Atmospheric CO2 growth is correlated with the El Niño–Southern Oscillation (ENSO), showing enhanced growth associated with strong El Niño events. Here, we combine GOSAT satellite observations with new ground-based total column measurements from Palau in the Tropical Western Pacific, with surface ocean pCO2 data, to analyze the 2015/16 and 2023/24 El Niño events. We show that the CO2 response to the 2023/24 event was strong and delayed, with the largest concentration increase occurring after the El Niño peak, and it remained elevated as El Niño conditions weakened. This led to a pronounced accumulation of atmospheric CO2 over the tropical Pacific during 2024–2025. A linear regression framework explains about one-third of the historical inter-annual variability in global CO2 growth, yet it underestimates the recent increase in 2025, which cannot be explained by early oceanic forcing and exceeds expectations based on linear ENSO scaling. After oceanic forcing weakens, terrestrial carbon-cycle processes persist, as drought and heat stress in a warming climate suppress photosynthesis and enhance respiration. Such delayed amplification is characteristic of terrestrial carbon-cycle processes that integrate climate anomalies over time in a warming regime, leading to more persistent CO2 accumulation. Our results indicate that post–El Niño terrestrial amplification has shifted from a passive response to a dominant driver of sustained CO2 growth, representing a critical but previously under-observed mechanism of the global carbon cycle in a changing climate.

Chlorofluorocarbons (CFCs) have played a major role in the depletion of stratospheric ozone. Understanding historical trends in emissions and atmospheric concentrations is crucial for quantifying their impact. The first measurements of atmospheric CFCs were performed in-situ by Lovelock in 1970. We have analyzed historical 1951 solar absorption infrared spectra, recorded at Jungfraujoch, to retrieve the surface mole fraction. We compare to routine solar absorption measurements starting at Jungfraujoch in 1986 and emission-based forward models. The surface mole fraction derived from the historical spectra is compared to from a reconstructed history model, and from the Advanced Global Atmospheric Gases Experiment 12-Box model. The 1951 measurement and model values agree within error bars although the models may be biased low due to unreported emissions. Our result represents the earliest atmospheric CFC measurement, predating Lovelock's detection by over two decades.

Abstract The Orbiting Carbon Observatory‐2 and ‐3 (collectively termed “OCO‐2/3,” hereafter) missions, together, provide precise and accurate global data records that contribute to a better understanding of the variability in atmospheric carbon dioxide (CO 2 ). The retrieval algorithm used to process the satellite data, Atmospheric Carbon Observations from Space (ACOS), continues to be updated. The latest v11.2 OCO‐2 and v11 OCO‐3 data releases include significant improvements compared to past data versions. The Total Carbon Column Observing Network (TCCON), a network of ground‐based Fourier Transform Spectrometers (FTS), has historically, been the validation data source for OCO‐2/3. The COllaborative Carbon Column Observing Network (COCCON), an emerging network of ground‐based portable Fourier Transform Infrared Spectrometers (EM27/SUN), aids satellite validation efforts by providing observations in geographic locations where there are no TCCON measurements being made. This study provides the first global comparison between the OCO‐2/3 data sets and the COCCON observations. Currently, the COCCON data set, most useful for global analysis, is version 1 (v1), which our analysis shows is typically lower than the satellites by ∼0.50–1.00 ppm, with scatter against OCO‐2/3 roughly similar to TCCON versus OCO‐2/3. This analysis includes comparisons of results from the latest v2.x COCCON data version for the sites with the data available. Comparisons with the v2.x COCCON data version generally show improved comparisons against OCO‐2/3. Our analysis illustrates the ways in which the COCCON data set, used globally, regionally, or in specific locations, can provide insight into the quality of satellite observations of atmospheric CO 2 .

The Belait River is a typical blackwater river in Brunei, characterized by deep tea-brown water rich in dissolved organic matter (DOM) derived from surrounding peat swamps and rainforests. This study provides a systematic assessment of the chemical composition, transformation, and transport processes of dissolved nitrogen (N) in the Belait River during alternating wet and dry seasons by integrating nitrate (NO3-) stable isotope techniques with microbial analyses. Results indicate that dissolved organic nitrogen (DON) constitutes the primary component of total dissolved nitrogen (TDN), with its seasonal dynamics significantly controlled by hydrological climate and microbial activity. Along the salinity gradient, DON decreases due to dilution and degradation, while ammonium (NH4+) and NO3- accumulate significantly in densely populated areas owing to anthropogenic inputs. The extremely low dissolved inorganic phosphorus (DIP) concentrations in the Belait River highlight the crucial role of P limitation in maintaining the system’s oligotrophic state. Through the investigation in this representative blackwater system, this study highlights complex N cycling mechanisms in blackwater and provides a critical gauge in land-ocean material fluxes at the Indo-Pacific Convergence Area.

The Orbiting Carbon Observatory-2 and -3 (collectively termed "OCO-2/3," hereafter) missions, together, provide precise and accurate global data records that contribute to a better understanding of the variability in atmospheric carbon dioxide (CO2). The retrieval algorithm used to process the satellite data, Atmospheric Carbon Observations from Space (ACOS), continues to be updated. The latest v11.2 OCO-2 and v11 OCO-3 data releases include significant improvements compared to past data versions. The Total Carbon Column Observing Network (TCCON), a network of ground-based Fourier Transform Spectrometers (FTS), has historically, been the validation data source for OCO-2/3. The COllaborative Carbon Column Observing Network (COCCON), an emerging network of ground-based portable Fourier Transform Infrared Spectrometers (EM27/SUN), aids satellite validation efforts by providing observations in geographic locations where there are no TCCON measurements being made. This study provides the first global comparison between the OCO-2/3 data sets and the COCCON observations. Currently, the COCCON data set, most useful for global analysis, is version 1 (v1), which our analysis shows is typically lower than the satellites by similar to 0.50-1.00 ppm, with scatter against OCO-2/3 roughly similar to TCCON versus OCO-2/3. This analysis includes comparisons of results from the latest v2.x COCCON data version for the sites with the data available. Comparisons with the v2.x COCCON data version generally show improved comparisons against OCO-2/3. Our analysis illustrates the ways in which the COCCON data set, used globally, regionally, or in specific locations, can provide insight into the quality of satellite observations of atmospheric CO2.

How long a climate-driven carbon imbalance persists determines how long atmospheric carbon dioxide (CO₂) continues to accumulate after the forcing peaks. Here, using a four-decade surface CO₂ record, we quantify the post-peak atmospheric response toacross 12 El Niño events since 1980, including six strong events with peak Oceanic Niño Index values ≥ 1.5°C. The 2023/24 event reached its peak response later than any other event studied here and had one of the longest durations, even though its peak forcing was significantly weaker than that of the super El Niño events of 1997/98 and 2015/16. Atmospheric CO₂ growth reached its maximum seven months after the El Niño peak and remained above half of that maximum for 10 months. The detrended cumulative anomaly in 2023/24 was approximately twice than that following the 2015/16 event. The prolonged late response was not matched by the observed oceanic or fire signals, both of which were weaker than in 2015/16. Independent land-flux estimates showed a persistent late-phase shift toward greater net carbon release, temporally consistent with the delayed atmospheric CO₂ response and reproduced across multiple inversion products. These results identify a prolonged post-peak atmospheric response that has not previously been quantified systematically and is temporally consistent with a persistent late-phase imbalance in net land–atmosphere carbon exchange. Because atmospheric CO₂ integrates net surface carbon exchange, the persistence and cumulative magnitude of its anomaly provide a system-level record of how long climate-driven carbon imbalances endure. This imbalance can prolong CO₂ accumulation and might amplify positive carbon–climate feedbacks.

Accurate aerosol composition retrievals support radiative forcing assessment, source attribution, air quality analysis, and improved modeling of aerosol-cloud-radiation interactions. Aerosol retrievals based solely on visible-wavelength aerosol optical depth (AOD) observations provide limited spectral sensitivity, which may be insufficient to reliably distinguish among aerosol types with similar optical properties. In this study, we present a new retrieval framework that combines multi-wavelength AOD observations from both the visible and shortwave infrared spectrum, enhancing aerosol type discrimination. A neural network forward model trained on simulations from the Model for Optical Properties of Aerosols and Clouds (MOPSMAP), which relates aerosol optical properties to spectral AOD, is embedded in an optimal estimation method (OEM) to retrieve aerosol composition. This machine learning-based forward model achieves computational efficiency without making compromises in accuracy. The neural network forward model achieves a mean R2 of 0.99 with root-mean-square error below 0.01. The retrieval resolves up to four independent aerosol components, with degrees of freedom for signal about 3.75. We apply this hybrid method to ground-based observations, including data from the Aerosol Robotic Network (AERONET) and Fourier Transform Infrared spectrometer (FTIR) measurements. The retrieved aerosol compositions are consistent with physical expectations and validated through backward trajectory analysis.

Long-term greenhouse gas (GHG) measurements are essential for understanding the carbon cycle, detecting trends in atmospheric composition, and assessing the efficiency of climate change mitigation strategies. However, observational gaps over large geographic areas such as the Eastern Mediterranean and Middle East (EMME), a well-known regional GHG hotspot, are likely to increase uncertainties in estimations of their sources and sinks. Here, we describe a new Total Carbon Column Observing Network (TCCON) observatory for solar absorption spectroscopy measurements that has been operating in Nicosia, Cyprus, since September 2019. The site helps bridge a regional observational gap in the EMME, a strategic location at the crossroads of air masses from Europe, Asia, and Africa. Using near-infrared (NIR, InGaAs detector) solar absorption spectra, TCCON-Nicosia measures total column average dry-air mole fractions (Xgas) of key trace gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), carbon monoxide (CO), hydrogen fluoride (HF), water vapor (H2O), and semi-heavy water (HDO). These continuous observations, spanning more than 4 years, are presented along with a description of the quality control procedures, compliant with the TCCON standards, to ensure total column atmospheric data with minimal errors. In 2023, observations were extended into the mid-infrared (MIR) spectral region with the addition of a liquid-nitrogen-cooled InSb (LN2-InSb) detector enabling the retrieval of additional trace gases such as formaldehyde (HCHO), carbonyl sulfide (OCS), nitrogen monoxide (NO), nitrogen dioxide (NO2), and ethane (C2H6), herewith further contributing to the global Network for the Detection of Atmospheric Composition Change (NDACC). To tie the TCCON Nicosia with the WMO reference scale, an AirCore (AC) campaign conducted in June 2020 over Cyprus provided vertical in situ profiles, which were converted into total column quantities (AC.Xgas) and compared to TCCON observations (Xgas). The TCCON/in situ comparison showed agreement well within their respective uncertainty budget.

Abstract Methane (CH4) 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 Arctic does not show a uniform warming, some regions even experience a cooling. We show that during winter, the Arctic warming becomes self-limited once a temperature threshold is crossed. This behavior is controlled by dust-driven cloud microphysical processes, occurring near 251 K. Regions colder than 251 K on ground exhibit increasing cloud emissivity over time, enhancing downward longwave radiation and increasing the surface warming. In contrast, regions exceeding 251 K show decreasing emissivity with increasing dust, producing a cooling tendency. We attribute this nonlinear response to the onset of an efficient Wegener–Bergeron–Findeisen processing in clouds, where ice crystals grow at the expense of supercooled liquid droplets. As temperatures approach a critical value of 251 K, enhanced ice nucleation accelerates liquid droplet depletion, thus modifying cloud phase partitioning, thereby altering cloud longwave emissivity. CMIP6 experiments with doubled mineral dust emissions reproduce this temperature-dependent dust–cloud–radiative response.

Ethane is the most abundant non-methane hydrocarbon in Earth’s atmosphere and acts as an indirect greenhouse gas, influencing the atmospheric lifetime of methane. Therefore, understanding the development of trends and identifying trend reversals in atmospheric ethane is crucial. Ethane abundance is measured at different ground-based stations worldwide using Fourier transform infrared remote sensing techniques. We compile a new dataset comprising 26 ethane time series from the Northern and Southern Hemispheres. We analyze their long-term trends using different econometric techniques capable of handling missing data and strong seasonal components present in the data. The resulting trend patterns are consistent across the different methods, with similar estimated trends at the various stations. In the Northern Hemisphere, the common trend across stations declined from the 1990s to 2005, gradually increased over the next decade, and then resumed a similar downward trajectory from 2015 onward. The estimated trends reveal a pronounced peak around 2014/2015, marking a reversal from an upward to a downward trend.

We present observations of the daytime diurnal cycle of tropospheric column ozone over Palau in the tropical Pacific Warm Pool, based on high-resolution solar absorption Fourier Transform Infrared (FTIR) spectrometry during September-October 2022. The tropospheric column-averaged ozone (surface-10.2 km) showed a distinct diurnal cycle, with concentrations increasing from morning to a midday maximum and declining in the afternoon, primarily reflecting near-surface variability. Relative comparisons with ozonesonde profiles confirm this diurnal pattern. GEOS-Chem model simulations reproduce the daily mean variability but are not able to capture the observed diurnal cycle, underscoring the need for improved representation of local photochemistry and boundary-layer processes in models.Palau exhibited persistently low column-averaged ozone between 20-30 ppb during the campaign period, reflecting limited precursor availability, efficient convective washout, and advection of clean marine air from the eastern Pacific. Satellite and reanalysis data indicate low aerosol loadings and large cloud droplets, which suppress convective electrification and reduce lightning activity. With lightning providing a key natural source of NOx, this suppression limits upper-tropospheric ozone and OH production. GEOS-Chem sensitivity simulations confirm that removing Lightning emissions further decreases both species, underscoring how aerosol-cloud interactions indirectly shape a chemically low-oxidizing environment. Given that the Tropical Western Pacific (TWP) is a major pathway for troposphere-to-stratosphere transport, the persistence of low ozone and OH suggests that air can ascend into the stratosphere before reactive species are removed by oxidation, thereby influencing the chemical composition of the lower stratosphere.

The Wegener-Bergeron-Findeisen (WBF) process describes the growth of ice crystals at the expense of supercooled liquid droplets in mixed-phase clouds, driven by phase transitions at temperatures below 0 degrees C. In this study, we introduce a potential mechanism involving the transfer of water vapor from ice to cloud droplets formed on Giant Cloud Condensation Nuclei (GCCN). This process occurs under specific atmospheric conditions influenced by temperature and CCN size, particularly for CCN with diameters exceeding 1 mu m. We term this mechanism the Giant Cloud Condensation Nuclei-Enhanced Ice Sublimation Process (GCCN-ISP). We first conduct a theoretical analysis to develop a physical model for determining these specific atmospheric conditions, followed by validation through observations. Model simulations informed by observational data from aircraft indicate that when CCNs are sufficiently large and cold, the water vapor partial pressure over droplets formed on these CCNs can be lower than that over ice. Consequently, water vapor can transfer from ice to supercooled droplets, causing the droplets to grow. Eventually, the water vapor pressures of both reach equilibrium, resulting in their coexistence.

This study presents the Ozone Monitoring Instrument (OMI) Collection 4 formaldehyde (HCHO) retrieval developed with the Smithsonian Astrophysical Observatory’s (SAO) Making Earth System Data Records for Use in Research Environments (MEaSUREs) algorithm. The retrieval algorithm updates and makes improvements to the NASA operational OMI HCHO (OMI Collection 3 HCHO) algorithm, and has been transitioned to use OMI Collection 4 Level-1B radiances. This paper describes the updated retrieval algorithm and compares Collection 3 and Collection 4 data products. The OMI Collection 4 HCHO exhibits remarkably improved stability over time in comparison to the OMI Collection 3 HCHO product, with better precision and the elimination of artificial trends present in the Collection 3 during the later years of the mission. We validate the OMI Collection 4 HCHO data product using Fourier-Transform Infrared (FTIR) ground-based HCHO measurements. The climatological monthly averaged OMI Collection 4 HCHO vertical column densities (VCDs) agree well with the FTIR VCDs, with a correlation coefficient of 0.83, root-mean-square error (RMSE) of 2.98 × 10 molecules cm, regression slope of 0.79, and intercept of 8.21 × 10 molecules cm. Additionally, we compare the monthly averaged OMI Collection 4 HCHO VCDs to OMPS Suomi NPP, OMPS NOAA-20, and TROPOMI HCHO VCDs in overlapping years for twelve geographic regions. This comparison demonstrates high correlation coefficients of 0.98 (OMPS Suomi NPP), 0.97 (OMPS NOAA-20), and 0.90 (TROPOMI).

The Orbiting Carbon Observatory 2 (OCO‐2) is NASA's first Earth observation satellite mission dedicated to studying the sources and sinks of carbon dioxide (CO 2 ) on a global scale. The observations of reflected sunlight are inverted in a retrieval algorithm to produce estimates of the dry air mole‐fractions of CO 2 (X CO2 ). The OCO‐2 Level 2 data release, version 11.1 (v11.1) retrievals from the Atmospheric Carbon Observations from Space (ACOS) algorithm, includes significant improvements in the X CO2 data product compared to older OCO‐2 data versions. This work compares the v11.1 X CO2 from OCO‐2 against X CO2 estimates collected from a global ground‐based network known as the Total Carbon Column Observing Network (TCCON), OCO‐2's primary validation source. The OCO‐2 project provides a version of the Level 2 data product, called “lite” files that include calibrated and bias‐corrected X CO2 values, accessible together with all OCO‐2 data products through the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). This work shows that OCO‐2 X CO2 observations made between September 2014 and December 2023, after quality filtering and the application of an averaging kernel correction, agree well with coincident TCCON data for all OCO‐2 observational modes of land (nadir, glint, target) and ocean (glint). The aggregated, bias‐corrected, and quality‐filtered absolute average bias values are less than or equal to 0.20 parts per million (ppm) globally for all OCO‐2 observation modes, where the biases do not indicate a statistically significant time dependence. The land nadir/glint mode has the lowest bias value of −0.03 ± 0.85 ppm.

Methane (CH4) and carbon monoxide (CO) are gases with important climate impacts as direct and indirect greenhouse gases, respectively. Methane has a warming potential 28 times that of carbon dioxide on a 100-year timescale, and carbon monoxide is a precursor to ozone in the troposphere. Modeling trace gas concentrations in the Arctic atmosphere can be challenging due to Arctic conditions and sensitivity to long-range transport, and comparing model outputs to remote sensing measurements is essential for ensuring that models are performing well. Ground-based Arctic measurements are spatially sparse, so it is important to make use of all such available data sets. In this study, we assess eight atmospheric models, comparing their simulations of atmospheric CO and CH4 column-averaged dry-air mole fractions for 2014 and 2015 with ground-based retrievals of these species at three Arctic stations in the Total Carbon Column Observing Network (TCCON). The multi-model mean had mean biases (+/- one standard deviation of the mean) of -5.4% +/- 8% at Eureka, Canada, -6.5% +/- 8% at Ny-& Aring;lesund, Norway, and -11% +/- 7% at Sodankyl & auml;, Finland for CO, and mean biases of -0.25% +/- 0.5% at Eureka, -0.90% +/- 0.5% at Ny-& Aring;lesund, and -1.0% +/- 0.5% at Sodankyl & auml; for CH4. Individual model mean biases range from -33% to +35% for CO and -2.5% to +1.9% for CH4. These results indicate that models could benefit from improvements targeting simulations of Arctic CO.

At the crossroads of Europe, Africa, and Asia, the island of Cyprus receives long-range and regional pollution from various anthropogenic and natural sources. To assess the variability and amounts of greenhouse gases (GHG) in the region, we have set up, in 2019, a new Total Carbon Column Observing Network (TCCON) site, the TCCON Nicosia, at The Cyprus Institute. Herewith, we present the first time series of columnar amounts of the main GHGs in the region (Xgas; X stands for total column average dry-air mole fractions), namely carbon dioxide (XCO2), methane (XCH4), nitrous oxide (XN2O), carbon monoxide (XCO) and hydrogen fluoride (XHF). To evaluate the performance of TCCON, an AirCore campaign was conducted in Cyprus in June 2020, providing independent in-situ vertical profiles of CO2, CH4 and CO extending up to the stratosphere. The recent observations of XGHG data, together with the results of the AirCore, are presented. The observed variability in the columnar time series and its possible drivers are discussed.

Tropospheric ozone trends from models and satellites are found to diverge. Ground-based (GB) observations are used to reference models and satellites, but GB data themselves might display station biases and discontinuities. Reprocessing with uniform procedures, the TOAR-II working group Harmonization and Evaluation of Ground-based Instruments for Free-Tropospheric Ozone Measurements (HEGIFTOM) homogenized public data from five networks: ozonesondes, In-service Aircraft for a Global Observing System (IAGOS) profiles, solar absorption Fourier transform infrared (FTIR) spectrometer measurements, lidar observations, and Dobson Umkehr data. Amounts and uncertainties for total tropospheric ozone (TrOC; surface to 300 hPa), as well as free- and lower-tropospheric ozone, are calculated for each network. We report trends (2000 to 2022) for these segments using quantile regression (QR) and multiple linear regression (MLR) for 55 datasets, including six multi-instrument stations. The findings are that (1) median TrOC trends computed with QR and MLR trends are essentially the same; (2) pole-to-pole, across all longitudes, TrOC trends fall within +3 to −3 ppbv per decade, equivalent to (−4 % to +8 %) per decade depending on site; (3) the greatest fractional increases occur over most tropical and subtropical sites, with decreases at northern high latitudes, but these patterns are not uniform; (4) post-COVID trends are smaller than pre-COVID trends for Northern Hemisphere mid-latitude sites. In summary, this analysis conducted in the frame of TOAR-II/HEGIFTOM shows that high-quality, multi-instrument, harmonized data over a wide range of ground sites provide clear standard references for TOAR-II models and evolving tropospheric ozone satellite products for 2000–2022.

Quantifying long-term free-tropospheric ozone trends is essential for understanding the impact of human activities and climate change on atmospheric chemistry. However, this is complicated by two key challenges: the differences among existing satellite-derived tropospheric ozone products, which are not yet fully understood or reconciled, and the limited temporal and spatial coverage of ground-based reference measurements. Here, we explore if a more consistent understanding of the geographical distribution of tropospheric ozone column (TrOC) trends can be obtained by focusing on regional trends from ground-based measurements. Regions were determined with a correlation analysis between modeled TrOCs at the site locations. For those regions, TrOC trends were estimated with quantile regression for the Trajectory-mapped Ozonesonde dataset for the Stratosphere and Troposphere (TOST) and with a linear mixed-effects modeling (LMM) approach to calculate synthesized trends from homogenized HEGIFTOM (Harmonization and Evaluation of Ground-based Instruments for Free-Tropospheric Ozone Measurements) individual site trends. For different periods (1990-2021/22, 1995-2021/22, 2000-2021/22), both approaches give increasing (partial) tropospheric ozone column amounts over almost all Asian regions (median confidence) and negative trends over Arctic regions (very high confidence). Trends over Europe and North America are mostly weakly positive (LMM) or negative (TOST). For both approaches, the 2000-2021/22 trends decreased in magnitude compared to 1995-2021/22 for most regions; and for all time periods and regions, the pre-COVID trends are larger than the post-COVID trends. Our results enable the validation of global satellite TrOC trends and assessment of the performance of atmospheric chemistry models to represent the distribution and variation of TrOC.