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We measured CO2 and CH4 fluxes using chambers and eddy covariance (only CO2) from a moist moss tundra in Svalbard. The average net ecosystem exchange (NEE) during the summer (9 June–31 August) was negative (sink), with -0.139 ± 0.032 µmol m-2 s-1 corresponding to -11.8 g C m-2 for the whole summer. The cumulated NEE over the whole growing season (day no. 160 to 284) was -2.5 g C m-2. The CH4 flux during the summer period showed a large spatial and temporal variability. The mean value of all 214 samples was 0.000511 ± 0.000315 µmol m-2 s-1, which corresponds to a growing season estimate of 0.04 to 0.16 g CH4 m-2. Thus, we find that this moss tundra ecosystem is closely in balance with the atmosphere during the growing season when regarding exchanges of CO2 and CH4. The sink of CO2 and the source of CH4 are small in comparison with other tundra ecosystems in the high Arctic.Air temperature, soil moisture and the greenness index contributed significantly to explaining the variation in ecosystem respiration (Reco), while active layer depth, soil moisture and the greenness index were the variables that best explained CH4 emissions. An estimate of temperature sensitivity ofReco and gross primary productivity (GPP) showed that the sensitivity is slightly higher for GPP than for Reco in the interval 0–4.5 ∘C; thereafter, the difference is small up to about 6 ∘C and then begins to rise rapidly for Reco. The consequence of this, for a small increase in air temperature of 1∘ (all other variables assumed unchanged), was that the respiration increased more than photosynthesis turning the small sink into a small source (4.5 g C m-2) during the growing season. Thus, we cannot rule out that the reason why the moss tundra is close to balance today is an effect of the warming that has already taken place in Svalbard.

Rangelands provide significant environmental benefits through many ecosystem services, which may include soil organic carbon (SOC) sequestration. However, quantifying SOC stocks and monitoring carbon (C) fluxes in rangelands are challenging due to the considerable spatial and temporal variability tied to rangeland C dynamics as well as limited data availability. We developed the Rangeland Carbon Tracking and Management (RCTM) system to track long‐term changes in SOC and ecosystem C fluxes by leveraging remote sensing inputs and environmental variable data sets with algorithms representing terrestrial C‐cycle processes. Bayesian calibration was conducted using quality‐controlled C flux data sets obtained from 61 Ameriflux and NEON flux tower sites from Western and Midwestern US rangelands to parameterize the model according to dominant vegetation classes (perennial and/or annual grass, grass‐shrub mixture, and grass‐tree mixture). The resulting RCTM system produced higher model accuracy for estimating annual cumulative gross primary productivity (GPP) (R2 > 0.6, RMSE <390 g C m−2) relative to net ecosystem exchange of CO2 (NEE) (R2 > 0.4, RMSE <180 g C m−2). Model performance in estimating rangeland C fluxes varied by season and vegetation type. The RCTM captured the spatial variability of SOC stocks with R2 = 0.6 when validated against SOC measurements across 13 NEON sites. Model simulations indicated slightly enhanced SOC stocks for the flux tower sites during the past decade, which is mainly driven by an increase in precipitation. Future efforts to refine the RCTM system will benefit from long‐term network‐based monitoring of vegetation biomass, C fluxes, and SOC stocks. Plain Language Summary Rangelands play a crucial role in providing various ecosystem services, including potential climate change mitigation through increased soil organic carbon (SOC) storage. Accurate estimates of changes in carbon (C) storage are challenging due to the heterogeneous nature of rangelands and the limited availability of field observations. In this work, we leveraged remote sensing observations, tower‐based C flux measurements from over 60 rangeland sites in the Western and Midwestern US, and other environmental data sets to build the process‐based Rangeland Carbon Tracking and Management (RCTM) modeling system. The RCTM system is designed to simulate the past 20 years of rangeland C dynamics and is regionally calibrated. The RCTM system performs well in estimating spatial and temporal rangeland C fluxes as well as spatial SOC storage. Model simulation results revealed increased SOC storage and rangeland productivity driven by annual precipitation patterns. The RCTM system developed by this work can be used to generate accurate spatial and temporal estimates of SOC storage and C fluxes at fine spatial (30 m) and temporal (every 5 days) resolutions, and is well‐suited for informing rangeland C management strategies and improving broad‐scale policy making. Key Points The Rangeland Carbon Tracking and Monitoring System was calibrated to simulate vegetation type‐specific rangeland C dynamics Regional variability in carbon fluxes and soil organic carbon is well represented by a remote sensing‐driven process modeling approach Soil organic carbon stocks in Western and Midwestern US rangelands increased over the past 20 years due to increased precipitation

Macroalgal beds have drawn attention as one of the vegetated coastal ecosystems that act as atmospheric CO2 sinks. Although macroalgal metabolism as well as inorganic and organic carbon flows are important pathways for CO2 uptake by macroalgal beds, the relationships between macroalgal metabolism and associated carbon flows are still poorly understood. In the present study, we investigated carbon flows, including air–water CO2 exchange and budgets of dissolved inorganic carbon, total alkalinity, and dissolved organic carbon (DOC), in a temperate macroalgal bed during the productive months of the year. To assess the key mechanisms responsible for atmospheric CO2 uptake by the macroalgal bed, we estimated macroalgal metabolism and lateral carbon flows (i.e., carbon exchanges between the macroalgal bed and the offshore area) by using field measurements of carbon species, a field-bag method, a degradation experiment, and mass-balance modeling in a temperate Sargassum bed over a diurnal cycle. Our results showed that macroalgal metabolism and lateral carbon flows driven by water exchange affected air–water CO2 exchange in the macroalgal bed and the surrounding waters. Macroalgal metabolism caused overlying waters to contain low concentrations of CO2 and high concentrations of DOC that were efficiently exported offshore from the macroalgal bed. These results indicate that the exported water can potentially lower CO2 concentrations in the offshore surface water and enhance atmospheric CO2 uptake. Furthermore, the Sargassum bed exported 6 %–35 % of the macroalgal net community production (NCP; 302–1378 mmol C m−2 d−1) as DOC to the offshore area. The results of degradation experiments showed that 56 %–78 % of macroalgal DOC was refractory DOC (RDOC) that persisted for 150 d; thus, the Sargassum bed exported 5 %–20 % of the macroalgal NCP as RDOC. Our findings suggest that macroalgal beds in habitats associated with high water exchange rates can create significant CO2 sinks around them and export a substantial amount of DOC to offshore areas.

Mapping in situ eddy covariance measurements of terrestrial land–atmosphere fluxes to the globe is a key method for diagnosing the Earth system from a data-driven perspective. We describe the first global products (called X-BASE) from a newly implemented upscaling framework, FLUXCOM-X, representing an advancement from the previous generation of FLUXCOM products in terms of flexibility and technical capabilities. The X-BASE products are comprised of estimates of CO2 net ecosystem exchange (NEE), gross primary productivity (GPP), evapotranspiration (ET), and for the first time a novel, fully data-driven global transpiration product (ETT), at high spatial (0.05°) and temporal (hourly) resolution. X-BASE estimates the global NEE at −5.75 ± 0.33 Pg C yr−1 for the period 2001–2020, showing a much higher consistency with independent atmospheric carbon cycle constraints compared to the previous versions of FLUXCOM. The improvement of global NEE was likely only possible thanks to the international effort to increase the precision and consistency of eddy covariance collection and processing pipelines, as well as to the extension of the measurements to more site years resulting in a wider coverage of bioclimatic conditions. However, X-BASE global net ecosystem exchange shows a very low interannual variability, which is common to state-of-the-art data-driven flux products and remains a scientific challenge. With 125 ± 2.1 Pg C yr−1 for the same period, X-BASE GPP is slightly higher than previous FLUXCOM estimates, mostly in temperate and boreal areas. X-BASE evapotranspiration amounts to 74.7×103 ± 0.9×103 km3 globally for the years 2001–2020 but exceeds precipitation in many dry areas, likely indicating overestimation in these regions. On average 57 % of evapotranspiration is estimated to be transpiration, in good agreement with isotope-based approaches, but higher than estimates from many land surface models. Despite considerable improvements to the previous upscaling products, many further opportunities for development exist. Pathways of exploration include methodological choices in the selection and processing of eddy covariance and satellite observations, their ingestion into the framework, and the configuration of machine learning methods. For this, the new FLUXCOM-X framework was specifically designed to have the necessary flexibility to experiment, diagnose, and converge to more accurate global flux estimates.

Oceanic dissolved inorganic carbon (TC) is the largest pool of carbon that substantially interacts with the atmosphere on human timescales. Oceanic TC is increasing through uptake of anthropogenic carbon dioxide (CO2), and seawater pH is decreasing as a consequence. Both the exchange of CO2 between the ocean and atmosphere and the pH response are governed by a set of parameters that interact through chemical equilibria, collectively known as the marine carbonate system. To investigate these processes, at least two of the marine carbonate system's parameters are typically measured – most commonly, two from TC, total alkalinity (AT), pH, and seawater CO2 fugacity (fCO2; or its partial pressure, pCO2, or its dry-air mole fraction, xCO2) – from which the remaining parameters can be calculated and the equilibrium state of seawater solved. Several software tools exist to carry out these calculations, but no fully functional and rigorously validated tool written in Python, a popular scientific programming language, was previously available. Here, we present PyCO2SYS, a Python package intended to fill this capability gap. We describe the elements of PyCO2SYS that have been inherited from the existing CO2SYS family of software and explain subsequent adjustments and improvements. For example, PyCO2SYS uses automatic differentiation to solve the marine carbonate system and calculate chemical buffer factors, ensuring that the effect of every modelled solute and reaction is accurately included in all its results. We validate PyCO2SYS with internal consistency tests and comparisons against other software, showing that PyCO2SYS produces results that are either virtually identical or different for known reasons, with the differences negligible for all practical purposes. We discuss insights that guided the development of PyCO2SYS: for example, the fact that the marine carbonate system cannot be unambiguously solved from certain pairs of parameters. Finally, we consider potential future developments to PyCO2SYS and discuss the outlook for this and other software for solving the marine carbonate system. The code for PyCO2SYS is distributed via GitHub (https://github.com/mvdh7/PyCO2SYS, last access: 23 December 2021) under the GNU General Public License v3, archived on Zenodo , and documented online (https://pyco2sys.readthedocs.io/en/latest/, last access: 23 December 2021).

The effects of dams on carbon dioxide (CO2) fluxes in downstream lakes remain elusive. Here we combined eddy covariance observations and random forest models to examine multi‐decadal variations in CO2 fluxes in the Poyang Lake, the largest freshwater lake in China, and quantified the contribution of the Three Gorges Dam (TGD), the world's largest hydraulic project. We found the lake fluctuated between CO2 source and sink in 1961–2016, and tended to be CO2 sink in the post‐TGD period (2003–2016) when vegetation expanded early and spatially due to declining water level. TGD can explain approximately 6% of the total differences in annual CO2 fluxes, with major contributions in the impoundment period (up to 22% in middle September to October). The results show a positive side of operational major hydraulic projects on lake carbon sink, and probably caution the negative side of carbon release after dam removal. Plain Language Summary In the past century, dams have significantly altered the hydrological connectivity between rivers and lakes, which affect CO2 exchange in the downstream lake systems. As the largest freshwater lake in China, Poyang Lake has also undergone drastic hydrological changes, attributable largely to the operation of the Three Gorges Dam (TGD), the world's largest hydraulic project ever, in 2003. Based on flux observations and machine learning method, we show that annual lake CO2 exchange shifted toward carbon sink during 1961–2016. The TGD has a major impact on lake CO2 fluxes, especially during the impoundment stage in middle September–October, explaining 22% of the flux differences between the pre‐ and post‐TGD period. The results show a positive side of hydraulic projects albeit their adverse impact on ecological protection. Key Points Poyang Lake as a CO2 source or sink significantly depends on water level Poyang Lake became a CO2 sink since the Three Gorges Dam operation in 2003 Dam explains 22% of differences in CO2 fluxes in autumn impoundment period

Rapid climate warming and sea-ice loss have induced major changes in the sea surface partial pressure of CO2 (pCO2). However, the long-term trends in the western Arctic Ocean are unknown. Here we show that in 1994–2017, summer pCO2 in the Canada Basin increased at twice the rate of atmospheric increase. Warming and ice loss in the basin have strengthened the pCO2 seasonal amplitude, resulting in the rapid decadal increase. Consequently, the summer air–sea CO2 gradient has reduced rapidly, and may become near zero within two decades. In contrast, there was no significant pCO2 increase on the Chukchi Shelf, where strong and increasing biological uptake has held pCO2 low, and thus the CO2 sink has increased and may increase further due to the atmospheric CO2 increase. Our findings elucidate the contrasting physical and biological drivers controlling sea surface pCO2 variations and trends in response to climate change in the Arctic Ocean.Surface CO2 concentrations in the western Arctic Ocean differ due to local processes. During the period 1994–2017, the Canada Basin has shown rapid increases as warming and ice loss enhance air–sea exchange of CO2, whereas the Chukchi Shelf has strong biological activity, resulting in a CO2 sink.

In recent decades, the ocean CO2 uptake has increased in response to rising atmospheric CO2. Yet, physical climate change also affects the ocean CO2 uptake, but magnitude and driving processes are poorly understood. Using a global ocean biogeochemistry model, we find that without climate change, the mean carbon uptake 2000–2019 would have been 13% higher and the trend 1958–2019 would have been 27% higher. Changes in wind are the dominant driver of the climate effect on CO2 uptake as they affect advective carbon transport and mixing, but the effect of warming increases over time. Roughly half of the globally integrated wind‐driven trend stems from the subpolar Southern Ocean and polar oceans in both hemispheres. Warming reduces the solubility of CO2 and acts rather homogeneously over the world oceans. However, the warming effect on pCO2 is dampened by limited exchange of surface and deep waters. Plain Language Summary At the ocean surface, the greenhouse gas CO2 is exchanged between atmosphere and ocean. Because the concentration of CO2 in the atmosphere has increased through man‐made CO2 emissions, the ocean has taken up an increasing amount of CO2 (about 25% of the emissions). Beside the atmospheric CO2 concentration, other climate variables affect the oceanic CO2 uptake: Firstly, winds set the ocean in motion, drive ocean currents and thus control the transport of dissolved forms of CO2 with ocean circulation. In particular, winds drive the exchange between the surface ocean and the deep ocean, where the bigger part of the ocean's carbon is stored. Secondly, global warming affects the oceanic CO2 uptake because the solubility of CO2 in water is temperature‐dependent. In recent decades, changes in winds and global warming have reduced the capacity of the ocean to remove CO2 from the atmosphere. Yet, this climate effect is not well understood. Here, we use computer simulations from 1958 to 2019 to quantify the climate effect and find that climate change reduced the oceanic CO2 uptake of the last two decades by 13%, with winds having more of an effect than sea surface warming. The effect of warming increases over time. Key Points Climate change reduced ocean CO2 uptake by 13% (2000–2019) primarily induced by wind‐driven changes in dissolved inorganic carbon transport A feedback between the surface dissolved inorganic carbon concentration and air‐sea flux dampens warming‐driven outgassing of natural carbon The effect of wind changes stems primarily from high latitudes, whereas the effect of warming is globally relatively uniform

Forests play a crucial role in the global carbon (C) cycle by storing and sequestering a substantial amount of C in the terrestrial biosphere. Due to temporal dynamics in climate and vegetation activity, there are significant regional variations in carbon dioxide (CO2) fluxes between the biosphere and atmosphere in forests that are affecting the global C cycle. Current forest CO2 flux dynamics are controlled by instantaneous climate, soil, and vegetation conditions, which carry legacy effects from disturbances and extreme climate events. Our level of understanding from the legacies of these processes on net CO2 fluxes is still limited due to their complexities and their long-term effects. Here, we combined remote sensing, climate, and eddy-covariance flux data to study net ecosystem CO2 exchange (NEE) at 185 forest sites globally. Instead of commonly used non-dynamic statistical methods, we employed a type of recurrent neural network (RNN), called Long Short-Term Memory network (LSTM) that captures information from the vegetation and climate's temporal dynamics. The resulting data-driven model integrates interannual and seasonal variations of climate and vegetation by using Landsat and climate data at each site. The presented LSTM algorithm was able to effectively describe the overall seasonal variability (Nash-Sutcliffe efficiency, NSE = 0.66) and across-site (NSE = 0.42) variations in NEE, while it had less success in predicting specific seasonal and interannual anomalies (NSE = 0.07). This analysis demonstrated that an LSTM approach with embedded climate and vegetation memory effects outperformed a non-dynamic statistical model (i.e. Random Forest) for estimating NEE. Additionally, it is shown that the vegetation mean seasonal cycle embeds most of the information content to realistically explain the spatial and seasonal variations in NEE. These findings show the relevance of capturing memory effects from both climate and vegetation in quantifying spatio-temporal variations in forest NEE.

The China Seas include the South China Sea, East China Sea, Yellow Sea, and Bohai Sea. Located off the Northwestern Pacific margin, covering 4700000 km 2 from tropical to northern temperate zones, and including a variety of continental margins/basins and depths, the China Seas provide typical cases for carbon budget studies. The South China Sea being a deep basin and part of the Western Pacific Warm Pool is characterized by oceanic features; the East China Sea with a wide continental shelf, enormous terrestrial discharges and open margins to the West Pacific, is featured by strong cross-shelf materials transport; the Yellow Sea is featured by the confluence of cold and warm waters; and the Bohai Sea is a shallow semi-closed gulf with strong impacts of human activities. Three large rivers, the Yangtze River, Yellow River, and Pearl River, flow into the East China Sea, the Bohai Sea, and the South China Sea, respectively. The Kuroshio Current at the outer margin of the Chinese continental shelf is one of the two major western boundary currents of the world oceans and its strength and position directly affect the regional climate of China. These characteristics make the China Seas a typical case of marginal seas to study carbon storage and fluxes. This paper systematically analyzes the literature data on the carbon pools and fluxes of the Bohai Sea, Yellow Sea, East China Sea, and South China Sea, including different interfaces (land-sea, sea-air, sediment-water, and marginal sea-open ocean) and different ecosystems (mangroves, wetland, seagrass beds, macroalgae mariculture, coral reefs, euphotic zones, and water column). Among the four seas, the Bohai Sea and South China Sea are acting as CO 2 sources, releasing about 0.22 and 13.86–33.60 Tg C yr −1 into the atmosphere, respectively, whereas the Yellow Sea and East China Sea are acting as carbon sinks, absorbing about 1.15 and 6.92–23.30 Tg C yr −1 of atmospheric CO 2 , respectively. Overall, if only the CO 2 exchange at the sea-air interface is considered, the Chinese marginal seas appear to be a source of atmospheric CO 2 , with a net release of 6.01–9.33 Tg C yr −1 , mainly from the inputs of rivers and adjacent oceans. The riverine dissolved inorganic carbon (DIC) input into the Bohai Sea and Yellow Sea, East China Sea, and South China Sea are 5.04, 14.60, and 40.14 Tg C yr −1 , respectively. The DIC input from adjacent oceans is as high as 144.81 Tg C yr −1 , significantly exceeding the carbon released from the seas to the atmosphere. In terms of output, the depositional fluxes of organic carbon in the Bohai Sea, Yellow Sea, East China Sea, and South China Sea are 2.00, 3.60, 7.40, and 5.92 Tg C yr −1 , respectively. The fluxes of organic carbon from the East China Sea and South China Sea to the adjacent oceans are 15.25–36.70 and 43.93 Tg C yr −1 , respectively. The annual carbon storage of mangroves, wetlands, and seagrass in Chinese coastal waters is 0.36–1.75 Tg C yr −1 , with a dissolved organic carbon (DOC) output from seagrass beds of up to 0.59 Tg C yr −1 . Removable organic carbon flux by Chinese macroalgae mariculture account for 0.68 Tg C yr −1 and the associated POC depositional and DOC releasing fluxes are 0.14 and 0.82 Tg C yr −1 , respectively. Thus, in total, the annual output of organic carbon, which is mainly DOC, in the China Seas is 81.72–104.56 Tg C yr −1 . The DOC efflux from the East China Sea to the adjacent oceans is 15.00–35.00 Tg C yr −1 . The DOC efflux from the South China Sea is 31.39 Tg C yr −1 . Although the marginal China Seas seem to be a source of atmospheric CO 2 based on the CO 2 flux at the sea-air interface, the combined effects of the riverine input in the area, oceanic input, depositional export, and microbial carbon pump (DOC conversion and output) indicate that the China Seas represent an important carbon storage area.