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Warming of northern high latitude regions (NHL, > 50 °N) has increased both photosynthesis and respiration which results in considerable uncertainty regarding the net carbon dioxide (CO 2 ) balance of NHL ecosystems. Using estimates constrained from atmospheric observations from 1980 to 2017, we find that the increasing trends of net CO 2 uptake in the early-growing season are of similar magnitude across the tree cover gradient in the NHL. However, the trend of respiratory CO 2 loss during late-growing season increases significantly with increasing tree cover, offsetting a larger fraction of photosynthetic CO 2 uptake, and thus resulting in a slower rate of increasing annual net CO 2 uptake in areas with higher tree cover, especially in central and southern boreal forest regions. The magnitude of this seasonal compensation effect explains the difference in net CO 2 uptake trends along the NHL vegetation- permafrost gradient. Such seasonal compensation dynamics are not captured by dynamic global vegetation models, which simulate weaker respiration control on carbon exchange during the late-growing season, and thus calls into question projections of increasing net CO 2 uptake as high latitude ecosystems respond to warming climate conditions., (© 2022. The Author(s).)

Carbon dioxide (CO 2 ) and methane (CH 4 ) are the most important greenhouse gases (GHGs) due to their significant role in anthropogenic global climate change. The spatio-temporal variations of their concentration are characterized by the terrestrial biosphere, seasonal weather patterns and anthropogenic emissions. Hence, to understand the variability in regional surface GHG fluxes, high precision GHGs measurements were initiated by the National Remote Sensing Center (NRSC) of India. We report continuous CO 2 and CH 4 measurements during 2014 to 2017 for the first time from Shadnagar, a suburban site in India. Annual mean CO 2 and CH 4 concentrations are 399.56 ± 5.46 ppm and 1.929 ± 0.09 ppm, respectively, for 2017. After the strong El Niño of 2015-2016, an abnormal rise in CO 2 growth rate of 5.5 ppm year -1 was observed in 2017 at the study site, compared to 3.03 ppm year -1 at Mauna Loa. Thus, the repercussion of the El Niño effect diminishes the net uptake by the terrestrial biosphere accompanied by increased soil respiration. Seasonal tracer to tracer correlation between CO 2 and CH 4 was also analyzed to characterize the possible source-sink relationship between the species. We compared CO 2 and CH 4 concentrations to simulations from an atmospheric chemistry transport model (ACTM). The seasonal phases of CH 4 were well captured by the ACTM, whereas the seasonal cycle amplitude of CO 2 was underestimated by about 30%., Competing Interests: Declaration of competing interest The authors declare that they have no conflict of interest., (Copyright © 2022 Elsevier B.V. All rights reserved.)

The COVID-19 pandemic caused drastic reductions in carbon dioxide (CO 2 ) emissions, but due to its large atmospheric reservoir and long lifetime, no detectable signal has been observed in the atmospheric CO 2 growth rate. Using the variabilities in CO 2 (ΔCO 2 ) and methane (ΔCH 4 ) observed at Hateruma Island, Japan during 1997-2020, we show a traceable CO 2 emission reduction in China during February-March 2020. The monitoring station at Hateruma Island observes the outflow of Chinese emissions during winter and spring. A systematic increase in the ΔCO 2 /ΔCH 4 ratio, governed by synoptic wind variability, well corroborated the increase in China's fossil-fuel CO 2 (FFCO 2 ) emissions during 1997-2019. However, the ΔCO 2 /ΔCH 4 ratios showed significant decreases of 29 ± 11 and 16 ± 11 mol mol -1 in February and March 2020, respectively, relative to the 2011-2019 average of 131 ± 11 mol mol -1 . By projecting these observed ΔCO 2 /ΔCH 4 ratios on transport model simulations, we estimated reductions of 32 ± 12% and 19 ± 15% in the FFCO 2 emissions in China for February and March 2020, respectively, compared to the expected emissions. Our data are consistent with the abrupt decrease in the economic activity in February, a slight recovery in March, and return to normal in April, which was calculated based on the COVID-19 lockdowns and mobility restriction datasets.

Robust estimates of CO 2 budget, CO 2 exchanged between the atmosphere and terrestrial biosphere, are necessary to better understand the role of the terrestrial biosphere in mitigating anthropogenic CO 2 emissions. Over the past decade, this field of research has advanced through understanding of the differences and similarities of two fundamentally different approaches: "top-down" atmospheric inversions and "bottom-up" biosphere models. Since the first studies were undertaken, these approaches have shown an increasing level of agreement, but disagreements in some regions still persist, in part because they do not estimate the same quantity of atmosphere-biosphere CO 2 exchange. Here, we conducted a thorough comparison of CO 2 budgets at multiple scales and from multiple methods to assess the current state of the science in estimating CO 2 budgets. Our set of atmospheric inversions and biosphere models, which were adjusted for a consistent flux definition, showed a high level of agreement for global and hemispheric CO 2 budgets in the 2000s. Regionally, improved agreement in CO 2 budgets was notable for North America and Southeast Asia. However, large gaps between the two methods remained in East Asia and South America. In other regions, Europe, boreal Asia, Africa, South Asia, and Oceania, it was difficult to determine whether those regions act as a net sink or source because of the large spread in estimates from atmospheric inversions. These results highlight two research directions to improve the robustness of CO 2 budgets: (a) to increase representation of processes in biosphere models that could contribute to fill the budget gaps, such as forest regrowth and forest degradation; and (b) to reduce sink-source compensation between regions (dipoles) in atmospheric inversion so that their estimates become more comparable. Advancements on both research areas will increase the level of agreement between the top-down and bottom-up approaches and yield more robust knowledge of regional CO 2 budgets., (© 2019 John Wiley & Sons Ltd.)

We present improved estimates of air–sea CO2 exchange over three latitude bands of the Southern Ocean using atmospheric CO2 measurements from global airborne campaigns and an atmospheric 4-box inverse model based on a mass-indexed isentropic coordinate (Mθe). These flux estimates show two features not clearly resolved in previous estimates based on inverting surface CO2 measurements: a weak winter-time outgassing in the polar region and a sharp phase transition of the seasonal flux cycles between polar/subpolar and subtropical regions. The estimates suggest much stronger summer-time uptake in the polar/subpolar regions than estimates derived through neural-network interpolation of pCO2 data obtained with profiling floats but somewhat weaker uptake than a recent study by Long et al. [Science 374, 1275–1280 (2021)], who used the same airborne data and multiple atmospheric transport models (ATMs) to constrain surface fluxes. Our study also uses moist static energy (MSE) budgets from reanalyses to show that most ATMs tend to have excessive diabatic mixing (transport across moist isentrope, θe, or Mθe surfaces) at high southern latitudes in the austral summer, which leads to biases in estimates of air–sea CO2 exchange. Furthermore, we show that the MSE-based constraint is consistent with an independent constraint on atmospheric mixing based on combining airborne and surface CO2 observations. [ABSTRACT FROM AUTHOR]

East Asia (China, Japan, Koreas, and Mongolia) has been the world's economic engine over at least the past two decades, exhibiting a rapid increase in fossil fuel emissions of greenhouse gases (GHGs) and has expressed the recent ambition to achieve climate neutrality by mid‐century. However, the GHG balance of its terrestrial ecosystems remains poorly constrained. Here, we present a synthesis of the three most important long‐lived greenhouse gases (CO2, CH4, and N2O) budgets over East Asia during the decades of 2000s and 2010s, following a dual constraint approach. We estimate that terrestrial ecosystems in East Asia is close to neutrality of GHGs, with a magnitude of between −46.3 ± 505.9 Tg CO2eq yr−1 (the top‐down approach) and −36.1 ± 207.1 Tg CO2eq yr−1 (the bottom‐up approach) during 2000–2019. This net GHG sink includes a large land CO2 sink (−1229.3 ± 430.9 Tg CO2 yr−1 based on the top‐down approach and −1353.8 ± 158.5 Tg CO2 yr−1 based on the bottom‐up approach) being offset by biogenic CH4 and N2O emissions, predominantly coming from the agricultural sectors. Emerging data sources and modeling capacities have helped achieve agreement between the top‐down and bottom‐up approaches, but sizable uncertainties remain in several flux terms. For example, the reported CO2 flux from land use and land cover change varies from a net source of more than 300 Tg CO2 yr−1 to a net sink of ∼−700 Tg CO2 yr−1. Although terrestrial ecosystems over East Asia is close to GHG neutral currently, curbing agricultural GHG emissions and additional afforestation and forest managements have the potential to transform the terrestrial ecosystems into a net GHG sink, which would help in realizing East Asian countries' ambitions to achieve climate neutrality. Plain Language Summary: East Asia (China, Japan, Koreas and Mongolia) is not only the hotspot of anthropogenic greenhouse gas (GHG, including CO2, CH4 and N2O) emissions, but also a region with large CO2 sink. However, the greenhouse gas balance of greenhouse gases over the region is poorly understood. In this study, we performed a synthesis for over 40 flux terms to provide the first‐of‐its‐kind GHG budget assessment over the decades of 2000s and 2010s. We find terrestrial ecosystems in East Asia is close to neutrality of GHGs. The bottom‐up approach summing up component fluxes estimated a net balance of −36.1 ± 207.1 Tg CO2eq yr−1, while the top‐down approach based on atmospheric inversions estimated a net balance of −46.3 ± 505.9 Tg CO2eq yr−1. This results from compensation of the large CO2 sink by CH4 and N2O emissions, and from compensation of net GHG sink over natural ecosystems by net GHG source over agricultural ecosystems. Thus, curbing agricultural GHG emissions has the potential to realizing the ambitious goal of achieving climate neutrality over East Asia. Key Points: A comprehensive greenhouse gas (CO2, CH4 and N2O) accounting including about 40 flux terms over East Asia is reportedTerrestrial ecosystems in East Asia are close to greenhouse gas neutralNatural ecosystems is a net greenhouse gas sink, compensated by a net source from agricultural ecosystems [ABSTRACT FROM AUTHOR]

A key goal of the Paris Agreement (PA) is to reach net-zero greenhouse gas (GHG) emissions by 2050 globally, which requires mitigation efforts from all countries. Africa's rapidly growing population and gross domestic product (GDP) make this continent important for GHG emission trends. In this paper, we study the emissions of carbon dioxide (CO 2), methane (CH 4) and nitrous oxide (N 2 O) in Africa over 3 decades (1990–2018). We compare bottom-up (BU) approaches, including United Nations Convention Framework on Climate Change (UNFCCC) national inventories, FAO, PRIMAP-hist, process-based ecosystem models for CO 2 fluxes in the land use, land use change and forestry (LULUCF) sector and global atmospheric inversions. For inversions, we applied different methods to separate anthropogenic CH 4 emissions. The BU inventories show that, over the decade 2010–2018, fewer than 10 countries represented more than 75 % of African fossil CO 2 emissions. With a mean of 1373 Mt CO 2 yr -1 , total African fossil CO 2 emissions over 2010–2018 represent only 4 % of global fossil emissions. However, these emissions grew by +34% from 1990–1999 to 2000–2009 and by +31% from 2000–2009 to 2010–2018, which represents more than a doubling in 30 years. This growth rate is more than 2 times faster than the global growth rate of fossil CO 2 emissions. The anthropogenic emissions of CH 4 grew by 5 % from 1990–1999 to 2000–2009 and by 14.8 % from 2000–2009 to 2010–2018. The N 2 O emissions grew by 19.5 % from 1990–1999 to 2000–2009 and by 20.8 % from 2000–2009 to 2010–2018. When using the mean of the estimates from UNFCCC reports (including the land use sector) with corrections from outliers, Africa was a mean source of greenhouse gases of 262221863239 Mt CO 2 eq. yr -1 from all BU estimates (the subscript and superscript indicate min–max range uncertainties) and of +263717615873 Mt CO 2 eq. yr -1 from top-down (TD) methods during their overlap period from 2001 to 2017. Although the mean values are consistent, the range of TD estimates is larger than the one of the BU estimates, indicating that sparse atmospheric observations and transport model errors do not allow us to use inversions to reduce the uncertainty in BU estimates. The main source of uncertainty comes from CO 2 fluxes in the LULUCF sector, for which the spread across inversions is larger than 50 %, especially in central Africa. Moreover, estimates from national UNFCCC communications differ widely depending on whether the large sinks in a few countries are corrected to more plausible values using more recent national sources following the methodology of Grassi et al. (2022). The medians of CH 4 emissions from inversions based on satellite retrievals and surface station networks are consistent with each other within 2 % at the continental scale. The inversion ensemble also provides consistent estimates of anthropogenic CH 4 emissions with BU inventories such as PRIMAP-hist. For N 2 O, inversions systematically show higher emissions than inventories, on average about 4.5 times more than PRIMAP-hist, either because natural N 2 O sources cannot be separated accurately from anthropogenic ones in inversions or because BU estimates ignore indirect emissions and underestimate emission factors. Future improvements can be expected thanks to a denser network of monitoring atmospheric concentrations. This study helps to introduce methods to enhance the scope of use of various published datasets and allows us to compute budgets thanks to recombinations of those data products. Our results allow us to understand uncertainty and trends in emissions and removals in a region of the world where few observations exist and where most inventories are based on default IPCC guideline values. The results can therefore serve as a support tool for the Global Stocktake (GST) of the Paris Agreement. The referenced datasets related to the figures are available at 10.5281/zenodo.7347077 (Mostefaoui et al., 2022). [ABSTRACT FROM AUTHOR]

Inverse estimation of carbon dioxide (CO2) sources and sinks uses atmospheric CO2 observations, mostly made near the Earth's surface. However, transport models used in such studies lack perfect representation of atmospheric dynamics and thus often fail to produce unbiased forward simulations. The error is generally larger for observations over the land than those over the remote/marine locations. The range of this error is estimated by using multiple transport models (16 are used here). We have estimated the remaining differences in CO2 fluxes due to the use of ocean-only versus all-sites (i.e., over ocean and land) observations of CO2 in a time-independent inverse modeling framework. The fluxes estimated using the ocean-only networks are more robust compared to those obtained using all-sites networks. This makes the global, hemispheric, and regional flux determination less dependent on the selection of transport model and observation network.

El Niño Southern Oscillation (ENSO) influence on carbon dioxide (CO2) growth rates are not spatially uniform or simultaneous around the globe. Using atmospheric CO2 observations and atmospheric chemistry‐transport model (ACTM), we show that anomalies in CO2 fluxes and growth rate originate in the tropics, as an effect of ENSO. The CO2 anomalies are then propagated from the equator toward the poles with asymmetric delays. A maximum delay of about 8 and 4 months are found at the northern hemisphere (NH) and southern hemisphere (SH) high latitudes, respectively. This asymmetric time delay is because of the CO2 flux anomaly mainly originate in the tropical SH land and transport of SH air into the NH is slower than that for NH air into the SH. The poleward increase of time delay is more homogeneous in the upper troposphere, as per the ACTM simulations, but observational evidence suffers from gaps in long‐term measurements. Plain Language Summary: El Niño Southern Oscillation (ENSO) is a dominant tropical climate mode that has uneven impact on carbon dioxide (CO2) growth rates in different parts of the globe. In this study, we show an asymmetric meridional evolution of ENSO signal imprinted on CO2 growth rate using observations and chemistry‐transport model simulations. A time delay of 8 and 4 months between ENSO and CO2 growth rate are found near northern and southern high latitudes. This propagation asymmetry near the surface is linked to asymmetry in poleward transport of airmass and dominance of southern tropical flux variability to ENSO. The propagation is more homogenous in the upper atmosphere, where the flux signals are mixed. The terrestrial biosphere flux and fire emissions are reiterated as the underlying biophysical process in CO2 anomaly influenced by ENSO. Key Points: El Niño Southern Oscillation (ENSO) events strongly influence tropical land carbon dioxide (CO2) flux variability, and global CO2 variability both at surface and upper atmosphereAsymmetrical meridional propagation of the ENSO‐CO2 flux signal is imprinted on CO2 growth rate near the surfaceThe terrestrial biosphere fluxes and fire induced emissions from the tropical southern hemisphere show dominant CO2 flux variability [ABSTRACT FROM AUTHOR]

Methane (CH4) is the second major greenhouse gas after carbon dioxide (CO2) which is substantially increased during last decades in the atmosphere, raising serious sustainability and climate change issues. Here, we develop a data assimilation system for in situ and column averaged concentrations using Local ensemble transform Kalman filter (LETKF) to estimate surface emissions of CH4. The data assimilation performance is tested and optimized based on idealized settings using Observation System Simulation Experiments (OSSEs) where a known surface emission distribution (the truth) is retrieved from synthetic observations. We tested three covariance inflation methods to avoid covariance underestimation in the emission estimates, namely; fixed multiplicative (FM), relaxation to prior spread (RTPS) and adaptive multiplicative. First, we assimilate the synthetic observations at every grid point at the surface level. In such a case of dense observational network, the normalized Root Mean Square Error (RMSE) in the analyses over global land regions are smaller by 10–15 % in case of RTPS covariance inflation method compared to FM. We have shown that integrated estimated flux seasonal cycles over 15 regions using RTPS inflation are in reasonable agreement between true and estimated flux with 0.04 global absolute normalized annual mean bias. We have then assimilated the column averaged CH4 concentration by sampling the model simulations at GOSAT observation locations and time for another OSSE experiment. Similar to the case of dense observational network, RTPS covariance inflation method performs better than FM for GOSAT synthetic observation in terms of normalized RMSE (2–3 %) and integrated flux estimation comparison with the true flux. The annual mean averaged normalized RMSE (normalized absolute mean bias) in LETKF CH4 flux estimation in case of RTPS and FM covariance inflation is found to be 0.59 (0.18) and 0.61 (0.23) respectively. The chi-square test performed for GOSAT synthetic observations assimilation suggests high underestimation of background error covariance in both RTPS and FM covariance inflation methods, however, the underestimation is much high (>100 % always) for FM compared to RTPS covariance inflation method. [ABSTRACT FROM AUTHOR]

Global and regional sources and sinks of carbon across the earth's surface have been studied extensively using atmospheric carbon dioxide (CO 2) 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 CO 2 fluxes for 84 regions over the period 2000–2020. The inversion ensembles provide a mean flux field that is consistent with the global CO 2 growth rate, land and ocean sink partitioning of - 2.9 ± 0.3 (± 1 σ 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 CO 2 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 CO 2 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 CO 2 distributions from independent (not used in the inversions) aircraft and surface measurements, suggesting that the ensemble mean flux (model–observation mean ± 1 σ standard deviation = - 0.3 ± 3 ppm) is best suited for global and regional CO 2 flux budgets than an individual inversion (model–observation 1 σ 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 CO 2 emission mitigation policies. [ABSTRACT FROM AUTHOR]

Long‐term measurements at the Mauna Loa Observatory (MLO) show that the CO2 seasonal cycle amplitude (SCA) increased from 1959 to 2019 at an overall rate of 0.22 ± $\pm $ 0.034 ppm decade−1 while also varying on interannual to decadal time scales. These SCA changes are a signature of changes in land ecological CO2 fluxes as well as shifting winds. Simulations with the TM3 tracer transport model and CO2 fluxes from the Jena CarboScope CO2 Inversion suggest that shifting winds alone have contributed to a decrease in SCA of −0.10 ± $\pm $ 0.022 ppm decade−1 from 1959 to 2019, partly offsetting the observed long‐term SCA increase associated with enhanced ecosystem net primary production. According to these simulations and MIROC‐ACTM simulations, the shorter‐term variability of MLO SCA is nearly equally driven by varying ecological CO2 fluxes (49%) and varying winds (51%). We also show that the MLO SCA is strongly correlated with the Pacific Decadal Oscillation (PDO) due to varying winds, as well as with a closely related wind index (U‐PDO). Since 1980, 44% of the wind‐driven SCA decrease has been tied to a secular trend in the U‐PDO, which is associated with a progressive weakening of westerly winds at 700 mbar over the central Pacific from 20°N to 40°N. Similar impacts of varying winds on the SCA are seen in simulations at other low‐latitude Pacific stations, illustrating the difficulty of constraining trend and variability of land CO2 fluxes using observations from low latitudes due to the complexity of circulation changes. Plain Language Summary: The CO2 seasonal amplitude is an indicator of the growing‐season productivity of land ecosystems. CO2 observation at Mauna Loa Observatory (MLO) showed an increasing amplitude from 1959 to 2019, probably due to significantly enhanced productivity over the Eurasia temperate and boreal forest. In this study, we show that about a third of this observed amplitude increase at MLO was offset by long‐term circulation changes, which isolated the air at low latitudes from the influence of northern high‐latitude ecosystems. This wind‐driven impact is also important at other low‐latitude Pacific stations. The CO2 seasonal amplitude at MLO also has considerable short‐term variability (on the scale of multiple years to decades), roughly half of which is regulated by the changing winds, while the remaining half is driven by the variability of ecological CO2 fluxes. We show that the wind influence is closely associated with a climate index known as the Pacific Decadal Oscillation. Our study indicates that the attribution of amplitude changes at low‐latitudes stations to ecosystem changes is complicated by wind shifts. Key Points: The increasing CO2 seasonal amplitude observed at the Mauna Loa Observatory since 1959 has been significantly offset by circulation changesVariability of CO2 seasonal cycles at Mauna Loa is partly regulated by circulation changes associated with the Pacific Decadal OscillationCirculation changes complicate using low‐latitudes CO2 observation to constrain changing land carbon cycle [ABSTRACT FROM AUTHOR]

Regional land carbon budgets provide insights into the spatial distribution of the land uptake of atmospheric carbon dioxide and can be used to evaluate carbon cycle models and to define baselines for land-based additional mitigation efforts. The scientific community has been involved in providing observation-based estimates of regional carbon budgets either by downscaling atmospheric CO2 observations into surface fluxes with atmospheric inversions, by using inventories of carbon stock changes in terrestrial ecosystems, by upscaling local field observations such as flux towers with gridded climate and remote sensing fields, or by integrating data-driven or process-oriented terrestrial carbon cycle models. The first coordinated attempt to collect regional carbon budgets for nine regions covering the entire globe in the RECCAP-1 project has delivered estimates for the decade 2000–2009, but these budgets were not comparable between regions due to different definitions and component fluxes being reported or omitted. The recent recognition of lateral fluxes of carbon by human activities and rivers that connect CO2 uptake in one area with its release in another also requires better definitions and protocols to reach harmonized regional budgets that can be summed up to a globe scale and compared with the atmospheric CO2 growth rate and inversion results. In this study, using the international initiative RECCAP-2 coordinated by the Global Carbon Project, which aims to be an update to regional carbon budgets over the last 2 decades based on observations for 10 regions covering the globe with a better harmonization than the precursor project, we provide recommendations for using atmospheric inversion results to match bottom-up carbon accounting and models, and we define the different component fluxes of the net land atmosphere carbon exchange that should be reported by each research group in charge of each region. Special attention is given to lateral fluxes, inland water fluxes, and land use fluxes. [ABSTRACT FROM AUTHOR]

The measurements of one of the major greenhouse gases, carbon dioxide (CO2), are being made using dedicated satellite remote sensing since the launch of the greenhouse gases observing satellite (GOSAT) by a three-way partnership between the Japan Aerospace Exploration Agency (JAXA), the Ministry of Environment (MoE) and the National Institute for Environmental Studies (NIES), and the National Aeronautics and Space Administration (NASA) Orbiting Carbon Observatory-2 (OCO-2). In the past 10 years, estimation of CO2 fluxes from land and ocean using the earth system models (ESMs) and inverse modelling of in situ atmospheric CO2 data have also made significant progress. We attempt, for the first time, to evaluate the CO2 fluxes simulated by an earth system model (MIROC-ES2L) and the fluxes estimated by an inverse model (MIROC4-Inv) using in situ data by comparing with GOSAT and OCO-2 observations. Both MIROC-ES2L and MIROC4-Inv fluxes are used in the MIROC4-atmospheric chemistry transport model (referred to as ACTM_ES2LF and ACTM_InvF, respectively) for calculating total column CO2 mole fraction (XCO2) that are sampled at the time and location of the satellite measurements. Both the ACTM simulations agreed well with the GOSAT and OCO-2 satellite observations, within 2 ppm for the spatial maps and time evolutions of the zonal mean distributions. Our results suggest that the inverse model using in situ data is more consistent with the OCO-2 retrievals, compared with those of the GOSAT XCO2 data due to the higher accuracy of the former. This suggests that the MIROC4-Inv fluxes are of sufficient quality to evaluate MIROC-ES2L simulated fluxes. The ACTM_ES2LF simulation shows a slightly weaker seasonal cycle for the meridional profiles of CO2 fluxes, compared with that from the ACTM_InvF. This difference is revealed by greater XCO2 differences for ACTM_ES2LF vs GOSAT, compared with those of ACTM_InvF vs GOSAT. Using remote sensing–based global products of leaf area index (LAI) and gross primary productivity (GPP) over land, we show a weaker sensitivity of MIROC-ES2L biospheric activities to the weather and climate in the tropical regions. Our results clearly suggest the usefulness of XCO2 measurements by satellite remote sensing for evaluation of large-scale ESMs, which so far remained untested by the sparse in situ data. [ABSTRACT FROM AUTHOR]

There is more useful information in the time series of satellite-derived column-averaged carbon dioxide (XCO2) than is typically characterized. Often, the entire time series is treated at once without considering detailed features at shorter timescales, such as nonstationary changes in signal characteristics – amplitude, period and phase. In many instances, signals are visually and analytically differentiable from other portions in a time series. Each rise (increasing) and fall (decreasing) segment in the seasonal cycle is visually discernable in a graph of the time series. The rise and fall segments largely result from seasonal differences in terrestrial ecosystem production, which means that the segment's signal characteristics can be used to establish observational benchmarks because the signal characteristics are driven by similar underlying processes. We developed an analytical segmentation algorithm to characterize the rise and fall segments in XCO2 seasonal cycles. We present the algorithm for general application of the segmentation analysis and emphasize here that the segmentation analysis is more generally applicable to cyclic time series. We demonstrate the utility of the algorithm with specific results related to the comparison between satellite- and model-derived XCO2 seasonal cycles (2009–2012) for large bioregions across the globe. We found a seasonal amplitude gradient of 0.74–0.77 ppm for every 10 ∘ of latitude in the satellite data, with similar gradients for rise and fall segments. This translates to a south–north seasonal amplitude gradient of 8 ppm for XCO2 , about half the gradient in seasonal amplitude based on surface site in situ CO2 data (∼19 ppm). The latitudinal gradients in the period of the satellite-derived seasonal cycles were of opposing sign and magnitude (-9 d per 10 ∘ latitude for fall segments and 10 d per 10 ∘ latitude for rise segments) and suggest that a specific latitude (∼2 ∘ N) exists that defines an inversion point for the period asymmetry. Before (after) the point of asymmetry inversion, the periods of rise segments are lesser (greater) than the periods of fall segments; only a single model could reproduce this emergent pattern. The asymmetry in amplitude and the period between rise and fall segments introduces a novel pattern in seasonal cycle analyses, but, while we show these emergent patterns exist in the data, we are still breaking ground in applying the information for science applications. Maybe the most useful application is that the segmentation analysis allowed us to decompose the model biases into their correlated parts of biases in amplitude, period and phase independently for rise and fall segments. We offer an extended discussion on how such information about model biases and the emergent patterns in satellite-derived seasonal cycles can be used to guide future inquiry and model development. [ABSTRACT FROM AUTHOR]

There is more useful information in the time series of satellite-derived column-averaged carbon dioxide (XCO2) than is typically characterized. Often, the entire time series is treated at once without considering detailed features at shorter timescales, such as non-stationary changes in signal characteristics – amplitude, period, and phase. In many instances, signals are visually and analytically differentiable from other portions in a time series. Each Rise (increasing) and Fall (decreasing) segment, in the seasonal cycle is visually discernable in a graph of the time series. The Rise and Fall segments largely result from seasonal differences in terrestrial ecosystem production, which means that the segment’s signal characteristics can be used to establish observational benchmarks because the signal characteristics are driven by similar underlying processes. We developed an analytical segmentation algorithm to characterize the Rise and Fall segments in XCO2 seasonal cycles. We present the algorithm for general application of the segmentation analysis and emphasize here that the segmentation analysis is more generally applicable to cyclic time series. We demonstrate the utility of the algorithm with specific results related to the comparison between satellite- and model-derived XCO2 seasonal cycles (2009–2012) for large bioregions on the globe. We found a seasonal amplitude gradient of 0.74–0.77 ppm for every 10˚ degrees of latitude for the satellite data, with similar gradients for Rise and Fall segments. This translates to a south-north seasonal amplitude gradient of 8ppm for XCO2, about half the gradient in seasonal amplitude based on surface site in-situ CO2 data (~19ppm). The latitudinal gradients in period of the satellite-derived seasonal cycles were of opposing sign and magnitude (-9 days/10˚ latitude for Fall segments, and 10 days/10˚ latitude for Rise segments), and suggests that a specific latitude (~2˚N) exists which defines an inversion point for the period asymmetry. Before (after) the point of asymmetry inversion, the periods of Rise segments are less (greater) than the periods of Fall segments; only a single model could reproduce this emergent pattern. The asymmetry in amplitude and period between Rise and Fall segments introduces a novel pattern in seasonal cycle analyses, but while we show these emergent patterns exist in the data, we are still breaking ground in applying the information for science applications. Maybe the most useful application is that the segmentation analysis allowed us to decompose the model biases into their correlated parts of biases in amplitude, period, and phase, independently for Rise and Fall segments. We offer an extended discussion on how such information on model biases and the emergent patterns in satellite-derived seasonal cycles can be used to guide future inquiry and model development. [ABSTRACT FROM AUTHOR]

We estimate the uncertainty of CO2 flux estimates in atmospheric inversions stemming from differences between different global transport models. Using a set of observing system simulation experiments (OSSEs), we estimate this uncertainty as represented by the spread between five different state-of-the-art global transport models (ACTM, LMDZ, GEOS-Chem, PCTM and TM5), for both traditional in situ CO2 inversions and inversions of XCO2 estimates from the Orbiting Carbon Observatory 2 (OCO-2). We find that, in the absence of relative biases between in situ CO2 and OCO-2 XCO2, OCO-2 estimates of terrestrial flux for TRANSCOM-scale land regions can be more robust to transport model differences than corresponding in situ CO2 inversions. This is due to a combination of the increased spatial coverage of OCO-2 samples and the total column nature of OCO-2 estimates. We separate the two effects by constructing hypothetical in situ networks with the coverage of OCO-2 but with only near-surface samples. We also find that the transport-driven uncertainty in fluxes is comparable between well-sampled northern temperate regions and poorly sampled tropical regions. Furthermore, we find that spatiotemporal differences in sampling, such as between OCO-2 land and ocean soundings, coupled with imperfect transport, can produce differences in flux estimates that are larger than flux uncertainties due to transport model differences. This highlights the need for sampling with as complete a spatial and temporal coverage as possible (e.g., using both land and ocean retrievals together for OCO-2) to minimize the impact of selective sampling. Finally, our annual and monthly estimates of transport-driven uncertainties can be used to evaluate the robustness of conclusions drawn from real OCO-2 and in situ CO2 inversions. [ABSTRACT FROM AUTHOR]

Investigation of spatio-temporal variability of column-averaged dry-air mole fraction of CO 2 (XCO 2) over the Indian region using remote sensing satellite measurements is of interest due to the sparseness of ground-based observations. In this study, we utilized OCO-2 satellite retrievals in conjunction with an atmospheric chemistry-transport model (ACTM) simulations for a set of known (bottom-up) and optimized (top-down) flux for the period September 2014 to December 2018. Results showed the highest XCO 2 during the pre-monsoon season, due to prevailing seasonal-high temperatures and drier soil conditions that resulted in increased respiration and suppressed photosynthesis. In contrast, a reduction in XCO 2 during the monsoon season is found as precipitation increased soil moisture and moderated the air temperature, driving vegetation growth by which photosynthesis exceeded respiration by the land biosphere. Model - observation differences of XCO 2 have shown the overestimation (underestimation) during monsoon (pre-monsoon) especially over the central India region, which might be due to underestimation of the modeled peak-to-trough biospheric fluxes. Analyses of the latitudinal distribution of XCO 2 averaged over Indian landmass shows a clear increasing trend and seasonality. An enormous increase in XCO 2 of about ~12 ppm during 2015–2018 is estimated from OCO-2, which is in good agreement with model-simulated XCO 2 (optimized flux case) and consistent with the global growth rate from surface observations. The time series and seasonal cycle of XCO 2 have also been examined using model simulations over different parts of the Indian region and agreed well with those from OCO-2. Over the northern regions, especially over the Indo Gangetic Plain, the peak-to-trough seasonal cycle amplitudes of 2–3 ppm are twice than those in the southern and oceanic regions,~1–1.5 ppm. An annual total CO 2 flux of −397 ± 99 TgC/yr is estimated by the inversion and that is consistent with the XCO 2 measurements. • Study investigated the seasonality and variability of XCO 2 using satellite observations and model simulations over India. • Highest XCO 2 noted during the pre-monsoon season, likely due to increased respiration and suppressed photosynthesis. • Strong Seasonal Cycle Amplitude (SCA) is found over the IGP region, while SCAs are lower over southern and oceanic regions. • An annual total CO 2 flux of -397 ± 99 TgC/yr is estimated by the inversion and it is consistent with the XCO 2 measurements. [ABSTRACT FROM AUTHOR]

Carbon dioxide (CO2) growth rates are estimated for a period 1959–2004 from atmospheric CO2 measurements at Mauna Loa by the Scripps Institute of Oceanography. Only during a few short periods, 1965–1966, 1972–1973, 1987–1988 and 1997–1998, in the last 45 yr have growth rates of atmospheric CO2 been of a similar magnitude or higher than that due to the total emission from burning of fossil fuels. Using results from a time-dependent inverse (TDI) model, based on observations of atmospheric CO2 at 87 stations, we establish that El Niño-induced climate variations in the tropics and large-scale forest fires in the boreal regions are the main causes of anomalous growth rates of atmospheric CO2. The high growth rate of 2.8 ppm yr−1 in 2002 can be predicted fairly successfully by using the correlations between (1) the peak-to-trough amplitudes in the El Niño Southern Oscillation (ENSO) index and tropical flux anomaly, and (2) anomalies in CO2 flux and area burned by fire from the boreal regions. We suggest that the large interannual changes in CO2 growth rates can mostly be explained by natural climate variability. Our analysis also shows that the decadal average growth rate, linked primarily to human activity, has fluctuated around an all-time high value of ∼1.5 ppm yr−1 over the past 20 yr. A statistical model analysis is performed to identify the regions which have the maximum influence on the observed growth rate anomaly at Mauna Loa. [ABSTRACT FROM AUTHOR]

Focuses on a study which determined optimal extensions of the surface carbon dioxide observation network using several global transport models and a time-dependent inverse model. Atmospheric observations of carbon dioxide and carbon isotopes; Methodology of the study; Results and discussion.

abstract We use an inverse model to evaluate the effects of the recent CO2 observations over Asia on estimates of regional CO2 sources and sinks. Global CO2 flux distribution is evaluated using several atmospheric transport models, atmospheric CO2 observations and a “time-independent” inversion procedure adopted in the basic synthesis inversion by the Transcom-3 inverse model intercomparison project. In our analysis we include airborne and tower observations in Siberia, continuous monitoring and airborne observations over Japan, and airborne monitoring on regular flights on Tokyo–Sydney route. The inclusion of the new data reduces the uncertainty of the estimated regional CO2 fluxes for Boreal Asia (Siberia), Temperate Asia and South-East Asia. The largest effect is observed for the emission/sink estimate for the Boreal Asia region, where introducing the observations in Siberia reduces the source uncertainty by almost half. It also produces an uncertainty reduction for Boreal North America. Addition of the Siberian airborne observations leads to projecting extra sinks in Boreal Asia of 0.2 Pg C yr-1 , and a smaller change for Europe. The Tokyo–Sydney observations reduce and constrain the Southeast Asian source. [ABSTRACT FROM AUTHOR]

To understand the spatio-temporal variations of the atmospheric greenhouse gases in the Arctic region, we have conducted systematic observations and numerical model simulations. Long-term observations for CO 2 , CH 4 and related constituents at Ny-Ålesund helped in separating the oceanic and terrestrial uptake rates of CO 2 and revealed the importance of biogenic CH 4 emissions to atmospheric CH 4 variations. Shipboard observations of atmospheric CH 4 in the Arctic Ocean indicated several peaks associated with long-range transports of the continental source signals. An underestimation of climatological O 2 flux is demonstrated by the shipboard O 2 /N 2 observations over the North Pacific and Arctic Sea. Clear seasonal cycles and long-term trends of the greenhouse gases were observed in the upper troposphere and lower stratosphere over the Eurasian continent at 8.5–12.5 km in altitude. Forward and inverse modeling studies and direct observations of CH 4 emission from West Siberian wetlands were conducted to evaluate the CH 4 fluxes estimated by previous bottom-up studies. To be consistent with the atmospheric CH 4 observations, the prescribed CH 4 emission needs downward and upward revisions at Hudson Bay Lowland and West Siberia, respectively. The terrestrial ecosystem model study showed increasing trends in the CH 4 emission in inland North America, Alaska and northern part of West Siberian Lowland. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic caused drastic reductions in carbon dioxide (CO2) emissions, but due to its large atmospheric reservoir and long lifetime, no detectable signal has been observed in the atmospheric CO2 growth rate. Using the variabilities in CO2 (ΔCO2) and methane (ΔCH4) observed at Hateruma Island, Japan during 1997–2020, we show a traceable CO2 emission reduction in China during February–March 2020. The monitoring station at Hateruma Island observes the outflow of Chinese emissions during winter and spring. A systematic increase in the ΔCO2/ΔCH4 ratio, governed by synoptic wind variability, well corroborated the increase in China's fossil-fuel CO2 (FFCO2) emissions during 1997–2019. However, the ΔCO2/ΔCH4 ratios showed significant decreases of 29 ± 11 and 16 ± 11 mol mol−1 in February and March 2020, respectively, relative to the 2011–2019 average of 131 ± 11 mol mol−1. By projecting these observed ΔCO2/ΔCH4 ratios on transport model simulations, we estimated reductions of 32 ± 12% and 19 ± 15% in the FFCO2 emissions in China for February and March 2020, respectively, compared to the expected emissions. Our data are consistent with the abrupt decrease in the economic activity in February, a slight recovery in March, and return to normal in April, which was calculated based on the COVID-19 lockdowns and mobility restriction datasets. [ABSTRACT FROM AUTHOR]

Diagnosing carbon dioxide (CO2) and methane (CH4) fluxes at subcontinental scales is complicated by sparse observations, limited knowledge of prior fluxes and their uncertainties, and background and transport errors. Multispecies measurements in flasks sampled during the wintertime ACT‐America campaign were used for background characterization and source apportionment of regional anthropogenic CO2 and CH4 fluxes when ecosystem CO2 exchange is likely to be least active. Continental background trace gas mole fractions for regional enhancements are defined using samples from the upper troposphere and assessed using model products. Trace gas enhancements taken from flask samples in the lower troposphere with background levels subtracted out are then interpreted to inform CO2 and CH4 enhancement variability in the eastern United States. Strong correlations between CO2 and CH4 enhancements in the Midwestern and Mid‐Atlantic United States indicated colocated anthropogenic sources. Oil and natural gas influence was also broadly observed throughout the entire observational domain. In the Midwestern United States, agricultural influence on CO2 and CH4 enhancement variability was evident during above‐average wintertime temperatures. Weaker correlations between CO2 and anthropogenic tracer enhancements in the Southeastern United States indicated potentially nonnegligible wintertime ecosystem CO2 exchange, with biogenic tracers indicating more active surface processing than other regions. These whole‐air flask samples illuminated significant regional CO2 and CH4 sources or sinks during Atmospheric Carbon and Transport‐America (ACT‐America) and can provide additional information for informing regional inverse modeling efforts. Key Points: Multispecies flask samples are used to characterize regional wintertime eastern U.S. CO2 and CH4 enhancement variabilityMid‐Atlantic and Midwestern U.S. CO2 and CH4 enhancements are primarily anthropogenicWinter Southeastern U.S. CO2 and CH4 enhancements indicate influence from biogenic exchange and surface processing [ABSTRACT FROM AUTHOR]

To understand the changes in CH 4 emissions in East Asia, mainly in China, we analyzed the synoptic scale variability (SSV) of CO 2 and CH 4 mole fractions at Anmyeondo (AMY; 36.53°N, 126.32°E, 46 m above sea level) in South Korea using near-surface in-situ observations and an atmospheric chemical transport model (ACTM) to investigate the role of prescribed surface fluxes on SSV. The SSV of the tracers at AMY was mainly caused by emission variations from eastern and northeastern China during the winters in 2010–2020, as indicated by the simulated concentration footprints at AMY observed using the WRF-STILT model. The estimated SSV of ΔCH 4 /ΔCO 2 mole fraction ratios from the observations was 4.2–6.2 ppb ppm−1, with a mean of 5.7 ± 0.93 ppb ppm−1 during the winters in 2010–2020. We also calculated the CH 4 /CO 2 emission ratio from China's total annual fossil-fuel emissions, excluding seasonal sources estimated from the Emission Database for Global Atmospheric Research. We found that the ratio was overestimated by ∼98.1% compared to observations, which was likely driven either due to the overestimation of CH 4 or underestimation of CO 2. We discerned that the model-simulated SSV of the ΔCH 4 /ΔCO 2 ratios generally reproduced the observations when the CH 4 FixCoal simulation case was used. This implies that CH 4 from coal was a major contributor to the observed variations in the winter period ΔCH 4 /ΔCO 2 ratios. However, the model did not reproduce the observed ratio in 2012—this discrepancy requires further study. The trend of ΔCH 4 /ΔCO 2 was a slight decrease over time, which was attributed to the earlier onset of CH 4 emissions relative to fossil-fuel CO 2 , predominantly due to the reduction of CH 4 emissions from coal. In 2013, a relatively low tracer ratio was noted, which was caused by the large contribution of CO 2 from biomass burning. Overall, the observed AMY ΔCH 4 /ΔCO 2 ratios provide an efficient approach for validating the existing estimates of East Asian sectoral emissions of CH 4. • Synoptic scale variability (SSV) of CH 4 and CO 2 at Anmyeondo, South Korea was mainly caused by emission variations from eastern and northeastern China. • Simulated wintertime SSV of CH 4 a from coal was well matched with observations at Anmyeondo during 2010–2020. • EDGAR v6 China anthropogenic CH 4 /CO 2 emission ratio was overestimated against observation based SSV ΔCH 4 /ΔCO 2 mole fractions ratios. • Decline trend of wintertime SSV ΔCH 4 /ΔCO 2 mole fractions ratios Anmyeondo was attributed to a slower growth rate of China anthropogenic CH 4 emission compared to CO 2. • Observed low ΔCH 4 /ΔCO 2 ratio in 2013 was most likely caused by large contribution CO 2 from biomass burning. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]