ALBERT

Description: Understanding the variations in global land carbon uptake, and their driving mechanisms, is essential if we are to predict future carbon-cycle feedbacks on global environmental changes. Satellite observations of vegetation greenness have shown consistent greening across the globe over the past three decades. Such greening has driven the increasing land carbon sink, especially over the growing season in northern latitudes. On the other hand, interannual variations in land carbon uptake are strongly influenced by El Niño–Southern Oscillation (ENSO) climate variations. Marked reductions in land uptake and strong positive anomalies in the atmospheric CO2 growth rates occur during El Niño events. Here we use the year 2015 as a natural experiment to examine the possible response of land ecosystems to a combination of vegetation greening and an El Niño event. The year 2015 was the greenest year since 2000 according to satellite observations, but a record atmospheric CO2 growth rate also occurred due to a weaker than usual land carbon sink. Two atmospheric inversions indicate that the year 2015 had a higher than usual northern land carbon uptake in boreal spring and summer, consistent with the positive greening anomaly and strong warming. This strong uptake was, however, followed by a larger source of CO2 in the autumn. For the year 2015, enhanced autumn carbon release clearly offset the extra uptake associated with greening during the summer. This finding leads us to speculate that a long-term greening trend may foster more uptakes during the growing season, but no large increase in annual carbon sequestration. For the tropics and Southern Hemisphere, a strong transition towards a large carbon source for the last 3 months of 2015 is discovered, concomitant with El Niño development. This transition of terrestrial tropical CO2 fluxes between two consecutive seasons is the largest ever found in the inversion records. The strong transition to a carbon source in the tropics with the peak of El Niño is consistent with historical observations, but the detailed mechanisms underlying such an extreme transition remain to be elucidated.

Description: The terrestrial carbon fluxes show the largest variability among the components of the global carbon cycle and drive most of the temporal variations in the growth rate of atmospheric CO2 (Le Quéré 2014). Understanding the environmental controls and trends of the terrestrial carbon budget is therefore essential to predict the future trajectories of the CO2 airborne fraction and atmospheric concentrations. In the present work, patterns and controls of the inter-annual variability (IAV) of carbon Net Ecosystem Exchange (NEE) have been analysed using three different data-streams: ecosystem level observations from the FLUXNET database (La Thuille and 2015 releases), the MPI-MTE bottom-up product resulting from the global up-scaling of site-level fluxes, and the Jena CarboScope Inversion, a top-down estimate of surface fluxes obtained from observed CO2 concentrations and an atmospheric transport model. Consistencies and discrepancies in the temporal and spatial patterns and in the climatic and physiological controls of IAV were investigated between the three data sources. The global average of IAV at FLUXNET sites (~ 120 gC m−2 y−1), quantified as the standard deviation of annual NEE, was observed to peak in arid ecosystems and to be almost six times larger than the values calculated from the two global products (15 and 20 gC m−2 y−1 for MPI-MTE and Jena inversion, respectively). The two data-driven global products show that most of the temporal variability observed in the last three decades is due to yearly anomalies, whereas the temporal trends explain only about 15 % of the variability in the MPI-MTE product and 20 % in the Jena Inversion product. Both at site level and at global scale, the IAV of NEE is driven by the gross primary productivity and in particular by the cumulative carbon flux during the months when land acts as a sink. Altogether these results offer a broad view on the magnitude, spatial patterns and environmental drivers of IAV from a variety of data sources, that can be instrumental to improve our understanding of the terrestrial carbon budget and to validate the predictions of land surface models.

Description: Atmospheric inversions are widely used in the optimization of surface carbon fluxes on a regional scale using information from atmospheric CO2 dry mole fractions. In many studies the prior flux uncertainty applied to the inversion schemes does not directly reflect the true flux uncertainties but is used to regularize the inverse problem. Here, we aim to implement an inversion scheme using the Jena inversion system and applying a prior flux error structure derived from a model–data residual analysis using high spatial and temporal resolution over a full year period in the European domain. We analyzed the performance of the inversion system with a synthetic experiment, in which the flux constraint is derived following the same residual analysis but applied to the model–model mismatch. The synthetic study showed a quite good agreement between posterior and true fluxes on European, country, annual and monthly scales. Posterior monthly and country-aggregated fluxes improved their correlation coefficient with the known truth by 7 % compared to the prior estimates when compared to the reference, with a mean correlation of 0.92. The ratio of the SD between the posterior and reference and between the prior and reference was also reduced by 33 % with a mean value of 1.15. We identified temporal and spatial scales on which the inversion system maximizes the derived information; monthly temporal scales at around 200 km spatial resolution seem to maximize the information gain.

Description: Optimized biogenic carbon fluxes for Europe were estimated from high-resolution regional-scale inversions, utilizing atmospheric CO2 measurements at 16 stations for the year 2007. Additional sensitivity tests with different data-driven error structures were performed. As the atmospheric network is rather sparse and consequently contains large spatial gaps, we use a priori biospheric fluxes to further constrain the inversions. The biospheric fluxes were simulated by the Vegetation Photosynthesis and Respiration Model (VPRM) at a resolution of 0.1° and optimized against eddy covariance data. Overall we estimate an a priori uncertainty of 0.54 GtC yr−1 related to the poor spatial representation between the biospheric model and the ecosystem sites. The sink estimated from the atmospheric inversions for the area of Europe (as represented in the model domain) ranges between 0.23 and 0.38 GtC yr−1 (0.39 and 0.71 GtC yr−1 up-scaled to geographical Europe). This is within the range of posterior flux uncertainty estimates of previous studies using ground-based observations.

Description: Inaccurate representation of atmospheric processes by transport models is a dominant source of uncertainty in inverse analyses and can lead to large discrepancies in the retrieved flux estimates. We investigate the impact of uncertainties in vertical transport as simulated by atmospheric transport models on fluxes retrieved using vertical profiles from aircraft as an observational constraint. Our numerical experiments are based on synthetic data with realistic spatial and temporal sampling of aircraft measurements. The impact of such uncertainties on the flux retrieved using the ground-based network and those retrieved using the aircraft profiles are compared. We find that the posterior flux retrieved using aircraft profiles is less susceptible to errors in boundary layer height, compared to the ground-based network. This finding highlights a benefit of utilizing atmospheric observations made onboard aircraft over surface measurements for flux estimation using inverse methods. We further use synthetic vertical profiles of CO2 in an inversion to estimate the potential of these measurements, which will be made available through the IAGOS (In-service Aircraft for a Global Observing System) project in the future, in constraining the regional carbon budget. Our results show that the regions of tropical Africa and temperate Eurasia, that are under-constrained by the existing surface-based network, will benefit the most from these measurements, with a reduction of posterior flux uncertainty of about 7 to 10 %.

Description: The response of the terrestrial net ecosystem exchange (NEE) of CO2 to climate variations and trends may crucially determine the future climate trajectory. Here we directly quantify this response on inter-annual timescales by building a linear regression of inter-annual NEE anomalies against observed air temperature anomalies into an atmospheric inverse calculation based on long-term atmospheric CO2 observations. This allows us to estimate the sensitivity of NEE to inter-annual variations in temperature (seen as a climate proxy) resolved in space and with season. As this sensitivity comprises both direct temperature effects and the effects of other climate variables co-varying with temperature, we interpret it as inter-annual climate sensitivity. We find distinct seasonal patterns of this sensitivity in the northern extratropics that are consistent with the expected seasonal responses of photosynthesis, respiration, and fire. Within uncertainties, these sensitivity patterns are consistent with independent inferences from eddy covariance data. On large spatial scales, northern extratropical and tropical inter-annual NEE variations inferred from the NEE–T regression are very similar to the estimates of an atmospheric inversion with explicit inter-annual degrees of freedom. The results of this study offer a way to benchmark ecosystem process models in more detail than existing effective global climate sensitivities. The results can also be used to gap-fill or extrapolate observational records or to separate inter-annual variations from longer-term trends.

Description: Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere – the "global carbon budget" – is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties. CO2 emissions from fossil fuels and industry (EFF) are based on energy statistics and cement production data, respectively, while emissions from land-use change (ELUC), mainly deforestation, are based on land-cover change data and bookkeeping models. The global atmospheric CO2 concentration is measured directly and its rate of growth (GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) and terrestrial CO2 sink (SLAND) are estimated with global process models constrained by observations. 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 our imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the last decade available (2007–2016), EFF was 9.4 ± 0.5 GtC yr−1, ELUC 1.3 ± 0.7 GtC yr−1, GATM 4.7 ± 0.1 GtC yr−1, SOCEAN 2.4 ± 0.5 GtC yr−1, and SLAND 3.0 ± 0.8 GtC yr−1, with a budget imbalance BIM of 0.6 GtC yr−1 indicating overestimated emissions and/or underestimated sinks. For year 2016 alone, the growth in EFF was approximately zero and emissions remained at 9.9 ± 0.5 GtC yr−1. Also for 2016, ELUC was 1.3 ± 0.7 GtC yr−1, GATM was 6.1 ± 0.2 GtC yr−1, SOCEAN was 2.6 ± 0.5 GtC yr−1 and SLAND was 2.7 ± 1.0 GtC yr−1, with a small BIM of −0.3 GtC. GATM continued to be higher in 2016 compared to the past decade (2007–2016), reflecting in part the higher fossil emissions and smaller SLAND for that year consistent with El Niño conditions. The global atmospheric CO2 concentration reached 402.8 ± 0.1 ppm averaged over 2016. For 2017, preliminary data indicate a renewed growth in EFF of +2.0 % (range of 0.8 % to 3.0 %) based on national emissions projections for China, USA, and India, and projections of Gross Domestic Product corrected for recent changes in the carbon intensity of the economy for the rest of the world. For 2017, initial data indicate an increase in atmospheric CO2 concentration of around 5.3 GtC (2.5 ppm), attributed to a combination of increasing emissions and receding El Niño conditions. This living data update documents changes in the methods and data sets used in this new global carbon budget compared with previous publications of this data set (Le Quéré et al., 2016; 2015b; 2015a; 2014; 2013). All results presented here can be downloaded from https://doi.org/10.18160/GCP-2017.

Description: The terrestrial carbon fluxes show the largest variability among the components of the global carbon cycle and drive most of the temporal variations in the growth rate of atmospheric CO2. Understanding the environmental controls and trends of the terrestrial carbon budget is therefore essential to predict the future trajectories of the CO2 airborne fraction and atmospheric concentrations. In the present work, patterns and controls of the inter-annual variability (IAV) of carbon net ecosystem exchange (NEE) have been analysed using three different data streams: ecosystem-level observations from the FLUXNET database (La Thuile and 2015 releases), the MPI-MTE (model tree ensemble) bottom–up product resulting from the global upscaling of site-level fluxes, and the Jena CarboScope Inversion, a top–down estimate of surface fluxes obtained from observed CO2 concentrations and an atmospheric transport model. Consistencies and discrepancies in the temporal and spatial patterns and in the climatic and physiological controls of IAV were investigated between the three data sources. Results show that the global average of IAV at FLUXNET sites, quantified as the standard deviation of annual NEE, peaks in arid ecosystems and amounts to  ∼  120 gC m−2 y−1, almost 6 times more than the values calculated from the two global products (15 and 20 gC m−2 y−1 for MPI-MTE and the Jena Inversion, respectively). Most of the temporal variability observed in the last three decades of the MPI-MTE and Jena Inversion products is due to yearly anomalies, whereas the temporal trends explain only about 15 and 20 % of the variability, respectively. Both at the site level and on a global scale, the IAV of NEE is driven by the gross primary productivity and in particular by the cumulative carbon flux during the months when land acts as a sink. Altogether these results offer a broad view on the magnitude, spatial patterns and environmental drivers of IAV from a variety of data sources that can be instrumental to improve our understanding of the terrestrial carbon budget and to validate the predictions of land surface models.

Description: Understanding the global carbon (C) cycle is of crucial importance to map current and future climate dynamics relative to global environmental change. A full characterization of C cycling requires detailed information on spatiotemporal patterns of surface–atmosphere fluxes. However, relevant C cycle observations are highly variable in their coverage and reporting standards. Especially problematic is the lack of integration of the carbon dioxide (CO2) exchange of the ocean, inland freshwaters and the land surface with the atmosphere. Here we adopt a data-driven approach to synthesize a wide range of observation-based spatially explicit surface–atmosphere CO2 fluxes from 2001 to 2010, to identify the state of today's observational opportunities and data limitations. The considered fluxes include net exchange of open oceans, continental shelves, estuaries, rivers, and lakes, as well as CO2 fluxes related to net ecosystem productivity, fire emissions, loss of tropical aboveground C, harvested wood and crops, as well as fossil fuel and cement emissions. Spatially explicit CO2 fluxes are obtained through geostatistical and/or remote-sensing-based upscaling, thereby minimizing biophysical or biogeochemical assumptions encoded in process-based models. We estimate a bottom-up net C exchange (NCE) between the surface (land, ocean, and coastal areas) and the atmosphere. Though we provide also global estimates, the primary goal of this study is to identify key uncertainties and observational shortcomings that need to be prioritized in the expansion of in situ observatories. Uncertainties for NCE and its components are derived using resampling. In many regions, our NCE estimates agree well with independent estimates from other sources such as process-based models and atmospheric inversions. This holds for Europe (mean ± 1 SD: 0.8 ± 0.1 PgC yr−1, positive numbers are sources to the atmosphere), Russia (0.1 ± 0.4 PgC yr−1), East Asia (1.6 ± 0.3 PgC yr−1), South Asia (0.3 ± 0.1 PgC yr−1), Australia (0.2 ± 0.3 PgC yr−1), and most of the Ocean regions. Our NCE estimates give a likely too large CO2 sink in tropical areas such as the Amazon, Congo, and Indonesia. Overall, and because of the overestimated CO2 uptake in tropical lands, our global bottom-up NCE amounts to a net sink of −5.4 ± 2.0 PgC yr−1. By contrast, the accurately measured mean atmospheric growth rate of CO2 over 2001–2010 indicates that the true value of NCE is a net CO2 source of 4.3 ± 0.1 PgC yr−1. This mismatch of nearly 10 PgC yr−1 highlights observational gaps and limitations of data-driven models in tropical lands, but also in North America. Our uncertainty assessment provides the basis for setting priority regions where to increase carbon observations in the future. High on the priority list are tropical land regions, which suffer from a lack of in situ observations. Second, extensive pCO2 data are missing in the Southern Ocean. Third, we lack observations that could enable seasonal estimates of shelf, estuary, and inland water–atmosphere C exchange. Our consistent derivation of data uncertainties could serve as prior knowledge in multicriteria optimization such as the Carbon Cycle Data Assimilation System (CCDAS) and atmospheric inversions, without over- or under-stating bottom-up data credibility. In the future, NCE estimates of carbon sinks could be aggregated at national scale to compare with the official national inventories of CO2 fluxes in the land use, land use change, and forestry sector, upon which future emission reductions are proposed.