over tropical Africa are more correlated with water‐related factors such as vapor pressure deficit (VPD) and soil moisture, while in tropical Southeast Asia, GPP is more correlated with energy‐related factors such as photosynthetically active radiation (PAR) and surface temperature. Amazonia is itself heterogeneous: with an energy‐limited regime in the north and water‐limited regime in the south. The correlations of GPP with climate variables are supported by other observation‐based products, such as Orbiting Carbon Observatory‐2 (OCO2) SIF and FluxSat GPP. In each tropical continent, the coupling between SIF and VPD increases with the mean VPD. Even on the interannual timescale, the correlation of GPP with VPD is still discernable, but the sensitivity is smaller than the intra‐annual correlation. By and large, the dynamic global vegetation models in the TRENDY v8 project do not capture the high GPP seasonal sensitivity to VPD in dry tropics. The complex interactions between carbon and water cycles in the tropics illustrated in this study and the poor representation of this coupling in the current suite of vegetation models suggest that projections of future changes in carbon dynamics based on these models may not be robust. [ABSTRACT FROM AUTHOR]

Evaluating the response of the land carbon sink to the anomalies in temperature and drought imposed by El Niño events provides insights into the present-day carbon cycle and its climate-driven variability. It is also a necessary step to build confidence in terrestrial ecosystems models' response to the warming and drying stresses expected in the future over many continents, and particularly in the tropics. Here we present an in-depth analysis of the response of the terrestrial carbon cycle to the 2015/2016 El Niño that imposed extreme warming and dry conditions in the tropics and other sensitive regions. First, we provide a synthesis of the spatiotemporal evolution of anomalies in net land--atmosphere CO2 fluxes estimated by two in situ measurements based on atmospheric inversions and 16 land-surface models (LSMs) from TRENDYv6. Simulated changes in ecosystem productivity, decomposition rates and fire emissions are also investigated. Inversions and LSMs generally agree on the decrease and subsequent recovery of the land sink in response to the onset, peak and demise of El Niño conditions and point to the decreased strength of the land carbon sink: by 0.4-0.7 PgC yr-1 (inversions) and by 1.0 PgC yr-1 (LSMs) during 2015/2016. LSM simulations indicate that a decrease in productivity, rather than increase in respiration, dominated the net biome productivity anomalies in response to ENSO throughout the tropics, mainly associated with prolonged drought conditions. This article is part of a discussion meeting issue 'The impact of the 2015/2016 El Niño on the terrestrial tropical carbon cycle: patterns, mechanisms and implications'. [ABSTRACT FROM AUTHOR]

Summary: Climate warming is causing compositional changes in Andean tropical montane forests (TMFs). These shifts are hypothesised to result from differential responses to warming of cold‐ and warm‐affiliated species, with the former experiencing mortality and the latter migrating upslope. The thermal acclimation potential of Andean TMFs remains unknown.Along a 2000 m Andean altitudinal gradient, we planted individuals of cold‐ and warm‐affiliated species (under common soil and irrigation), exposing them to the hot and cold extremes of their thermal niches, respectively. We measured the response of net photosynthesis (Anet), photosynthetic capacity and leaf dark respiration (Rdark) to warming/cooling, 5 months after planting.In all species, Anet and photosynthetic capacity at 25°C were highest when growing at growth temperatures (Tg) closest to their thermal means, declining with warming and cooling in cold‐affiliated and warm‐affiliated species, respectively. When expressed at Tg, photosynthetic capacity and Rdark remained unchanged in cold‐affiliated species, but the latter decreased in warm‐affiliated counterparts. Rdark at 25°C increased with temperature in all species, but remained unchanged when expressed at Tg.Both species groups acclimated to temperature, but only warm‐affiliated species decreased Rdark to photosynthetic capacity ratio at Tg as temperature increased. This could confer them a competitive advantage under future warming. [ABSTRACT FROM AUTHOR]

Significant land greening since the 1980s has been detected through satellite observation, forest inventory, and Earth system modeling. However, whether and to what extent global land greening enhances ecosystem carbon stock remains uncertain. Here, using 40 global models, we first detected a positive correlation between the terrestrial ecosystem carbon stock and leaf area index (LAI) over time. Then, we diagnose the source of uncertainty of simulated the sensitivities of ecosystem carbon stock to LAI based on a traceability analysis. We found that the sensitivity of gross primary productivity (GPP) to LAI is the largest contributor to the model uncertainty in more than 60% of the vegetated grids. Using the ensemble of four long‐term global data sets of GPP and three satellite LAI products from 1982 to 2014, we provided an emergent constraint on the ecosystem carbon stock increase as 0.75 ± 0.46 kg C m−2 per unit LAI over global land areas. Furthermore, the biome‐based results reveal that the tropical forest regions have the highest inter‐model variation and model bias. Overall, this study identifies the uncertainty source and provides constrained estimates of the greening effect on ecosystem carbon stock at the global scale. Plain Language Summary: In the last few decades, we can see that there has been more green land from looking at satellite images, checking forest records, and using computer models. However, we do not know if having more green land is helping the ecosystem store more carbon. This study investigated the greening effect on ecosystem carbon stock among the 40 global models, but this effect varied between models. Therefore, we diagnosed the simulated source of uncertainty based on the traceability analysis framework. Based on satellite‐derived data sets, we further constrained the greening effect on ecosystem carbon stock at the global scale. Land greening enhances ecosystem carbon stock, although this increase is highly heterogeneous on a global scale. Key Points: A globally enhanced ecosystem C stock (ECS) by land greening has been detected since the 1980s from 40 global modelsThe sensitivity of gross primary productivity to leaf area index (LAI) is the largest contributor to the modeled greening effect on ECS uncertaintyThis study estimated the effect of global greening on ECS with the emergent constraint method [ABSTRACT FROM AUTHOR]

The Australian continent contributes substantially to the year-to-year variability of the global terrestrial carbon dioxide (CO2) sink. However, the scarcity of in situ observations in remote areas prevents the deciphering of processes that force the CO2 flux variability. In this study, by examining atmospheric CO2 measurements from satellites in the period 2009–2018, we find recurrent end-of-dry-season CO2 pulses over the Australian continent. These pulses largely control the year-to-year variability of Australia’s CO2 balance. They cause two to three times larger seasonal variations compared with previous top-down inversions and bottom-up estimates. The pulses occur shortly after the onset of rainfall and are driven by enhanced soil respiration preceding photosynthetic uptake in Australia’s semiarid regions. The suggested continental-scale relevance of soil-rewetting processes has substantial implications for our understanding and modeling of global climate–carbon cycle feedbacks. [ABSTRACT FROM AUTHOR]

Satellite XCO2 retrievals could improve the estimates of surface carbon fluxes, but it remains unknown on what scales these estimates are robust. Here, we use the time‐dependent Bayesian synthesis top‐down method and prior net ecosystem exchanges (NEEs) from 12 terrestrial biosphere models (TBMs) to infer the monthly carbon fluxes of 51 land regions with constraints by GOSAT XCO2 retrievals. We find that the uncertainty (standard deviation of 12 TBMs) reduction rates (uncertainty reduction rate (URR)) decrease significantly at decreasing spatial scales. On the continental‐scale, the mean URR is about 57%, and the annual and seasonal cycle estimates of NEE are rather robust. The evaluation shows that the posterior CO2 concentrations are significantly improved at the continental scale. Our study suggests that the GOSAT XCO2 can only promise a robust continental‐scale NEE estimate, and improving the XCO2 accuracy is an effective way to achieve robust estimates on smaller scales under current spatial coverage. Plain Language Summary: Remotesensing‐based CO2 measurement can improve the estimates of surface carbon fluxes due to its relatively well global coverage, but it remains unknown on what spatial scales the satellite observation could provide a robust estimate. Here, net ecosystem exchanges (NEEs) from 12 terrestrial biosphere models (TBMs) of 51 land regions for the period of 2011–2014 are constrained using GOSAT XCO2 retrievals, and the uncertainty (standard deviation of 12 TBMs) reduction rates (uncertainty reduction rate (URR)) at different spatial scales are analyzed. We find that (a) from the whole globe to the mean of 51 regions, the URR decreases from 95% to 16%. (b) On the continental‐scale, the mean URR is about 57%, and the annual NEEs in Asia, N. America, Europe, S. America, Africa, and Australia are estimated to be −2.07 ± 0.25, −0.85 ± 0.09, −0.76 ± 0.21, −0.36 ± 0.17, −0.36 ± 0.30, and −0.15 ± 0.12 PgC yr−1, respectively. Our study suggests that the GOSAT XCO2 can only promise a robust continental‐scale NEE estimate, and improving the XCO2 accuracy is an effective way to achieve robust estimates on smaller scales under current satellite observing capacity. Key Points: Terrestrial carbon sinks estimated based on GOSAT XCO2 and net ecosystem exchanges (NEE) from 12 terrestrial biosphere models using atmospheric inverse methodThe uncertainty reduction rates decrease significantly at decreasing spatial scalesThe GOSAT XCO2 can only promise a robust continental‐scale NEE estimate [ABSTRACT FROM AUTHOR]

Vegetation greening has been widely occurring on the Chinese Loess Plateau, and the contributions of human land-use management have been well-understood. However, the influences of climatic change and CO2 fertilization on reported vegetation variations remain difficult to determine. Therefore, we quantified the impacts of multiple factors on vegetation changes for the Chinese Loess Plateau from 2000 to 2019 by integrating satellite-based leaf area index (LAI) and simulated LAI from dynamic global vegetation models. More than 96% of the vegetated areas of the Loess Plateau exhibited greening trends, with an annually averaged satellite-based LAI rate of 0.037 ± 0.006 m2 m−2 a−1 (P < 0.01). Human land-use management and environmental change have jointly accelerated vegetation growth, explaining 54% and 46% of the overall greening trend, respectively. CO2 fertilization and climate change explain 55% and 45% of the greening trend due to environmental change, respectively; solar radiation and precipitation were the main driving factors for climate-induced vegetation greenness (P < 0.05). Spatially, the eastern part of the Loess Plateau was dominated by CO2 fertilization, while the western part was mainly affected by climate change. Furthermore, solar radiation was the key limiting factor affecting LAI variations in the relatively humid area, while precipitation was the major influencing factor in relatively arid areas. This study highlights the important roles that climate change and CO2 fertilization have played in vegetation greenness in recent decades of the Loess Plateau, despite strong influences of anthropogenic footprint. [ABSTRACT FROM AUTHOR]

As the focus of climate policy shifts from pledges to implementation, there is a growing need to track progress on climate change mitigation at the country level, particularly for the land-use sector. Despite new tools and models providing unprecedented monitoring opportunities, striking differences remain in estimations of anthropogenic land-use CO 2 fluxes between, on the one hand, the national greenhouse gas inventories (NGHGIs) used to assess compliance with national climate targets under the Paris Agreement and, on the other hand, the Global Carbon Budget and Intergovernmental Panel on Climate Change (IPCC) assessment reports, both based on global bookkeeping models (BMs). Recent studies have shown that these differences are mainly due to inconsistent definitions of anthropogenic CO 2 fluxes in managed forests. Countries assume larger areas of forest to be managed than BMs do, due to a broader definition of managed land in NGHGIs. Additionally, the fraction of the land sink caused by indirect effects of human-induced environmental change (e.g. fertilisation effect on vegetation growth due to increased atmospheric CO 2 concentration) on managed lands is treated as non-anthropogenic by BMs but as anthropogenic in most NGHGIs. We implement an approach that adds the CO 2 sink caused by environmental change in countries' managed forests (estimated by 16 dynamic global vegetation models, DGVMs) to the land-use fluxes from three BMs. This sum is conceptually more comparable to NGHGIs and is thus expected to be quantitatively more similar. 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 regions, countries and land categories (i.e. forest land, deforestation, organic soils, other land uses). Our results confirm a large difference (6.7 GtCO 2 yr -1) in global land-use CO 2 fluxes between the ensemble mean of the BMs, which estimate a source of 4.8 GtCO 2 yr -1 for the period 2000–2020, and NGHGIs, which estimate a sink of -1.9 GtCO 2 yr -1 in the same period. Most of the gap is found on forest land (3.5 GtCO 2 yr -1), with differences also for deforestation (2.4 GtCO 2 yr -1), for fluxes from other land uses (1.0 GtCO 2 yr -1) and to a lesser extent for fluxes from organic soils (0.2 GtCO 2 yr -1). By adding the DGVM ensemble mean sink arising from environmental change in managed forests (-6.4 GtCO 2 yr -1) to BM estimates, the gap between BMs and NGHGIs becomes substantially smaller both globally (residual gap: 0.3 GtCO 2 yr -1) and in most regions and countries. However, some discrepancies remain and deserve further investigation. For example, the BMs generally provide higher emissions from deforestation than NGHGIs and, when adjusted with the sink in managed forests estimated by DGVMs, yield a sink that is often greater than NGHGIs. In summary, this study provides a blueprint for harmonising the estimations of anthropogenic land-use fluxes, allowing for detailed comparisons between global models and national inventories at global, regional and country levels. This is crucial to increase confidence in land-use emissions estimates, support investments in land-based mitigation strategies and assess the countries' collective progress under the Global Stocktake of the Paris Agreement. Data from this study are openly available online via the Zenodo portal (Grassi et al., 2023) at 10.5281/zenodo.7650360. [ABSTRACT FROM AUTHOR]

Plot-based monitoring has yielded much information on the taxonomic diversity and carbon (C) storage in tropical lowland forests of the Amazon basin. This has resulted in an improved understanding of the relationship between lowland forest biomass dynamics and global change drivers, such as climate change and atmospheric CO2 concentration. Much less attention has been paid to the mountain ecosystems of South America that comprise montane forests and alpine vegetation (páramo, puna, high Andean grasslands, wetlands, and alpine heath). This vegetation complex provides a variety of ecosystem services and forms a natural laboratory along various physiographic, geological and evolutionary history/biogeography, and land use history gradients. Here, we review existing empirical understanding and model-based approaches to quantify the contribution of mountain ecosystems to ecosystem service provision in the rapidly changing socioecological setting of the South American mountains. The objective of this paper is to outline a broad road map for the implementation of mountain vegetation into dynamic global vegetation models (DGVM) for use in Earth System Models (ESM), based on our current understanding of their structure and function and of their responsiveness to global change drivers. We also identify treeline processes, critical in mountain ecosystems, as key missing elements in DGVMs/ESMs, and thus explore in addition a treeline model. Stocktaking of the availability of empirical data was undertaken from eight research sites along the Andes and in south-eastern Brazil. Out of eight sites, two (one each in Venezuela and Brazil) had some climate, ecological and ecophysiological data potentially suitable to parametrise a DGVM. Tree biomass data were available for six sites. A preliminary assessment of the Joint UK Land Environment Simulator (JULES) DGVM was made to identify gaps in available data and their impacts on model parametrisation and calibration. Additionally, the potential climate-determined elevation of the treeline was modelled to check the DGVM for its ability to identify the transition between the montane forest and alpine vegetation. Outcomes of the evaluation of the JULES land surface model identified the following key processes in montane forests: temperature-related decrease in net primary production, respiration, and allocation to above-ground biomass and increase in soil C stocks with elevation. There was a variable agreement between simulated biomass and those derived from field measurements via allometric equations. We identified major gaps between data availability and the needs for process-based modelling of South American mountain vegetation and its dynamics in DGVMs. To bridge this gap, we propose a transdisciplinary network, composed of members of the theoretical/modelling and empirical scientific communities, to study the natural dynamics of mountain ecosystems and their responses to global change drivers locally, regionally and at the continental scale, within a social-ecological system framework. The work presented here forms the basis for the design of data collection from field measurements and instrumental monitoring stations to parametrise and verify DGVMs. The network is designed to collaborate with and complement existing long-term research initiatives in the region and will adopt existing standard field protocols. Complementary protocols will ensure compatibility between field data collection and data needed for process-based and empirical models. [ABSTRACT FROM AUTHOR]

Most land surface models (LSMs), i.e. the land components of Earth system models (ESMs), include representation of nitrogen (N) limitation on ecosystem productivity. However, only a few of these models have incorporated phosphorus (P) cycling. In tropical ecosystems, this is likely to be important as N tends to be abundant, whereas the availability of rock-derived elements, such as P, can be very low. Thus, without a representation of P cycling, tropical forest response in areas such as Amazonia to rising atmospheric CO 2 conditions remain highly uncertain. In this study, we introduced P dynamics and its interactions with the N and carbon (C) cycles into the Joint UK Land Environment Simulator (JULES). The new model (JULES-CNP) includes the representation of P stocks in vegetation and soil pools, as well as key processes controlling fluxes between these pools. We develop and evaluate JULES-CNP using in situ data collected at a low-fertility site in the central Amazon, with a soil P content representative of 60 % of soils across the Amazon basin, to parameterize, calibrate, and evaluate JULES-CNP. Novel soil and plant P pool observations are used for parameterization and calibration, and the model is evaluated against C fluxes and stocks and those soil P pools not used for parameterization or calibration. We then evaluate the model at additional P-limited test sites across the Amazon and in Panama and Hawaii, showing a significant improvement over the C- and CN-only versions of the model. The model is then applied under elevated CO 2 (600 ppm) at our study site in the central Amazon to quantify the impact of P limitation on CO 2 fertilization. We compare our results against the current state-of-the-art CNP models using the same methodology that was used in the AmazonFACE model intercomparison study. The model is able to reproduce the observed plant and soil P pools and fluxes used for evaluation under ambient CO 2. We estimate P to limit net primary productivity (NPP) by 24 % under current CO 2 and by 46 % under elevated CO 2. Under elevated CO 2 , biomass in simulations accounting for CNP increase by 10 % relative to contemporary CO 2 conditions, although it is 5 % lower compared to CN- and C-only simulations. Our results highlight the potential for high P limitation and therefore lower CO 2 fertilization capacity in the Amazon rainforest with low-fertility soils. [ABSTRACT FROM AUTHOR]

The 2019 extreme positive Indian Ocean dipole drove climate extremes over Indian Ocean rim countries with unclear carbon‐cycle responses. We investigated its impact on net biome productivity (NBP) and its constituent fluxes, using the Global Carbon Assimilation System (GCASv2) product, process‐based model simulations from TRENDYv9, and satellite‐based gross primary productivity (GPP). By distinguishing two separate regions, the India‐Africa and Asia‐Pacific, GCASv2 indicated enhanced terrestrial carbon uptake of 0.23 ± 0.20 PgC and release of 0.38 ± 0.15 PgC, respectively, during September–December (SOND) 2019. These NBP anomalies had comparable magnitudes to those following the 2015 extreme El Niño which, however, caused the consistent carbon release in both regions. The TRENDYv9 model ensemble confirmed these NBP responses, albeit with smaller magnitudes. These regional NBP anomalies were related to soil moisture variations with a dominant role of GPP. Understanding the impact of IOD provides new insights into mechanisms driving interannual variations in regional carbon cycling. Plain Language Summary: The extreme Indian Ocean Dipole (IOD) can drive climate extremes, such as floods, heatwaves, droughts, and wildfires, over the Indian Ocean rim countries. However, responses of regional terrestrial carbon cycling to IOD remained unclear. We used the net biome productivity (NBP) from an atmospheric inversion and multiple terrestrial biosphere models to demonstrate an enhanced terrestrial carbon uptake and release over the India‐Africa and Asia‐Pacific regions, respectively, during the extreme positive IOD (September–December) in 2019. These IOD‐induced regional NBP anomalies showed comparable magnitudes but different patterns to those following the 2015 extreme El Niño. Along with the more frequent extreme IOD under future greenhouse warming, IOD will be an important mechanism driving interannual variations in regional carbon cycling. Key Points: The 2019 extreme positive Indian Ocean Dipole caused the enhanced land carbon uptake over India‐Africa and release over Asia‐Pacific during September–DecemberThese regional net biome productivity (NBP) anomalies were closely related to soil moisture variations with a dominant role of gross primary productivityThese Indian Ocean Dipole‐induced regional NBP anomalies showed comparable magnitudes but different patterns to those following the 2015 extreme El Niño [ABSTRACT FROM AUTHOR]

Accurate assessment of anthropogenic carbon dioxide (CO 2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate is critical to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe and synthesize data sets and methodologies to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO 2 emissions (EFOS) are based on energy statistics and cement production data, while emissions from land-use change (ELUC), mainly deforestation, are based on land use and land-use change data and bookkeeping models. Atmospheric CO 2 concentration is measured directly, and its growth rate (GATM) is computed from the annual changes in concentration. The ocean CO 2 sink (SOCEAN) is estimated with global ocean biogeochemistry models and observation-based data products. The terrestrial CO 2 sink (SLAND) is estimated with dynamic global vegetation models. The resulting carbon budget imbalance (BIM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ± 1 σ. For the year 2021, EFOS increased by 5.1 % relative to 2020, with fossil emissions at 10.1 ± 0.5 GtC yr -1 (9.9 ± 0.5 GtC yr -1 when the cement carbonation sink is included), and ELUC was 1.1 ± 0.7 GtC yr -1 , for a total anthropogenic CO 2 emission (including the cement carbonation sink) of 10.9 ± 0.8 GtC yr -1 (40.0 ± 2.9 GtCO 2). Also, for 2021, GATM was 5.2 ± 0.2 GtC yr -1 (2.5 ± 0.1 ppm yr -1), SOCEAN was 2.9 ± 0.4 GtC yr -1 , and SLAND was 3.5 ± 0.9 GtC yr -1 , with a BIM of - 0.6 GtC yr -1 (i.e. the total estimated sources were too low or sinks were too high). The global atmospheric CO 2 concentration averaged over 2021 reached 414.71 ± 0.1 ppm. Preliminary data for 2022 suggest an increase in EFOS relative to 2021 of + 1.0 % (0.1 % to 1.9 %) globally and atmospheric CO 2 concentration reaching 417.2 ppm, more than 50 % above pre-industrial levels (around 278 ppm). Overall, the mean and trend in the components of the global carbon budget are consistently estimated over the period 1959–2021, but discrepancies of up to 1 GtC yr -1 persist for the representation of annual to semi-decadal variability in CO 2 fluxes. Comparison of estimates from multiple approaches and observations shows (1) a persistent large uncertainty in the estimate of land-use change emissions, (2) a low agreement between the different methods on the magnitude of the land CO 2 flux in the northern extratropics, and (3) a discrepancy between the different methods on the strength of the ocean sink over the last decade. This living data update documents changes in the methods and data sets used in this new global carbon budget and the progress in understanding of the global carbon cycle compared with previous publications of this data set. The data presented in this work are available at 10.18160/GCP-2022 (Friedlingstein et al., 2022b). [ABSTRACT FROM AUTHOR]

Recent wildfire outbreaks around the world have prompted concern that climate change is increasing fire incidence, threatening human livelihood and biodiversity, and perpetuating climate change. Here, we review current understanding of the impacts of climate change on fire weather (weather conditions conducive to the ignition and spread of wildfires) and the consequences for regional fire activity as mediated by a range of other bioclimatic factors (including vegetation biogeography, productivity and lightning) and human factors (including ignition, suppression, and land use). Through supplemental analyses, we present a stocktake of regional trends in fire weather and burned area (BA) during recent decades, and we examine how fire activity relates to its bioclimatic and human drivers. Fire weather controls the annual timing of fires in most world regions and also drives inter‐annual variability in BA in the Mediterranean, the Pacific US and high latitude forests. Increases in the frequency and extremity of fire weather have been globally pervasive due to climate change during 1979–2019, meaning that landscapes are primed to burn more frequently. Correspondingly, increases in BA of ∼50% or higher have been seen in some extratropical forest ecoregions including in the Pacific US and high‐latitude forests during 2001–2019, though interannual variability remains large in these regions. Nonetheless, other bioclimatic and human factors can override the relationship between BA and fire weather. For example, BA in savannahs relates more strongly to patterns of fuel production or to the fragmentation of naturally fire‐prone landscapes by agriculture. Similarly, BA trends in tropical forests relate more strongly to deforestation rates and forest degradation than to changing fire weather. Overall, BA has reduced by 27% globally in the past two decades, due in large part to a decline in BA in African savannahs. According to climate models, the prevalence and extremity of fire weather has already emerged beyond its pre‐industrial variability in the Mediterranean due to climate change, and emergence will become increasingly widespread at additional levels of warming. Moreover, several of the major wildfires experienced in recent years, including the Australian bushfires of 2019/2020, have occurred amidst fire weather conditions that were considerably more likely due to climate change. Current fire models incompletely reproduce the observed spatial patterns of BA based on their existing representations of the relationships between fire and its bioclimatic and human controls, and historical trends in BA also vary considerably across models. Advances in the observation of fire and understanding of its controlling factors are supporting the addition or optimization of a range of processes in models. Overall, climate change is exerting a pervasive upwards pressure on fire globally by increasing the frequency and intensity of fire weather, and this upwards pressure will escalate with each increment of global warming. Improvements to fire models and a better understanding of the interactions between climate, climate extremes, humans and fire are required to predict future fire activity and to mitigate against its consequences. Plain Language Summary: In this review, with supplemental data analyses, we focus on the global and regional impacts of climate change on the frequency and intensity of fire weather (conditions conducive to fire ignition and spread) and the consequences for fire activity. We find that significant increases in fire weather have occurred in most world regions during recent decades due to climate change. Corresponding increases in the area burned by fires have been seen in some regions, most notably in mesic forests, however, in many regions fire is controlled by a range of other bioclimatic and human factors whose influences mediate or override those of fire weather. Weather conditions affecting vegetation growth and the build‐up of fuels, the presence of human ignitions in regions that are not naturally fire‐prone, and the fragmentation of fire‐prone landscapes by agriculture are key examples of factors that can locally or regionally outweigh fire weather as controls on fire activity. Climate models project that fire weather will become increasingly frequent and intense under future warming, and at an increasing rate with each additional increment of warming. The outcomes for fire activity in future will depend on other regionally important factors that control fire ignition and spread. Existing fire models represent the controls on fire incompletely and so they reproduce observed patterns of fire with only limited success. Models also disagree on historical trends, leading to low confidence in their simulations of future fire activity. Various efforts to improve the representation of fire in models are underway and should yield greater capacity to predict the future of fire activity. Key Points: The frequency and severity of fire weather has increased in recent decades and is projected to escalate with each added increment of warmingFire weather is one of the major controls on fire activity, and is the dominant control on variability in burned area (BA) in many mesic forest ecoregionsVarious human and bioclimatic factors also control fire, modulating the relationship between BA and fire weather in many regions [ABSTRACT FROM AUTHOR]

In the circumpolar north (45–90°N), permafrost plays an important role in vegetation and carbon (C) dynamics. Permafrost thawing has been accelerated by the warming climate and exerts a positive feedback to climate through increasing soil C release to the atmosphere. To evaluate the influence of permafrost on C dynamics, changes in soil temperature profiles should be considered in global C models. This study incorporates a sophisticated soil thermal model (STM) into a dynamic global vegetation model (LPJ-DGVM) to improve simulations of changes in soil temperature profiles from the ground surface to 3 m depth, and its impacts on C pools and fluxes during the 20th and 21st centuries. With cooler simulated soil temperatures during the summer, LPJ-STM estimates ~ 0.4 Pg C yr − 1 lower present-day heterotrophic respiration but ~ 0.5 Pg C yr − 1 higher net primary production than the original LPJ model resulting in an additional 0.8 to 1.0 Pg C yr − 1 being sequestered in circumpolar ecosystems. Under a suite of projected warming scenarios, we show that the increasing active layer thickness results in the mobilization of permafrost C, which contributes to a more rapid increase in heterotrophic respiration in LPJ-STM compared to the stand-alone LPJ model. Except under the extreme warming conditions, increases in plant production due to warming and rising CO 2 , overwhelm the e nhanced ecosystem respiration so that both boreal forest and arctic tundra ecosystems remain a net C sink over the 21st century. This study highlights the importance of considering changes in the soil thermal regime when quantifying the C budget in the circumpolar north. [ABSTRACT FROM AUTHOR]

Fossil fuel combustion, land use change and other human activities have increased the atmospheric carbon dioxide (CO2) abundance by about 50% since the beginning of the industrial age. The atmospheric CO2 growth rates would have been much larger if natural sinks in the land biosphere and ocean had not removed over half of this anthropogenic CO2. As these CO2 emissions grew, uptake by the ocean increased in response to increases in atmospheric CO2 partial pressure (pCO2). On land, gross primary production also increased, but the dynamics of other key aspects of the land carbon cycle varied regionally. Over the past three decades, CO2 uptake by intact tropical humid forests declined, but these changes are offset by increased uptake across mid- and high-latitudes. While there have been substantial improvements in our ability to study the carbon cycle, measurement and modeling gaps still limit our understanding of the processes driving its evolution. Continued ship-based observations combined with expanded deployments of autonomous platforms are needed to quantify ocean-atmosphere fluxes and interior ocean carbon storage on policy-relevant spatial and temporal scales. There is also an urgent need for more comprehensive measurements of stocks, fluxes and atmospheric CO2 in humid tropical forests and across the Arctic and boreal regions, which are experiencing rapid change. Here, we review our understanding of the atmosphere, ocean, and land carbon cycles and their interactions, identify emerging measurement and modeling capabilities and gaps and the need for a sustainable, operational framework to ensure a scientific basis for carbon management. [ABSTRACT FROM AUTHOR]

The Global Carbon Project estimates that the terrestrial biosphere has absorbed about one‐third of anthropogenic CO2 emissions during the 1959–2019 period. This sink‐estimate is produced by an ensemble of terrestrial biosphere models and is consistent with the land uptake inferred from the residual of emissions and ocean uptake. The purpose of our study is to understand how well terrestrial biosphere models reproduce the processes that drive the terrestrial carbon sink. One challenge is to decide what level of agreement between model output and observation‐based reference data is adequate considering that reference data are prone to uncertainties. To define such a level of agreement, we compute benchmark scores that quantify the similarity between independently derived reference data sets using multiple statistical metrics. Models are considered to perform well if their model scores reach benchmark scores. Our results show that reference data can differ considerably, causing benchmark scores to be low. Model scores are often of similar magnitude as benchmark scores, implying that model performance is reasonable given how different reference data are. While model performance is encouraging, ample potential for improvements remains, including a reduction in a positive leaf area index bias, improved representations of processes that govern soil organic carbon in high latitudes, and an assessment of causes that drive the inter‐model spread of gross primary productivity in boreal regions and humid tropics. The success of future model development will increasingly depend on our capacity to reduce and account for observational uncertainties. Plain Language Summary: Earth's natural vegetation absorbs about one‐third of CO2 emissions caused by human activities. This value is produced by a group of models rather than through direct observations. Our study assesses how well models reproduce the processes that drive the CO2 exchange between land and atmosphere using a wide range of data sets that are mainly derived from field measurements and satellite images. These reference data sets are prone to errors that are not quantified in a consistent manner. To account for such errors, we first compare different reference data sets against each other. We then compare model output against reference data and assess whether the differences are comparable to the differences among the reference data sets. We conclude that the performance of models is encouraging given how uncertain reference data are, but that ample potential for improvements remains. Key Points: Differences between model and observations are often similar compared to differences between independently derived observation‐based dataWe quantify differences between independently derived observations to disentangle model deficiencies from observational uncertaintiesFuture work should address biases in soil organic carbon, leaf area index, and the large spread of gross primary productivity among models [ABSTRACT FROM AUTHOR]

Accurate assessment of anthropogenic carbon dioxide (CO 2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate is critical to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe and synthesize datasets and methodology to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO 2 emissions (EFOS) are based on energy statistics and cement production data, while emissions from land-use change (ELUC), mainly deforestation, are based on land use and land-use change data and bookkeeping models. Atmospheric CO 2 concentration is measured directly, and its growth rate (GATM) is computed from the annual changes in concentration. The ocean CO 2 sink (SOCEAN) is estimated with global ocean biogeochemistry models and observation-based data products. The terrestrial CO 2 sink (SLAND) is estimated with dynamic global vegetation models. The resulting carbon budget imbalance (BIM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ± 1 σ. For the first time, an approach is shown to reconcile the difference in our ELUC estimate with the one from national greenhouse gas inventories, supporting the assessment of collective countries' climate progress. For the year 2020, EFOS declined by 5.4 % relative to 2019, with fossil emissions at 9.5 ± 0.5 GtC yr -1 (9.3 ± 0.5 GtC yr -1 when the cement carbonation sink is included), and ELUC was 0.9 ± 0.7 GtC yr -1 , for a total anthropogenic CO 2 emission of 10.2 ± 0.8 GtC yr -1 (37.4 ± 2.9 GtCO 2). Also, for 2020, GATM was 5.0 ± 0.2 GtC yr -1 (2.4 ± 0.1 ppm yr -1), SOCEAN was 3.0 ± 0.4 GtC yr -1 , and SLAND was 2.9 ± 1 GtC yr -1 , with a BIM of - 0.8 GtC yr -1. The global atmospheric CO 2 concentration averaged over 2020 reached 412.45 ± 0.1 ppm. Preliminary data for 2021 suggest a rebound in EFOS relative to 2020 of + 4.8 % (4.2 % to 5.4 %) globally. Overall, the mean and trend in the components of the global carbon budget are consistently estimated over the period 1959–2020, but discrepancies of up to 1 GtC yr -1 persist for the representation of annual to semi-decadal variability in CO 2 fluxes. Comparison of estimates from multiple approaches and observations shows (1) a persistent large uncertainty in the estimate of land-use changes emissions, (2) a low agreement between the different methods on the magnitude of the land CO 2 flux in the northern extra-tropics, and (3) a discrepancy between the different methods on the strength of the ocean sink over the last decade. This living data update documents changes in the methods and datasets used in this new global carbon budget and the progress in understanding of the global carbon cycle compared with previous publications of this dataset (Friedlingstein et al., 2020, 2019; Le Quéré et al., 2018b, a, 2016, 2015b, a, 2014, 2013). The data presented in this work are available at 10.18160/gcp-2021 (Friedlingstein et al., 2021). [ABSTRACT FROM AUTHOR]

In support of the global stocktake of the Paris Agreement on climate change, this study presents a comprehensive framework to process the results of an ensemble of atmospheric inversions in order to make their net ecosystem exchange (NEE) carbon dioxide (CO 2) flux suitable for evaluating national greenhouse gas inventories (NGHGIs) submitted by countries to the United Nations Framework Convention on Climate Change (UNFCCC). From inversions we also deduced anthropogenic methane (CH 4) emissions regrouped into fossil and agriculture and waste emissions, as well as anthropogenic nitrous oxide (N 2 O) emissions. To compare inversion results with national reports, we compiled a new global harmonized database of emissions and removals from periodical UNFCCC inventories by Annex I countries, and from sporadic and less detailed emissions reports by non-Annex I countries, given by national communications and biennial update reports. No gap filling was applied. The method to reconcile inversions with inventories is applied to selected large countries covering ∼90 % of the global land carbon uptake for CO 2 and top emitters of CH 4 and N 2 O. Our method uses results from an ensemble of global inversions produced by the Global Carbon Project for the three greenhouse gases, with ancillary data. We examine the role of CO 2 fluxes caused by lateral transfer processes from rivers and from trade in crop and wood products and the role of carbon uptake in unmanaged lands, both not accounted for by NGHGIs. Here we show that, despite a large spread across the inversions, the median of available inversion models points to a larger terrestrial carbon sink than inventories over temperate countries or groups of countries of the Northern Hemisphere like Russia, Canada and the European Union. For CH 4 , we find good consistency between the inversions assimilating only data from the global in situ network and those using satellite CH 4 retrievals and a tendency for inversions to diagnose higher CH 4 emission estimates than reported by NGHGIs. In particular, oil- and gas-extracting countries in central Asia and the Persian Gulf region tend to systematically report lower emissions compared to those estimated by inversions. For N 2 O, inversions tend to produce higher anthropogenic emissions than inventories for tropical countries, even when attempting to consider only managed land emissions. In the inventories of many non-Annex I countries, this can be tentatively attributed to a lack of reporting indirect N 2 O emissions from atmospheric deposition and from leaching to rivers, to the existence of natural sources intertwined with managed lands, or to an underestimation of N 2 O emission factors for direct agricultural soil emissions. Inversions provide insights into seasonal and interannual greenhouse gas fluxes anomalies, e.g., during extreme events such as drought or abnormal fire episodes, whereas inventory methods are established to estimate trends and multi-annual changes. As a much denser sampling of atmospheric CO 2 and CH 4 concentrations by different satellites coordinated into a global constellation is expected in the coming years, the methodology proposed here to compare inversion results with inventory reports (e.g., NGHGIs) could be applied regularly for monitoring the effectiveness of mitigation policy and progress by countries to meet the objective of their pledges. The dataset constructed by this study is publicly available at 10.5281/zenodo.5089799 (Deng et al., 2021). [ABSTRACT FROM AUTHOR]

Understanding historical changes in gross primary productivity (GPP) is essential for better predicting the future global carbon cycle. However, the historical trends of terrestrial GPP, due to the CO 2 fertilization effect, climate, and land-use change, remain largely uncertain. Using long-term satellite-based near-infrared radiance of vegetation (NIRv), a proxy for GPP, and multiple GPP datasets derived from satellite-based products, dynamic global vegetation model (DGVM) simulations, and an upscaled product from eddy covariance (EC) measurements, here we comprehensively investigated their trends and analyzed the causes for any discrepancies during 1982–2015. Although spatial patterns of climatological annual GPP from all products and NIRv are highly correlated (r>0.84), the spatial correlation coefficients of trends between DGVM GPP and NIRv significantly decreased (with the ensemble mean of r=0.49) and even the spatial correlation coefficients of trends between other GPP products and NIRv became negative. By separating the global land into the tropics plus extratropical Southern Hemisphere (Trop + SH) and extratropical Northern Hemisphere (NH), we found that, during 1982–2015, simulated GPP from most of the models showed a stronger increasing trend over Trop + SH than NH. In contrast, the satellite-based GPP products indicated a substantial increase over NH. Mechanistically, model sensitivity experiments indicated that the increase of annual global total GPP was dominated by the CO 2 fertilization effect (83.9 % contribution), however, with the largest uncertainty in magnitude in individual simulations among the three drivers of CO 2 fertilization, climate, and land-use change. Interestingly, the spatial distribution of inter-model spreads of GPP trends resulted mainly from climate and land-use change rather than CO 2 fertilization effect. After 2000, trends from satellite-based GPP products were different from the full time series, suggesting weakened rising trends over NH and even significantly decreasing trends over Trop + SH, while the trends from DGVMs and NIRv kept increasing. The inconsistencies of GPP trends are very likely caused by the contrasting performance between satellite-derived and DGVM simulated vegetation structure parameter (leaf area index, LAI). Therefore, the uncertainty in satellite-based GPP products induced by highly uncertain LAI data in the tropics undermines their roles in assessing the performance of DGVM simulations and understanding the changes of global carbon sinks. The higher consistency between DGVM GPP and NIRv suggests that the trends from a DGVM ensemble might even have better performance than satellite-based GPP products. [ABSTRACT FROM AUTHOR]

Long-term global monitoring of terrestrial gross primary production (GPP) is crucial for assessing ecosystem responses to global climate change. In recent decades, great advances have been made in estimating GPP and many global GPP datasets have been published. These datasets are based on observations from optical remote sensing, are upscaled from in situ measurements, or rely on process-based models. Although these approaches are well established within the scientific community, datasets nevertheless differ significantly. Here, we introduce the new VODCA2GPP dataset, which utilizes microwave remote sensing estimates of vegetation optical depth (VOD) to estimate GPP at the global scale for the period 1988–2020. VODCA2GPP applies a previously developed carbon-sink-driven approach (Teubner et al., 2019, 2021) to estimate GPP from the Vegetation Optical Depth Climate Archive (Moesinger et al., 2020; Zotta et al., 2022​​​​​​​), which merges VOD observations from multiple sensors into one long-running, coherent data record. VODCA2GPP was trained and evaluated against FLUXNET in situ observations of GPP and compared against largely independent state-of-the-art GPP datasets from the Moderate Resolution Imaging Spectroradiometer (MODIS), FLUXCOM, and the TRENDY-v7 process-based model ensemble. The site-level evaluation with FLUXNET GPP indicates an overall robust performance of VODCA2GPP with only a small bias and good temporal agreement. The comparisons with MODIS, FLUXCOM, and TRENDY-v7 show that VODCA2GPP exhibits very similar spatial patterns across all biomes but with a consistent positive bias. In terms of temporal dynamics, a high agreement was found for regions outside the humid tropics, with median correlations around 0.75. Concerning anomalies from the long-term climatology, VODCA2GPP correlates well with MODIS and TRENDY-v7 (Pearson's r 0.53 and 0.61) but less well with FLUXCOM (Pearson's r 0.29). A trend analysis for the period 1988–2019 did not exhibit a significant trend in VODCA2GPP at the global scale but rather suggests regionally different long-term changes in GPP. For the shorter overlapping observation period (2003–2015) of VODCA2GPP, MODIS, and the TRENDY-v7 ensemble, significant increases in global GPP were found. VODCA2GPP can complement existing GPP products and is a valuable dataset for the assessment of large-scale and long-term changes in GPP for global vegetation and carbon cycle studies. The VODCA2GPP dataset is available at the TU Data Repository of TU Wien (10.48436/1k7aj-bdz35, Wild et al., 2021). [ABSTRACT FROM AUTHOR]

The African humid tropical biome constitutes the second largest rainforest region, significantly impacts global carbon cycling and climate, and has undergone major changes in functioning owing to climate and land-use change over the past century. We assess changes and trends in CO2 fluxes from 1901 to 2010 using nine land surface models forced with common driving data, and depict the inter-model variability as the uncertainty in fluxes. The biome is estimated to be a natural (no disturbance) net carbon sink (20.02 kg C m-2 yr-1 or -0.04 Pg C yr-1, p, 0.05)with increasing strength fourfold in the second half of the century. The models were in close agreement on net CO2 flux at the beginning of the century (σ1901 = 0.02 kg C m-2 yr-1), but diverged exponentially throughout the century (σ2010 = 0.03 kg C m-2 yr-1). The increasing uncertainty is due to differences in sensitivity to increasing atmospheric CO2,but not increasing water stress, despite a decrease in precipitation and increase in air temperature. However, the largest uncertainties were associated with the most extreme drought events of the century. These results highlight the need to constrain modelled CO2 fluxes with increasing atmospheric CO2 concentrations and extreme climatic events, as the uncertainties will only amplify in the next century. [ABSTRACT FROM AUTHOR]

Most Land Surface Models (LSMs), the land components of Earth system models (ESMs), include representation of N limitation on ecosystem productivity. However only few of these models have incorporated phosphorus (P) cycling. In tropical ecosystems, this is likely to be particularly important as N tends to be abundant but the availability of rock-derived elements, such as P, can be very low. Thus, without a representation of P cycling, tropical forest response in areas such as Amazonia to rising atmospheric CO2 conditions remains highly uncertain. In this study, we introduced P dynamics and its interactions with the N and carbon (C) cycles into the Joint UK Land Environment Simulator (JULES). The new model (JULES-CNP) includes the representation of P stocks in vegetation and soil pools, as well as key processes controlling fluxes between these pools. We evaluate JULES-CNP at the Amazon nutrient fertilization experiment (AFEX), a low fertility site, representative of about 60 % of Amazon soils. We apply the model under ambient CO2 and elevated CO2. The model is able to reproduce the observed plant and soil P pools and fluxes under ambient CO2. We estimate P to limit net primary productivity (NPP) by 24 % under current CO2 and by 46 % under elevated CO2. Under elevated CO2, biomass in simulations accounting for CNP increase by 10 % relative to at contemporary CO2, although it is 5 % lower compared with CN and C-only simulations. Our results highlight the potential for high P limitation and therefore lower CO2 fertilization capacity in the Amazon forest with low fertility soils. [ABSTRACT FROM AUTHOR]

Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate is critical to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe and synthesize data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (EFOS) are based on energy statistics and cement production data, while emissions from land-use change (ELUC), mainly deforestation, are based on land-use and land-use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly, and its growth rate (GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) is estimated with global ocean biogeochemistry models and observation-based data-products. The terrestrial CO2 sink (SLAND) is estimated with dynamic global vegetation models. The resulting carbon budget imbalance (BIM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the first time, an approach is shown to reconcile the difference in our ELUC estimate with the one from national greenhouse gases inventories, supporting the assessment of collective countries' climate progress. For the year 2020, EFOS declined by 5.4 % relative to 2019, with fossil emissions at 9.5 ± 0.5 GtC yr-1 (9.3 ± 0.5 GtC yr-1 when the cement carbonation sink is included), ELUC was 0.9 ± 0.7 GtC yr-1, for a total anthropogenic CO2 emission of 10.2 ± 0.8 GtC yr-1 (37.4 ± 2.9 GtCO2). Also, for 2020, GATM was 5.0 ± 0.2 GtC yr-1 (2.4 ± 0.1 ppm yr-1), SOCEAN was 3.0 ± 0.4 GtC yr-1 and SLAND was 2.9 ± 1 GtC yr-1, with a BIM of -0.8 GtC yr-1. The global atmospheric CO2 concentration averaged over 2020 reached 412.45 ± 0.1 ppm. Preliminary data for 2021, suggest a rebound in EFOS relative to 2020 of +4.9 % (4.1 % to 5.7 %) globally. Overall, the mean and trend in the components of the global carbon budget are consistently estimated over the period 1959-2020, but discrepancies of up to 1 GtC yr-1 persist for the representation of annual to semi-decadal variability in CO2 fluxes. Comparison of estimates from multiple approaches and observations shows: (1) a persistent large uncertainty in the estimate of land-use changes emissions, (2) a low agreement between the different methods on the magnitude of the land CO2 flux in the northern extra-tropics, and (3) a discrepancy between the different methods on the strength of the ocean sink over the last decade. This living data update documents changes in the methods and data sets used in this new global carbon budget and the progress in understanding of the global carbon cycle compared with previous publications of this data set (Friedlingstein et al., 2020; Friedlingstein et al., 2019; Le Quéré et al., 2018b, 2018a, 2016, 2015b, 2015a, 2014, 2013). The data presented in this work are available at https://doi.org/10.18160/gcp-2021 (Friedlingstein et al., 2021). [ABSTRACT FROM AUTHOR]

Tropospheric ozone (O3) is a global air pollutant that causes billions of dollars in lost plant productivity annually. It is an important anthropogenic greenhouse gas, and as a secondary air pollutant, it is present at high concentrations in rural areas far from industrial sources. It also reduces plant productivity by entering leaves through the stomata, generating other reactive oxygen species and causing oxidative stress, which in turn decreases photosynthesis, plant growth, and biomass accumulation. The deposition of O3 into vegetation through stomata is an important sink for tropospheric O3, but this sink is modified by other aspects of environmental change, including rising atmospheric carbon dioxide concentrations, rising temperature, altered precipitation, and nitrogen availability. We review the atmospheric chemistry governing tropospheric O3 mass balance, the effects of O3 on stomatal conductance and net primary productivity, and implications for agriculture, carbon sequestration, and climate change. [ABSTRACT FROM AUTHOR]

•The large-scale loss of Amazonian rainforest under some future climate scenarios has generally been considered to be driven by increased drying over Amazonia predicted by some general circulation models (GCMs). However, the importance of rainfall relative to other drivers has never been formally examined. •Here, we conducted factorial simulations to ascertain the contributions of four environmental drivers (precipitation, temperature, humidity and CO2) to simulated changes in Amazonian vegetation carbon ( Cveg), in three dynamic global vegetation models (DGVMs) forced with climate data based on HadCM3 for four SRES scenarios. •Increased temperature was found to be more important than precipitation reduction in causing losses of Amazonian Cveg in two DGVMs (Hyland and TRIFFID), and as important as precipitation reduction in a third DGVM (LPJ). Increases in plant respiration, direct declines in photosynthesis and increases in vapour pressure deficit (VPD) all contributed to reduce Cveg under high temperature, but the contribution of each mechanism varied greatly across models. Rising CO2 mitigated much of the climate-driven biomass losses in the models. •Additional work is required to constrain model behaviour with experimental data under conditions of high temperature and drought. Current models may be overly sensitive to long-term elevated temperatures as they do not account for physiological acclimation. [ABSTRACT FROM AUTHOR]

Global environmental changes may be altering the ecology of tropical forests. Long-term monitoring plots have provided much of the evidence for large scale, directional changes in tropical forests, but the results have been controversial. Here we review evidence from six complementary approaches to understanding possible changes: plant physiology experiments, long-term monitoring plots, ecosystem flux techniques, atmospheric measurements, Earth observations, and global-scale vegetation models. Evidence from four of these approaches suggests that large-scale, directional changes are occurring in the ecology of tropical forests, with the other two approaches providing inconclusive results. Collectively, the evidence indicates that both gross and net primary productivity has likely increased over recent decades as have tree growth, recruitment, and mortality rates, and forest biomass These results suggest a profound reorganization of tropical forest ecosystems. We evaluate the most likely drivers of the suite of changes, and suggest increasing resource availability, potentially from rising atmospheric CO2 concentrations, is the most likely cause. [ABSTRACT FROM AUTHOR]

Plant photosynthesis tends to increase with irradiance. However, recent theoretical and observational studies have demonstrated that photosynthesis is also more efficient under diffuse light conditions. Changes in cloud cover or atmospheric aerosol loadings, arising from either volcanic or anthropogenic emissions, alter both the total photosynthetically active radiation reaching the surface and the fraction of this radiation that is diffuse, with uncertain overall effects on global plant productivity and the land carbon sink. Here we estimate the impact of variations in diffuse fraction on the land carbon sink using a global model modified to account for the effects of variations in both direct and diffuse radiation on canopy photosynthesis. We estimate that variations in diffuse fraction, associated largely with the ‘global dimming’ period, enhanced the land carbon sink by approximately one-quarter between 1960 and 1999. However, under a climate mitigation scenario for the twenty-first century in which sulphate aerosols decline before atmospheric CO2 is stabilized, this ‘diffuse-radiation’ fertilization effect declines rapidly to near zero by the end of the twenty-first century. [ABSTRACT FROM AUTHOR]

In 2018 and 2019, central Europe was affected by two consecutive extreme dry and hot summers (DH18 and DH19). The DH18 event had severe impacts on ecosystems and likely affected vegetation activity in the subsequent year, for example through depletion of carbon reserves or damage from drought. Such legacies from drought and heat stress can further increase vegetation susceptibility to additional hazards. Temporally compound extremes such as DH18 and DH19 can, therefore, result in an amplification of impacts due to preconditioning effects of past disturbance legacies. Here, we evaluate how these two consecutive extreme summers impacted ecosystems in central Europe and how the vegetation responses to the first compound event (DH18) modulated the impacts of the second (DH19). To quantify changes in vegetation vulnerability to each compound event, we first train a set of statistical models for the period 2001–2017, which are then used to predict the impacts of DH18 and DH19 on enhanced vegetation index (EVI) anomalies from MODIS. These estimates correspond to expected EVI anomalies in DH18 and DH19 based on past sensitivity to climate. Large departures from the predicted values can indicate changes in vulnerability to dry and hot conditions and be used to identify modulating effects by vegetation activity and composition or other environmental factors on observed impacts. We find two regions in which the impacts of the two compound dry and hot (DH) events were significantly stronger than those expected based on previous climate–vegetation relationships. One region, largely dominated by grasslands and crops, showed much stronger impacts than expected in both DH events due to an amplification of their sensitivity to heat and drought, possibly linked to changing background CO2 and temperature conditions. A second region, dominated by forests and grasslands, showed browning from DH18 to DH19, even though dry and hot conditions were partly alleviated in 2019. This browning trajectory was mainly explained by the preconditioning role of DH18 on the impacts of DH19 due to interannual legacy effects and possibly by increased susceptibility to biotic disturbances, which are also promoted by warm conditions. Dry and hot summers are expected to become more frequent in the coming decades, posing a major threat to the stability of European forests. We show that state-of-the-art process-based models could not represent the decline in response to DH19 because they missed the interannual legacy effects from DH18 impacts. These gaps may result in an overestimation of the resilience and stability of temperate ecosystems in future model projections. [ABSTRACT FROM AUTHOR]

Our study suggests that the global CO2 fertilization effect (CFE) on vegetation photosynthesis has declined during the past four decades. The Comments suggest that the temporal inconsistency in AVHRR data and the attribution method undermine the results’ robustness. Here, we provide additional evidence that these arguments did not affect our finding and that the global decline in CFE is robust. [ABSTRACT FROM AUTHOR]

Understanding historical changes in gross primary productivity (GPP) is essential for better predicting the future global carbon cycle. However, the historical trends of terrestrial GPP, owing to the CO2 fertilization effect, climate, and land-use change, remain largely uncertain. Using long-term satellite-based near-infrared radiance of vegetation (NIRv), a proxy for GPP, and multiple GPP datasets derived from satellite-based products, Dynamic Global Vegetation Model (DGVM) simulations, and machine learning techniques, here we comprehensively investigated their trends and analyzed the causes for any discrepancies during 1982-2015. Although spatial patterns of climatological annual GPP from all products and NIRv are highly correlated (r > 0.84), the spatial correlation coefficients of trends between DGVM GPP and NIRv significantly decreased (with the ensemble mean of r = 0.49) and even the spatial correlation coefficients of trends between other GPP products and NIRv became negative. By separating the global land into the tropics plus extra-tropical southern hemisphere (Trop+SH) and extra-tropical northern hemisphere (NH), we found that, during 1982-2015, simulated GPP from most of the models showed a stronger increasing trend over Trop+SH than NH. In contrast, the satellite-based GPP products indicated a substantial increase over NH. Mechanistically, model sensitivity experiments indicated that the increase of annual GPP was dominated by the CO2 fertilization effect (Global: 83.9 %), albeit a large uncertainty in magnitude among individual simulations. However, the spatial distribution of inter-model spreads of GPP trends resulted mainly from climate and land-use change rather than CO2 fertilization effect. Trends after 2000 were different from the full time-series, showing that satellite-based GPP products suggested weakened rising trends over NH and even significantly decreasing trends over Trop+SH, while the trends from DGVMs kept increasing. The inconsistencies are very likely caused by the contrasting performances between satellite-derived and DGVM simulated vegetation structure parameter (leaf area index, LAI). Therefore, the uncertainty in satellite-based GPP products induced by highly uncertain LAI data in the tropics undermines their roles in assessing the performance of DGVM simulations and understanding the changes of global carbon sinks. [ABSTRACT FROM AUTHOR]