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Atmospheric CH 4 observations from two ground-based stations within Indian subcontinent, namely, Sinhagad (SNG) and Cape Rama station (CRI) showed a strong seasonality with a minima (∼1800 ± 20 ppb) during southwest monsoon (SWM; i.e. June–September, JJAS) and a maxima (2000 ± 30 ppb) during northeast monsoon (NEM i.e. December–February, DJF) with a peak-to-peak seasonality close to 200 ppb. The Indian summer (winter) monsoon is characterized with strong southwesterly (northeasterly) winds of oceanic (land) origin at the surface level and strong easterly (westerly) jet streams aloft. The monsoon dynamics has pronounced impact on CH 4 variability over India and is analyzed with winds, Lagrangian trajectories, and 3-dimentional distributions of CH 4 simulated by a general circulation model. The model simulations suggest a consistent annual vertical structure (mean and sub-seasonal uncertainty) of CH 4 over India with a stark contrast in concentration from summer to winter at surface levels (below 750 mb) in confirmation with what is identified by the ground-based observations. During SWM (NEM) the air with comparatively lower (higher) CH 4 concentrations from southern (northern) hemisphere reduces the CH 4 over India by 1814 ± 26 ppb (enhances by 1950 ± 51 ppb). The contribution of local fluxes to this seasonality appears to be albeit weak as the synthesized CH4 fluxes (from EDGAR dataset) of the Indian peninsula itself show a peak in summer and a dip in winter. Similar property of CH 4 is also common to nearby oceanic region (i.e. over Arabian Sea, 1765 ± 10 ppb during summer) suggesting the role of monsoon dynamics as the controlling factor. Further the mixing and convection carries the CH 4 to the upper atmosphere and advect inward or outward aloft according the seasonal monsoon dynamics. [ABSTRACT FROM AUTHOR]

This study investigates the impact of ENSO on the CO 2 variability over the Indian subcontinent for the period 2003–2011 based on the relationships between NINO indices derived from the sea surface temperature (SST) and AIRS-retrieved mid-tropospheric CO 2 concentrations. The NINO4 region exhibits positive influence on the variability of CO 2 almost during the entire year except for the post-monsoon/winter months (October through December; OND). Significant positive relationship (correlation coefficient r = +0.68) between NINO4 index and CO 2 levels is observed for the month of June, while negative relationship (r = −0.73) for the month of October, and the negative relationship tends to continue till November with decreasing magnitudes (r = −0.41). The spatial distribution of mid-tropospheric CO 2 concentrations during El Niño and La Niña periods also indicate large-scale impact over the Indian subcontinent with positive (negative) anomalies of about 1–2 ppm during El Niño (La Niña). [ABSTRACT FROM AUTHOR]

In this study, we have examined the impact of droughts on the atmospheric CO 2 spatial variability and changes its emission scenario over India. Surface monitoring over India is very sparse and have started recently. Satellite retrievals from AIRS/Aqua during 2004–2011 are used here to understand CO 2 variability over India. During recent decade, Indian region witnessed two drought years 2004 and 2009 and no flood. Year 2009 was mega drought for India ever seen, when actual rainfall was approx −23% below the mean rainfall over most part of India. Satellite retrieved mid-tropospheric CO 2 indicates increase of about 3 ppm during summer monsoon drought. Enhanced impact of drought conditions is also seen on subsequent seasons, winter (JF) and hot pre-monsoon season (MAM). Post-monsoon season (OND) does not show any clear impact, as rainfall during this season has its own variability quite different than summer monsoon (JJAS) may be compensating changes in CO 2 values. Decrease in vegetation and weak circulation patterns during drought conditions may have influences on distribution of CO 2 over Indian region. [ABSTRACT FROM AUTHOR]

This study examines the role of Asian monsoons on transport and spatial variability of atmospheric CO 2 over the Indian subcontinent, using transport modeling tools and available surface observations from two atmospheric CO 2 monitoring sites Sinhagad (SNG) and Cape Rama (CRI) in the western part of peninsular India. The regional source contributions to these sites arise from the horizontal flow in conduits within the planetary boundary layer. Greater CO 2 variability, greater than 15 ppm, is observed during winter, while it is reduced nearly by half during summer. The SNG air sampling site is more susceptible to narrow regional terrestrial fluxes transported from the Indo-Gangetic Plains in January, and to wider upwind marine source regions from the Arabian Sea in July. The Western Ghats mountains appear to play a role in the seasonal variability at SNG by trapping polluted air masses associated with weak monsoonal winds. A Lagrangian back-trajectory analysis further suggests that the horizontal extent of regional sensitivity increases from north to south over the Indian subcontinent in January (Boreal winter). [ABSTRACT FROM AUTHOR]

Abstract: In this paper we have studied variability and growth rate of surface observed atmospheric Carbon Dioxide (CO2) concentrations over Cape Rama, west coast of India and its association with rainfall and vegetation over this region. Cape Rama is a maritime site which experiences a seasonal reversal wind pattern receiving air masses having marine (continental) signatures during summer (winter) monsoon season. This study reveals that summer monsoon (JJAS) precipitation and monthly values of atmospheric CO2 concentration during the season are well correlated. Negative correlations are seen with CO2 concentrations of concurrent months of the season as well as subsequent months. However the magnitudes of correlation coefficients are decreased till hot pre-monsoon season (MAM). Annual cycle and interannual variability show negative relationship between CO2 concentration and vegetation over the region. CO2 concentration shows increasing trend and NDVI shows decreasing trend. However, the magnitude of increasing trend of CO2 concentration is higher. Amplitude of decreasing phase of vegetation is higher than the amplitude of increasing phase. Though the results show certain link between CO2 and climate variability, further examination with dense and longer data may be needed to confirm the result. [Copyright &y& Elsevier]

India has the second largest population, one of fastest growing economies and is ranked third in greenhouse gas emissions by fossil-fuel burning in the world. However, there has been little monitoring of atmospheric CO2 concentration over India to date. Here we reanalyse pioneering atmospheric CO2 observations at Cape Rama, India (CRI) during the period from February 1993 to October 2002, using three forward transport models to simulate atmospheric CO2 and separate tracers of terrestrial and oceanic fluxes, and fossil-fuel emissions. The CO2 seasonal behaviour at this site has clear signals from monsoon-driven meteorology and terrestrial ecosystem activity, which are generally captured by all three models. The quality of the agreement between the simulations and the observations varies with season, with better results obtained during the southwest monsoon months when the CRI site observes the oceanic air of mostly southern hemispheric origin. Relatively poor model-data agreements in the other seasons, when air originating from the Indian subcontinent passes over the site, arise from the inability of coarse-resolution global models to represent CRI appropriately. In addition, limited atmospheric CO2 measurements in the South Asia region only provide poor constraint on inversion fluxes. Flux signal footprint analysis of the CRI station highlights the need of extending the observation network inland and to different parts of the country for better understanding of the carbon cycle of India. [ABSTRACT FROM AUTHOR]

Atmospheric methane (CH 4) is considered to be one of the most important greenhouse gases due to its increasing atmospheric concentrations and the fact that it has a warming potential 28 times that of atmospheric carbon dioxide (CO 2). Over the Indian sub-continent, fluxes and transport both contribute towards CH 4 seasonal variability. Its intra-seasonal variability however is more complex as it is additionally influenced by monsoonal activity during the Asian Summer Monsoon (ASM) period. In this study, the intra-seasonal variability of atmospheric CH 4 is examined using ground-based observations at two sites located in the Southern Indian Peninsula, Sinhagad (SNG) and Cape Rama (CRI); and outputs from three different model simulations. Both, the ground based observations and multi-model simulations show that the dominant spectral variability of CH 4 is coherent with 20–90 day oscillations in the dynamics of the monsoon (termed hereafter as Intra-Seasonal Oscillations, ISOs). The multi-model analysis revealed that CH 4 is heavily influenced by advection due to this intra-seasonal variability. The simulations also display a clear northward propagation of CH 4 anomalies over India. The co-evolution of CH 4 , outgoing long wave radiation (to represent convection) and OH radicals (proxy to CH 4 sinks) is presented. The study quantifies CH 4 variability at intra-seasonal timescales and also its spatial extent. The results suggest that the effect of ISOs on CH 4 needs to be considered along with the corresponding observations for future inverse modeling. Image 1 Composite analysis shows a clear northward propagation of atmospheric CH 4 concentration anomalies with amplitude of ±10 ppb and at a speed of approximately 1.5° latitude per day. On the day when the active spell commences (i.e. at zero on the time axis), the CH 4 concentrations switches from positive to negative at surface, middle and upper troposphere. This is visible in three different model outputs analyzed here although with some differences from model to model.Figure: Active-break composite evolution of atmospheric CH4 anomalies (ppb) over India, as simulated by models LMDz (upper left panel), ACTM (upper right panel), and CAM-Chem (lower panel). The data was averaged from 55 °E to 110 °E and is shown from the equator to 30 °N. • Surface CH 4 observations in India show coherence of 30–90 day oscillations. • Advected CH 4 signal shows clear northward propagation of anomalies during JJAS. • The co-evolutions of CH 4 , OLR, and OH radicals are presented for the JJAS period. [ABSTRACT FROM AUTHOR]

In a study based on a data assimilation product of the terrestrial biospheric fluxes of CO 2 over India, the subcontinent was hypothesized to be an anomalous source (sink) of CO 2 during the active (break) spells of rain in the summer monsoon from June to September (Valsala et al., 2013). We test this hypothesis here by investigating intraseasonal variability in the atmospheric CO 2 concentrations over India by utilizing a combination of ground-based and satellite observations and model outputs. The results show that the atmospheric CO 2 concentration also varies in synchrony with the active and break spells of rainfall with amplitude of ±2 ppm which is above the instrumental uncertainty of the present day techniques of atmospheric CO 2 measurements. The result is also consistent with the signs of the Net Ecosystem Exchange (NEE) flux anomalies estimated in our earlier work. The study thus offers the first observational affirmation of the above hypothesis although the data gap in the satellite measurements during monsoon season and the limited ground-based stations over India still leaves some uncertainty in the robust assertion of the hypothesis. The study highlights the need to capture these subtle variabilities and their responses to climate variability and change since it has implications for inverse estimates of terrestrial CO 2 fluxes. [ABSTRACT FROM AUTHOR]

In this paper we examine CO2 emission hot spots and sink regions over India as identified from global model simulations during the period 2000-2009. CO2 emission hot spots overlap with locations of densely clustered thermal power plants, coal mines and other industrial and urban centres; CO2 sink regions coincide with the locations of dense forest. Fossil fuel CO2 emissions are compared with two bottom-up inventories: the Regional Emission inventories in ASia (REAS v1.11; 2000-2009) and the Emission Database for Global Atmospheric Research (EDGAR v4.2) (2000-2009). Estimated fossil fuel emis sions over the hot spot region are ~ 500-950 gCm-2yr-1 as obtained from the global model simulation, EDGAR v4.2 and REAS v1.11 emission inventory. Simulated total fluxes show increasing trends, from 1.39 ± 1.01% yr-1 (19.8 ± 1.9 TgC yr-1) to 6.7 ± 0.54 % yr-1 (97 ± 12 TgC yr-1) over the hot spot regions and decreasing trends of -0.95 ± 1.51 % yr-1 (-1 ± 2 TgC yr-1) to -5.7 ± 2.89 % yr-1 (-2.3 ± 2 TgC yr-1) over the sink regions. Model-simulated terrestrial ecosystem fluxes show decreasing trends (increasing CO2 uptake) over the sink regions. Decreasing trends in terrestrial ecosystem fluxes imply that forest cover is increasing, which is consistent with India State of Forest Report (2009). Fossil fuel emissions show statistically significant increasing trends in all the data sets considered in this study. Estimated trend in simulated total fluxes over the Indian region is ~ 4.72 ± 2.25 % yr-1 (25.6 TgC yr-1) which is slightly higher than global growth rate ~ 3.1 % yr-1 during 2000-2010. [ABSTRACT FROM AUTHOR]

Due to human activities, the atmospheric concentration of Carbon Dioxide (CO 2 ) has been rising extensively since the Industrial Revolution. Indian summer monsoon (ISM) has a dominant westerly component from ocean to land with a strong tendency to ascend and hence may have role in CO 2 distribution in lower and middle troposphere over Indian sub-continent. A substantial component of ISM variability arises from the fluctuations on the intra-seasonal scale between active and break phases which correspond to strong and weak monsoon circulation. In view of the above, an attempt is made in this study to examine the AIRS/AQUA satellite retrieved CO 2 distribution in response to atmospheric circulation with focus on active and break phase. Correlation analysis indicates the increase in AIRS CO 2 linked with strong monsoon circulation. Study also reveals that anomalous circulation pattern during active and break phase show resemblance with high and low values of AIRS CO 2 . Homogeneous monsoon regions of India show substantial increase in CO 2 levels during active phase. Hilly regions of India show strong contrast in CO 2 and vertical velocity during active and break phases. [ABSTRACT FROM AUTHOR]

Abstract: Atmospheric Infrared Sounder (AIRS) retrieved mid-tropospheric Carbon Dioxide (CO2) have been used to study the variability and its association with the climatic parameters over India during 2004 to 2011. The study also aims in understanding transport of CO2 from surface to mid-troposphere over India. The annual cycle of mid-tropospheric CO2 shows gradual increase in concentration from January till the month of May at the rate ~0.6ppm/month. It decreases continuously in summer monsoon (JJAS) at the same rate during which strong westerlies persists over the region. A slight increase is seen during winter monsoon (DJF). Being a greenhouse gas, annual cycle of CO2 show good resemblance with annual cycle of surface air temperature with correlation coefficient (CC) of +0.8. Annual cycle of vertical velocity indicate inverse pattern compared to annual cycle of CO2. High values of mid-tropospheric CO2 correspond to upward wind, while low values of mid-tropospheric CO2 correspond to downward wind. In addition to vertical motion, zonal winds are also contributing towards the transport of CO2 from surface to mid-troposphere. Vegetation as it absorbs CO2 at surface level, show inverse annual cycle to that of annual cycle of CO2 (CC-0.64). Seasonal variation of rainfall-CO2 shows similarities with seasonal variation of NDVI-CO2. However, the use of long period data sets for CO2 at the surface and at the mid-troposphere will be an advantage to confirm these results. [Copyright &y& Elsevier]

• Calibration and validation of a process-based ecosystem model at an Indian forest. • Modelled and observed annual GPP are 2433 gC m−2 y−1 and 2399 gC m−2 y−1. • Mismatch exists between empirically derived and modelled photosynthetic parameters. • Model simulated sensible and latent heat fluxes concur with direct observations. The ecosystem-atmosphere exchanges of terrestrial ecosystems are key drivers of global carbon, water, and energy cycles which are crucial to be measured accurately and represented in bottom-up biogeochemically and biogeophysically coupled ecosystem, Earth system, and climate models for improving the model predictions and impact assessment of climate change on these ecosystems. The diverse natural ecosystems in India are poorly represented in these models due to the absence any of integrated data-model framework and intercomparison study so far. To partially address this, we have used flux-tower measurements in this study to simulate the gross primary productivity (GPP), sensible (H), and latent heat fluxes (LE) of a moist semi-evergreen deciduous forest in the Kaziranga National Park in subtropical Northeast India using a process-based model ISAM. The model is calibrated using two years of measurement which improves its performance when subsequently run to simulate these fluxes for a year. The model produced annual GPP, mean maximum H, and LE are 2432.26 gC m−2 y−1, 29 and 82 W m−2 respectively as compared to their measured values i.e. 2398.47 gC m−2 y−1, 26 and 73 W m−2 correspondingly. Additionally, we report the calibrated model biogeochemical and biogeophysical parameters which will be useful to simulate the fluxes in this forest using the aforementioned models. [Display omitted] [ABSTRACT FROM AUTHOR]

Amongst all the anthropogenically produced greenhouse gases (GHGs), carbon dioxide (CO2) and methane (CH4) are the most important, owing to their maximum contribution to the net radiative forcing of the Earth. India is undergoing rapid economic development, where fossil fuel emissions have increased drastically in the last three decades. Apart from the anthropogenic activities, the GHGs dynamics in India are governed by the biospheric process and monsoon circulation; however, these aspects are not well addressed yet. Towards this, we have measured CO2 and CH4 concentration at Sinhagad, located on the Western Ghats in peninsular India. The average concentrations of CO2 and CH4 observed during the study period are 406.05 ± 6.36 and 1.97 ± 0.07 ppm (µ ± 1σ), respectively. They also exhibit significant seasonal variabilities at this site. CH4 (CO2) attains its minimum concentration during monsoon (post-monsoon), whereas CO2 (CH4) reaches its maximum concentration during pre-monsoon (post-monsoon). CO2 poses significant diurnal variations in monsoon and post-monsoon. However, CH4 exhibits a dual-peak like pattern in pre-monsoon. The study suggests that the GHG dynamics in the western region of India are significantly influenced by monsoon circulation, especially during the summer season. [ABSTRACT FROM AUTHOR]

Climate impacts agriculture in various complex ways at different levels and scales depending on the local natural crop growth limitations. Our objective in this study, therefore, is to understand how different is the atmosphere–biosphere exchange of CO2 under contrasting subtropical and boreal agricultural (an oilseed crop and a bioenergy crop, respectively) climates. The oilseed crop in subtropical climate continued to uptake CO2 from the atmosphere throughout the year, with maximum uptake occurring in the monsoon season, and drastically reduced uptake during drought. The boreal ecosystem, on the other hand, was a sustained, small source of CO2 to the atmosphere during the snow-covered winter season. Higher rates of CO2 uptake were observed owing to greater day-length in the growing season in the boreal ecosystem. The optimal temperature for photosynthesis by the subtropical ecosystem was close to the regional normal mean temperature. An enhanced photosynthetic response to the incident radiation was found for the boreal ecosystem implying the bioenergy crop to be more efficient than the oilseed crop in utilizing the available light. This comparison of the CO2 exchange patterns will help strategising the carbon management under different climatic conditions. [ABSTRACT FROM AUTHOR]

The present study demonstrates the distribution of carbon dioxide (CO2) concentration over the Indian region and the surrounding oceanic regions during 2009-2012, using measurements from satellites viz., Greenhouse Gases Observing Satellite (GOSAT) and Atmospheric Infrared Sounder, Carbon Tracker (CT) model simulations and flask measurements from two Indian stations Sinhagad (SNG) (73°45′ E, 18°21′36″ N) and Cape Rama (CRI) (73°54′ E, 15°6′ N). The concentration of CO2 is observed to be maximum during pre-monsoon and shows a decreasing phase during the post-monsoon season. In a regional scale, it is found that Indo-Gangetic Plain and northern India have relatively higher concentrations compared to the other regions. The probability distribution of the concentration differences shows that for most of the time, the differences lie between ±3 ppmv between GOSAT and CT. The comparison between the CO2 flask measurements over SNG and CRI with respect to that of GOSAT and CT clearly reveals that the differences in CO2 are as high as 10 ppmv between the ground- and satellite-based measurements. Further, we utilized the Lagrangian model FLEXible PARTicle (FLEXPART) to understand the source‒receptor relationship over CRI, SNG, and over the equatorial Indian Ocean (IO). The source contributions from the northern and eastern continental regions of the Indian region are found to be more influential over SNG compared to CRI. It is also found from simulations that the equatorial IO has less influence from the continental source and therefore has a reduced seasonal variability compared to the other regions considered in the present study. [ABSTRACT FROM AUTHOR]

The terrestrial biosphere has a crucial role in controlling the rate of CO 2 accumulation in the atmosphere. The quantification of Net Ecosystem Exchange (NEE) is critical to determine whether the regional terrestrial ecosystem is a net sink or source of CO 2. This study uses a satellite-data derived light-use-efficiency model (the Vegetation Photosynthesis and Respiration Model, VPRM) to compute the biospheric CO 2 fluxes over India from 2011–2020. A very high-resolution Land Use-Land Cover (LULC) data from the IRS-P6 satellite and MODIS derived surface reflectance are utilised to compute the NEE over distinct vegetation types at ∼ 9 km × 9 km. The VPRM model for the Indian region captures the spatial pattern and seasonal features of NEE over the country. The magnitude of annual mean NEE over India from the VPRM model is relatively high compared to that from Carbon Tracker, FLUXCOM, and Soil Moisture Active Passive Level-4 carbon data products. However, the VPRM model simulated NEE agrees with the NEE observations from the eddy covariance estimates, which are accepted as the ground truth. The fusion of high-resolution LULC and surface reflectance data in VPRM suggests that the average NEE over the Indian region during 2011–2020 is −0.16 ± 0.02 PgC yr−1. Among this, the contribution from Gross Ecosystem Exchange is about −0.47 ± 0.02 PgC yr−1, and Respiration is 0.31 ± 0.01 PgC yr−1. The maximum biospheric CO 2 absorption is during the post-monsoon season, and the minimum is during the pre-monsoon season. The very high spatial resolution and heterogeneity resolved in the NEE data derived in this work offer the first data product of this kind for a variety of NEE analyses over India. [Display omitted] • The Vegetation Photosynthesis and Respiration Model (VPRM) is configured over India. • VPRM computes high-resolution biospheric CO 2 flux over India during 2011-20. • The mean NEE over India during 2011–20 is −0.16 ± 0.02 PgC yr−1. • CO 2 flux optimisation using inverse modelling can benefit from this biospheric flux. [ABSTRACT FROM AUTHOR]

• OSSE is done for Indian Ocean (IO) pCO 2 using RAMA + OMNI moorings, Bio-Argo floats and SOOP ship-tracks. • pCO 2 data from moorings and ship-tracks are tested for air-sea CO 2 flux inversions. • The maximum CO 2 flux uncertainty reduction (UR) with potential pCO 2 data is found. • IO-UR with pCO 2 data from moorings and ship-tracks are 30% and 62%, respectively. • Ship-track pCO 2 is more efficient than the mooring pCO 2 data in CO 2 flux inversion. An observing system simulation experiment (OSSE) is conducted to identify potential locations for making surface ocean pCO 2 measurements in the Indian Ocean using the Bayesian Inversion method. As of the SOCATv3 release, the pCO 2 data is limited in the Indian Ocean. To improve our modeling of this region, we need to identify where and what observation systems would produce the most good or benefit for their cost. The potential benefits of installing pCO 2 sensors in the existing RAMA and OMNI moorings of the Indian Ocean, the potential of Bio-Argo floats (with pH measurements), and the implementation of the ship of opportunity program (SOOP) for underway sampling of pCO 2 are evaluated. A cost function of dissolved inorganic carbon as a model state vector and CO 2 flux mismatch as the source of error is minimized, and the basin-wide CO 2 flux uncertainty reduction is estimated for different seasons. The maximum flux uncertainty reduction achievable by installing pCO 2 sensors in the existing RAMA and OMNI moorings is limited to 30% during different seasons. One may consider that around 20 Bio-Argos are still the right choice over installing mooring based pCO 2 sensors and achieve uncertainty reduction up to 50% with additional benefit of profiling the sub-surface upto 1000–2000 m. However, a single track SOOP has the potential to reduce the uncertainty by approximately 62%. This study identifies vital RAMA and OMNI moorings and SOOP tracks for observing Indian Ocean pCO 2. Plain Language Summary. Surface ocean partial pressure of CO 2 (pCO 2) information is vital for estimating sea-to-air CO 2 exchanges. This parameter is least available from the Indian Ocean as compared to other global tropical and southern oceans. There has been no effort made so far to measure surface ocean pCO 2 in the Indian Ocean with routine monitoring such as by mounting instruments to moorings or by underway sampling via any ship of opportunity program. Therefore there is a considerable demand to start pCO 2 observations in the Indian Ocean. However, one key question that emerges is where to deploy pCO 2 instruments in the Indian Ocean to learn the most with limited resources. This study addresses this question with inverse modeling techniques. The study finds that the existing moorings of the Indian Ocean are capable of hosting pCO 2 sensors, and data from those are useful to reduce the uncertainty in the surface sea-to-air CO 2 flux estimation by a quarter magnitude. In contrast, the Bio-Argo floats with pH sensors, and the ship of opportunity underway sampling of pCO 2 may benefit from reducing the same up to 50% and 62%, respectively. [ABSTRACT FROM AUTHOR]

We employed a global high-resolution inverse model to optimize the CH4 emission using Greenhouse gas Observing Satellite (GOSAT) and surface observation data for a period from 2011–2017 for the two main source categories of anthropogenic and natural emissions. We used the Emission Database for Global Atmospheric Research (EDGAR v4.3.2) for anthropogenic methane emission and scaled them by country to match the national inventories reported to the United Nations Framework Convention on Climate Change (UNFCCC). Wetland and soil sink prior fluxes were simulated using the Vegetation Integrative Simulator of Trace gases (VISIT) model. Biomass burning prior fluxes were provided by the Global Fire Assimilation System (GFAS). We estimated a global total anthropogenic and natural methane emissions of 340.9 Tg CH4 yr−1 and 232.5 Tg CH4 yr−1, respectively. Country-scale analysis of the estimated anthropogenic emissions showed that all the top-emitting countries showed differences with their respective inventories to be within the uncertainty range of the inventories, confirming that the posterior anthropogenic emissions did not deviate from nationally reported values. Large countries, such as China, Russia, and the United States, had the mean estimated emission of 45.7 ± 8.6, 31.9 ± 7.8, and 29.8 ± 7.8 Tg CH4 yr−1, respectively. For natural wetland emissions, we estimated large emissions for Brazil (39.8 ± 12.4 Tg CH4 yr−1), the United States (25.9 ± 8.3 Tg CH4 yr−1), Russia (13.2 ± 9.3 Tg CH4 yr−1), India (12.3 ± 6.4 Tg CH4 yr−1), and Canada (12.2 ± 5.1 Tg CH4 yr−1). In both emission categories, the major emitting countries all had the model corrections to emissions within the uncertainty range of inventories. The advantages of the approach used in this study were: (1) use of high-resolution transport, useful for simulations near emission hotspots, (2) prior anthropogenic emissions adjusted to the UNFCCC reports, (3) combining surface and satellite observations, which improves the estimation of both natural and anthropogenic methane emissions over spatial scale of countries. [ABSTRACT FROM AUTHOR]

Optimal network design, for ground-based monitoring of atmospheric mole fractions of carbon dioxide (CO 2) over India to better constrain the Indian terrestrial surface fluxes, is proposed here using a Lagrangian Particle Dispersion Model FLEXPART and Bayesian inversion methods. Potential emission sensitivity from FLEXPART, prior flux uncertainties from CASA-GFED biosphere fluxes and CDIAC fossil fuel fluxes, and assumed uniform observational uncertainty of 2 ppm are used to calculate prior and posterior cost functions. A total of 73 grid cells are identified over the Indian region in 2°x2° latitude by longitude resolution assuming each cell as a potential site, ' a-priori'. Further, the effectiveness of CO 2 observations from these locations to reduce the Indian terrestrial flux uncertainty is quantified using an incremental optimization method. Based on the above objective, we have (a) ranked the existing nine stations, (b) devised a methodology to design an extended network by adding a few more potential stations to the existing stations, and (c) identified a completely new set of optimal stations for measuring atmospheric CO 2 over India. The study shows that the base network of existing stations scattered over India could reduce the uncertainty of Indian terrestrial surface flux estimation in an inversion framework, at this resolution, by more than 30% during post-monsoon and winter season, and by more than 15% during other parts of the year. Addition of five new stations to the base network could reduce the uncertainty by an additional 15% for all the seasons reaching up to 45%. In the 'new network', twelve stations are able to achieve an equivalent flux uncertainty reduction of fourteen stations achieved by the 'extended network' during each season. Locations of stations are more important than the magnitude of reduction since it depends on the values of the prior and observational uncertainties. The most important and essential 'new' stations in the extended network are over the northeastern region and Indo-Gangetic plain, during most of the year. Only during the post-monsoon season, additional stations are required in the western peninsular region. Finally, a consolidated set for all seasons comprising seventeen members is proposed. The study highlights a major zone of CO 2 'observational void' that exists in potential locations near east and northeast parts of India. Immediate requirement of CO 2 monitoring initiative in these areas is highly recommended. • Designed an optimal network for monitoring CO2 fluxes over India. • Existing CO2 monitoring stations over India could reduce flux uncertainty by 30%. • The most required stations are over the northeastern and Indo-Gangetic region. • New optimal network highlights 'observational void' over east and northeast India. [ABSTRACT FROM AUTHOR]