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Atmospheric non-methane hydrocarbon concentrations began declining in the 1970s. Surface and column measurements show that Northern Hemisphere ethane concentrations are now rising, probably due to North American oil and natural gas emissions. Non-methane hydrocarbons such as ethane are important precursors to tropospheric ozone and aerosols. Using data from a global surface network and atmospheric column observations we show that the steady decline in the ethane mole fraction that began in the 1970s 1 , 2 , 3 halted between 2005 and 2010 in most of the Northern Hemisphere and has since reversed. We calculate a yearly increase in ethane emissions in the Northern Hemisphere of 0.42 (±0.19) Tg yr −1 between mid-2009 and mid-2014. The largest increases in ethane and the shorter-lived propane are seen over the central and eastern USA, with a spatial distribution that suggests North American oil and natural gas development as the primary source of increasing emissions. By including other co-emitted oil and natural gas non-methane hydrocarbons, we estimate a Northern Hemisphere total non-methane hydrocarbon yearly emission increase of 1.2 (±0.8) Tg yr −1 . Atmospheric chemical transport modelling suggests that these emissions could augment summertime mean surface ozone by several nanomoles per mole near oil and natural gas production regions. Methane/ethane oil and natural gas emission ratios could suggest a significant increase in associated methane emissions; however, this increase is inconsistent with observed leak rates in production regions and changes in methane’s global isotopic ratio.

We present a consistent intercomparison of the mean age of air (AoA) according to five modern reanalyses: the European Centre for Medium-Range Weather Forecasts Interim Reanalysis (ERA-Interim), the Japanese Meteorological Agency's Japanese 55-year Reanalysis (JRA-55), the National Centers for Environmental Prediction Climate Forecast System Reanalysis (CFSR) and the National Aeronautics and Space Administration's Modern Era Retrospective analysis for Research and Applications version 1 (MERRA) and version 2 (MERRA-2). The modeling tool is a kinematic transport model driven only by the surface pressure and wind fields. It is validated for ERA-I through a comparison with the AoA computed by another transport model. The five reanalyses deliver AoA which differs in the worst case by 1 year in the tropical lower stratosphere and more than 2 years in the upper stratosphere. At all latitudes and altitudes, MERRA-2 and MERRA provide the oldest values (∼5–6 years in midstratosphere at midlatitudes), while JRA-55 and CFSR provide the youngest values (∼4 years) and ERA-I delivers intermediate results. The spread of AoA at 50 hPa is as large as the spread obtained in a comparison of chemistry–climate models. The differences between tropical and midlatitude AoA are in better agreement except for MERRA-2. Compared with in situ observations, they indicate that the upwelling is too fast in the tropical lower stratosphere. The spread between the five simulations in the northern midlatitudes is as large as the observational uncertainties in a multidecadal time series of balloon observations, i.e., approximately 2 years. No global impact of the Pinatubo eruption can be found in our simulations of AoA, contrary to a recent study which used a diabatic transport model driven by ERA-I and JRA-55 winds and heating rates. The time variations are also analyzed through multiple linear regression analyses taking into account the seasonal cycles, the quasi-biennial oscillation and the linear trends over four time periods. The amplitudes of AoA seasonal variations in the lower stratosphere are significantly larger when using MERRA and MERRA-2 than with the other reanalyses. The linear trends of AoA using ERA-I confirm those found by earlier model studies, especially for the period 2002–2012, where the dipole structure of the latitude–height distribution (positive in the northern midstratosphere and negative in the southern midstratosphere) also matches trends derived from satellite observations of SF6. Yet the linear trends vary substantially depending on the considered period. Over 2002–2015, the ERA-I results still show a dipole structure with positive trends in the Northern Hemisphere reaching up to 0.3 yr dec−1. No reanalysis other than ERA-I finds any dipole structure of AoA trends. The signs of the trends depend strongly on the input reanalysis and on the considered period, with values above 10 hPa varying between approximately −0.4 and 0.4 yr dec−1. Using ERA-I and CFSR, the 2002–2015 trends are negative above 10 hPa, but using the three other reanalyses these trends are positive. Over the whole period (1989–2015) each reanalysis delivers opposite trends; i.e., AoA is mostly increasing with CFSR and ERA-I but mostly decreasing with MERRA, JRA-55 and MERRA-2. In view of this large disagreement, we urge great caution for studies aiming to assess AoA trends derived only from reanalysis winds. We briefly discuss some possible causes for the dependency of AoA on the input reanalysis and highlight the need for complementary intercomparisons using diabatic transport models.

Only very few long-term records of formaldehyde (HCHO) exist that are suitable for trend analysis. Furthermore, many uncertainties remain as to its diurnal cycle, representing a large short-term variability superimposed on seasonal and inter-annual variations that should be accounted for when comparing ground-based observations to, e.g., model results. In this study, we derive a multi-decadal time series (January 1988–June 2015) of HCHO total columns from ground-based high-resolution Fourier transform infrared (FTIR) solar spectra recorded at the high-altitude station of Jungfraujoch (Swiss Alps, 46.5° N, 8.0° E, 3580 m a. s. l. ), allowing for the characterization of the mid-latitudinal atmosphere for background conditions. First we investigate the HCHO diurnal variation, peaking around noontime and mainly driven by the intra-day insolation modulation and methane (CH4) oxidation. We also characterize quantitatively the diurnal cycles by adjusting a parametric model to the observations, which links the daytime to the HCHO columns according to the monthly intra-day regimes. It is then employed to scale all the individual FTIR measurements on a given daytime in order to remove the effect of the intra-day modulation for improving the trend determination and the comparison with HCHO columns simulated by the state-of-the-art GEOS-Chem v9-02 chemical transport model. Such a parametric model will be useful to scale the Jungfraujoch HCHO columns on satellite overpass times in the framework of future calibration/validation efforts of space-borne sensors. GEOS-Chem sensitivity tests suggest then that the seasonal and inter-annual HCHO column variations above Jungfraujoch are predominantly led by the atmospheric CH4 oxidation, with a maximum contribution of 25 % from the anthropogenic non-methane volatile organic compound precursors during wintertime. Finally, trend analysis of the so-scaled 27-year FTIR time series reveals a long-term evolution of the HCHO columns in the remote troposphere to be related to the atmospheric CH4 fluctuations and the short-term OH variability: +2.9 % year−1 between 1988 and 1995, −3.7 % year−1 over 1996–2002 and +0.8 % year−1 from 2003 onwards.

This paper studies the seasonal variation of surface and column CO at three different sites (Paris, Jungfraujoch and Wollongong), with an emphasis on establishing a link between the CO vertical distribution and the nature of CO emission sources. We find the first evidence of a time lag between surface and free tropospheric CO seasonal variations in the Northern Hemisphere. The CO seasonal variability obtained from the total columns and free tropospheric partial columns shows a maximum around March–April and a minimum around September–October in the Northern Hemisphere (Paris and Jungfraujoch). In the Southern Hemisphere (Wollongong) this seasonal variability is shifted by about 6 months. Satellite observations by the IASI–MetOp (Infrared Atmospheric Sounding Interferometer) and MOPITT (Measurements Of Pollution In The Troposphere) instruments confirm this seasonality. Ground-based FTIR (Fourier transform infrared) measurements provide useful complementary information due to good sensitivity in the boundary layer. In situ surface measurements of CO volume mixing ratios at the Paris and Jungfraujoch sites reveal a time lag of the near-surface seasonal variability of about 2 months with respect to the total column variability at the same sites. The chemical transport model GEOS-Chem (Goddard Earth Observing System chemical transport model) is employed to interpret our observations. GEOS-Chem sensitivity runs identify the emission sources influencing the seasonal variation of CO. At both Paris and Jungfraujoch, the surface seasonality is mainly driven by anthropogenic emissions, while the total column seasonality is also controlled by air masses transported from distant sources. At Wollongong, where the CO seasonality is mainly affected by biomass burning, no time shift is observed between surface measurements and total column data.

The Measurements of Pollution in the Troposphere (MOPITT) satellite instrument provides the longest continuous dataset of carbon monoxide (CO) from space. We perform the first validation of MOPITT version 6 retrievals using total column CO measurements from ground-based remote-sensing Fourier transform infrared spectrometers (FTSs). Validation uses data recorded at 14 stations, that span a wide range of latitudes (80° N to 78° S), in the Network for the Detection of Atmospheric Composition Change (NDACC). MOPITT measurements are spatially co-located with each station, and different vertical sensitivities between instruments are accounted for by using MOPITT averaging kernels (AKs). All three MOPITT retrieval types are analyzed: thermal infrared (TIR-only), joint thermal and near infrared (TIR–NIR), and near infrared (NIR-only). Generally, MOPITT measurements overestimate CO relative to FTS measurements, but the bias is typically less than 10 %. Mean bias is 2.4 % for TIR-only, 5.1 % for TIR–NIR, and 6.5 % for NIR-only. The TIR–NIR and NIR-only products consistently produce a larger bias and lower correlation than the TIR-only. Validation performance of MOPITT for TIR-only and TIR–NIR retrievals over land or water scenes is equivalent. The four MOPITT detector element pixels are validated separately to account for their different uncertainty characteristics. Pixel 1 produces the highest standard deviation and lowest correlation for all three MOPITT products. However, for TIR-only and TIR–NIR, the error-weighted average that includes all four pixels often provides the best correlation, indicating compensating pixel biases and well-captured error characteristics. We find that MOPITT bias does not depend on latitude but rather is influenced by the proximity to rapidly changing atmospheric CO. MOPITT bias drift has been bound geographically to within ±0.5 % yr−1 or lower at almost all locations.

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 2.98×1015$2.98\\times 1{0}^{15}$molecules cm−2${\\text{cm}}^{-2}$ , regression slope of 0.79, and intercept of 8.21×1014$8.21\\times 1{0}^{14}$molecules cm−2${\\text{cm}}^{-2}$ . 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 12 geographic regions. This comparison demonstrates high correlation coefficients of 0.98 (OMPS Suomi NPP), 0.97 (OMPS NOAA‐20), and 0.90 (TROPOMI). Key Points We present the ozone monitoring instrument (OMI) Collection 4 formaldehyde (HCHO) product, retrieved using the SAO retrieval algorithm OMI Collection 4 HCHO product is well‐correlated with the ground‐based Fourier‐Transform Infrared Spectrometers (FTIR) HCHO measurements OMI Collection 4 HCHO provides a stable long‐term data record and shows strong agreement with OMPS and TROPOMI HCHO products

Understanding carbon dioxide (CO2) biospheric processes is of great importance because the terrestrial exchange drives the seasonal and interannual variability of CO2 in the atmosphere. Atmospheric inversions based on CO2 concentration measurements alone can only determine net biosphere fluxes, but not differentiate between photosynthesis (uptake) and respiration (production). Carbonyl sulfide (OCS) could provide an important additional constraint: it is also taken up by plants during photosynthesis but not emitted during respiration, and therefore is a potential means to differentiate between these processes. Solar absorption Fourier Transform InfraRed (FTIR) spectrometry allows for the retrievals of the atmospheric concentrations of both CO2 and OCS from measured solar absorption spectra. Here, we investigate co-located and quasi-simultaneous FTIR measurements of OCS and CO2 performed at five selected sites located in the Northern Hemisphere. These measurements are compared to simulations of OCS and CO2 using a chemical transport model (GEOS-Chem). The coupled biospheric fluxes of OCS and CO2 from the simple biosphere model (SiB) are used in the study. The CO2 simulation with SiB fluxes agrees with the measurements well, while the OCS simulation reproduced a weaker drawdown than FTIR measurements at selected sites, and a smaller latitudinal gradient in the Northern Hemisphere during growing season when comparing with HIPPO (HIAPER Pole-to-Pole Observations) data spanning both hemispheres. An offset in the timing of the seasonal cycle minimum between SiB simulation and measurements is also seen. Using OCS as a photosynthesis proxy can help to understand how the biospheric processes are reproduced in models and to further understand the carbon cycle in the real world.

SF6 total columns were successfully retrieved from FTIR (Fourier transform infrared) measurements (Saint Denis and Maïdo) on Reunion Island (21∘ S, 55∘ E) between 2004 and 2016 using the SFIT4 algorithm: the retrieval strategy and the error budget were presented. The FTIR SF6 retrieval has independent information in only one individual layer, covering the whole of the troposphere and the lower stratosphere. The trend in SF6 was analysed based on the FTIR-retrieved dry-air column-averaged mole fractions (XSF6) on Reunion Island, the in situ measurements at America Samoa (SMO) and the collocated satellite measurements (Michelson Interferometer for Passive Atmospheric Sounding, MIPAS, and Atmospheric Chemistry Experiment Fourier Transform Spectrometer, ACE-FTS) in the southern tropics. The SF6 annual growth rate from FTIR retrievals is 0.265±0.013 pptv year−1 for 2004–2016, which is slightly weaker than that from the SMO in situ measurements (0.285±0.002 pptv year−1) for the same time period. The SF6 trend in the troposphere from MIPAS and ACE-FTS observations is also close to the ones from the FTIR retrievals and the SMO in situ measurements.

Atmospheric acidity is increasingly determined by carbon dioxide and organic acids 1 – 3 . Among the latter, formic acid facilitates the nucleation of cloud droplets 4 and contributes to the acidity of clouds and rainwater 1 , 5 . At present, chemistry–climate models greatly underestimate the atmospheric burden of formic acid, because key processes related to its sources and sinks remain poorly understood 2 , 6 – 9 . Here we present atmospheric chamber experiments that show that formaldehyde is efficiently converted to gaseous formic acid via a multiphase pathway that involves its hydrated form, methanediol. In warm cloud droplets, methanediol undergoes fast outgassing but slow dehydration. Using a chemistry–climate model, we estimate that the gas-phase oxidation of methanediol produces up to four times more formic acid than all other known chemical sources combined. Our findings reconcile model predictions and measurements of formic acid abundance. The additional formic acid burden increases atmospheric acidity by reducing the pH of clouds and rainwater by up to 0.3. The diol mechanism presented here probably applies to other aldehydes and may help to explain the high atmospheric levels of other organic acids that affect aerosol growth and cloud evolution. The oxidation of hydrated formaldehyde from cloud droplets is the dominant source of atmospheric formic acid, increasing atmospheric acidity by reducing cloud and rainwater pH.

In this work, sulphur hexafluoride (SF6), which is often used as a tracer for stratospheric transport due to its inertness in the stratosphere and nearly linear growth rate in the troposphere, is included in the chemistry transport model (CTM) of the Belgian Assimilation System for Chemical ObsErvations (BASCOE). Sink and recovery reactions for this species are implemented in the model, which has a top in the mesosphere at 0.01 hPa. The simulated SF6 distributions are compared with MIPAS and ACE-FTS observations and the global atmospheric lifetime is computed from CTM runs driven by three recent meteorological reanalyses: ERA5, MERRA2 and JRA-3Q. The results show that BASCOE SF6 profiles are generally within 10 % of the satellite observations below 10 hPa, although discrepancies increase at higher altitudes. The global atmospheric lifetime is used as an additional diagnostic for the implementation of the chemistry in the mesosphere, where satellite measurements are unavailable. The derived SF6 lifetimes are 2646 years with ERA5, 1909 years with MERRA2 and 2147 years with JRA-3Q, in accordance with recent literature. Due to the large spread of published lifetimes for SF6, the study is extended to N2O, CH4, CFC-11, CFC-12 and HCFC-22, to validate the SF6 results. The lifetimes for these species are in agreement with previously reported values, and their spread between simulations is smaller compared to SF6. This analysis highlights the sensitivity of SF6 to the input reanalysis data sets and thus to differences in dynamics.