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Southeast Asian peatlands represent a globally significant carbon store that is destabilized by land-use changes like deforestation and the conversion into plantations, causing high carbon dioxide (CO2) emissions from peat soils and increased leaching of peat carbon into rivers. While this high carbon leaching and consequentially high DOC concentrations suggest that CO2 emissions from peat-draining rivers would be high, estimates based on field data suggest they are only moderate. In this study, we offer an explanation for this phenomenon by showing that carbon decomposition is hampered by the low pH in peat-draining rivers. This limits CO2 production in and emissions from these rivers. We find an exponential pH limitation that shows good agreement with laboratory measurements from high-latitude peat soils. Additionally, our results suggest that enhanced input of carbonate minerals increases CO2 emissions from peat-draining rivers by counteracting the pH limitation. As such inputs of carbonate minerals can occur due to human activities like deforestation of river catchments, liming in plantations, and enhanced weathering application, our study points out an important feedback mechanism of those practices.

Urban areas are home to many people on the globe, and centres of industry. Emissions from cities contribute to atmospheric concentrations of greenhouse gases like CO2 and CH4, which influence the Earth’s energy budget. The Ruisdael Observatory is a Dutch research infrastructure to investigate the atmosphere over the Netherlands by bringing together measurements and high resolution modelling. One part of it is a semi-mobile trailer to be flexibly deployed as measurement station for targeted field measurements. Mounted on the roof of this trailer are a Bruker EM27/SUN (EM27) for columnar trace gas measurements and a Cimel for columnar aerosol measurements. A targeted field campaign was conducted in August and September 2022 to study Rotterdam, one of the biggest city in the Netherlands and the biggest harbour in Europe. Three EM27s were set up around Rotterdam in an upwind-downwind configuration. To ensure the comparability of the data, the instruments measured in parallel for three days before and after the measurement period, which showed good agreement between the instruments. Four different configurations of instrument locations were used during the three week campaign to account for changes in wind direction and investigate specific targets as well as separate between the influence of the harbour area and the city itself. Enhancements in the CO2 column were around 1-3 ppm across the harbour and about 1 ppm across the city. CH4 columnar concentrations were not significantly enhanced across the city, but increased by several ppb across the harbour area. The CO columnar concentrations increased across the harbour by up to 10 ppb and 5 ppb across the city area.

The ATTO (Amazon Tall Tower Observatory) tower is a 325m tall tower located in the middle of the pristine Amazon rainforest. Since 2022, continuous greenhouse gas concentrations at different heights (4m, 42m, 81m, 150m, 273m, 321m) are monitored by use of a Spectronus FTIR-spectrometer, measuring CO2, CH4, CO, N2O and del13CO2 hourly, compliant to WMO/GAW standards. This unique measurement system is the first set up which measures greenhouse gases continuously until 325m above a tropical rainforest, which fill an important gap in the global continous observation network. The measurements can be used for regional and global modelling, and can be used for biosphere-atmosphere exchange flux estimates. In this presentation, we will show the main observations of the first year of data collection, and will present the typical daily cycles observed for the different gases at different heights.

Two global networks of ground-based Fourier transform spectrometers are measuring abundances of atmospheric trace gases that absorb in the near and mid infrared, Network for the Detection of Atmospheric Composition Change (NDACC) and Total Carbon Column Observing Network (TCCON). The first lacks a CO2 product, therefore this study focuses on developing a xCO2 retrieval method for NDACC from a spectral window in the 4800 cm−1 region. This retrieval will allow to extend ground-based measurements back in time, which we will demonstrate with historical data available from Ny-Ålesund. This region is covered by both TCCON and NDACC, which is an advantage for collocated comparisons where available. The results are compared with collocated TCCON measurements of column-averaged dry-air mole fractions of CO2 (denoted by xCO2) in Ny-Ålesund, Svalbard and only TCCON in Burgos, Philippines. We found that it is possible to retrieve xCO2 from NDACC spectra with a precision from 0.2 % . The comparison between the new retrieval to TCCON showed that the sensitivity of the new retrieval is high in the troposphere and lower in the upper stratosphere, similar to TCCON, and that the seasonality is well captured. We determined an optimal retrieval setup covered in section 7.

Satellite gravimetry is a key component in the investigation of groundwater depletion on the Indian subcontinent. Terrestrial mass loss by sediment transport in rivers is assumed to be below the detection limit of current satellites like GRACE-FO. Thus, it is not considered in the calculation of terrestrial water budgets. However, the Indian subcontinent is drained by the Ganges and Brahmaputra rivers, which constitute one of the world’s most sediment rich river systems. We find that the impact of sediment mass loss within the combined Ganges-Brahmaputra-Meghna catchment accounts for (4±2)% of the long-term gravity decrease currently attributed to groundwater depletion. For erosion-prone Himalaya regions, the correction for sediment mass loss reduces the local trend in equivalent water height by 0.22 cm/yr, which is 14% of the observed trend., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

The Thermal And Near infrared Sensor for carbon Observation – Fourier Transform Spectrometer (TANSO-FTS) on the Japanese Greenhouse gases Observing SATellite (GOSAT) has been returning data since April 2009. The version 9 (v9) Atmospheric Carbon Observations from Space (ACOS) Level 2 Full Physics (L2FP) retrieval algorithm (Kiel et al., 2019) was used to derive estimates of carbon dioxide (CO2) dry air mole fraction (XCO2) from the TANSO-FTS measurements collected over its first 11 years of operation. The bias correction and quality filtering of the L2FP XCO2 product were evaluated using estimates derived from the Total Carbon Column Observing Network (TCCON) as well as values simulated from a suite of global atmospheric inversion systems (models) which do not assimilate satellite-derived CO2. In addition, the v9 ACOS GOSAT XCO2 results were compared with collocated XCO2 estimates derived from NASA's Orbiting Carbon Observatory-2 (OCO-2), using the version 10 (v10) ACOS L2FP algorithm. These tests indicate that the v9 ACOS GOSAT XCO2 product has improved throughput, scatter, and bias, when compared to the earlier v7.3 ACOS GOSAT product, which extended through mid 2016. Of the 37 million soundings collected by GOSAT through June 2020, approximately 20 % were selected for processing by the v9 L2FP algorithm after screening for clouds and other artifacts. After post-processing, 5.4 % of the soundings (2×106 out of 37×106) were assigned a “good” XCO2 quality flag, as compared to 3.9 % in v7.3 (

Satellite-based observations of atmospheric carbon dioxide (CO 2 ) provide measurements in remote regions, such as the biologically sensitive but undersampled northern high latitudes, and are progressing toward true global data coverage. Recent improvements in satellite retrievals of total column-averaged dry air mole fractions of CO 2 ( X CO 2 ) from the NASA Orbiting Carbon Observatory 2 (OCO-2) have allowed for unprecedented data coverage of northern high-latitude regions, while maintaining acceptable accuracy and consistency relative to ground-based observations, and finally providing sufficient data in spring and autumn for analysis of satellite-observed X CO 2 seasonal cycles across a majority of terrestrial northern high-latitude regions. Here, we present an analysis of X CO 2 seasonal cycles calculated from OCO-2 data for temperate, boreal, and tundra regions, subdivided into 5 ∘ latitude by 20 ∘ longitude zones. We quantify the seasonal cycle amplitudes (SCAs) and the annual half drawdown day (HDD). OCO-2 SCAs are in good agreement with ground-based observations at five high-latitude sites, and OCO-2 SCAs show very close agreement with SCAs calculated for model estimates of X CO 2 from the Copernicus Atmosphere Monitoring Services (CAMS) global inversion-optimized greenhouse gas flux model v19r1 and the CarbonTracker2019 model (CT2019B). Model estimates of X CO 2 from the GEOS-Chem CO 2 simulation version 12.7.2 with underlying biospheric fluxes from CarbonTracker2019 (GC-CT2019) yield SCAs of larger magnitude and spread over a larger range than those from CAMS, CT2019B, or OCO-2; however, GC-CT2019 SCAs still exhibit a very similar spatial distribution across northern high-latitude regions to that from CAMS, CT2019B, and OCO-2. Zones in the Asian boreal forest were found to have exceptionally large SCA and early HDD, and both OCO-2 data and model estimates yield a distinct longitudinal gradient of increasing SCA from west to east across the Eurasian continent. In northern high-latitude regions, spanning latitudes from 47 to 72 ∘ N, longitudinal gradients in both SCA and HDD are at least as pronounced as latitudinal gradients, suggesting a role for global atmospheric transport patterns in defining spatial distributions of X CO 2 seasonality across these regions. GEOS-Chem surface contact tracers show that the largest X CO 2 SCAs occur in areas with the greatest contact with land surfaces, integrated over 15–30 d. The correlation of X CO 2 SCA with these land surface contact tracers is stronger than the correlation of X CO 2 SCA with the SCA of CO 2 fluxes or the total annual CO 2 flux within each 5 ∘ latitude by 20 ∘ longitude zone. This indicates that accumulation of terrestrial CO 2 flux during atmospheric transport is a major driver of regional variations in X CO 2 SCA.

A better understanding of greenhouse gas surface sources and sinks is required in order to address the global challenge of climate change. Space-borne remote estimations of greenhouse gas atmospheric concentrations can offer the global coverage that is necessary to improve the constraint on their fluxes, thus enabling a better monitoring of anthropogenic emissions. In this work, we introduce the Adaptable 4A Inversion (5AI) inverse scheme that aims to retrieve geophysical parameters from any remote sensing observation. The algorithm is based on the Optimal Estimation algorithm, relying on the Operational version of the Automatized Atmospheric Absorption Atlas (4A/OP) radiative transfer forward model along with the Gestion et Étude des Informations Spectroscopiques Atmosphériques: Management and Study of Atmospheric Spectroscopic Information (GEISA) spectroscopic database. Here, the 5AI scheme is applied to retrieve the column-averaged dry air mole fraction of carbon dioxide (XCO2) from a sample of measurements performed by the Orbiting Carbon Observatory-2 (OCO-2) mission. Those have been selected as a compromise between coverage and the lowest aerosol content possible, so that the impact of scattering particles can be neglected, for computational time purposes. For air masses below 3.0, 5AI XCO2 retrievals successfully capture the latitudinal variations of CO2 and its seasonal cycle and long-term increasing trend. Comparison with ground-based observations from the Total Carbon Column Observing Network (TCCON) yields a bias of 1.30±1.32 ppm (parts per million), which is comparable to the standard deviation of the Atmospheric CO2 Observations from Space (ACOS) official products over the same set of soundings. These nonscattering 5AI results, however, exhibit an average difference of about 3 ppm compared to ACOS results. We show that neglecting scattering particles for computational time purposes can explain most of this difference that can be fully corrected by adding to OCO-2 measurements an average calculated–observed spectral residual correction, which encompasses all the inverse setup and forward differences between 5AI and ACOS. These comparisons show the reliability of 5AI as an optimal estimation implementation that is easily adaptable to any instrument designed to retrieve column-averaged dry air mole fractions of greenhouse gases.

We examined biases in the global GEOS-Chem chemical transport model for the period of February–May 2010 using weak-constraint (WC) four-dimensional variational (4D-Var) data assimilation and dry-air mole fractions of CH4 (XCH4) from the Greenhouse gases Observing SATellite (GOSAT). The ability of the observations and the WC 4D-Var method to mitigate model errors in CH4 concentrations was first investigated in a set of observing system simulation experiments (OSSEs). We then assimilated the GOSAT XCH4 retrievals and found that they were capable of providing information on the vertical structure of model errors and of removing a significant portion of biases in the modeled CH4 state. In the WC 4D-Var assimilation, corrections were added to the modeled CH4 state at each model time step to account for model errors and improve the model fit to the assimilated observations. Compared to the conventional strong-constraint (SC) 4D-Var assimilation, the WC method was able to significantly improve the model fit to independent observations. Examination of the WC state corrections suggested that a significant source of model errors was associated with discrepancies in the model CH4 in the stratosphere. The WC state corrections also suggested that the model vertical transport in the troposphere at middle and high latitudes is too weak. The problem was traced back to biases in the uplift of CH4 over the source regions in eastern China and North America. In the tropics, the WC assimilation pointed to the possibility of biased CH4 outflow from the African continent to the Atlantic in the mid-troposphere. The WC assimilation in this region would greatly benefit from glint observations over the ocean to provide additional constraints on the vertical structure of the model errors in the tropics. We also compared the WC assimilation at 4∘ × 5∘ and 2∘ × 2.5∘ horizontal resolutions and found that the WC corrections to mitigate the model errors were significantly larger at 4∘ × 5∘ than at 2∘ × 2.5∘ resolution, indicating the presence of resolution-dependent model errors. Our results illustrate the potential utility of the WC 4D-Var approach for characterizing model errors. However, a major limitation of this approach is the need to better characterize the specified model error covariance in the assimilation scheme.

Accurate accounting of emissions and removals of CO2 is critical for the planning and verification of emission reduction targets in support of the Paris Agreement. Here, we present a pilot dataset of country-specific net carbon exchange (NCE; fossil plus terrestrial ecosystem fluxes) and terrestrial carbon stock changes aimed at informing countries' carbon budgets. These estimates are based on “top-down” NCE outputs from the v10 Orbiting Carbon Observatory (OCO-2) modeling intercomparison project (MIP), wherein an ensemble of inverse modeling groups conducted standardized experiments assimilating OCO-2 column-averaged dry-air mole fraction (XCO2) retrievals (ACOS v10), in situ CO2 measurements or combinations of these data. The v10 OCO-2 MIP NCE estimates are combined with “bottom-up” estimates of fossil fuel emissions and lateral carbon fluxes to estimate changes in terrestrial carbon stocks, which are impacted by anthropogenic and natural drivers. These flux and stock change estimates are reported annually (2015–2020) as both a global 1∘ × 1∘ gridded dataset and a country-level dataset and are available for download from the Committee on Earth Observation Satellites' (CEOS) website: https://doi.org/10.48588/npf6-sw92 (Byrne et al., 2022). Across the v10 OCO-2 MIP experiments, we obtain increases in the ensemble median terrestrial carbon stocks of 3.29–4.58 Pg CO2 yr−1 (0.90–1.25 Pg C yr−1). This is a result of broad increases in terrestrial carbon stocks across the northern extratropics, while the tropics generally have stock losses but with considerable regional variability and differences between v10 OCO-2 MIP experiments. We discuss the state of the science for tracking emissions and removals using top-down methods, including current limitations and future developments towards top-down monitoring and verification systems.

This work presents the latest release (v9.0) of the University of Leicester GOSAT Proxy XCH4 dataset. Since the launch of the GOSAT satellite in 2009, these data have been produced by the UK National Centre for Earth Observation (NCEO) as part of the ESA Greenhouse Gas Climate Change Initiative (GHG-CCI) and Copernicus Climate Change Services (C3S) projects. With now over a decade of observations, we outline the many scientific studies achieved using past versions of these data in order to highlight how this latest version may be used in the future. We describe in detail how the data are generated, providing information and statistics for the entire processing chain from the L1B spectral data through to the final quality-filtered column-averaged dry-air mole fraction (XCH4) data. We show that out of the 19.5 million observations made between April 2009 and December 2019, we determine that 7.3 million of these are sufficiently cloud-free (37.6 %) to process further and ultimately obtain 4.6 million (23.5 %) high-quality XCH4 observations. We separate these totals by observation mode (land and ocean sun glint) and by month, to provide data users with the expected data coverage, including highlighting periods with reduced observations due to instrumental issues. We perform extensive validation of the data against the Total Carbon Column Observing Network (TCCON), comparing to ground-based observations at 22 locations worldwide. We find excellent agreement with TCCON, with an overall correlation coefficient of 0.92 for the 88 345 co-located measurements. The single-measurement precision is found to be 13.72 ppb, and an overall global bias of 9.06 ppb is determined and removed from the Proxy XCH4 data. Additionally, we validate the separate components of the Proxy (namely the modelled XCO2 and the XCH4∕XCO2 ratio) and find these to be in excellent agreement with TCCON. In order to show the utility of the data for future studies, we compare against simulated XCH4 from the TM5 model. We find a high degree of consistency between the model and observations throughout both space and time. When focusing on specific regions, we find average differences ranging from just 3.9 to 15.4 ppb. We find the phase and magnitude of the seasonal cycle to be in excellent agreement, with an average correlation coefficient of 0.93 and a mean seasonal cycle amplitude difference across all regions of −0.84 ppb. These data are available at https://doi.org/10.5285/18ef8247f52a4cb6a14013f8235cc1eb (Parker and Boesch, 2020).

Methane (CH4) is one of the most important anthropogenic greenhouse gases. Despite its importance, natural sources of methane, such as tropical wetlands and termites, are still not well understood and a large source of uncertainty in the tropical CH4 budget. The Amazon rainforest is a key region for the (global) CH4 budget but, due to its remote location, local CH4 concentration and flux measurements are still rare.Fieldsite ZF2 (60 km NW of Manaus, Brazil) is located in pristine tropical rain forest. At this fieldsite, a Spectronus FTIR-analyzer (measuring CO2, CO, CH4, N2O & δ13CO2) was installed at the foot of the K34 tower, set up to measure different heights above and below the canopy continuously. In addition, by use of a Los Gatos portable analyzer (measuring CO2 & CH4), additional semi-continuous concentration measurements were performed at the valley tower (studying the nighttime build up of valley CH4), above the igarapé (capturing the CH4 ebullition bubbles leaving the water surface), and on the plateau (studying the spatial horizontal heterogeneity of CH4 concentrations within the canopy). Furthermore, the portable analyzer was used for soil, water, termite mound, and termites flux measurements.By combining tower and flux chamber measurements, the role and magnitude of different ecosystem sources could be assessed. We observed that, while soils in the valley are a small source of CH4 (0.1 to 0.2 nmol CH4 m-2 s-1), overall the soils of this ecosystem are expected to be a net CH4 sink (-0.3 to -0.5 nmol m-2 s-1 ). Estimated total ecosystem CH4 flux, based on nighttime concentration analyses of the tower data, indicate that the ecosystem is a net CH4 source (~1 to 2 nmol CH4 m-2 s-1). We propose that the net CH4 emission of the ecosystem is driven by local emitting hotspots, such as the valley stream and standing water, termites and termite mounds (~1 nmol CH4 m-2 s-1), anoxic soil spots and decaying dead wood.

To assess realistic climate change mitigation strategies, it is important to research and understand the global carbon cycle. Carbon dioxide (CO2) and methane (CH4) are the two most important anthropogenic greenhouse gases. Their atmospheric concentrations are affected by anthropogenic emissions as well as exchange fluxes with oceans and the terrestrial biosphere. For the prediction of future atmospheric CO2 and CH4 concentrations, it is critical to understand how the natural exchange fluxes respond to a changing climate. One of the factors that impact these fluxes is the changing hydrological cycle. In our project, we combine information about the hydrological cycle from geodetic satellites (e.g. GRACE & GRACE-FO) with carbon cycle observations from other satellites (e.g. TROPOMI & OCO-2). Specifically, we plan to investigate the impact of a changing water level in soils on CH4 emissions from wetlands and on the photosynthetic CO2 uptake of plants. Details of our approach and first results will be presented.

Recently, the Fast atmOspheric traCe gAs retrievaL (FOCAL) algorithm has been applied to measurements of the Greenhouse gases Observing SATellite (GOSAT) and its successor GOSAT-2. FOCAL has been originally developed for Orbiting Carbon Observatory-2 (OCO-2) retrievals with the focus on the derivation of carbon dioxide (XCO2). However, depending on the available spectral windows, FOCAL also successfully retrieves total column amounts for other atmospheric species. Here, we show new results from updated GOSAT and GOSAT-2 FOCAL retrievals. The main focus is placed on methane (XCH4; full physics and proxy product), water vapour (XH2O) and the relative ratio of semi-heavy water (HDO) to water vapour (δD). Due to the extended spectral range of GOSAT-2 it is also possible to derive information on carbon monoxide (XCO) and nitrous oxide (XN2O) for which we also show first results. We also present an update on XCO2 from both instruments. Compared to the previous product version (v1), the number of valid XCO2 data could be significantly increased in the updated version (v3.0) by 50 % for GOSAT and about a factor of two for GOSAT-2. All FOCAL data products show reasonable spatial distribution and temporal variations. Comparisons with TCCON (Total Carbon Column Observing Network) result in station-to-station biases which are generally in line with the reported TCCON uncertainties. With this updated version of the GOSAT-2 FOCAL data, we provide a first total column average XN2O product. Global XN2O maps show a gradient from the tropics to higher latitudes in the order of 15 ppb, which can be explained by variations in tropopause height. The new GOSAT-2 XN2O product compares well with TCCON. Its station-to-station variability is lower than 2 ppb, which is about the magnitude of the typical N2O variations close to the surface. However, both GOSAT-2 and TCCON measurements show that the seasonal variations in the total column average XN2O are in the order of 8 ppb peak-to-peak, which can be easily resolved by the GOSAT-2 FOCAL data.

We present a multiyear time series of column abundances of carbon monoxide (CO), hydrogen cyanide (HCN), and ethane (C2H6) measured using Fourier-transform infrared (FTIR) spectrometers at 10 sites affiliated with the Network for the Detection of Atmospheric Composition Change (NDACC). Six are high-latitude sites: Eureka, Ny-Ålesund, Thule, Kiruna, Poker Flat, and St. Petersburg, and four are midlatitude sites: Zugspitze, Jungfraujoch, Toronto, and Rikubetsu. For each site, the interannual trends and seasonal variabilities of the CO time series are accounted for, allowing background column amounts to be determined. Enhancements above the seasonal background were used to identify possible wildfire pollution events. Since the abundance of each trace gas emitted in a wildfire event is specific to the type of vegetation burned and the burning phase, correlations of CO to the long-lived wildfire tracers HCN and C2H6 allow for further confirmation of the detection of wildfire pollution. A GEOS-Chem tagged CO simulation with Global Fire Assimilation System (GFASv1.2) biomass burning emissions was used to determine the source attribution of CO concentrations at each site from 2003 to 2018. For each detected wildfire pollution event, FLEXPART back-trajectory simulations were performed to determine the transport times of the smoke plume. Accounting for the loss of each species during transport, the enhancement ratios of HCN and C2H6 with respect to CO were converted to emission ratios. We report mean emission ratios with respect to CO for HCN and C2H6 of 0.0047 and 0.0092, respectively, with a standard deviation of 0.0014 and 0.0046, respectively, determined from 23 boreal North American wildfire events. Similarly, we report mean emission ratios for HCN and C2H6 of 0.0049 and 0.0100, respectively, with a standard deviation of 0.0025 and 0.0042, respectively, determined from 39 boreal Asian wildfire events. The agreement of our emission ratios with literature values illustrates the capability of ground-based FTIR measurements to quantify biomass burning emissions. We provide a comprehensive dataset that quantifies HCN and C2H6 emission ratios from 62 wildfire pollution events. Our dataset provides novel emission ratio estimates, which are sparsely available in the published literature, particularly for boreal Asian sources.

This chapter provides background information on peat and more specifically on Indonesian peatlands and their role in the global carbon cycle and summarizes information on human-induced CO2 emissions caused by peat oxidation and fires. In contrast to these so-called on-site CO2 emissions, not much was known about off-site CO2 emissions prior to the joint Indonesian–German project Science for the Protection of Indonesian Coastal Ecosystems (SPICE). Off-site CO2 emissions are CO2 emissions caused by the mobilization of peat carbon along the land–ocean continuum, which were studied by us in the framework of SPICE. Our results allowed us to establish comprehensive carbon budgets showing that peatland preservation and restoration are crucial measures to combat global warming and mitigate climate change impacts caused. e.g., by sea level rise. Furthermore, we conducted socioeconomic experiments and used the established carbon budget to demonstrate the economic conflict that arises between restoration and transformation of peatlands into plantations. Abstrak Bab ini memberikan informasi mengenai gambut dan lebih khusus lagi tentang lahan gambut di Indonesia dan peranannya dalam siklus karbon global, serta merangkum informasi mengenai emisi CO2 yang disebabkan oleh aktivitas manusia sehingga mengakibatkan terjadinya oksidasi dan kebakaran lahan gambut. Terkait emisi CO2 yang terdapat di tempat ini telah banyak di ketahui, namun hal yang kontras dengan informasi terkait emisi CO2 di luar lokasi masih kurang diketahui sebelum program kerjasama riset SPICE antara Indonesia dan Jerman dilakukan. Emisi CO2 di luar lokasi merupakan emisi CO2 yang disebabkan oleh mobilisasi karbon yang berasal dari gambut di sepanjang kontinum darat hingga laut, yang diteliti dalam kerangka kerja program SPICE. Riset ini menunjukkan hasil pengukuran karbon budget yang komprehensif dimana aspek pelestarian dan restorasi lahan gambut merupakan upaya yang sangat penting untuk memerangi pemanasan global dan mitigasi perubahan iklim yang ditimbulkan, misalnya akibat kenaikan permukaan laut dan meningkatnya emisi CO2. Lebih lanjut, dalam riset SPICE ini dilakukan juga eksperimen sosial ekonomi dan menggunakan pengukuran karbon budget yang telah ditetapkan untuk menunjukkan potensi konflik ekonomi yang mungkin terjadi antara aktivitas restorasi dan transformasi lahan gambut menjadi perkebunan, pertanian atau tujuan lainnya.

The Thermal And Near infrared Sensor for carbon Observation – Fourier Transform Spectrometer (TANSO-FTS) on the Japanese Greenhouse gases Observing SATellite (GOSAT) has been returning data since April 2009. The version 9 (v9) Atmospheric Carbon Observations from Space (ACOS) Level 2 Full Physics (L2FP) retrieval algorithm (Kiel et al., 2019) was used to derive estimates of carbon dioxide (CO2) dry air mole fraction (XCO2) from the TANSO-FTS measurements collected over it's first eleven years of operation. The bias correction and quality filtering of the L2FP XCO2 product were evaluated using estimates derived from the Total Carbon Column Observing Network (TCCON) as well as values simulated from a suite of global atmospheric inverse modeling systems (models). In addition, the v9 ACOS GOSAT XCO2 results were compared with collocated XCO2 estimates derived from NASA's Orbiting Carbon Observatory-2 (OCO-2), using the version 10 (v10) ACOS L2FP algorithm. These tests indicate that the v9 ACOS GOSAT XCO2 product has improved throughput, scatter and bias, when compared to the earlier v7.3 ACOS GOSAT product, which extended through mid 2016. Of the 37 million (M) soundings collected by GOSAT through June 2020, approximately 20 % were selected for processing by the v9 L2FP algorithm after screening for clouds and other artifacts. After post-processing, 5.4 % of the soundings (2M out of 37M) were assigned a “good” XCO2 quality flag, as compared to 3.9 % in v7.3 (

The Total Carbon Column Observing Network (TCCON) column-averaged dry air mole fraction of CH4 ( X CH 4 ) measurements have been widely used to validate satellite observations and to estimate model simulations. The GGG2014 code is the standard TCCON retrieval software used in performing a profile scaling retrieval. In order to obtain several vertical pieces of information in addition to the total column, in this study, the SFIT4 retrieval code is applied to retrieve the CH4 mole fraction vertical profile from the Fourier transform spectrometer (FTS) spectrum at six sites (Ny-Alesund, Sodankyla, Bialystok, Bremen, Orleans and St Denis) during the time period of 2016–2017. The retrieval strategy of the CH4 profile retrieval from ground-based FTS near-infrared (NIR) spectra using the SFIT4 code (SFIT4NIR) is investigated. The degree of freedom for signal (DOFS) of the SFIT4NIR retrieval is about 2.4, with two distinct pieces of information in the troposphere and in the stratosphere. The averaging kernel and error budget of the SFIT4NIR retrieval are presented. The data accuracy and precision of the SFIT4NIR retrievals, including the total column and two partial columns (in the troposphere and stratosphere), are estimated by TCCON standard retrievals, ground-based in situ measurements, Atmospheric Chemistry Experiment – Fourier Transform Spectrometer (ACE-FTS) satellite observations, TCCON proxy data and AirCore and aircraft measurements. By comparison against TCCON standard retrievals, it is found that the retrieval uncertainty of SFIT4NIR X CH 4 is similar to that of TCCON standard retrievals with systematic uncertainty within 0.35 % and random uncertainty of about 0.5 %. The tropospheric and stratospheric X CH 4 from SFIT4NIR retrievals are assessed by comparison with AirCore and aircraft measurements, and there is a 1.0 ± 0.3 % overestimation in the SFIT4NIR tropospheric X CH 4 and a 4.0 ± 2.0 % underestimation in the SFIT4NIR stratospheric X CH 4 , which are within the systematic uncertainties of SFIT4NIR-retrieved partial columns in the troposphere and stratosphere respectively.

Column-averaged dry-air mole fraction of CO (XCO) measurements are obtained from two ground-based Fourier transform infrared (FTIR) spectrometer networks: the Total Carbon Column Observing Network (TCCON) and the Network for the Detection of Atmospheric Composition Change (NDACC). In this study, the differences between the TCCON and NDACC XCO measurements are investigated and discussed based on six NDACC–TCCON sites using data over the period 2007–2017. A direct comparison shows that the NDACC XCO measurements are about 5.5 % larger than the TCCON data at Ny-Ålesund, Bremen, and Izaña (Northern Hemisphere), and the absolute bias between the NDACC and TCCON data is within 2 % at Saint-Denis, Wollongong and Lauder (Southern Hemisphere). The hemispheric dependence of the bias is mainly attributed to their smoothing errors. The systematic smoothing error of the TCCON XCO data varies in the range between 0.2 % (Bremen) and 7.9 % (Lauder), and the random smoothing error varies in the range between 2.0 % and 3.6 %. The systematic smoothing error of NDACC data is between 0.1 % and 0.8 %, and the random smoothing error of NDACC data is about 0.3 %. For TCCON data, the smoothing error is significant because it is higher than the reported uncertainty, particularly at Southern Hemisphere sites. To reduce the influence from the a priori profiles and different vertical sensitivities, the scaled NDACC a priori profiles are used as the common a priori profiles for comparing TCCON and NDACC retrievals. As a result, the biases between TCCON and NDACC XCO measurements become more consistent (5.6 %–8.5 %) with a mean value of 6.8 % at these sites. To determine the sources of the remaining bias, regular AirCore measurements at Orléans and Sodankylä are compared to co-located TCCON measurements. It is found that TCCON XCO measurements are 6.1 ± 1.6 % and 8.0 ± 3.2 % smaller than the AirCore measurements at Orléans and Sodankylä, respectively, indicating that the scaling factor of TCCON XCO data should be around 1.0000 instead of 1.0672. Further investigations should be carried out in the TCCON community to determine the correct scaling factor to be applied to the TCCON XCO data. This paper also demonstrates that the smoothing error must be taken into account when comparing FTIR XCO data, and especially TCCON XCO data, with model or satellite data.

Observations of carbon monoxide (CO) from the Measurements Of Pollution In The Troposphere (MOPITT) instrument aboard the Terra spacecraft were expected to have an accuracy of 10 % prior to the launch in 1999. Here we evaluate MOPITT Version 7 joint (V7J) thermal-infrared and near-infrared (TIR–NIR) retrieval accuracy and precision and suggest ways to further improve the accuracy of the observations. We take five steps involving filtering or bias corrections to reduce scatter and bias in the data relative to other MOPITT soundings and ground-based measurements. (1) We apply a preliminary filtering scheme in which measurements over snow and ice are removed. (2) We find a systematic pairwise bias among the four MOPITT along-track detectors (pixels) on the order of 3–4 ppb with a small temporal trend, which we remove on a global scale using a temporally trended bias correction. (3) Using a small-region approximation (SRA), a new filtering scheme is developed and applied based on additional quality indicators such as the signal-to-noise ratio (SNR). After applying these new filters, the root-mean-squared error computed using the local median from the SRA over 16 years of global observations decreases from 3.84 to 2.55 ppb. (4) We also use the SRA to find variability in MOPITT retrieval anomalies that relates to retrieval parameters. We apply a bias correction to one parameter from this analysis. (5) After applying the previous bias corrections and filtering, we compare the MOPITT results with the GGG2014 ground-based Total Carbon Column Observing Network (TCCON) observations to obtain an overall global bias correction. These comparisons show that MOPITT V7J is biased high by about 6 %–8 %, which is similar to past studies using independent validation datasets on V6J. When using TCCON spectrometric column retrievals without the standard airmass correction or scaling to aircraft (WMO scale), the ground- and satellite-based observations overall agree to better than 0.5 %. GEOS-Chem data assimilations are used to estimate the influence of filtering and scaling to TCCON on global CO and tend to pull concentrations away from the prior fluxes and closer to the truth. We conclude with suggestions for further improving the MOPITT data products.

On 13 October 2017, the Tropospheric Monitoring Instrument (TROPOMI) was launched on the Copernicus Sentinel-5 Precursor satellite in a sun-synchronous orbit. One of the mission's operational data products is the total column concentration of carbon monoxide (CO), which was released to the public in July 2018. The current TROPOMI CO processing uses the HITRAN 2008 spectroscopic data with updated water vapor spectroscopy and produces a CO data product compliant with the mission requirement of 10 % precision and 15 % accuracy for single soundings. Comparison with ground-based CO observations of the Total Carbon Column Observing Network (TCCON) show systematic differences of about 6.2 ppb and single-orbit observations are superimposed by a significant striping pattern along the flight path exceeding 5 ppb. In this study, we discuss possible improvements of the CO data product. We found that the molecular spectroscopic data used in the retrieval plays a key role for the data quality where the use of the Scientific Exploitation of Operational Missions – Improved Atmospheric Spectroscopy Databases (SEOM-IAS) and the HITRAN 2012 and 2016 releases reduce the bias between TROPOMI and TCCON due to improved CH4 spectroscopy. SEOM-IAS achieves the best spectral fit quality (root-mean-square, rms, differences between the simulated and measured spectrum) of 1.5×10-10 mol s−1 m−2 nm−1 sr−1 and reduces the bias between TROPOMI and TCCON to 3.4 ppb, while HITRAN 2012 and HITRAN 2016 decrease the bias even further below 1 ppb. HITRAN 2012 shows the worst fit quality (rms = 2.5×10-10 mol s−1 m−2 nm−1 sr−1) of the tested cross sections and furthermore introduces an artificial bias of about -1.5×1017 molec cm−2 between TROPOMI CO and the CAMS-IFS model in the Tropics caused by the H2O spectroscopic data. Moreover, analyzing 1 year of TROPOMI CO observations, we identified increased striping patterns by about 16 % percent from November 2017 to November 2018. For that, we defined a measure γ, quantifying the relative pixel-to-pixel variation in CO in the cross-track and along-track directions. To mitigate this effect, we discuss two destriping methods applied to the CO data a posteriori. A destriping mask calculated per orbit by median filtering of the data in the cross-track direction significantly reduced the stripe pattern from γ=2.1 to γ=1.6. However, the destriping can be further improved, achieving γ=1.2 by deploying a Fourier analysis and filtering of the data, which not only corrects for stripe patterns in the cross-track direction but also accounts for the variability of stripes along the flight path.

Methane (CH4) is one of the most important anthropogenic greenhouse gases. Despite its importance, natural sources of methane, such as tropical wetlands and termites, are still not well understood and a large source of uncertainty in the tropical CH4 budget. The Amazon rainforest is a key region for the (global) CH4 budget but, due to its remote location, continous CH4 concentration and flux measurements are still rare. The 50 m high K34 tower (field site ZF2) is located in a pristine ‘Terra Firme’ tropical forest region 60 km northwest of Manaus (Brazil), and is located next to a waterlogged valley, a possible location for anaerobic CH4 production. In October 2018, in addition to the existing EC CO2 system, an in-situ FTIR-analyzer (measuring CO2, CO, CH4, N2O and δ13CO2) was set up to measure tower profile concentrations, above and below the canopy, continuously. By analyses of vertical and temporal nighttime concentrations patterns, an emission estimate for all gases could be made, and an ecosystem emission of ~1 nmol CH4 m-2 s-1 was estimated. In addition, by use of different types of flux chambers, possible CH4 sinks and sources such as soils, trees, water and termite mounds were measured. By combining tower and flux chamber measurements, the role and magnitude of different ecosystem sources could be assessed. In this presentation, an overview of the measured CH4 forest concentrations and fluxes will be given.

Southeast Asian peatlands represent a globally significant carbon store that is destabilized by deforestation and the transformation into plantations, causing high carbon dioxide (CO2) emissions from peat soils and increased leaching rates of peat carbon into rivers. While global model studies assumed that CO2 emissions from peat-draining rivers would be high, estimates based on field data suggest they are only moderate. In this study we offer an explanation for this phenomenon and show that carbon decomposition is hampered by the low pH in peat-draining rivers, which limits CO2 production in and emissions from these rivers. We find an exponential pH limitation that shows good agreement with laboratory measurements from high latitude peat soils. Additionally, our results suggest that enhanced input of carbonate minerals increase CO2 emissions from peat-draining rivers by counteracting the pH limitation. As such inputs of carbonate minerals occur due to human activities like deforestation of river catchments, liming in plantations and enhanced weathering projects, our study points out an important feedback mechanism of those practices.

The anthropogenic impact is a major factor of the climate change which is highest in industrial regions and modern megacities. Megacities are a significant source of emissions of various substances into the atmosphere, including CO2 which is the most important anthropogenic greenhouse gas. In 2019 and 2020, the mobile experiment EMME (Emission Monitoring Mobile Experiment) was carried out on the territory of St. Petersburg which is the second largest industrial city in Russia with a population of more than 5 million people. In 2020, several measurement data sets were obtained during the lockdown period caused by the COVID-19 (COronaVIrus Disease of 2019) pandemic. One of the goals of EMME was to evaluate the CO2 emission from the St. Petersburg agglomeration. Previously, the CO2 area flux has been obtained from the data of the EMME-2019 experiment using the mass balance approach. The value of the CO2 area flux for St. Petersburg has been estimated as 89±28 kt km−2 yr−1 which is three times higher than the corresponding value reported in the official municipal inventory. The present study is focused on the derivation of the integral CO2 emission from St. Petersburg by coupling the results of the EMME observational campaigns of 2019 and 2020 and the HYSPLIT (HYbrid Single-Particle Lagrangian Integrated Trajectories) model. The ODIAC (Open-source Data Inventory for Anthropogenic CO2) database is used as the source of the a priori information on the CO2 emissions for the territory of St. Petersburg. The most important finding of the present study based on the analysis of two observational campaigns is a significantly higher CO2 emission from the megacity of St. Petersburg as compared to the data of municipal inventory: ~75800±5400 kt yr−1 for 2019, ~68400±7100 kt yr−1 for 2020 (~70000±16000 kt yr−1 during the lockdown) versus ~30000 kt yr−1 reported by official inventory. The comparison of the CO2 emissions obtained during the COVID-19 lockdown period in 2020 to the results obtained during the same period of 2019 demonstrated the decrease in emission of 8 % or 5800 kt yr−1.

The Sentinel-5 Precursor (S5P) mission with the TROPOspheric Monitoring Instrument (TROPOMI) onboard has been measuring solar radiation backscattered by the Earth's atmosphere and its surface since its launch on 13 October 2017. Methane (CH4) and carbon monoxide (CO) data with a spatial resolution (initially 7 x 7 km2, upgraded to 5.5 x 7 km2 on 6th of August 2019) have been retrieved from shortwave infrared (SWIR) and near-infrared (NIR) measurements since the end of November 2017 and made available to the experts for early validation and quality checks before the official product release. In this paper, we present for the first time the S5P CH4 and CO validation results (covering a period from November 2017 to September 2020) using global Total Carbon Column Observing Network (TCCON) and Infrared Working Group of the Network for the Detection of Atmospheric Composition Change (NDACC-IRWG) network data, accounting for a priori alignment and smoothing uncertainties in the validation, and testing the sensitivity of validation results towards the application of advanced co-location criteria.We found that the required bias (systematic error) of 1.5 % and random error of 1 % for the S5P standard and bias-corrected methane data are met for measurements over land surfaces with pixels having quality assurance (QA) value > 0.5. The systematic difference between the S5P standard XCH4 and TCCON data is on average −0.69 ± 0.73 %. The systematic difference changes to a value of −0.25 ± 0.57 % for the S5P bias-corrected XCH4 data. We found a correlation of above 0.6 for most stations, which is mostly dominated by the seasonal cycle. The contributions of smoothing uncertainty at the individual stations are estimated and found to be dependent on the location. The highest contribution of the smoothing uncertainty is observed for mid-latitude TCCON stations and high latitude stations for NDACC. A seasonal dependency of the relative bias is seen. We observe a high bias during the springtime measurements at high SZA and a decreasing bias with increasing SZA for the rest of the year.We found that the required bias (systematic error) of 15 % and random error of < 10 % for the S5P carbon monoxide data are met in general for measurements over all surfaces with pixels having quality assurance value of > 0.5. There are a few stations where this is not the case, mostly due to co-location mismatches and the limited availability of co-located data. We compared the S5P XCO data with respect to standard TCCON XCO and unscaled TCCON XCO (without application of the empirical scaling factor) data sets. The systematic difference between the S5P XCO and the TCCON data is on average 9.14 ± 3.33 % (standard TCCON XCO data) and 2.36 ± 3.22 % (unscaled TCCON XCO data). We found that the systematic difference between the S5P CO column and NDACC CO column data (excluding two stations that were obvious outliers) is on average 6.44 ± 3.79 %. We found a correlation of above 0.9 for most TCCON and NDACC stations indicating that the temporal variations in CO column captured by the ground-based instruments are reproduced very similarly by the S5P CO column. The contribution of smoothing uncertainty at the individual stations is estimated and found to be significant. They are found to be dependent on the location with large changes seen for stations located in the Southern Hemisphere as compared to the Northern Hemisphere and at highly polluted stations. A cone co-location criterion, which gives a better match between the ground-based instrument's line-of-sight and satellite pixels, seems to give better results for high latitude stations and stations located close to emission sources. The validation results for the clear-sky and cloud cases of S5P pixels are comparable to the validation results including all pixels with quality assurance value of > 0.5. We observe that the relative bias increases with increasing SZA. We estimated this increase is about 10 % over the complete range of measurement SZAs.The study shows the high quality of S5P CH4 and CO data by validating the products against reference global TCCON and NDACC stations covering a wide range of latitudinal bands, atmospheric conditions, and surface conditions.

Since 2009, the Greenhouse gases Observing SATellite (GOSAT) performs radiance measurements in the shortwave-infrared (SWIR) spectral region. From February 2019 onward, data from GOSAT-2 are also available. We present first results from the application of the Fast atmOspheric traCe gAs retrieval (FOCAL) algorithm to derive column-averaged dry-air mole fractions of carbon dioxide (XCO2) from GOSAT and GOSAT-2 radiances and their validation. FOCAL has initially been developed for OCO-2 XCO2 retrievals and allows simultaneous retrievals of several gases over both land and ocean. Because FOCAL is accurate and numerically very fast it is currently considered as a candidate algorithm for the forthcoming European anthropogenic CO2 Monitoring (CO2M) mission, to be launched in 2025. We present the adaptation of FOCAL to GOSAT and discuss the changes made and GOSAT specific additions. This includes particularly modifications in pre-processing (e.g. cloud detection) and post-processing (bias correction and filtering). A feature of the new application of FOCAL to GOSAT/GOSAT-2 is the independent use of both S and P polarisation spectra in the retrieval. This is not possible for OCO-2, which measures only one polarisation direction. Additionally, we make use of GOSAT’s wider spectral coverage compared to OCO-2 and derive not only XCO2, water vapour (H2O) and solar induced fluorescence (SIF) but also methane (XCH4), with the potential for further atmospheric constituents and parameters like semiheavy water vapour (HDO) and (in the case of GOSAT-2) also carbon monoxide (CO) total columns and possibly nitrous oxide (XN2O). Here, we concentrate on the new FOCAL XCO2 data products. We describe the generation of the products as well as applied filtering and bias correction procedures. GOSAT-FOCAL XCO2 data have been produced for the time interval 2009 to 2019. Comparisons with other independent GOSAT data sets reveal an agreement of long-term temporal variations within about 1 ppm over one decade; differences in seasonal variations of about 0.5 ppm are observed. Furthermore, we obtain a mean regional bias of the new GOSAT-FOCAL product to the ground based Total Carbon Column Observing Network (TCCON) of 0.56 ppm with a mean scatter of 1.89 ppm. The GOSAT-2-FOCAL XCO2 product is generated in a similar way as the GOSAT-FOCAL product, but with adapted settings. All GOSAT-2 data until end of 2019 have been processed. Because of this limited time interval, the GOSAT-2 results are considered to be preliminary only, but first comparisons show that these data compare well with the GOSAT-FOCAL results.

TanSat is the 1st Chinese carbon dioxide (CO2) measurement satellite, launched in 2016. In this study, the University of Leicester Full Physics (UoL‐FP) algorithm is implemented for TanSat nadir mode XCO2 retrievals. We develop a spectrum correction method to reduce the retrieval errors by the online fitting of an 8th order Fourier series. The spectrum‐correction model and its a priori parameters are developed by analyzing the solar calibration measurement. This correction provides a significant improvement to the O2 A band retrieval. Accordingly, we extend the previous TanSat single CO2 weak band retrieval to a combined O2 A and CO2 weak band retrieval. A Genetic Algorithm (GA) has been applied to determine the threshold values of post‐screening filters. In total, 18.3% of the retrieved data is identified as high quality compared to the original measurements. The same quality control parameters have been used in a footprint independent multiple linear regression bias correction due to the strong correlation with the XCO2 retrieval error. Twenty sites of the Total Column Carbon Observing Network (TCCON) have been selected to validate our new approach for the TanSat XCO2 retrieval. We show that our new approach produces a significant improvement on the XCO2 retrieval accuracy and precision when compared to TCCON with an average bias and RMSE of −0.08 ppm and 1.47 ppm, respectively. The methods used in this study can help to improve the XCO2 retrieval from TanSat and subsequently the Level‐2 data production, and hence will be applied in the TanSat operational XCO2 processing., Key Points First using O2 A and 1.61 um CO2 band approaching TanSat XCO2 retreivalDevelopment a method on radiometric correction of TanSat L1B data in O2 A and 1.61 um CO2 Validation of new TanSat retrieval against TCCON and recived significant improved results compare to previously retrieval

The magnitude of termite methane (CH4) emissions is still an uncertain part of the global CH4 budget and current emission estimates are based on limited field studies. We present in-situ CH4 emission measurements of termite mounds and termite mound sub samples, performed in the Amazon rain forest. Emissions of five termite mounds of the species Neocapritermes brasiliensis were measured by use of a large flux chamber connected to a portable gas analyser, measuring CH4 and CO2. In addition, the emission of mound sub samples was measured, after which termites were counted, so that a termite CH4 and CO2 emission factor could be determined. Mound emissions were found to range between 17.0–34.8 nmol mound−1 s−1 for CH4 and between 1.6–13.5 μmol mound−1 s−1 for CO2. A termite emission factor of 0.32 μmol CH4 gtermite−1 h−1 was found, which is twice as high as the only other reported average value for the Amazon. By combining mound emission measurements with the termite emission factor, colony sizes could be estimated, which were found to range between 50–120 thousand individuals. Estimates were similar to literature values, and we therefore propose that this method can be used as a quick non-intrusive method to estimate termite colony size in the field. The role of termites in the ecosystems CH4 budget was evaluated by use of two approaches. Termite mound emission values were combined with local termite mound density numbers, leading to an estimate of 0.15–0.71 nmol CH4 m−2 s−1 on average emitted by termite mounds. In addition, the termite CH4 emission factor from this study was combined with termite density numbers, resulting in an estimate of termite emitted CH4 of ~1.0 nmol m−2 s−1. Considering the relatively low net CH4 emissions previously measured at this ecosystem, we expect that termites play an important role in the CH4 budget of this Terra Firme ecosystem.

A better understanding of greenhouse gas surface sources and sinks is required in order to address the global challenge of climate change. Spaceborne remote estimations of greenhouse gas atmospheric concentrations can offer the global coverage that is necessary to improve the constraint on their fluxes, thus enabling a better monitoring of anthropogenic emissions. In this work, we introduce the Adaptable 4A Inversion (5AI) inverse scheme that aims to retrieve geophysical parameters from any remote sensing observation. The algorithm is based on Bayesian optimal estimation relying on the Operational version of the Automatized Atmospheric Absorption Atlas (4A/OP) radiative transfer forward model along with the Gestion et Étude des Informations Spectroscopiques Atmosphériques: Management and Study of Atmospheric Spectroscopic Information (GEISA) spectroscopic database. Here, the 5AI scheme is applied to retrieve the column-averaged dry-air mole fraction of carbon dioxide (XCO2) from measurements performed by the Orbiting Carbon Observatory-2 (OCO-2) mission, and uses an empirically corrected absorption continuum in the O2 A-band. For airmasses below 3.0, XCO2 retrievals successfully capture the latitudinal variations of CO2, as well as its seasonal cycle and long-term increasing trend. Comparison with ground-based observations from the Total Carbon Column Observing Network (TCCON) yields a difference of 1.33 ± 1.29 ppm, which is similar to the standard deviation of the Atmospheric CO2 Observations from Space (ACOS) official products. We show that the systematic differences between 5AI and ACOS results can be fully removed by adding an average calculated – observed spectral residual correction to OCO-2 measurements, thus underlying the critical sensitivity of retrieval results to forward modelling. These comparisons show the reliability of 5AI as a Bayesian optimal estimation implementation that is easily adaptable to any instrument designed to retrieve column-averaged dry-air mole fractions of greenhouse gases.

We present a new remote sensing based method to estimate dissolved organic carbon (DOC) flux discharged from rivers into coastal waters off the Sarawak region in Borneo. This method comprises three steps. In the first step, we developed an algorithm for estimating DOC concentrations using the ratio of Landsat-8 Red to Green bands B4/B3 (DOC (μM C) = 89.86 ·e0.27·(B4/B3)), which showed good correlation (R = 0.88) and low mean relative error (+5.71%) between measured and predicted DOC. In the second step, we used TRMM Multisatellite Precipitation Analysis (TMPA) precipitation data to estimate river discharge for the river basins. In the final step, DOC flux for each river catchment was then estimated by combining Landsat-8 derived DOC concentrations and TMPA derived river discharge. The analysis of remote sensing derived DOC flux (April 2013 to December 2018) shows that Sarawak coastal waters off the Rajang river basin, received the highest DOC flux (72% of total) with an average of 168 Gg C per year in our study area, has seasonal variability. The whole of Sarawak represents about 0.1% of the global annual riverine and estuarine DOC flux. The results presented in this study demonstrate the ability to estimate DOC flux using satellite remotely sensed observations.

The TROPOspheric Monitoring Instrument (TROPOMI) aboard of the Sentinel 5 Precursor (S5-P) satellite provides methane (CH4) measurements with high accuracy and exceptional temporal and spatial resolution. TROPOMI CH4 measurements are highly valuable to constrain emissions inventories and for trend analysis, with strict requirements on the data quality. This study describes the improvements that we have implemented to retrieve CH4 from TROPOMI using the RemoTeC full-physics algorithm. The updated TROPOMI CH4 product features a constant regularization scheme of the inversion that stabilizes the retrieval and yields less scatter in the data, and includes a higher resolution surface altitude database. We have tested the impact of three state-of-the-art molecular spectroscopic databases (HITRAN 2008, HITRAN 2016 and Scientific Exploitation of Operational Missions – Improved Atmospheric Spectroscopy Databases SEOM-IAS) and found that SEOM-IAS provides the best fitting results. The most relevant update in the TROPOMI XCH4 data product is the implementation of a posteriori correction fully independent of any reference data that is more accurate and corrects for the underestimation at low surface albedo scenes and the overestimation at high surface albedo scenes. After applying the correction, the albedo dependence is removed to a large extent in the TROPOMI versus satellite (Greenhouse gases Observing SATellite – GOSAT) and TROPOMI versus ground-based observations (Total Carbon Column Observing Network – TCCON) comparison, which is an independent verification of the correction scheme. We validate two years of TROPOMI CH4 data that shows the good agreement of the updated TROPOMI CH4 with TCCON (−3.4 ± 5.6 ppb) and GOSAT (−10.3 ± 16.8 pbb) (mean bias and standard deviation). Low and high albedo scenes as well as snow covered scenes are the most challenging for the CH4 retrieval algorithm, and although the posteriori correction accounts for most of the bias, there is a need to further investigate the underlying cause.

This work presents the latest release (v9.0) of the University of Leicester GOSAT Proxy XCH4 dataset. Since the launch of the GOSAT satellite in 2009, this data has been produced by the UK National Centre for Earth Observation (NCEO) as part of the ESA Greenhouse Gas Climate Change Initiative (GHG-CCI) and Copernicus Climate Change Services (C3S) projects. With now over a decade of observations, we outline the many scientific studies achieved using past versions of this data in order to highlight how this latest version may be used in the future. We describe in detail how the data is generated, providing information and statistics for the entire processing chain from the L1B spectral data through to the final quality-filtered column-averaged dry-air mole fraction (XCH4) data. We show that out of the 19.5 million observations made between April 2009 and December 2019, we determine that 7.3 million of these are sufficiently cloud-free (37.6 %) to process further and ultimately obtain 4.6 million (23.5 %) high-quality XCH4 observations. We separate these totals by observation mode (land and ocean sun-glint) and by month, to provide data users with the expected data coverage, including highlighting periods with reduced observations due to instrumental issues. We perform extensive validation of the data against the Total Carbon Column Observing Network (TCCON), comparing to ground-based observations at 22 locations worldwide. We find excellent agreement to TCCON, with an overall correlation coefficient of 0.92 for the 88,345 co-located measurements. The single measurement precision is found to be 13.72 ppb and an overall global bias of 9.06 ppb is determined and removed from the Proxy XCH4 data. Additionally, we validate the separate components of the Proxy (namely the modelled XCO2 and the XCH4/XCO2 ratio) and find these to be in excellent agreement with TCCON. In order to show the utility of the data for future studies, we compare against simulated XCH4 from the TM5 model. We find a high degree of consistency between the model and observations throughout both space and time. When focusing on specific regions, we find average differences ranging from just 3.9 ppb to 15.4 ppb. We find the phase and magnitude of the seasonal cycle to be in excellent agreement, with an average correlation coefficient of 0.93 and a mean seasonal cycle amplitude difference across all regions of −0.84 ppb. This data is available at https://doi.org/10.5285/18ef8247f52a4cb6a14013f8235cc1eb (Parker and Boesch, 2020).