CO2 Flux Estimates Research Articles

Temporal Variability in Gas Emissions at Bagana Volcano Revealed by Aerial, Ground, and Satellite Observations

AbstractBagana is a remote, highly active volcano, located on Bougainville Island in southeastern Papua New Guinea. The volcano has exhibited sustained and prodigious sulfur dioxide gas emissions in recent decades, accompanied by frequent episodes of lava extrusion. The remote location of Bagana and its persistent activity have made it a valuable case study for satellite observations of active volcanism. This remoteness has also left many features of Bagana relatively unexplored. Here, we present the first measurements of volcanic gas composition, achieved by unoccupied aerial system (UAS) flights through the volcano's summit plume, and a payload comprising a miniaturized MultiGAS. We combine our measurements of the molar CO2/SO2 ratio in the plume with coincident remote sensing measurements (ground‐ and satellite‐based) of SO2 emission rate to compute the first estimate of CO2 flux at Bagana. We report low SO2 and CO2 fluxes at Bagana from our fieldwork in September 2019, ∼320 ± 76 td−1 and ∼320 ± 84 td−1, respectively, which we attribute to the volcano's low level of activity at the time of our visit. We use satellite observations to demonstrate that Bagana's activity and emissions behavior are highly variable and advance the argument that such variability is likely an inherent feature of many volcanoes worldwide and yet is inadequately captured by our extant volcanic gas inventories, which are often biased to sporadic measurements. We argue that there is great value in the use of UAS combined with MultiGAS‐type instruments for remote monitoring of gas emissions from other inaccessible volcanoes.

Soil gas CO2 emissions from active faults: a case study from the Anninghe—Zemuhe fault, Southeastern Tibetan Plateau, China

Introduction: Carbon dioxide emissions from non-volcanic areas are undervalued in the carbon cycle.Methods: First estimates of diffuse CO2 flux from the Anninghe—Zemuhe fault (AZF), Southeastern Tibetan Plateau, China, which suggests this could equal 15% emissions from all volcanoes in China. Following the accumulation chamber method, CO2 flux was investigated at 1,483 points, and along 67 profiles crossing the AZF. Results and discussion: Total CO2 emissions from the AZF were estimated 1.2 Mt yr-1. The relationship between soil gas CO2 fluxes, earthquakes, and fault activity was discussed. The intense fault activity in the southern part of the Zemuhe fault (ZMHF) and the northern part of the Anninghe fault (ANH) was inferred, which could have enhanced the porosity of the soil, and accelerated the water-rock interactions and soil gas emission within the fault zone. The chemical and isotopic data indicated that biogenic CO2 was the primary source of CO2 from the AZF. Produced by interactions between groundwaters and carbonates, soil gas CO2 could migrate to the near surface through cracks. Spatial variations of CO2 flux in soil gas indicate that seismic activity could be responsible for the jumpy variations of CO2 flux. The diffuse CO2 from deep faults may contribute considerably to the greenhouse gas cycles.

Intra-urban Variations of the CO2 Fluxes at the Surface-Atmosphere Interface in the Seoul Metropolitan Area

Severe spatiotemporal heterogeneity of emissions sources and limited measurement networks have been hampering the monitoring and understanding of CO2 fluxes in large cities, a great concern in climate research as big cities are among the major sources of anthropogenic CO2 in the climate system. To understand the CO2 fluxes in Seoul, Korea, CO2 fluxes at eight surface energy balance sites, six urban (vegetation-area fraction < 15%) and two suburban (vegetation-area fraction > 60%), for 2017–2018 are analyzed and attributed to the local land-use and business types. The analyses show that the CO2 flux variations at the suburban sites are mainly driven by vegetation and that the CO2 flux differences between the urban and suburban sites originate from the differences in the vegetation-area fraction and anthropogenic CO2 emissions. For the CO2 fluxes at the urban sites; (1) vehicle traffic (traffic) and heating-fuel consumption (heating) contribute > 80% to the total, (2) vegetation effects are minimal, (3) the seasonal cycle is driven mainly by heating, (4) the contribution of heating is positively related to the building-area fraction, (5) the annual total is positively (negatively) correlated with the commercial-area (residential-area) fraction, and (6) the traffic at the commercial sites depend further on the main business types to induce distinct CO2 flux weekly cycles. This study shows that understanding and estimation of CO2 fluxes in large urban areas require careful site selections and analyses based on detailed consideration of the land-use and business types refined beyond the single representative land-use type widely-used in contemporary studies.

Deep magma degassing and volatile fluxes through volcanic hydrothermal systems: Insights from the Askja and Kverkfjöll volcanoes, Iceland

Mantle volatiles are transported to Earth's crust and surface by basaltic volcanism. During subaerial eruptions, vast amounts of carbon, sulfur and halogens can be released to the atmosphere during a short time-interval, with impacts ranging in scale from the local environment to the global climate. By contrast, passive volatile release at the surface originating from magmatic intrusions is characterized by much lower flux, yet may outsize eruptive volatile quantities over long timescales. Volcanic hydrothermal systems (VHSs) act as conduits for such volatile release from degassing intrusions and can be used to gauge the contribution of intrusive magmatism to global volatile cycles. Here, we present new compositional and isotopic (δD and δ18O-H2O, 3He/4He, δ13C-CO2, Δ33S-δ34S-H2S and SO4) data for thermal waters and fumarole gases from the Askja and Kverkfjöll volcanoes in central Iceland. We use the data together with magma degassing modelling and mass balance calculations to constrain the sources of volatiles in VHSs and to assess the role of intrusive magmatism to the volcanic volatile emission budgets in Iceland.The CO2/ΣS (10−30), 3He/4He (8.3–10.5 RA; 3He/4He relative to air), δ13C-CO2 (−4.1 to −0.2 ‰) and Δ33S-δ34S-H2S (−0.031 to 0.003 ‰ and −1.5 to +3.6‰) values in high-gas flux fumaroles (CO2 > 10 mmol/mol) are consistent with an intrusive magmatic origin for CO2 and S at Askja and Kverkfjöll. We demonstrate that deep (0.5–5 kbar, equivalent to ∼2–18 km crustal depth) decompression degassing of basaltic intrusions in Iceland results in CO2 and S fluxes of 330–5060 and 6–210 kt/yr, respectively, which is sufficient to account for the estimated CO2 flux of Icelandic VHSs (3365–6730 kt/yr), but not the VHS S flux (220–440 kt/yr). Secondary, crystallization-driven degassing from maturing intrusions and leaching of crustal rocks are suggested as additional sources of S. Only a minor proportion of the mantle flux of Cl is channeled via VHSs whereas the H2O flux remains poorly constrained, because magmatic signals in Icelandic VHSs are masked by a dominant shallow groundwater component of meteoric water origin. These results suggest that the bulk of the mantle CO2 and S flux to the atmosphere in Iceland is supplied by intrusive, not eruptive magmatism, and is largely vented via hydrothermal fields.

Spatiotemporal distributions of air-sea CO2 flux modulated by windseas in the Southern Indian Ocean

The Southern Indian Ocean is a major reservoir for rapid carbon exchange with the atmosphere, plays a key role in the world’s carbon cycle. To understand the importance of anthropogenic CO2 uptake in the Southern Indian Ocean, a variety of methods have been used to quantify the magnitude of the CO2 flux between air and sea. The basic approach is based on the bulk formula—the air-sea CO2 flux is commonly calculated by the difference in the CO2 partial pressure between the ocean and the atmosphere, the gas transfer velocity, the surface wind speed, and the CO2 solubility in seawater. However, relying solely on wind speed to measure the gas transfer velocity at the sea surface increases the uncertainty of CO2 flux estimation. Recent studies have shown that the generation and breaking of ocean waves also significantly affect the gas transfer process at the air-sea interface. In this study, we highlight the impact of windseas on the process of air-sea CO2 exchange and address its important role in CO2 uptake in the Southern Indian Ocean. We run the WAVEWATCH III model to simulate surface waves in this region over the period from January 1st 2002 to December 31st 2021. Then, we use the spectral partitioning method to isolate windseas and swells from total wave fields. Finally, we calculate the CO2 flux based on the new semiempirical equation for gas transfer velocity considering only windseas. We found that after considering windseas’ impact, the seasonal mean zonal flux (mmol/m2·d) increased approximately 10%-20% compared with that calculated solely on wind speed in all seasons. Evolution of air-sea net carbon flux (PgC) increased around 5.87%-32.12% in the latest 5 years with the most significant seasonal improvement appeared in summer. Long-term trend analysis also indicated that the CO2 absorption capacity of the whole Southern Indian Ocean gradually increased during the past 20 years. These findings extend the understanding of the roles of the Southern Indian Ocean in the global carbon cycle and are useful for making management policies associated with marine environmental protection and global climatic change mitigation.

Independent Bias Correction Method for Satellite Observation Data Introduced to CO&lt;sub&gt;2&lt;/sub&gt; Flux Inversion

Satellite observations are expected to play an important role in studying carbon fluxes. However, it is necessary to properly remove spatiotemporal bias from these observations. In this study, we estimated the spatiotemporal bias in satellite XCO2 data by making an inversion of in-situ observations. Compared to the GOSAT XCO2 concentrations derived from this analysis, the global average difference is −1.26 ppm, with a difference of −0.76 ppm over the land and −1.54 ppm over the ocean. We then developed a method to correct the bias between the satellite XCO2 and the in-situ inversed XCO2 data. Using this bias correction method, we performed an inverse analysis using in-situ and satellite observations and found that the error relative to the independent observations decreased mainly in the middle and upper troposphere compared to the in-situ inversion. Compared to previous studies, the introduction of satellite observations also suppressed large variations in the regional CO2 flux, except in regions with few observations. After correction using the proposed method, the estimated CO2 flux can be used to provide improved estimates of regional CO2 fluxes; however, the results also show a need for increasing the CO2 observation sites and improving the analysis system.

Optimizing 4 years of CO2 biospheric fluxes from OCO-2 and in situ data in TM5: fire emissions from GFED and inferred from MOPITT CO data

Abstract. Column mixing ratio of carbon dioxide (CO2) data alone do not provide enough information for source attribution. Carbon monoxide (CO) is a product of inefficient combustion often co-emitted with CO2. CO data can then provide a powerful constraint on fire emissions, supporting more accurate estimation of biospheric CO2 fluxes. In this framework and using the chemistry transport model TM5, a CO inversion using Measurements of Pollution in The Troposphere (MOPITT) v8 data is performed to estimate fire emissions which are then converted into CO2 fire emissions (called FIREMo) through the use of the emission ratio. These optimized CO2 fire emissions are used to rebalance the CO2 net ecosystem exchange (NEEMo) and respiration (RhMo) with the global CO2 growth rate. Subsequently, in a second step, these rebalanced fluxes are used as priors for a CO2 inversion to derive the NEE and ocean fluxes constrained either by the Orbiting Carbon Observatory 2 (OCO-2) v9 or by in situ (IS) CO2 data. For comparison purpose, we also balanced the respiration using fire emissions from the Global Fire Emissions Database (GFED) version 3 (GFED3) and version 4.1s (GFED4.1s). We hence study the impact of CO fire emissions in our CO2 inversions at global, latitudinal, and regional scales over the period 2015–2018 and compare our results to the two other similar approaches using GFED3 (FIRE3) and GFED4.1s (FIRE4) fires, as well as with an inversion using both Carnegie–Ames–Stanford Approach (CASA)-GFED3 NEE and GFED3 fire priors (priorCMS). After comparison at the different scales, the inversions are evaluated against Total Carbon Column Observing Network (TCCON) data. Comparison of the flux estimates shows that at the global scale posterior net flux estimates are more robust than the different prior flux estimates. However, at the regional scale, we can observe differences in fire emissions among the priors, resulting in differences among the NEE prior emissions. The derived NEE prior emissions are rebalanced in concert with the fires. Consequently, the differences observed in the NEE posterior emissions are a result of the balancing with fires and the constraints provided by CO2 observations. Tropical net flux estimates from in situ inversions are highly sensitive to the prior flux assumed, of which fires are a significant component. Slightly larger net CO2 sources are derived with posterior fire emissions using either FIRE4 or FIREMo in the OCO-2 inversion, in particular for most tropical regions during the 2015 El Niño year. Similarly, larger net CO2 sources are also derived with posterior fire emissions in the in situ data inversion for Tropical Asia. Evaluation with CO2 TCCON data shows lower biases with the three rebalanced priors than with the prior using CASA-GFED3. However, posteriors have average bias and scatter very close each other, making it difficult to conclude which simulation performs better than the other. We observe that the assimilated CO2 data have a strong influence on the global net fluxes among the different inversions. Inversions using OCO-2 (or IS) data have similar emissions, mostly as a result of the observational constraints and to a lesser extent because of the fire prior used. But results in the tropical regions suggest net flux sensitivity to the fire prior for both the IS and OCO-2 inversions. Further work is needed to improve prior fluxes in tropical regions where fires are a significant component. Finally, even if the inversions using the FIREMo prior did enhance the biases over some TCCON sites, it is not the case for the majority of TCCON sites. This study consequently pushes forward the development of a CO–CO2 joint inversion with multi-observations for a possible stronger constraint on posterior CO2 fire and biospheric emissions.

High resolution modeling of vegetation reveals large summertime biogenic CO2 fluxes in New York City

Abstract Cities are beginning to monitor atmospheric carbon dioxide (CO2) to assess the efficacy of their climate policies. However, changes in anthropogenic CO2 emissions must be separated from biospheric CO2 fluxes which have a large seasonal cycle. Urban vegetation (e.g. lawns, trees along street and in parks, etc) in developed land covers is often omitted in regional biogenic CO2 flux models. We set up a biosphere model to estimate the regional biogenic CO2 fluxes in New York City (NYC) and assess the importance of vegetation within developed land covers. The model incorporates a high-resolution (30 m) land cover map which identifies the mixture of impervious surfaces and vegetation that is ubiquitous across developed land covers. We designed three model scenarios to evaluate the role of developed land covers in regional biogenic CO2 fluxes by assuming (a) there is no vegetation versus scenarios where all remotely sensed vegetation in developed land covers is either (b) grassland or (c) deciduous forest. Despite relatively low tree canopy cover in NYC, the regional biogenic CO2 fluxes are surprisingly large when vegetation within the developed land covers is included. Furthermore, the types of vegetation within the developed land covers are crucially important for estimating regional biogenic CO2 fluxes, demonstrated by a doubling in estimates of total biogenic CO2 flux when this vegetation is assumed to be grassland compared to forest. Using a Lagrangian atmospheric transport model, we find that the regional biogenic CO2 uptake offsets up to 40% of atmospheric CO2 enhancements attributed to anthropogenic emissions in summer afternoons and completely balances on-road traffic in one of the most congested cities in the United States. Accurate characterization of the vegetation and biogenic carbon fluxes in cities are essential to the development of effective atmospheric monitoring tools. Future measurements should focus on constraining CO2 fluxes in urban grasslands (i.e. lawns).

Inconsistency between ship- and Argo float-based pCO2 at the intense upwelling region of the Drake Passage, Southern Ocean

The Southern Ocean absorbs a quarter of anthropogenic carbon dioxide (CO2) from the atmosphere to modulate the climate system. However, less attention has been paid to the CO2 outgassing phenomenon at the Antarctic Circumpolar Current (ACC) region of the Southern Ocean due to strong upwelling. Recent studies using autonomous biogeochemical-Argo float revealed a greater winter CO2 outgassing than previously estimated at ACC zone of the Southern Ocean, which, however, remains controversial and urgently needs to be validated. Here we take the Drake Passage as a case study to present new insights into the Southern Ocean carbon cycle and examine the validity of float-based CO2 outgassing. Upon integrating the ship-based data over the past two decades, we investigate the spatiotemporal variability of sea surface CO2 partial pressure (pCO2) in Drake Passage. We show that Drake Passage is acting as a year-round weak CO2 sink, although some CO2 uptake is counteracted by winter CO2 outgassing. The float-based pCO2 values are overall higher than ship-based values in winter, by 6 to 20 µatm (averaged 14 µatm) at the most intensive upwelling region. We then develop a surface carbon balance calculation (considering mixing between surface, subsurface, and upwelled waters) to estimate the potential of surface pCO2 increase due to upwelling, and we find that upwelling of CO2-rich subsurface waters in Drake Passage cannot support an excess ΔpCO2 of 14 µatm as suggested by float detections. We further compare our results to previous study and find that, although we used same datasets and obtained comparable results, the way to conclude the bias in float-based pCO2 would cause significant difference: an uncertainty of ±2.7% (i.e., ± 11 µatm) in float-based pCO2 estimated by other study seems acceptable, however, it is five times larger than the typical ship-based pCO2 uncertainty ( ± 2 µatm), and would cause ~180% bias in CO2 flux estimates. Going forward, there is special need for caution when interpreting the float-based CO2 flux; meanwhile, further comparisons and corrections between float- and ship-based pCO2 are clearly warranted.

Explicit Physical Knowledge in Machine Learning for Ocean Carbon Flux Reconstruction: The pCO2‐Residual Method

AbstractThe ocean reduces human impacts on global climate by absorbing and sequestering CO2 from the atmosphere. To quantify global, time‐resolved air‐sea CO2 fluxes, surface ocean pCO2 is needed. A common approach for estimating full‐coverage pCO2 is to train a machine learning algorithm on sparse in situ pCO2 data and associated physical and biogeochemical observations. Though these associated variables have understood relationships to pCO2, it is often unclear how they drive pCO2 outputs. Here, we make two advances that enhance connections between physical understanding and reconstructed pCO2. First, we apply pre‐processing to the pCO2 data to remove the direct effect of temperature. This enhances the biogeochemical/physical component of pCO2 in the target variable and reduces the complexity that the machine learning must disentangle. Second, we demonstrate that the resulting algorithm has physically understandable connections between input data and the output biogeochemical/physical component of pCO2. The final pCO2 reconstruction agrees modestly better with independent data than most other approaches. Uncertainties in the reconstructed pCO2 and impacts on the estimated CO2 fluxes are quantified. Uncertainty in piston velocity drives substantial flux uncertainties in some regions, but does not increase globally integrated estimates of uncertainty in CO2 fluxes from observation‐based products. Our reconstructed CO2 fluxes show larger interannual variability than smoother neural network approaches, but a lesser trend since 2005. We estimate an air‐sea flux of −1.8 PgC/yr (anthropogenic flux of −2.3 ± 0.5 PgC/yr) for 1990–2019, agreeing with other data products and the Global Carbon Budget 2020 (−2.3 ± 0.4 PgC/yr).

Variability in Sea Surface pCO2 and Controlling Factors in the Bay of Bengal Based on buoy Observations at 15°N, 90°E

AbstractDiurnal variability of the CO2 partial pressure (pCO2) at the sea surface (pCO2sea) and its impact on air‐sea CO2 flux estimations are currently poorly understood. In this work, diurnal variations in pCO2sea were examined in the Bay of Bengal (BoB) based on 3‐hourly observations collected at the BoB Ocean Acidification moored buoy between November 2013 and November 2018 by a moored autonomous pCO2 system. Significant mean diurnal pCO2sea cycles and a mean diurnal amplitude of 11 ± 9 μatm were observed based on 1215 complete diurnal cycles. One‐dimensional mass budget model calculations suggest that temperature was responsible for these mean diurnal variations in pCO2sea and that these variations were intensified by biological activity. The effects of air‐sea gas fluxes and mixing on the mean diurnal pCO2sea variability were very low. A weak annual net source of 12.1 mmol m−2 year−1 was estimated at this site. The sampling frequency greatly impacted the annual air‐sea CO2 flux estimations. Compared to the 3‐hr sampling frequency, random daily sampling resulted in a bias of ±7.7 mmol m−2 year−1 or 64%, and regular daily sampling resulted in a maximum bias of 400%. Factorial experiments showed that wind speed had the largest impact on the air‐sea CO2 flux estimations, followed by pCO2sea and atmospheric pCO2 (pCO2air). These findings indicate the importance of obtaining continuous pCO2 measurements to accurately estimate air‐sea CO2 flux; additionally, pCO2 and wind speed measurements should be obtained at frequencies higher than 6 hr.

A Numerical reassessment of the Gulf of Mexico carbon system in connection with the Mississippi River and global ocean

Abstract. Coupled physical–biogeochemical models can fill the spatial and temporal gap in ocean carbon observations. Challenges of applying a coupled physical–biogeochemical model in the regional ocean include the reasonable prescription of carbon model boundary conditions, lack of in situ observations, and the oversimplification of certain biogeochemical processes. In this study, we applied a coupled physical–biogeochemical model (Regional Ocean Modelling System, ROMS) to the Gulf of Mexico (GoM) and achieved an unprecedented 20-year high-resolution (5 km, 1/22∘) hindcast covering the period of 2000 to 2019. The biogeochemical model incorporated the dynamics of dissolved organic carbon (DOC) pools and the formation and dissolution of carbonate minerals. The biogeochemical boundaries were interpolated from NCAR's CESM2-WACCM-FV2 solution after evaluating the performance of 17 GCMs in the GoM waters. Model outputs included carbon system variables of wide interest, such as pCO2, pH, aragonite saturation state (ΩArag), calcite saturation state (ΩCalc), CO2 air–sea flux, and carbon burial rate. The model's robustness is evaluated via extensive model–data comparison against buoys, remote-sensing-based machine learning (ML) products, and ship-based measurements. A reassessment of air–sea CO2 flux with previous modeling and observational studies gives us confidence that our model provides a robust and updated CO2 flux estimation, and NGoM is a stronger carbon sink than previously reported. Model results reveal that the GoM water has been experiencing a ∼ 0.0016 yr−1 decrease in surface pH over the past 2 decades, accompanied by a ∼ 1.66 µatm yr−1 increase in sea surface pCO2. The air–sea CO2 exchange estimation confirms in accordance with several previous models and ocean surface pCO2 observations that the river-dominated northern GoM (NGoM) is a substantial carbon sink, and the open GoM is a carbon source during summer and a carbon sink for the rest of the year. Sensitivity experiments are conducted to evaluate the impacts of river inputs and the global ocean via model boundaries. The NGoM carbon system is directly modified by the enormous carbon inputs (∼ 15.5 Tg C yr−1 DIC and ∼ 2.3 Tg C yr−1 DOC) from the Mississippi–Atchafalaya River System (MARS). Additionally, nutrient-stimulated biological activities create a ∼ 105 times higher particulate organic matter burial rate in NGoM sediment than in the case without river-delivered nutrients. The carbon system condition of the open ocean is driven by inputs from the Caribbean Sea via the Yucatan Channel and is affected more by thermal effects than biological factors.

Using the atmospheric CO2 growth rate to constrain the CO2 flux from land use and land cover change since 1900.

We explore the ability of the atmospheric CO2 record since 1900 to constrain the source of CO2 from land use and land cover change (hereafter "land use"), taking account of uncertainties in other terms in the global carbon budget. We find that the atmospheric constraint favors land use CO2 flux estimates with lower decadal variability and can identify potentially erroneous features, such as emission peaks around 1960 and after 2000, in some published estimates. Furthermore, we resolve an offset in the global carbon budget that is most plausibly attributed to the land use flux. This correction shifts the mean land use flux since 1900 across 20 published estimates down by 0.35 PgC year-1 to 1.04 ± 0.57 PgC year-1 , which is within the range but at the low end of these estimates. We show that the atmospheric CO2 record can provide insights into the time history of the land use flux that may reduce uncertainty in this term and improve current understanding and projections of the global carbon cycle.

Surface ocean CO2 concentration and air-sea flux estimate by machine learning with modelled variable trends

The global ocean is a major sink of anthropogenic carbon dioxide (CO2) emitted into the atmosphere. Machine learning has been actively used in the past decades to estimate the oceanic sink, but it is still a challenge to obtain an accurate estimate due to scarcely available CO2 measurements. One of the methods to deal with data scarcity was normalizing multiple years’ CO2 values to a reference year to increase the spatial coverage. The practice assumed a constant CO2 trend for the normalization. Here, we used three machine learning models to extract variable ocean CO2 trends on a decadal scale and proposed a method to use the extracted ocean CO2 trends to correct the decadal atmospheric CO2 trends for data normalization. The method minimizes assumptions of using the extracted ocean CO2 trends directly. Comparisons of our CO2 flux estimate with machine learning products included in Global Carbon Budget 2021 indicates that using the variable trends improved the bias resulted from using a constant trend and that the trends are a critical factor for machine learning methods. Our dataset includes monthly distributions of surface ocean CO2 concentration and air-sea flux in 1980-2020 with a spatial resolution of 1×1 degree.

Update on the Temperature Corrections of Global Air‐Sea CO2 Flux Estimates

AbstractThe oceans are a major carbon sink. Sea surface temperature (SST) is a crucial variable in the calculation of the air‐sea carbon dioxide (CO2) flux from surface observations. Any bias in the SST or any upper ocean vertical temperature gradient (e.g., the cool skin effect) potentially generates a bias in the CO2 flux estimates. A recent study suggested a substantial increase (∼50% or ∼0.9 Pg C yr−1) in the global ocean CO2 uptake due to this temperature effect. Here, we use a gold standard buoy SST data set as the reference to assess the accuracy of insitu SST used for flux calculation. A physical model is then used to estimate the cool skin effect, which varies with latitude. The bias‐corrected SST (assessed by buoy SST) coupled with the physics‐based cool skin correction increases the average ocean CO2 uptake by ∼35% (0.6 Pg C yr−1) from 1982 to 2020, which is substantially smaller than the previous correction. After these temperature considerations, we estimate an average net ocean CO2 uptake of 2.2 ± 0.4 Pg C yr−1 from 1994 to 2007 based on an ensemble of surface observation‐based flux estimates, in line with the independent interior ocean carbon storage estimate corrected for the river induced natural outgassing flux (2.1 ± 0.4 Pg C yr−1).

The Space Carbon Observatory (SCARBO) concept: assessment of XCO2 and XCH4 retrieval performance

Abstract. Several single-platform satellite missions have been designed during the past decades in order to retrieve the atmospheric concentrations of anthropogenic greenhouse gases (GHG), initiating worldwide efforts towards better monitoring of their sources and sinks. To set up a future operational system for anthropogenic GHG emission monitoring, both revisit frequency and spatial resolution need to be improved. The Space Carbon Observatory (SCARBO) project aims at significantly increasing the revisit frequency of spaceborne GHG measurements, while reaching state-of-the-art precision requirements, by implementing a concept of small satellite constellation. It would accommodate a miniaturised GHG sensor named NanoCarb coupled with an aerosol instrument, the multi-angle polarimeter SPEXone. More specifically, the NanoCarb sensor is a static Fabry–Pérot imaging interferometer with a 2.3×2.3 km2 spatial resolution and 200 km swath. It samples a truncated interferogram at optical path differences (OPDs) optimally sensitive to all the geophysical parameters necessary to retrieve column-averaged dry-air mole fractions of CO2 and CH4 (hereafter XCO2 and XCH4). In this work, we present the Level 2 performance assessment of the concept proposed in the SCARBO project. We perform inverse radiative transfer to retrieve XCO2 and XCH4 directly from synthetic NanoCarb truncated interferograms and provide their systematic and random errors, column vertical sensitivities, and degrees of freedom as a function of five scattering-error-critical atmospheric and observational parameters. We show that NanoCarb XCO2 and XCH4 systematic retrieval errors can be greatly reduced with SPEXone posterior outputs used as improved prior aerosol constraints. For two-thirds of the soundings, located at the centre of the 200 km NanoCarb swath, XCO2 and XCH4 random errors span 0.5–1 ppm and 4–6 ppb, respectively, compliant with their respective 1 ppm and 6 ppb precision objectives. Finally, these Level 2 performance results are parameterised as a function of the explored scattering-error-critical atmospheric and observational parameters in order to time-efficiently compute extensive L2 error maps for future CO2 and CH4 flux estimation performance studies.

Estimated regional CO2flux and uncertainty based on an ensemble of atmospheric CO2inversions

Abstract. Global and regional sources and sinks of carbon across the earth's surface have been studied extensively using atmospheric carbon dioxide (CO2) observations and atmospheric chemistry-transport model (ACTM) simulations (top-down/inversion method). However, the uncertainties in the regional flux distributions remain unconstrained due to the lack of high-quality measurements, uncertainties in model simulations, and representation of data and flux errors in the inversion systems. Here, we assess the representation of data and flux errors using a suite of 16 inversion cases derived from a single transport model (MIROC4-ACTM) but different sets of a priori (bottom-up) terrestrial biosphere and oceanic fluxes, as well as prior flux and observational data uncertainties (50 sites) to estimate CO2 fluxes for 84 regions over the period 2000–2020. The inversion ensembles provide a mean flux field that is consistent with the global CO2 growth rate, land and ocean sink partitioning of −2.9 ± 0.3 (± 1σ uncertainty on the ensemble mean) and −1.6 ± 0.2 PgC yr−1, respectively, for the period 2011–2020 (without riverine export correction), offsetting about 22 %–33 % and 16 %–18 % of global fossil fuel CO2 emissions. The rivers carry about 0.6 PgC yr−1 of land sink into the deep ocean, and thus the effective land and ocean partitioning is −2.3 ± 0.3 and −2.2 ± 0.3, respectively. Aggregated fluxes for 15 land regions compare reasonably well with the best estimations for the 2000s (∼ 2000–2009), given by the REgional Carbon Cycle Assessment and Processes (RECCAP), and all regions appeared as a carbon sink over 2011–2020. Interannual variability and seasonal cycle in CO2 fluxes are more consistently derived for two distinct prior fluxes when a greater degree of freedom (increased prior flux uncertainty) is given to the inversion system. We have further evaluated the inversion fluxes using meridional CO2 distributions from independent (not used in the inversions) aircraft and surface measurements, suggesting that the ensemble mean flux (model–observation mean ± 1σ standard deviation = −0.3 ± 3 ppm) is best suited for global and regional CO2 flux budgets than an individual inversion (model–observation 1σ standard deviation = −0.35 ± 3.3 ppm). Using the ensemble mean fluxes and uncertainties for 15 land and 11 ocean regions at 5-year intervals, we show promise in the capability to track flux changes toward supporting the ongoing and future CO2 emission mitigation policies.

Worldwide Evaluation of CAMS-EGG4 CO2 Data Re-Analysis at the Surface Level.

This study systematically examines the global uncertainties and biases in the carbon dioxide (CO2) mixing ratio provided by the Copernicus Atmosphere Monitoring Service (CAMS). The global greenhouse gas re-analysis (EGG4) data product from the European Centre for Medium-Range Weather Forecasts (ECMWF) was evaluated against ground-based in situ measurements from more than 160 of stations across the world. The evaluation shows that CO2 re-analysis can capture the general features in the tracer distributions, including the CO2 seasonal cycle and its strength at different latitudes, as well as the global CO2 trend. The emissions and natural fluxes of CO2 at the surface are evaluated on a wide range of scales, from diurnal to interannual. The results highlight re-analysis compliance, reproducing biogenic fluxes as well the observed CO2 patterns in remote environments. CAMS consistently reproduces observations at marine and remote regions with low CO2 fluxes and smooth variability. However, the model’s weaknesses were observed in continental areas, regions with complex sources, transport circulations and large CO2 fluxes. A strong variation in the accuracy and bias are displayed among those stations with different flux profiles, with the largest uncertainties in the continental regions with high CO2 anthropogenic fluxes. Displaying biased estimation and root-mean-square error (RMSE) ranging from values below one ppmv up to 70 ppmv, the results reveal a poor response from re-analysis to high CO2 mixing ratio, showing larger uncertainty of the product in the boundaries where the CAMS system misses solving sharp flux variability. The mismatch at regions with high fluxes of anthropogenic emission indicate large uncertainties in inventories and constrained physical parameterizations in the CO2 at boundary conditions. The current study provides a broad uncertainty assessment for the CAMS CO2 product worldwide, suggesting deficiencies and methods that can be used in the future to overcome failures and uncertainties in regional CO2 mixing ratio and flux estimates.

Magnitude and Origin of CO2 Evasion From High‐Latitude Lakes

AbstractLakes evade significant amounts of carbon dioxide (CO2) to the atmosphere; yet the magnitude and origin of the evasion are still poorly constrained. We quantified annual CO2 evasion and its origin (in‐lake net ecosystem production vs. lateral inputs from terrestrial ecosystems) in 14 high‐latitude lakes through high‐frequency estimates of open water CO2 flux and ecosystem metabolism and inorganic carbon mass‐balance before and after ice breakup. Annual CO2 evasion ranged from 1 to 25 g C m−2 yr−1 of which an average of 57% was evaded over a short period at ice‐breakup. Annual internal CO2 production ranged from −6 to 21 g C m−2 yr−1, of which at least half was produced over winter. The contribution of internal versus external source contribution to annual CO2 evasion varied between lakes, ranging from fully internal to fully external with most lakes having over 75% of the evasion sustained through a single source. Overall, the study stresses the large variability in magnitude and control of CO2 evasion and suggests that environmental change impacts on CO2 evasion from high‐latitude lakes are not uniform.

Land-use change emissions based on high-resolution activity data substantially lower than previously estimated

Land-use and land-cover changes (LULCCs) contributed around one third to the cumulative, anthropogenic CO2 emissions from 1850 to 2019. Despite its great importance, estimates of the net CO2 fluxes from LULCC (ELUC) have high uncertainties, compared to other components of the global carbon cycle. One major source of uncertainty roots in the underlying LULCC forcing data. In this study, we implemented a new high-resolution LULCC dataset (HILDA+) in a bookkeeping model (BLUE) and compared the results to estimates from simulations based on LUH2, which is the LULCC dataset most commonly used in global carbon cycle models. Compared to LUH2-based estimates, results based on HILDA+ show lower total ELUC (global mean difference 1960–2019: 541 TgC yr−1, 65%) and large spatial and temporal differences in component fluxes (e.g. CO2 fluxes from deforestation). In general, the congruence of component fluxes is higher in the mid-latitudes compared to tropical and subtropical regions, which is to some degree explained with the different implementations of shifting cultivation in the underlying LULCC datasets. However, little agreement is reached on the trend of the last decade between ELUC estimates based on the two LULCC reconstructions. Globally and in many regions, ELUC estimates based on HILDA+ have decreasing trends, whereas estimates based on LUH2 indicate an increase. Furthermore, we analyzed the effect of different resolutions on ELUC estimates. By comparing estimates from simulations at 0.01∘ and 0.25∘ resolution, we find that component fluxes of estimates based on the coarser resolution tend to be larger compared to estimates based on the finer resolution, both in terms of sources and sinks (global mean difference 1960–2019: 36 TgC yr−1, 96%). The reason for these differences are successive transitions: these are not adequately represented at coarser resolution, which has the effect that—despite capturing the same extent of transition areas—overall less area remains pristine at the coarser resolution compared to the finer resolution.