David W. T. Griffith

Here we report a notable slowdown in the methane growth rate observed by Total Carbon Column Observing Network (TCCON) sites in the Southern Hemisphere in 2024.

Understanding the renewed and accelerating growth of atmospheric methane requires satellite records that are long enough to span decadal change and consistent enough that instrumental differences are not mistaken for atmospheric signals. We present version 2.0 of the University of Leicester Proxy retrieval of column-averaged dry-air methane (XCH4) from the GOSAT-2 TANSO-FTS-2 instrument, comprising approximately 4.4 million quality-filtered soundings in land nadir and ocean glint observation modes between 5 February 2019 and 31 December 2025. The data are processed with the same algorithm, a priori fields, and quality-filtering framework as the established GOSAT Proxy v10.0 product, with the aim of producing a consistent multi-instrument climate data record. The on-board intelligent pointing of GOSAT-2 improves the yield of usable land soundings, particularly at coastal and island sites. The GOSAT-2 bias correction is cross-anchored to GOSAT over 124,206 matched-pair footprints and depends linearly on solar zenith angle. Anchoring to GOSAT rather than directly to the Total Carbon Column Observing Network (TCCON) leaves the corrected GOSAT-2 with a residual mean TCCON bias of +5.52 ppb, in exchange for sub-ppb inter-instrument consistency over the overlap period. Validation against the TCCON GGG2020.1 release yields a standard deviation of the satellite-minus-TCCON differences of 16.41 ppb and a correlation coefficient of 0.910 across 135,110 co-locations at 24 sites, compared to 16.42 ppb and 0.950 for GOSAT over 25 sites, demonstrating that GOSAT-2 reaches the precision of the heritage instrument. Co-located monthly 2° grid-cell means from GOSAT and GOSAT-2 during the 2019–2025 overlap correlate at 0.95 (land) and 0.97 (glint), with mean biases of +1.9 and +3.0 ppb. Beyond the cross-anchored correction, no further adjustment for either observation mode is required to merge the two records. The combined record forms a continuous Proxy XCH4 time series spanning 2009 to 2025, which will be extended as both missions continue to operate, and provides a reference for inter-comparison with TROPOMI and the next generation of greenhouse-gas monitoring missions. The GOSAT-2 Proxy XCH4 v2.0 dataset is available from the Centre for Environmental Data Analysis at https://doi.org/10.5285/8209b9acb92e4ba69188c9c8ff7a3b76 (Parker et al., 2026a).

The TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel-5 Precursor satellite provides daily global observations of atmospheric methane (CH₄) and carbon monoxide (CO) at relatively high spatial resolution. The dense spatial and temporal coverage is achieved by the instrument's wide swath, which permits detailed mapping of the worldwide distribution of these important atmospheric constituents. The adaptation and optimisation of the Weighting Function Modified Differential Optical Absorption Spectroscopy (WFMD) algorithm for the simultaneous retrieval of the column-averaged dry-air mole fractions XCH₄ and XCO from TROPOMI's shortwave infrared (SWIR) radiance measurements has proven to be a valuable complement and alternative to the operational TROPOMI products. The latest release of the TROPOMI/WFMD product (version 2.0) includes several improvements expanding its suitability for a wider range of scientific applications. Data yield at mid and high latitudes has increased, accompanied by improved accuracy and precision according to the validation with the ground-based Total Carbon Column Observing Network (TCCON). These advancements are primarily due to more refined quality filtering that has been accomplished by replacing the previous Random Forest Classifier with the more efficient and potentially higher performing Extreme Gradient Boosting (XGBoost) algorithm in conjunction with improved training data incorporating an updated cloud product from the Visible Infrared Imaging Radiometer Suite (VIIRS) and the TROPOMI Aerosol Index. This enhanced training data set enables more reliable identification of cloudy scenes and mitigates issues related to specific aerosol events over bright surfaces. Importantly, as with previous product versions, the actual quality classification does not depend on the real-time availability of these external data products, which are only required during the training phase.

Methane (CH ₄ ) is a potent greenhouse gas with high radiative forcing and a relatively short atmospheric lifetime of around a decade. We used a decade‐long data set (2011–2022) from the Fourier transform spectrometer at the California Laboratory for Atmospheric Remote Sensing (CLARS‐FTS) to quantify a dramatic increase in methane observed in 2020. We report a significant acceleration of the short‐term growth rate of 1.37 ± 0.20 ppb/month starting in 2020 until the end of 2021, a substantial increase relative to the near‐zero and negative rates of the preceding 4 years (2016–2019). The observed increase in methane concentrations in 2020 is of significant concern due to its potential contribution to global warming. The Total Carbon Column Observing Network (TCCON) is then used to examine the global geospatial variability of the increase in methane. The results suggest an approximately uniform rise in methane globally. Finally, results from a two‐box model used to simulate atmospheric chemical processes of methane production and loss indicate that changes in OH alone are insufficient to explain the rise in atmospheric methane. Recent data from 2022 suggest a deceleration in the methane growth rate, indicating a potential slowdown in the methane increase observed in 2020.

The TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel-5 Precursor satellite provides daily global observations of atmospheric methane (CH₄) and carbon monoxide (CO) at relatively high spatial resolution. The dense spatial and temporal coverage is achieved by the instrument’s wide swath, which permits detailed mapping of the worldwide distribution of these important atmospheric constituents. The adaptation and optimisation of the Weighting Function Modified Differential Optical Absorption Spectroscopy (WFMD) algorithm for the simultaneous retrieval of the column-averaged dry-air mole fractions XCH₄ and XCO from TROPOMI’s shortwave infrared (SWIR) radiance measurements has proven to be a valuable complement and alternative to the operational TROPOMI products. The latest release of the TROPOMI/WFMD product (version 2.0) includes several improvements expanding its suitability for a wider range of scientific applications. Data yield at mid and high latitudes has increased, accompanied by improved accuracy and precision according to the validation with the ground-based Total Carbon Column Observing Network (TCCON). These advancements are primarily due to more refined quality filtering that has been accomplished by replacing the previous Random Forest Classifier with the more efficient and potentially higher performing Extreme Gradient Boosting (XGBoost) algorithm in conjunction with improved training data incorporating an updated cloud product from the Visible Infrared Imaging Radiometer Suite (VIIRS) and the TROPOMI Aerosol Index. This enhanced training data set enables more reliable identification of cloudy scenes and mitigates issues related to specific aerosol events over bright surfaces. Importantly, as with previous product versions, the actual quality classification does not depend on the real-time availability of these external data products, which are only required during the training phase.

The TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel-5 Precursor satellite provides daily global observations of atmospheric methane (CH₄) and carbon monoxide (CO) at relatively high spatial resolution. The dense spatial and temporal coverage is achieved by the instrument’s wide swath, which permits detailed mapping of the worldwide distribution of these important atmospheric constituents. The adaptation and optimisation of the Weighting Function Modified Differential Optical Absorption Spectroscopy (WFMD) algorithm for the simultaneous retrieval of the column-averaged dry-air mole fractions XCH₄ and XCO from TROPOMI’s shortwave infrared (SWIR) radiance measurements has proven to be a valuable complement and alternative to the operational TROPOMI products. The latest release of the TROPOMI/WFMD product (version 2.0) includes several improvements expanding its suitability for a wider range of scientific applications. Data yield at mid and high latitudes has increased, accompanied by improved accuracy and precision according to the validation with the ground-based Total Carbon Column Observing Network (TCCON). These advancements are primarily due to more refined quality filtering that has been accomplished by replacing the previous Random Forest Classifier with the more efficient and potentially higher performing Extreme Gradient Boosting (XGBoost) algorithm in conjunction with improved training data incorporating an updated cloud product from the Visible Infrared Imaging Radiometer Suite (VIIRS) and the TROPOMI Aerosol Index. This enhanced training data set enables more reliable identification of cloudy scenes and mitigates issues related to specific aerosol events over bright surfaces. Importantly, as with previous product versions, the actual quality classification does not depend on the real-time availability of these external data products, which are only required during the training phase.

The TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel-5 Precursor satellite provides daily global observations of atmospheric methane (CH₄) and carbon monoxide (CO) at relatively high spatial resolution. The dense spatial and temporal coverage is achieved by the instrument’s wide swath, which permits detailed mapping of the worldwide distribution of these important atmospheric constituents. The adaptation and optimisation of the Weighting Function Modified Differential Optical Absorption Spectroscopy (WFMD) algorithm for the simultaneous retrieval of the column-averaged dry-air mole fractions XCH₄ and XCO from TROPOMI’s shortwave infrared (SWIR) radiance measurements has proven to be a valuable complement and alternative to the operational TROPOMI products. The latest release of the TROPOMI/WFMD product (version 2.0) includes several improvements expanding its suitability for a wider range of scientific applications. Data yield at mid and high latitudes has increased, accompanied by improved accuracy and precision according to the validation with the ground-based Total Carbon Column Observing Network (TCCON). These advancements are primarily due to more refined quality filtering that has been accomplished by replacing the previous Random Forest Classifier with the more efficient and potentially higher performing Extreme Gradient Boosting (XGBoost) algorithm in conjunction with improved training data incorporating an updated cloud product from the Visible Infrared Imaging Radiometer Suite (VIIRS) and the TROPOMI Aerosol Index. This enhanced training data set enables more reliable identification of cloudy scenes and mitigates issues related to specific aerosol events over bright surfaces. Importantly, as with previous product versions, the actual quality classification does not depend on the real-time availability of these external data products, which are only required during the training phase.

Correct determination of the phase spectrum is a highly relevant task in Fourier transform spectrometry for concluding which spectral distribution most likely gave rise to the measured interferogram. We present implementation of an improved scheme for phase determination in the operational Collaborative Carbon Column Observing Network (COCCON) processor. We introduce a robust unwrapping scheme for retrieving a spectrally smooth phase spectrum at intermediate spectral resolution, which uses all spectral positions carrying enough signal to allow a significant determination of the phase. In the second step, we perform a least-squares fit of model parameters of a suitable analytical phase spectrum model through all reliable phase values constructed in the first step. The model fit exploits the fact that we expect the phase to be spectrally smooth. Still, it can be refined to reflect specific characteristics inherent to the optical and electronic layout of the interferometer. The proposed approach avoids the problems of the classical phase reconstruction method, which enforces a spectrally smooth phase by directly limiting spectral resolution when calculating the complex phase. Thereby, the phase is created from a very low number of interferogram points around the centerburst of the interferogram, which results in a suboptimal noise propagation from the interferogram into the spectral domain. Moreover, the interpolation of the phase spectrum across spectral subsections with reduced spectral signal is not well behaved, and results depend strongly on the numerical apodization function used for creating the low-resolution phase.

Urban areas are a major and growing contributor to anthropogenic greenhouse gas (GHG) emissions and are thus an important target for emission reduction efforts. However, measurement-based information for planning, implementing, and monitoring such reduction efforts on city scales is rarely available to policymakers and stakeholders. Such monitoring systems typically rely on three key components: measurements of GHG concentrations (or turbulent fluxes), modeling of the atmospheric transport and prior information on the spatial and/or temporal structure of the emissions. The high spatial and temporal heterogeneity of urban areas and their emissions is especially challenging for atmospheric transport models and gridded inventories, which are currently pushed to resolutions of kilometers and below in an effort to accurately represent these effects. However, GHG concentration measurements are often performed by in-situ systems and are thus not necessarily representative for the kilometer scales on which measurements, transport modeling and prior information are typically compiled. This becomes increasingly important with the ever-improving quality of measurements, models, and inventories themselves.We present a dataset of urban path averaged concentration measurements of CO2 and CH4 and their comparison to co-located in-situ measurements. The path averaged measurements are taken along a 1.55 km long path over the city of Heidelberg, Germany. The observatory utilizes FTIR spectroscopy and is now in continuous operation since February 2023. Analysis of the path averaged and co-located in-situ measurements reveals differences of up to 20 ppm in CO2 for specific wind directions, which are most likely a result of a local atmospheric transport phenomenon. Further, the two measurements show differences in CH4, which are likely a result of different sensitivities to local emissions. Overall, the data indicate a clear but different sensitivity of either measurement approach to localized source patterns. Thus, the dataset enables the assessment of the representativeness of the different measurement approaches and of the performance of atmospheric transport models and emission inventories in the urban environments.