Search

Three-hourly net ecosystem exchange (NEE) is estimated at spatial scales of 0.25∘ over the European continent, based on the pre-operational inverse modelling framework “CarboScope Regional” (CSR) for the years 2006 to 2019. To assess the uncertainty originating from the choice of a priori flux models and observational data, ensembles of inversions were produced using three terrestrial ecosystem flux models, two ocean flux models, and three sets of atmospheric stations. We find that the station set ensemble accounts for 61 % of the total spread of the annually aggregated fluxes over the full domain when varying all these elements, while the biosphere and ocean ensembles resulted in much smaller contributions to the spread of 28 % and 11 %, respectively. These percentages differ over the specific regions of Europe, based on the availability of atmospheric data. For example, the spread of the biosphere ensemble is prone to be larger in regions that are less constrained by CO2 measurements. We investigate the impact of unprecedented increase in temperature and simultaneous reduction in soil water content (SWC) observed in 2018 and 2019 on the carbon cycle. We find that NEE estimates during these 2 years suggest an impact of drought occurrences represented by the reduction in net primary productivity (NPP), which in turn leads to less CO2 uptake across Europe in 2018 and 2019, resulting in anomalies of up to 0.13 and 0.07 PgC yr−1 above the climatological mean, respectively. Annual temperature anomalies also exceeded the climatological mean by 0.46 ∘C in 2018 and by 0.56 ∘C in 2019, while Standardised Precipitation–Evaporation Index (SPEI) anomalies declined to −0.20 and −0.05 SPEI units below the climatological mean in both 2018 and 2019, respectively. Therefore, the biogenic fluxes showed a weaker sink of CO2 in both 2018 and 2019 (−0.22 ± 0.05 and −0.28 ± 0.06 PgC yr−1, respectively) in comparison with the mean −0.36 ± 0.07 PgC yr−1 calculated over the full analysed period (i.e. 14 years). These translate into a continental-wide reduction in the annual sink by 39 % and 22 %, respectively, larger than the typical year-to-year standard deviation of 19 % observed over the full period.

The atmospheric CO2 growth rate (AGR) shows large year-to-year variations, which are mainly driven by land and ocean carbon uptake variations. Recent studies suggested an approximate doubling of the AGR regressed onto tropical mean temperature anomalies (“sensitivity of AGR to tropical mean temperature anomalies”; Wang et al., 2014; Luo et al., 2022), which was attributed to increasing drought in tropical land vegetation areas in a warming climate (Wang et al., 2014). We hypothesise that at least part of this apparent sensitivity change may instead be explained by extratropical areas and by internal climate variability.Here, we study the apparent sensitivity changes of AGR to tropical mean temperature in observations, atmospheric inversions, and a large climate model ensemble of historical simulations. First, we identify the main regional drivers of the apparent sensitivity change, including the ocean and extratropical regions in all datasets. Then, we evaluate whether these sensitivity changes can be attributed to anthropogenic forcing in a large climate model ensemble, or whether they are mostly driven by internal climate variability. Our results show that other regions beyond the land tropics contribute to the change in apparent sensitivity of AGR to tropical mean temperature anomalies in atmospheric inversions and in the period 1960 to 2006. Furthermore, the climate model large ensemble shows that such "doubling sensitivity" events can occur due to internal climate variability only. This points to the importance of distinguishing internal climate variability from forced signals when attributing causes to observed changes in the carbon cycle.Wang, X., Piao, S., Ciais, P. et al. A two-fold increase of carbon cycle sensitivity to tropical temperature variations. Nature 506, 212–215 (2014). https://doi.org/10.1038/nature12915Luo, X., Keenan, T. F. Tropical extreme droughts drive long-term increase in atmospheric CO2 growth rate variability. Nat Commun 13, 1193 (2022). https://doi.org/10.1038/s41467-022-28824-5

Within the Integrated Greenhouse Gas Monitoring System for Germany (ITMS), a national initiative targeted at the provision of estimates of GHG fluxes for Germany, the CarboScope-Regional (CSR) inversion system operated at the MPI-BGC is envisioned as a back-bone and reference system for future developments using the ICON-ART modelling system at the German Weather Service (DWD). It utilizes ICOS atmospheric observationsAdditional focus of the research involving CSR is the evaluation and improvement of vertical transport in atmospheric models, as well as the inclusion of additional data streams (e.g. vertical profile information) to improve both, GHG flux estimates and their uncertainty characterization. In this context, the assessment of mixing heights as represented within CSR against independent information derived from radiosondes over a timespan of more than a decade has revealed problems vertical mixing during wintertime that have the potential to cause biased flux retrievals. First results related to this and other experiments will be presented.

This work was developed on the scope of the national initiative of the Integrated Greenhouse Gas Monitoring System for Germany – ITMS, which focus on reducing and characterizing uncertainties/errors introduced in the full retrieval chain of models, making use of observational data. Determining the magnitude, cause, and agents of carbon fluxes is important to advance our current understanding of the carbon budget and cycle, permitting more accurate predictions of its future behaviour. Sources and sinks of CO2at the Earth's surface can, in principle, be estimated from atmospheric concentrations by inverting atmospheric transport in the atmospheric tracer inversions.Solving for CO2 fluxes in the inversions is highly desirable to better understand the carbon cycle but also to support policies aimed at reducing CO2 emissions. The Jena CarboScope inversion developed based on Bayesian inverse methods is used to obtain data-driven estimates of trace gas exchange, quantifying the large-scale sources and sinks of CO2.To make Jena CarboScope estimates more reliable in understanding sources, sinks and transport of atmospheric CO2from the surface into the troposphere, the reliability of this data product should be evaluated based on independent observations. Quantifying the quality of the inversion estimation by decomposing the inherent uncertainty components is a challenging and key component in product reliability and its use. The overall objective in validating and evaluating uncertainties of the Bayesian data product provided by Jena CarboScope, is to explicitly answer the question: How good is the inversion estimation?The assessment of these products is of special importance for further development and possibly allows for judging the trustworthiness of the inversion outcome.This study explores the potential of CarboScope to reproduce CO2 concentrations recorded during regular flights and aircraft campaigns during the past two decades. The inversion estimations are accomplished by forward runs performed with the inversion having the measurement locations as receptors.This study examines biases and uncertainties in the CarboScope estimations evaluated against flights’ data. It has been found that the CO2 simulated by CarboScope in the forward run (using the transport model TM3 for background concentration and STILT for regional signal) agree reasonably well, into the 10/90th percentile for 3-sigma of the distribution. On the other hand, the inversion exhibit some systematic biases at the edges of the distribution under and overestimating at high and lower mixing ratios, respectively. The CarboScope strength and concerns were enhanced by understanding the differences among observations and the inversion estimation. In comprehensive statistics comparing measurement data from hundreds of flights, we assess the compliance CarboScope`s CO2 estimation. Furthermore, we discuss the estimation and observation mismatches, exploiting the model constraints to reproduce atmospheric transport from the boundary layer to the upper troposphere. Understanding such constraints has the potential to reduce uncertainties of the atmospheric inversion estimates.

Global net biome production (NBP), or net land carbon uptake, has been repeatedly shown to increase during recent decades. However, whether the temporal variability and autocorrelation of NBP has changed during this period remains elusive. Answering this question is particularly relevant given that an increase in both could indicate destabilising C sinks and potentially lead to abrupt changes. We investigated the trends and controls of net land C uptake and its temporal variability and autocorrelation, from 1981 to 2018, using two atmospheric inversion models, the amplitude of the seasonal cycle of atmospheric CO2 derived from nine monitoring stations distributed across the Pacific Ocean, and 12 dynamic global vegetation models. Spatially, we found that plant biodiversity presented a convex parabolic relationship with NBP and its temporal variability at the global scale while nitrogen deposition generally increased annual NBP. We also found that annual NBP and its interdecadal temporal variability globally increased, but temporal autocorrelation decreased. Regions characterized by increasingly variable NBP were usually with warmer and with increasingly variable temperatures, and lower and weaker trends in NBP compared to those where NBP variability did not increase, where NBP became stronger. Annual temperature increase and its increasing temporal variability were the most important drivers of declining NBP and increasingly its variability. Our results show that increasing regional NBP variability can be mostly attributed to climate change.

We present a comparison of atmospheric transport models that simulate carbonyl sulfide (COS). This is part II of the ongoing Atmospheric Transport Model (ATM) Inter-comparison Project (TransCom–COS). Differently from part I, we focus on seven model intercomparison by transporting two recent COS inversions of NOAA surface data within TM5-4DVAR and LMDz models. The main goals of TransCom-COS part II are (a) to compare the COS simulations using the two sets of optimized fluxes with simulations that use a control scenario (part I) and (b) to evaluate the simulated tropospheric COS abundance with aircraft-based observations from various sources. The output of the seven transport models are grouped in terms of their vertical mixing strength: strong and weak mixing. The results indicate that all transport models capture the meridional distribution of COS at the surface well. Model simulations generally match the aircraft campaigns HIPPO and ATom. Comparisons to HIPPO and ATom demonstrate a gap between observed and modelled COS over the Pacific Ocean at 0–40$\degree$N, indicating a potential missing source in the free troposphere. The effects of seasonal continental COS uptake by the biosphere, observed on HIPPO and ATom over oceans, is well reproduced by the simulations. We found that the strength of the vertical mixing within the column as represented in the various atmospheric transport models explains much of the model to model differences. We also found that weak-mixing models transporting the optimized flux derived from the strong-mixing TM5 model show a too strong seasonal cycle at high latitudes.

We present an analysis of atmospheric transport impact on estimating CO2 fluxes using two atmospheric inversion systems (CarboScope-Regional (CSR) and Lund University Modular Inversion Algorithm (LUMIA)) over Europe in 2018. The main focus of this study is to quantify the dominant drivers of spread amid CO2 estimates derived from atmospheric tracer inversions. The Lagrangian transport models STILT (Stochastic Time-Inverted Lagrangian Transport) and FLEXPART (FLEXible PARTicle) were used to assess the impact of mesoscale transport. The impact of lateral boundary conditions for CO2 was assessed by using two different estimates from the global inversion systems CarboScope (TM3) and TM5-4DVAR. CO2 estimates calculated with an ensemble of eight inversions differing in the regional and global transport models, as well as the inversion systems, show a relatively large spread for the annual fluxes, ranging between −0.72 and 0.20 PgC yr−1, which is larger than the a priori uncertainty of 0.47 PgC yr−1. The discrepancies in annual budget are primarily caused by differences in the mesoscale transport model (0.51 PgC yr−1), in comparison with 0.23 and 0.10 PgC yr−1 that resulted from the far-field contributions and the inversion systems, respectively. Additionally, varying the mesoscale transport caused large discrepancies in spatial and temporal patterns, while changing the lateral boundary conditions led to more homogeneous spatial and temporal impact. We further investigated the origin of the discrepancies between transport models. The meteorological forcing parameters (forecasts versus reanalysis obtained from ECMWF data products) used to drive the transport models are responsible for a small part of the differences in CO2 estimates, but the largest impact seems to come from the transport model schemes. Although a good convergence in the differences between the inversion systems was achieved by applying a strict protocol of using identical prior fluxes and atmospheric datasets, there was a non-negligible impact arising from applying a different inversion system. Specifically, the choice of prior error structure accounted for a large part of system-to-system differences.

Estimates of ocean CO 2 uptake from global ocean biogeochemistry models and p CO 2 -based data products differ substantially, especially in high latitudes and in the trend of the CO 2 uptake since 2000. Here, we assess the effect of data sparsity on two p CO 2 -based estimates by subsampling output from a global ocean biogeochemistry model. The estimates of the ocean CO 2 uptake are improved from a sampling scheme that mimics present-day sampling to an ideal sampling scheme with 1000 evenly distributed sites. In particular, insufficient sampling has given rise to strong biases in the trend of the ocean carbon sink in the p CO 2 products. The overestimation of the CO 2 flux trend by 20–35% globally and 50–130% in the Southern Ocean with the present-day sampling is reduced to less than 15 % with the ideal sampling scheme. A substantial overestimation of the decadal variability of the Southern Ocean carbon sink occurs in one product and appears related to a skewed data distribution in p CO 2 space. With the ideal sampling, the bias in the mean CO 2 flux is reduced from 9–12% to 2–9% globally and from 14–26% to 5–17% in the Southern Ocean. On top of that, discrepancies of about 0.4 PgC yr − 1 (15%) persist due to uncertainties in the gas-exchange calculation. This article is part of a discussion meeting issue ‘Heat and carbon uptake in the Southern Ocean: the state of the art and future priorities’.

Global net land carbon uptake or net biome production (NBP) has increased during recent decades1. Whether its temporal variability and autocorrelation have changed during this period, however, remains elusive, even though an increase in both could indicate an increased potential for a destabilized carbon sink2,3. Here, we investigate the trends and controls of net terrestrial carbon uptake and its temporal variability and autocorrelation from 1981 to 2018 using two atmospheric-inversion models, the amplitude of the seasonal cycle of atmospheric CO2 concentration derived from nine monitoring stations distributed across the Pacific Ocean and dynamic global vegetation models. We find that annual NBP and its interdecadal variability increased globally whereas temporal autocorrelation decreased. We observe a separation of regions characterized by increasingly variable NBP, associated with warm regions and increasingly variable temperatures, lower and weaker positive trends in NBP and regions where NBP became stronger and less variable. Plant species richness presented a concave-down parabolic spatial relationship with NBP and its variability at the global scale whereas nitrogen deposition generally increased NBP. Increasing temperature and its increasing variability appear as the most important drivers of declining and increasingly variable NBP. Our results show increasing variability of NBP regionally that can be mostly attributed to climate change and that may point to destabilization of the coupled carbon–climate system.

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 synthesise 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 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), ELUC was 1.1 ± 0.7 GtC yr-1, for a total anthropogenic CO2 emission of 11.1 ± 0.8 GtC yr-1 (40.8 ± 2.9 GtCO2). 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. total estimated sources too low or sinks too high). The global atmospheric CO2 concentration averaged over 2021 reached 414.71 ± 0.1 ppm. Preliminary data for 2022, suggest an increase in EFOS relative to 2021 of +1.1 % (0 % to 1.7 %) globally, and atmospheric CO2 concentration reaching 417.3 ppm, more than 50 % above pre-industrial level. 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 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., 2022a; 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-2022 (Friedlingstein et al., 2022b).

We present an analysis of atmospheric transport impact on estimating CO2 fluxes using two atmospheric inversion systems (CarboScope Regional (CSR) and LUMIA) over Europe for 2018. The main focus of this study is to quantify the dominant drivers of spread amid CO2 estimates derived from atmospheric tracer inversions. The Lagrangian transport models STILT and FLEXPART were used to assess the impact of mesoscale transport. The impact of lateral boundary conditions for CO2 was assessed by applying the global transport models TM3 and TM5. CO2 estimates calculated with an ensemble of eight inversions differing in the regional and global transport models, as well as the inversion systems show a relatively large spread for the annual domain wide flux ranging between -0.72 and 0.20 PgC yr-1 with a mean estimate of -0.29 PgC. The largest discrepancies resulted from varying the mesoscale transport model, which amounted to a difference of 0.51 (PgC yr-1), in comparison with 0.23 and 0.10 (PgC yr-1) that resulted from the far-field contributions and the inversion systems, respectively. Additionally, varying the mesoscale transport caused large discrepancies in spatial and temporal patterns, while changing the lateral boundary conditions lead to more homogeneous spatial and temporal impact. We further investigated the origin of the discrepancies between transport models. The meteorological forcing parameters (forecasts versus reanalysis obtained from ECMWF data products) used to drive the transport models are responsible for a small part of the differences in CO2 estimates, but the largest impact seems to come from the models themselves. Although a good convergence in the differences between the inversion systems was achieved by applying a strict protocol of using identical priors, and atmospheric datasets, there was a non-negligible impact arising from applying a different inversion system. Specifically, the choice of prior error structure accounted for a large part of system-to-system differences.

We developed a simple method to refine existing open-ocean maps and extend them towards different coastal seas. Using a multi-linear regression we produced monthly maps of surface ocean fCO2 in the northern European coastal seas (the North Sea, the Baltic Sea, the Norwegian Coast and the Barents Sea) covering a time period from 1998 to 2016. A comparison with gridded Surface Ocean CO2 Atlas (SOCAT) v5 data revealed mean biases and standard deviations of 0 ± 26 µatm in the North Sea, 0 ± 16 µatm along the Norwegian Coast, 0 ± 19 µatm in the Barents Sea and 2 ± 42 µatm in the Baltic Sea. We used these maps to investigate trends in fCO2, pH and air–sea CO2 flux. The surface ocean fCO2 trends are smaller than the atmospheric trend in most of the studied regions. The only exception to this is the western part of the North Sea, where sea surface fCO2 increases by 2 µatm yr−1, which is similar to the atmospheric trend. The Baltic Sea does not show a significant trend. Here, the variability was much larger than the expected trends. Consistently, the pH trends were smaller than expected for an increase in fCO2 in pace with the rise of atmospheric CO2 levels. The calculated air–sea CO2 fluxes revealed that most regions were net sinks for CO2. Only the southern North Sea and the Baltic Sea emitted CO2 to the atmosphere. Especially in the northern regions the sink strength increased during the studied period.

The Southern Ocean plays a major role in both the global oceanic uptake of anthropogenic CO2 and its interannual variations. The size and origin of the interannual variability in the Southern Ocean CO2 fluxes is debated. Observation-based estimates suggest a large variability (+/- 0.11 PgC/yr) while Global Ocean Biogeochemistry Models (GOBMs) simulate almost no variability. Studying the air-sea fluxes of O2 can provide independent information that help resolve this data-model inconsistency. Oceanic O2 is influenced by the same physical and biogeochemical processes as CO2, but unlike CO2, its variability is not masked by a large anthropogenic flux. Here, we used 26 years (1994-2019) of monthly O2 fluxes from 9 GOBMs. These model outputs were compared to air-sea O2 fluxes inferred from an atmospheric inversion of precisely quantified changes in atmospheric O2 and CO2 levels. The 26-year time series of air-sea O2 fluxes from all GOBMs and the atmospheric inversion exhibited similar temporal variations. This could be linked to the Southern Annular Mode and its influence on air-sea heat flux forcing that induced large-scale changes in observed wintertime Mixed Layer Depth (MLD). However, the amplitude of the interannual variability in air-sea O2 fluxes was two times higher in the atmospheric inversion than in GOBMs. It possible that this was induced by the general overestimation of the mean wintertime MLD by the GOBM and subsurface vertical gradients in oxygen saturation lower than observed. Implications of these results for the variability in air-sea fluxes of CO2 will be discussed.

In the last decades terrestrial ecosystems have reabsorbed on average more than one-quarter of anthropogenic emissions (Le Quéré et al., 2018). However, this large carbon sink is modulated by climate and is therefore highly variable in time and space. The magnitude and temporal changes in the sensitivity of terrestrial CO2 fluxes to climate drivers are key factors to determine future atmospheric CO2 concentration and climate trajectories. In the literature, there is so far a strong focus on the climatic controls of daily and long-term variability, while less is known about the key drivers at a seasonal timescale and about their variation over time (Wohlfahrt et al., 2008). This latter temporal scale is relevant to assess which climatic drivers dominate the seasonality of the fluxes and to understand which factors limit the CO2 exchange during the course of the year. Here, we investigate the global sensitivity of net terrestrial CO2 fluxes, derived from atmospheric inversion, to three key climate drivers (i.e. global radiation and temperature from WFDEI and soil water content from ERA-Interim) from weekly to seasonal temporal scales, in order to explore the short-term interdependence between climate and the terrestrial carbon budget. We observed that the CO2 exchange is controlled by temperature during the carbon uptake period over most of the land surface (from 55 % to 52 % of the total surface), while radiation is the most widespread dominant climate driver during the carbon release period (from 64 % to 70 % of the total surface). As expected, soil water content plays a key role in arid regions of the Southern Hemisphere during both the carbon uptake and the carbon release period. Looking at the decadal trend of these sensitivities (1985–2016) we observed that the importance of radiation as a driver is increasing over time, while we observed a decrease in sensitivity to temperature in Eurasia. Overall, we show that flux temporal variation due to a specific driver has been dominated by the temporal changes in ecosystem sensitivity (i.e. the response of ecosystem to climate) rather than to the temporal variability of the climate driver itself over the last decades. Ultimately, this analysis shows that the ecosystem response to climate is significantly changing both in space and in time, with potential repercussion on the future terrestrial CO2 sink and therefore on the role that land may play in climate trajectories.

Southeast Asia is a region known for active land-use changes (LUC) over the past 60 years; yet, how trends in net CO2 uptake and release resulting from LUC activities (net LUC flux) have changed through past decades remains uncertain. The level of uncertainty in net LUC flux from process-based models is so high that it cannot be concluded that newer estimates are necessarily more reliable than older ones. Here, we examined net LUC flux estimates of Southeast Asia for the 1980s−2010s from older and newer sets of Dynamic Global Vegetation Model simulations (TRENDY v2 and v7, respectively), and forcing data used for running those simulations, along with two book-keeping estimates (H&N and BLUE). These estimates yielded two contrasting historical LUC transitions, such that TRENDY v2 and H&N showed a transition from increased emissions from the 1980s to 1990s to declining emissions in the 2000s, while TRENDY v7 and BLUE showed the opposite transition. We found that these contrasting transitions originated in the update of LUC forcing data, which reduced the loss of forest area during the 1990s. Further evaluation of remote sensing studies, atmospheric inversions, and the history of forestry and environmental policies in Southeast Asia supported the occurrence of peak emissions in the 1990s and declining thereafter. However, whether LUC emissions continue to decline in Southeast Asia remains uncertain as key processes in recent years, such as conversion of peat forest to oil-palm plantation, are yet to be represented in the forcing data, suggesting a need for further revision., Global Biogeochemical Cycles, 36 (1), ISSN:0886-6236, ISSN:1944-9224

The Southern Ocean plays an important role in determining atmospheric carbon dioxide (CO2), yet estimates of air-sea CO2 flux for the region diverge widely. In this study, we constrained Southern Ocean air-sea CO2 exchange by relating fluxes to horizontal and vertical CO2 gradients in atmospheric transport models and applying atmospheric observations of these gradients to estimate fluxes. Aircraft-based measurements of the vertical atmospheric CO2 gradient provide robust flux constraints. We found an annual mean flux of –0.53 ± 0.23 petagrams of carbon per year (net uptake) south of 45°S during the period 2009–2018. This is consistent with the mean of atmospheric inversion estimates and surface-ocean partial pressure of CO2 (PCO2)–based products, but our data indicate stronger annual mean uptake than suggested by recent interpretations of profiling float observations.

This study considers year-to-year and decadal variations in as well as secular trends of the sea–air CO2 flux over the 1957–2020 period, as constrained by the pCO2 measurements from the SOCATv2021 database. In a first step, we relate interannual anomalies in ocean-internal carbon sources and sinks to local interannual anomalies in sea surface temperature (SST), the temporal changes in SST (dSST/dt), and squared wind speed (u2), employing a multi-linear regression. In the tropical Pacific, we find interannual variability to be dominated by dSST/dt, as arising from variations in the upwelling of colder and more carbon-rich waters into the mixed layer. In the eastern upwelling zones as well as in circumpolar bands in the high latitudes of both hemispheres, we find sensitivity to wind speed, compatible with the entrainment of carbon-rich water during wind-driven deepening of the mixed layer and wind-driven upwelling. In the Southern Ocean, the secular increase in wind speed leads to a secular increase in the carbon source into the mixed layer, with an estimated reduction in the sink trend in the range of 17 % to 42 %. In a second step, we combined the result of the multi-linear regression and an explicitly interannual pCO2-based additive correction into a “hybrid” estimate of the sea–air CO2 flux over the period 1957–2020. As a pCO2 mapping method, it combines (a) the ability of a regression to bridge data gaps and extrapolate into the early decades almost void of pCO2 data based on process-related observables and (b) the ability of an auto-regressive interpolation to follow signals even if not represented in the chosen set of explanatory variables. The “hybrid” estimate can be applied as an ocean flux prior for atmospheric CO2 inversions covering the whole period of atmospheric CO2 data since 1957.

Long-term measurements at the Mauna Loa Observatory (MLO) show that the CO

Atmospheric inversion approaches are expected to play a critical role in future observation-based monitoring systems for surface fluxes of greenhouse gases (GHGs), pollutants and other trace gases. In the past decade, the research community has developed various inversion software, mainly using variational or ensemble Bayesian optimization methods, with various assumptions on uncertainty structures and prior information and with various atmospheric chemistry–transport models. Each of them can assimilate some or all of the available observation streams for its domain area of interest: flask samples, in situ measurements or satellite observations. Although referenced in peer-reviewed publications and usually accessible across the research community, most systems are not at the level of transparency, flexibility and accessibility needed to provide the scientific community and policy makers with a comprehensive and robust view of the uncertainties associated with the inverse estimation of GHG and reactive species fluxes. Furthermore, their development, usually carried out by individual research institutes, may in the future not keep pace with the increasing scientific needs and technical possibilities. We present here the Community Inversion Framework (CIF) to help rationalize development efforts and leverage the strengths of individual inversion systems into a comprehensive framework. The CIF is primarily a programming protocol to allow various inversion bricks to be exchanged among researchers. In practice, the ensemble of bricks makes a flexible, transparent and open-source Python-based tool to estimate the fluxes of various GHGs and reactive species both at the global and regional scales. It will allow for running different atmospheric transport models, different observation streams and different data assimilation approaches. This adaptability will allow for a comprehensive assessment of uncertainty in a fully consistent framework. We present here the main structure and functionalities of the system, and we demonstrate how it operates in a simple academic case.

Continuous atmospheric CO2 monitoring data indicate an increase in the amplitude of seasonal CO2-cycle exchange (SCANBP) in northern high latitudes. The major drivers of enhanced SCANBP remain unclear and intensely debated, with land-use change, CO2 fertilization and warming being identified as likely contributors. We integrated CO2-flux data from two atmospheric inversions (consistent with atmospheric records) and from 11 state-of-the-art land-surface models (LSMs) to evaluate the relative importance of individual contributors to trends and drivers of the SCANBP of CO2 fluxes for 1980–2015. The LSMs generally reproduce the latitudinal increase in SCANBP trends within the inversions range. Inversions and LSMs attribute SCANBP increase to boreal Asia and Europe due to enhanced vegetation productivity (in LSMs) and point to contrasting effects of CO2 fertilization (positive) and warming (negative) on SCANBP. Our results do not support land-use change as a key contributor to the increase in SCANBP. The sensitivity of simulated microbial respiration to temperature in LSMs explained biases in SCANBP trends, which suggests that SCANBP could help to constrain model turnover times.

We have compared a suite of recent global CO2 atmospheric inversion results to independent airborne observations and to each other, to assess their dependence on differences in northern extratropical (NET) vertical transport and to identify some of the drivers of model spread. We evaluate posterior CO2 concentration profiles against observations from the High-Performance Instrumented Airborne Platform for Environmental Research (HIAPER) Pole-to-Pole Observations (HIPPO) aircraft campaigns over the mid-Pacific in 2009–2011. Although the models differ in inverse approaches, assimilated observations, prior fluxes, and transport models, their broad latitudinal separation of land fluxes has converged significantly since the Atmospheric Carbon Cycle Inversion Intercomparison (TransCom 3) and the REgional Carbon Cycle Assessment and Processes (RECCAP) projects, with model spread reduced by 80 % since TransCom 3 and 70 % since RECCAP. Most modeled CO2 fields agree reasonably well with the HIPPO observations, specifically for the annual mean vertical gradients in the Northern Hemisphere. Northern Hemisphere vertical mixing no longer appears to be a dominant driver of northern versus tropical (T) annual flux differences. Our newer suite of models still gives northern extratropical land uptake that is modest relative to previous estimates (Gurney et al., 2002; Peylin et al., 2013) and near-neutral tropical land uptake for 2009–2011. Given estimates of emissions from deforestation, this implies a continued uptake in intact tropical forests that is strong relative to historical estimates (Gurney et al., 2002; Peylin et al., 2013). The results from these models for other time periods (2004–2014, 2001–2004, 1992–1996) and re-evaluation of the TransCom 3 Level 2 and RECCAP results confirm that tropical land carbon fluxes including deforestation have been near neutral for several decades. However, models still have large disagreements on ocean–land partitioning. The fossil fuel (FF) and the atmospheric growth rate terms have been thought to be the best-known terms in the global carbon budget, but we show that they currently limit our ability to assess regional-scale terrestrial fluxes and ocean–land partitioning from the model ensemble.

Air-sea flux of carbon dioxide (CO2) is a critical component of the global carbon cycle and the climate system with the ocean removing about a quarter of the CO2 emitted into the atmosphere by human activities over the last decade. A common approach to estimate this net flux of CO2 across the air-sea interface is the use of surface ocean CO2 observations and the computation of the flux through a bulk parameterization approach. Yet, the details for how this is done in order to arrive at a global ocean CO2 uptake estimate varies greatly, unnecessarily enhancing the uncertainties. Here we reduce some of these uncertainties by harmonizing an ensemble of products that interpolate surface ocean CO2 observations to near global coverage. We propose a common methodology to fill in missing areas in the products and to calculate fluxes and present a new estimate of the net flux. The ensemble data product, SeaFlux (Fay et al. (2021), doi.org/10.5281/zenodo.4133802, https://github.com/luke-gregor/SeaFlux), accounts for the diversity of the underlying mapping methodologies. Utilizing six global observation-based mapping products (CMEMS-FFNN, CSIR-ML6, JENA-MLS, JMA-MLR, MPI-SOMFFN, NIES-FNN), the SeaFlux ensemble approach adjusts for methodological inconsistencies in flux calculations that can result in an average error of 15 % in global mean flux estimates. We address differences in spatial coverage of the surface ocean CO2 between the mapping products which ultimately yields an increase in CO2 uptake of up to 19 % for some products. Fluxes are calculated using three wind products (CCMPv2, ERA5, and JRA55). Application of an appropriately scaled gas exchange coefficient has a greater impact on the resulting flux than solely the choice of wind product. With these adjustments, we derive an improved ensemble of surface ocean pCO2 and air-sea carbon flux estimates. The SeaFlux ensemble suggests a global mean uptake of CO2 from the atmosphere of 1.92 +/- 0.35 PgC yr-1. This work aims to support the community effort to perform model-data intercomparisons which will help to identify missing fluxes as we strive to close the global carbon budget.

By means of a variety of international observing and modeling efforts, the ocean carbon community has developed numerous estimates for ocean carbon uptake. In this presentation, we report on the synthesis effort we are undertaking under the auspices of an Ocean Carbon and Biogeochemistry Working Group. Our initial goal for this working group is to determine the best estimate for the net and anthropogenic carbon sink from 1994-2007 based on three approaches that independently use interior data, surface data or hindcast ocean models. Combining two approaches that use interior ocean data to estimate anthropogenic carbon, Fant = -2.40+-0.21 PgC/yr (2 sigma uncertainty). Estimates for the net, or contemporary, ocean carbon uptake come from 6 products that interpolate surface ocean pCO2 data to global coverage: Fnet = -1.58+-0.19 PgC/yr for 1994-2007. Uncertain closure terms for naturally-outgassed river-derived carbon and non-steady state natural carbon fluxes in the open ocean are then added to derive Fant from surface observation-based Fnet. Ocean models do not include river-derived carbon, but do include non-steady state natural carbon fluxes, and thus a third estimate for Fant is derived. The combined best-estimate is Fant = -2.35+-0.53 PgC/yr. We detail the uncertainties and assumptions made in deriving these estimates, and suggest paths forward to further reduce uncertainties.

The oceanic CO2 sink displays year-to-year to decadal variabilities which are not fully reproduced by global ocean biogeochemistry models, especially in the high-latitude oceans. Oceanic CO2 is influenced by the same climate variability and the same ecosystem processes as oceanic oxygen (O2), although in different proportions. Unlike for CO2, oceanic O2 flux is not influenced directly by the rise in atmospheric CO2, and therefore its variability reflects purely climatic and biogeochemical variability and trends. Therefore, natural climate variability and changes in oceanic processes controlling air-sea exchanges of CO2 can be studied by focusing on oxygen (O2), where the signal is unencumbered by direct anthropogenic influence. A global time series of oceanic O2 flux was obtained by building a global O2 budget, with an approach similar to the one used for the global carbon budget. The global O2 budget is based on atmospheric O2 observations and fossil fuel statistics, and infers the partitioning of the land and ocean fluxes using constant C:O2 ratios for land processes. One key result of this analysis is that air-sea O2 exchange induced significant year-to-year variability in observed atmospheric O2. Estimates of regional oceanic O2 fluxes were obtained from an atmospheric transport inversion analysis that inferred air-sea O2 exchange based on global atmospheric O2 observations and a global atmospheric transport model. For the Southern Ocean, a comparison was made between time series of winter oceanic O2 fluxes from this inversion method and winter mixed layer depths from Argo floats. Results from this comparison confirmed the previously suggested relationship between the winter ocean mixing and air-sea O2 exchange, which might be controlled by the climate variability induced by the Southern Annular Mode. Finally, these global and regional air-sea O2 fluxes were compared with outputs from six global ocean biogeochemistry models to examine their current skills in simulating O2 variability. Preliminary results suggested that all models underestimated the interannual variability in oceanic O2 fluxes, however they were able to simulate some of the observed multi-annual variability in O2 fluxes at high latitudes. We discuss the implications for the model’s representation of the variability in CO2 fluxes.

Air–sea flux of carbon dioxide (CO2) is a critical component of the global carbon cycle and the climate system with the ocean removing about a quarter of the CO2 emitted into the atmosphere by human activities over the last decade. A common approach to estimate this net flux of CO2 across the air–sea interface is the use of surface ocean CO2 observations and the computation of the flux through a bulk parameterization approach. Yet, the details for how this is done in order to arrive at a global ocean CO2 uptake estimate vary greatly, enhancing the spread of estimates. Here we introduce the ensemble data product, SeaFlux (Gregor and Fay, 2021, https://doi.org/10.5281/zenodo.5482547​​​​​​​, https://github.com/luke-gregor/pySeaFlux, last access: 9 September 2021​​​​​​​); this resource enables users to harmonize an ensemble of products that interpolate surface ocean CO2 observations to near-global coverage with a common methodology to fill in missing areas in the products. Further, the dataset provides the inputs to calculate fluxes in a consistent manner. Utilizing six global observation-based mapping products (CMEMS-FFNN, CSIR-ML6, JENA-MLS, JMA-MLR, MPI-SOMFFN, NIES-FNN), the SeaFlux ensemble approach adjusts for methodological inconsistencies in flux calculations. We address differences in spatial coverage of the surface ocean CO2 between the mapping products, which ultimately yields an increase in CO2 uptake of up to 17 % for some products. Fluxes are calculated using three wind products (CCMPv2, ERA5, and JRA55). Application of a scaled gas exchange coefficient has a greater impact on the resulting flux than solely the choice of wind product. With these adjustments, we present an ensemble of global surface ocean pCO2 and air–sea carbon flux estimates. This work aims to support the community effort to perform model–data intercomparisons which will help to identify missing fluxes as we strive to close the global carbon budget.

Atmospheric inversion approaches are expected to play a critical role in future observation-based monitoring systems for surface greenhouse gas (GHG) fluxes. In the past decade, the research community has developed various inversion softwares, mainly using variational or ensemble Bayesian optimization methods, with various assumptions on uncertainty structures and prior information and with various atmospheric chemistry-transport models. Each of them can assimilate some or all of the available observation streams for its domain area of interest: flask samples, in-situ measurements or satellite observations. Although referenced in peer-reviewed publications and usually accessible across the research community, most systems are not at the level of transparency, flexibility and accessibility needed to provide the scientific community and policy makers with a comprehensive and robust view of the uncertainties associated with the inverse estimation of GHG fluxes. Furthermore, their development, usually carried out by individual research institutes, may in the future not keep pace with the increasing scientific needs and technical possibilities. We present here a Community Inversion Framework (CIF) to help rationalize development efforts and leverage the strengths of individual inversion systems into a comprehensive framework. The CIF is primarily a programming protocol to allow various inversion bricks to be exchanged among researchers. In practice, the ensemble of bricks makes a flexible, transparent and open-source python-based tool to estimate the fluxes of various GHGs both at global and regional scales. It will allow running different atmospheric transport models, different observation streams and different data assimilation approaches. This adaptability will allow a comprehensively assessment of uncertainty in a fully consistent framework. We present here the main structure and functionalities of the system, and demonstrate how it operates in a simple academic case.

Background: The climate mitigation target of limiting the temperature increase below 2 °C above the pre-industrial levels requires the efforts from all countries. Tracking the trajectory of the land carbon sink efficiency is thus crucial to evaluate the nationally determined contributions (NDCs). Here, we define the instantaneous land sink efficiency as the ratio of natural land carbon sinks to emissions from fossil fuel and land-use and land-cover change with a value of 1 indicating carbon neutrality to track its temporal dynamics in the past decades.Results: Land sink efficiency has been decreasing during 1957-1990 because of the increased emissions from fossil fuel. After the effect of the Pinatubo eruption diminished (after 1994), the land sink efficiency firstly increased before 2009 and then began to decrease again after 2009. This reversal around 2009 is mostly attributed to changes in land sinks in Latin America in response to climate variations.Conclusions: The decreasing trend of land sink efficiency in the recent years reveals greater challenges in climate change mitigation, and impacts of climate on land carbon sinks need to be accurately quantified to assess the implementation of climate mitigation policies.