Surface CO2 Flux Research Articles

Impact of the horizontal resolution of GEOS-Chem on land‒ocean and tropic‒extratropic partitioning and seasonal cycle in CO2 inversion

Abstract Credible regional carbon budget estimates from atmospheric CO2 measurements rely on the accuracy of atmospheric transport models (ATMs). However, the atmospheric transport in ATMs is usually simplified and spatiotemporally averaged, leading to systematic biases in simulating the atmospheric CO2 and estimating surface CO2 fluxes. We show that forward simulations of global CO2 using an ATM, GEOS-Chem, at a native resolution of 0.5°×0.625° and a coarse resolution of 4°×5° differ significantly near the surface in the Northern Hemisphere and the polar vortex, mainly because of advection in GEOS-Chem. Comparing observing system simulation experiments that assimilate synthetic observations sampled from the forward simulations, we separate the impact of coarse-resolution GEOS-Chem on regional flux estimates. The results suggest that a significant amount of annual carbon uptake from the ocean and tropics is improperly redistributed to the land and northern and southern extratropics, respectively. In addition, these errors lead to an underestimated seasonal amplitude in the northern extratropical land and a reversed sign of the seasonal cycle in the northern extratropical ocean. The reversed sign of the seasonal cycle has also been observed in a real data assimilation experiment and several state-of-the-art inversions, suggesting that reasonable ocean flux estimates depend strongly on the accuracy of the ATM.

Dynamic correlation between surface carbon response and underlying emissions from spontaneous combustion goaf: field study of an abandoned coal mine

Abandoned coal mine goaf is affected by air leakages and prone to spontaneous combustion, resulting in environmental pollution and geological disasters. Haizhou Open-pit Mine adopts both underground and open-pit mining methods. During the long-term mining process, the original stable stratum structure is constantly destroyed, and the slope slides, increasing cracks and severe air leakage around the goaf and roadway. The spontaneous combustion of coal is particularly prominent after the mine shut down. At present, there is no suitable indirect monitoring method to effectively explore the spontaneous combustion area in goaf. The study developed an all-weather monitoring plan and conducted multi-point continuous long-term measurements of the spontaneous combustion state in one abandoned coal mine goaf located in the eastern part of the Haizhou Open-pit Mine. We evaluated the dynamic correlation between surface CO2 flux (SCF) and changes in the underground fire areas, determined the scope and evolution trend of the fire areas, and identified the distribution and change laws of SCF. The results show a significant positive correlation between SCF and soil temperature; moreover, the SCF value was found to reflect the CO2 emission intensity of the goaf. The high SCF in the test area showed month-wise expansion and increase, while the CO2 emission gradually increased monthly, and the calculated annual total emission was approximately 7017 t. Hence, the study can further provide guidance for the monitoring of spontaneous combustion in shallow coal seams, goaf and the assessment of CO2 emissions from underground coal fires through the on-site monitoring and analysis results.

Potential of 14C-based vs. ΔCO-based ΔffCO2 observations to estimate urban fossil fuel CO2 (ffCO2) emissions

Abstract. Atmospheric transport inversions are a powerful tool for independently estimating surface CO2 fluxes from atmospheric CO2 concentration measurements. However, additional tracers are needed to separate the fossil fuel CO2 (ffCO2) emissions from non-fossil CO2 fluxes. In this study, we focus on radiocarbon (14C), the most direct tracer of ffCO2, and the continuously measured surrogate tracer carbon monoxide (CO), which is co-emitted with ffCO2 during incomplete combustion. In the companion paper by Maier et al. (2024), we determined discrete 14C-based and continuous ΔCO-based estimates of the ffCO2 excess concentration (ΔffCO2) compared with a clean-air reference for the urban Heidelberg observation site in southwestern Germany. The ΔCO-based ΔffCO2 concentration was calculated by dividing the continuously measured ΔCO excess concentration by an average 14C-basedΔCO/ΔffCO2 ratio. Here, we use the CarboScope inversion framework adapted for the urban domain around Heidelberg to assess the potential of both types of ΔffCO2 observations to investigate ffCO2 emissions and their seasonal cycle. We find that, although they are more precise, 14C-based ΔffCO2 observations from almost 100 afternoon flask samples collected in the 2 years of 2019 and 2020 are not well suited for estimating robust ffCO2 emissions in the main footprint of this urban area, which has a very heterogeneous distribution of sources including several point sources. The benefit of the continuous ΔCO-based ΔffCO2 estimates is that they can be averaged to reduce the impact of individual hours with an inadequate model performance. We show that the weekly averaged ΔCO-based ΔffCO2 observations allow for a robust reconstruction of the seasonal cycle of the area source ffCO2 emissions from temporally flat a priori emissions. In particular, the distinct COVID-19 signal – with a steep drop in emissions in spring 2020 – is clearly present in these data-driven a posteriori results. Moreover, our top-down results show a shift in the seasonality of the area source ffCO2 emissions around Heidelberg in 2019 compared with the bottom-up estimates from the Netherlands Organization for Applied Scientific Research (TNO). This highlights the huge potential of ΔCO-based ΔffCO2 to validate bottom-up ffCO2 emissions at urban stations if the ΔCO/ΔffCO2 ratios can be determined without biases.

Impact of vegetation composition and seasonality on sensitivity of modelled CO2 exchange in temperate raised bogs

Encroachment of vascular plants (VP) in temperate raised bogs, as a consequence of altered hydrological conditions and nutrient input, is widely observed. Effects of such vegetation shift on water and carbon cycles are, however, largely unknown and identification of responsible plant physiological traits is challenging. Process-based modelling offers the opportunity of gaining insights into ecosystem functioning beyond observations, and to infer decisive trait shifts of plant functional groups. We adapted the Soil–Vegetation–Atmosphere Transfer model pyAPES to a temperate raised bog site by calibration against measured peat temperature, water table and surface CO2 fluxes. We identified the most important traits determining CO2 fluxes by conducting Morris sensitivity analysis (MSA) under changing conditions throughout the year and simulated VP encroachment. We further investigated transferability of results to other sites by extending MSA to parameter ranges derived from literature review. We found highly variable intra-annual plant traits importance determining ecosystem CO2 fluxes, but only a partial shift of importance of photosynthetic processes from moss to VP during encroachment. Ecosystem respiration was dominated by peat respiration. Overall, carboxylation rate, base respiration rate and temperature sensitivity (Q10) were most important for determining bog CO2 balance and parameter ranking was robust even under the extended MSA.

Assessing the impact of global carbon dioxide changes on atmospheric fluctuations in Iran through satellite data analysis

ABSTRACT Atmospheric Carbon Dioxide (CO2), a significant greenhouse gas, drives climate change, influencing temperature, rainfall, and the hydrologic cycle. This alters precipitation patterns, intensifies storms, and changes drought frequency and timing of floods, impacting ecosystems, agriculture, water resources, and societies globally. Understanding how global CO2 fluctuations impact regional atmospheric CO2 levels can inform mitigation strategies and Facilitate water resources management. The study investigates how global CO2 fluctuations affect atmospheric CO2 concentrations (XCO2) in Iran from 2015 to 2020, aiming to inform mitigation strategies against climate change. XCO2 data OCO-2 satellite and CO2 surface flux data from the Copernicus Atmosphere Monitoring Service (CAMS) were analyzed. Over the 6 years, XCO2 in Iran increased steadily by 12.66 ppm, mirroring global rises. However, Iran's CO2 surface flux decreased, with slight increases in anthropogenic emissions but decreased natural and total fluxes. Monthly patterns of XCO2 and surface flux exhibited variations, with XCO2 reaching its zenith in spring and dipping to its lowest point during summer, while surface flux peaked during the summer months. The results reveal a significant discrepancy between Iran's surface CO2 flux and atmospheric XCO2 trends. While Iran's anthropogenic emissions increased barely from 2015–2020, its natural and total CO2 fluxes decreased. However, XCO2 increased steadily over this period, indicating the dominant impact of global rather than local factors on Iran's XCO2. Curbing worldwide greenhouse gas output is imperative to disrupt the current trajectory of climate change. Reporting CO2 levels can inform climate mitigation plans, reducing emissions to combat global warming and minimize global impacts on the hydrologic cycle.

Decadal changes in surface CO2 concentrations and CO2 fluxes in a mountain lake

AbstractClimate warming impacts biogeochemical cycles in lakes. However, the factors controlling CO2 dynamics in mountain lakes over multidecadal scales are poorly understood. Here, we capitalized on long‐term monthly data (1995–2022) of oligotrophic mountain Lake Tovel and calculated surface CO2 concentrations and flux by applying geochemical relationships and the thin boundary layer approach. Advanced time‐series and regression modeling was used to determine temporal patterns and environmental parameters explaining surface CO2 concentrations and flux. Surface CO2 concentrations were highest from 2009 to 2017 (annual mean: 109.1 μmol CO2 L−1) but lower before and after this period. Concomitantly, the air–water CO2 flux (μmol CO2 m−2 d−1) showed a period of lowest (mean1995–2010: 6.4 ± 0.7), highest (mean2011–2017: 35.7 ± 2.1), and intermediate emissions (mean2018–2022: 19.3 ± 4.7). Temporal modeling showed that hypolimnetic and deep hypolimnetic dissolved oxygen (DO) had the same change points and trends as surface CO2 concentrations. In multiple linear regression, hypolimnetic DO, silica, and the standardized precipitation index (pseudo‐R2adj. = 0.62; p < 0.01) best predicted annual mean surface CO2 concentrations. Regression results and the overlap between temporal trend patterns indicated that surface CO2 concentrations of Lake Tovel were positively influenced by external (loading of allochthonous carbon) and internal (lake autumn mixing) factors. The recent decline in surface CO2 concentrations from the year 2018 was attributed to increased stratification that offset lake autumn mixing and thus lead to the observed decline. These results help us to better understand the carbon cycle in mountain lakes in a changing climate.

Designing additional CO2 in-situ surface observation networks over South Korea using bayesian inversion coupled with Lagrangian modelling

Efforts to enhance greenhouse gas (GHG) emission reduction in East Asia play a pivotal role on both global and regional scales in advancing climate mitigation strategies. This study aimed to better constrain anthropogenic CO2 emission estimates by expanding the network of near-surface in-situ stations for CO2 observations across South Korea. To achieve an optimal CO2 network design, we conducted an Observing System Simulation Experiment (OSSE) coupled with the Stochastic Lagrangian Transport model (STILT), utilizing meteorological data from the Korean Integrated Model (KIM). Our inversion setup incorporated two CO2 emission datasets with a 0.1o resolution: EDGAR v6 for prior emissions and GRACED for truth emissions. A uniform model-mismatch error of 3 ppm was introduced across sites. The effectiveness of the existing five in-situ stations, termed the base network, in South Korea was evaluated to gauge their ability to constrain CO2 surface flux estimates. However, the findings revealed a reduction in flux uncertainty of only 29.2%, which fell short of the desired uncertainty reduction goal. In this base network, the Lotte World Tower (LWT: 37.5126°E, 127.1025°E) in Seoul and the Anmyeondo (AMY: 36.538576° N, 126.330071° E) site in Taean county stood as major contributors, with estimated reductions of 17.48% and 6.35%, respectively. Consequently, we proposed and developed an extended network, identifying seven candidate sites based on consideration of logistical factors, existing infrastructures, and proximity to the emission source regions. An incremental optimization scheme ranked their contributions, resulting in an additional 25% reduction, bringing the total to 54.13%. However, it is noteworthy that diminishing returns (ranging from 13% to less than 0.1%) were observed with an increase in station count mainly due to the possibility that adding a station earlier in the sequence might render subsequent stations redundant. Despite this, the proposed CO2 network successfully reduced uncertainty in emissions, narrowing the gap with the objectives of the Global Greenhouse Gas Watch (G3W).

Variability in surface CO2 flux: Implication for monitoring surface emission from geothermal fields

In the last decade the international push for the development of various renewable energy and the increase of the carbon tax for emitters, has put high enthalpy geothermal on a hot seat. Anthropic geothermal emissions in volcanic areas are not negligible due to the inherent nature of their volcanic setting but are counted regardless, similarly to a gas or coal power plant, despite their non-fossil carbon origin. Natural volcanic emissions are however poorly constrained due to the lack of field scale soil CO2 flux assessments. In this study, we analyse the temporal and spatial variations of soil CO2 flux and soil temperature in the Taupō region, Taupō Volcanic Zone, New Zealand, in the light of seasonal variation, field utilisation, and seismicity. Fortnightly monitoring between 2019 and 2023 at 8 sites across the Tauhara and Wairakei geothermal fields provides insight into changes of seasonal variations and geothermal activities. To understand the broader distribution of natural degassing and to compare the CO2 flux over decades, we repeated the CO2 flux survey between 2019 and 2023 and compared to previous results at Karapiti, Wairakei geothermal field (2004, 2018), Ring of Fire, Tauhara geothermal field (2006), and Rotokawa geothermal field (2003, 2011). Similar emissions but a change in the spatial distribution at Tauhara geothermal field suggests that human surface development has affected permeability in the shallow subsurface. In contrast, a similar spatial distribution of flux, but higher emissions, at Karapiti, Wairakei indicates the recovery or modification of reservoir pressure. Finally, a decrease of total emission flux of the natural geothermal features at Rotokawa coincides with the decrease of CO2eq emissions at the power stations. This study showcases the complexity and uncertainties of natural CO2 emission calculations and provides recommendations for future monitoring studies.

Development of the tangent linear and adjoint models of the global online chemical transport model MPAS-CO2 v7.3

Abstract. We describe the development of the tangent linear (TL) and adjoint models of the Model for Prediction Across Scales (MPAS)-CO2 transport model, which is a global online chemical transport model developed upon the non-hydrostatic Model for Prediction Across Scales – Atmosphere (MPAS-A). The primary goal is to make the model system a valuable research tool for investigating atmospheric carbon transport and inverse modeling. First, we develop the TL code, encompassing all CO2 transport processes within the MPAS-CO2 forward model. Then, we construct the adjoint model using a combined strategy involving re-calculation and storage of the essential meteorological variables needed for CO2 transport. This strategy allows the adjoint model to undertake a long-period integration with moderate memory demands. To ensure accuracy, the TL and adjoint models undergo vigorous verifications through a series of standard tests. The adjoint model, through backward-in-time integration, calculates the sensitivity of atmospheric CO2 observations to surface CO2 fluxes and the initial atmospheric CO2 mixing ratio. To demonstrate the utility of the newly developed adjoint model, we conduct simulations for two types of atmospheric CO2 observations, namely the tower-based in situ CO2 mixing ratio and satellite-derived column-averaged CO2 mixing ratio (XCO2). A comparison between the sensitivity to surface flux calculated by the MPAS-CO2 adjoint model with its counterpart from CarbonTracker–Lagrange (CT-L) reveals a spatial agreement but notable magnitude differences. These differences, particularly evident for XCO2, might be attributed to the two model systems' differences in the simulation configuration, spatial resolution, and treatment of vertical mixing processes. Moreover, this comparison highlights the substantial loss of information in the atmospheric CO2 observations due to CT-L's spatial domain limitation. Furthermore, the adjoint sensitivity analysis demonstrates that the sensitivities to both surface flux and initial CO2 conditions spread out throughout the entire Northern Hemisphere within a month. MPAS-CO2 forward, TL, and adjoint models stand out for their calculation efficiency and variable-resolution capability, making them competitive in computational cost. In conclusion, the successful development of the MPAS-CO2 TL and adjoint models, and their integration into the MPAS-CO2 system, establish the possibility of using MPAS's unique features in atmospheric CO2 transport sensitivity studies and in inverse modeling with advanced methods such as variational data assimilation.

A novel model for measuring carbon dioxide flux on soil surface using an accumulation chamber

Soil surface carbon dioxide (CO2) fluxes hold significant implications for comprehending carbon cycling, global climate change, and ecosystem functioning. Accurate understanding and quantification of this flux are crucial for assessing the carbon dynamics of ecosystems and predicting the impacts of climate change. In this study, we analyzed the variation pattern of CO2 concentration within the accumulated chamber from Fick's law, accounting for diffusion gradient effect and considering leakage effect. We developed a simple model to measure the soil surface CO2 flux, and used the model to calculate the values of CO2 fluxes on a specific soil surface. Comparing the fitting results of the proposed nonlinear model with traditional linear models for the measurement data, we found that the linear model underestimated by at least 18%, even up to 45%. Overall, our research indicates that the proposed model exhibits significant advancement and higher predictive reliability, successfully evaluating soil surface CO2 flux.

Effect of atmospheric conditions and VPRM parameters on high-resolution regional CO2 simulations over East Asia

Atmospheric CO2 concentrations are largely affected by the surface CO2 flux and atmospheric wind. To estimate atmospheric CO2 concentrations over East Asia, the effects of atmospheric conditions and the parameters of Vegetation Photosynthesis and Respiration Model (VPRM) that simulates biogenic CO2 concentrations were evaluated using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) model. The VPRM in WRF-Chem requires parameter optimization for the experimental period and region. Total six experiments with two atmospheric fields (final analysis; FNL and fifth generation of European Centre for Medium-range Weather Forecasts atmospheric reanalysis; ERA5) and three VPRM parameter tables (US, Li, and Dayalu) were conducted to investigate the appropriate atmospheric field and VPRM parameter table for East Asia. For validation, two types of wind observations (SYNOP and SONDE) and two types of CO2 observations (surface CO2 observations and OCO-2 XCO2 observations) were used. The experiments using FNL showed a lower RMSE for surface winds, whereas those using ERA5 showed a lower RMSE for upper-air winds. On average, the surface wind RMSE in the experiments using FNL was lower than that using ERA5. With respect to surface CO2 observations, the experiments using the Li table showed relatively lower RMSEs compared to those using other tables. With respect to OCO-2 XCO2 observations, the Li table with FNL showed lower RMSEs than other combinations. Overall, the combination of the Li table and FNL was the most appropriate for simulating CO2 concentrations in East Asia using WRF-Chem with VPRM.

A dataset of soil carbon flux in Xishuangbanna tropical rainforest ecosystem from 2003 to 2008

Long-term and large-scale carbon flux data supports research on key processes of the global carbon and water cycle. Soil is the largest carbon pool in the terrestrial ecosystem. Small variations in soil carbon can significantly impact the carbon budgets of terrestrial ecosystems. Tropical forest is a crucial carbon sink in the world. However, there have been some reports indicating a decrease in soli carbon levels, potentially approaching neutrality, as a result of soil carbon degradation caused by global changing. Thus, the long-term and continuous observation of soil carbon flux is of great significance for evaluating the role of soil carbon in the tropical carbon cycle. ChinaFLUX Observation and Research Network (ChinaFLUX) has not only conducted the observation and research on carbon flux by using vorticity correlation technology, but also carried out the long-term observation and research on surface CO2 flux by using the box method. In this dataset, we collected and sorted out the soil CO2 emission flux data and conventional meteorological data of Xishuangbanna tropical rain forest ecosystem from January 2003 to August 2008 according to the static box observation method and data quality control management of greenhouse gas emissions. The dataset consists of three types of files at daily, monthly and yearly scales, covering surface CO2 flux, pressure, atmospheric temperature, soil temperature (5 cm) and soil water content (5 cm). It is expected to provide essential data for estimating carbon balance and understanding carbon cycle processes at both tropical and global scales.

A high-resolution monitoring approach of urban CO2 fluxes. Part 2 – surface flux optimisation using eddy covariance observations

Achieving climate neutrality by 2050 requires ground-breaking technological and methodological advancements in climate change mitigation planning and actions from local to regional scales. Monitoring the cities' CO2 emissions with sufficient detail and accuracy is crucial for guiding sustainable urban transformation. Current methodologies for CO2 emission inventories rely on bottom-up (BU) approaches which do not usually offer information on the spatial or temporal variability of the emissions and present substantial uncertainties. This study develops a novel approach which assimilates direct CO2 flux observations from urban eddy covariance (EC) towers with very high spatiotemporal resolution information from an advanced urban BU surface flux model (Part 1 of this study, Stagakis et al., 2023) within a Bayesian inversion framework. The methodology is applied to the city centre of Basel, Switzerland (3 × 3 km domain), taking advantage of two long-term urban EC sites located 1.6 km apart. The data assimilation provides optimised gridded CO2 flux information individually for each urban surface flux component (i.e. building heating emissions, commercial/industrial emissions, traffic emissions, human respiration emissions, biogenic net exchange) at 20 m resolution and weekly time-step. The results demonstrate that urban EC observations can be consistently used to improve high-resolution BU surface CO2 flux model estimations, providing realistic seasonal variabilities of each flux component. Traffic emissions are determined with the greatest confidence among the five flux components during the inversions. The optimised annual anthropogenic emissions are 14.7 % lower than the prior estimate, the human respiration emissions have decreased by 12.1 %, while the biogenic components transformed from a weak sink to a weak source. The root-mean-square errors (RMSEs) of the weekly comparisons between EC observations and model outputs are consistently reduced. However, a slight underestimation of the total flux, especially in locations with complex CO2 source/sink mixture, is still evident in the optimised fluxes.

Segmentation of XCO2 images with deep learning: application to synthetic plumes from cities and power plants

Abstract. Under the Copernicus programme, an operational CO2 Monitoring Verification and Support system (CO2MVS) is being developed and will exploit data from future satellites monitoring the distribution of CO2 within the atmosphere. Methods for estimating CO2 emissions from significant local emitters (hotspots; i.e. cities or power plants) can greatly benefit from the availability of such satellite images that display the atmospheric plumes of CO2. Indeed, local emissions are strongly correlated to the size, shape, and concentration distribution of the corresponding plume, which is a visible consequence of the emission. The estimation of emissions from a given source can therefore directly benefit from the detection of its associated plumes in the satellite image. In this study, we address the problem of plume segmentation (i.e. the problem of finding all pixels in an image that constitute a city or power plant plume). This represents a significant challenge, as the signal from CO2 plumes induced by emissions from cities or power plants is inherently difficult to detect, since it rarely exceeds values of a few parts per million (ppm) and is perturbed by variable regional CO2 background signals and observation errors. To address this key issue, we investigate the potential of deep learning methods and in particular convolutional neural networks to learn to distinguish plume-specific spatial features from background or instrument features. Specifically, a U-Net algorithm, an image-to-image convolutional neural network with a state-of-the-art encoder, is used to transform an XCO2 field into an image representing the positions of the targeted plume. Our models are trained on hourly 1 km simulated XCO2 fields in the regions of Paris, Berlin, and several power plants in Germany. Each field represents the plume of the hotspot, with the background consisting of the signal of anthropogenic and biogenic CO2 surface fluxes near to or far from the targeted source and the simulated satellite observation errors. The performance of the deep learning method is thereafter evaluated and compared with a plume segmentation technique based on thresholding in two contexts, namely (1) where the model is trained and tested on data from the same region and (2) where the model is trained and tested in two different regions. In both contexts, our method outperforms the usual segmentation technique based on thresholding and demonstrates its ability to generalise in various cases, with respect to city plumes, power plant plumes, and areas with multiple plumes. Although less accurate than in the first context, the ability of the algorithm to extrapolate on new geographical data is conclusive, paving the way to a promising universal segmentation model trained on a well-chosen sample of power plants and cities and able to detect the majority of the plumes from all of them. Finally, the highly accurate results for segmentation suggest the significant potential of convolutional neural networks for estimating local emissions from spaceborne imagery.

Optimal design of surface CO2 observation network to constrain China’s land carbon sink

Accurate estimate of the size of land carbon sink is essential for guiding climate mitigation actions to fulfill China’s net-zero ambitions before 2060. The atmospheric inversion is an effective approach to provide spatially explicit estimate of surface CO2 fluxes that are optimally consistent with atmospheric CO2 measurements. But atmospheric inversion of China’s land carbon sink has enormous uncertainties, with one major source arising from the poor coverage of CO2 observation stations. Here we use a regional atmospheric inversion framework to design an observation network that could minimize uncertainties in inverted estimate of China’s land carbon sink. Compared with the large spread of inverted sink (∼1PgCa−1) from state-of-the-art inversions using existing CO2 observations, the uncertainty is constrained within 0.3PgCa−1 when a total of 30 stations were deployed, and is further reduced to approximately 0.2PgCa−1 when 60 stations were deployed. The proposed stations are mostly distributed over areas with high biosphere productivity during the growing season, such as Southeast China, Northeast China, North China, and the Tibetan Plateau. Moreover, the proposed stations can cover areas where existing satellites have limited coverage due to cloud shadowing in the monsoon season or over complex topography. Such ground-based observation network will be a critical component in the future integrated observing system for monitoring China’s land carbon fluxes.

High-resolution GEOS-Chem model for Indian monsoon region: Seasonal cycle and budget of tropospheric CO2

A nested GEOS-Chem atmosphere transport model has been set up for the Indian monsoon region (40° E-110° E & 15° S–45° N at 0.250X0.3125°) spatial resolution to study the variability of the tropospheric CO2 over India and surrounding oceans during 2012–2020. The model has been constrained by the influxes of CO2 at its four lateral boundaries derived from the GEOS-Chem global simulations and surface fluxes of CO2 over the land and oceans based on historical databases. It is initialized from a known state and driven by the GEOS reanalysis's meteorological forcing. Model simulations were evaluated at three in situ measurement locations and with respect to the satellite retrievals from OCO-2 and GOSAT. Model parameters were tuned to have reasonable solutions to resolve observed latitudinal gradient and seasonal and inter-annual variability. Three additional model runs were made to evaluate the role of long-range transport, net terrestrial ecosystem exchange, air-sea fluxes, and regional emissions in controlling seasonal oscillations of tropospheric CO2 over the study region. It revealed that the net terrestrial ecosystem exchanges are responsible for 35% control in the surface layer CO2 seasonal tendency for India and the Bay of Bengal domains, and 10% for the Arabian Sea. The air-sea fluxes have 8% control on the CO2 seasonal tendency for the Arabian Sea and Bay of Bengal and 5% for India. The anthropogenic emissions have 4% control for India and <1% for the Arabian Sea, and 9% for the Bay of Bengal. The long-range transport, as the residual, is responsible for 60% variability in the seasonal cycle of CO2 over India and the Bay of Bengal and 75% for the Arabian Sea domain.

Contribution of microbial activity and vegetation cover to the spatial distribution of soil respiration in mountains.

The patterns of change in bioclimatic conditions determine the vegetation cover and soil properties along the altitudinal gradient. Together, these factors control the spatial variability of soil respiration (RS) in mountainous areas. The underlying mechanisms, which are poorly understood, shape the resulting surface CO2 flux in these ecosystems. We aimed to investigate the spatial variability of RS and its drivers on the northeastern slope of the Northwest Caucasus Mountains, Russia (1,260-2,480 m a.s.l.), in mixed, fir, and deciduous forests, as well as subalpine and alpine meadows. RS was measured simultaneously in each ecosystem at 12 randomly distributed points using the closed static chamber technique. After the measurements, topsoil samples (0-10 cm) were collected under each chamber (n = 60). Several soil physicochemical, microbial, and vegetation indices were assessed as potential drivers of RS. We tested two hypotheses: (i) the spatial variability of RS is higher in forests than in grasslands; and (ii) the spatial variability of RS in forests is mainly due to soil microbial activity, whereas in grasslands, it is mainly due to vegetation characteristics. Unexpectedly, RS variability was lower in forests than in grasslands, ranging from 1.3-6.5 versus 3.4-12.7 μmol CO2 m-1 s-1, respectively. Spatial variability of RS in forests was related to microbial functioning through chitinase activity (50% explained variance), whereas in grasslands it was related to vegetation structure, namely graminoid abundance (27% explained variance). Apparently, the chitinase dependence of RS variability in forests may be related to soil N limitation. This was confirmed by low N content and high C:N ratio compared to grassland soils. The greater sensitivity of grassland RS to vegetation structure may be related to the essential root C allocation for some grasses. Thus, the first hypothesis concerning the higher spatial variability of RS in forests than in grasslands was not confirmed, whereas the second hypothesis concerning the crucial role of soil microorganisms in forests and vegetation in grasslands as drivers of RS spatial variability was confirmed.

High-Resolution Bayesian Inversion of Carbon Dioxide Flux Over Peninsular India

Accurate estimation of the flux of the most important greenhouse gas, carbon dioxide (CO2), plays a decisive role in designing the policies to reduce the atmospheric CO2 burden to combat climate change. Here we integrate recent (2017–2020) in-situ measurements with a high-resolution particle dispersion model for optimising CO2 flux over peninsular India based on a top-down approach. Using weekly observations of atmospheric boundary layer CO2 from three distinct locations representing peninsular India, the total surface fluxes of CO2 are optimised based on the fluxes from fossil fuel, wildfire, terrestrial biosphere and ocean. Our Bayesian mode inversion using the Lagrangian Particle Dispersion Model FLEXPART suggests that the peninsular Indian region is a slightly stronger source of CO2 (3.34 TgC yr−1) than what is synthesised in the fossil fuel and ecosystem exchanges based on independent estimations. On seasonal scale, the flux corrections to the prior fluxes over peninsular India are 4.68, 6.53, −2.28 and 4.41 TgC yr−1during winter, pre-monsoon, monsoon and post-monsoon seasons, respectively. The CO2 measurements from the stations attempted in the present inversion experiments could capture the footprints of peninsular India, giving better constraints to fluxes in the inversion. However, the data gap, pristineness of the samples, and large variabilities in the observations call for sustained monitoring for a longer term to further reduce the uncertainty of the estimated flux.

Heat and carbon uptake in the Southern Ocean: the state of the art and future priorities.

Heat and carbon uptake in the Southern Ocean: the state of the art and future priorities.

Earth’s CO2 battle: a view from space

Abstract Our environment is undergoing rapid change as greenhouse gases warm the planet. Noel Cressie, Andrew Zammit-Mangion, Josh Jacobson, and Michael Bertolacci use WOMBAT, a Bayesian hierarchical statistical framework, to infer the spatio-temporal distribution of CO2 surface fluxes