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During the 2020 austral summer, the pristine atmosphere of the southwest Indian Ocean (SWIO) basin experienced significant perturbations. This study examines the variability of aerosols and carbon monoxide (CO) over this remote oceanic region and investigates the underlying processes in the upper troposphere–lower stratosphere (UT-LS). Aerosol profiles in January and February 2020 revealed a multi-layer structure in the tropical UT-LS. Numerical models – the FLEXible PARTicle dispersion model (FLEXPART) and the Modèle Isentropique de transport Mésoéchelle de l'Ozone Stratosphérique par Advection (MIMOSA) – indicated that the lower-stratospheric aerosol content was influenced by the intense and persistent stratospheric aerosol layer generated during the 2019–2020 extreme Australian bushfire events. A portion of this layer was transported eastward by prevailing easterly winds, leading to increased aerosol extinction profiles over Réunion on 27 and 28 January. Analysis of advected potential vorticity revealed isentropic transport of air masses containing Australian biomass burning aerosols from extratropical latitudes to Réunion at the 400 K isentropic level on 28 January. Interestingly, we found that biomass burning (BB) activity in eastern Africa, though weak during this season, significantly influenced (contributed up to 90 % of) the vertical distribution of CO and aerosols in the upper troposphere over the SWIO basin. Ground-based observations at Réunion confirmed the simultaneous presence of African and Australian aerosol layers. This study provides the first evidence of African BB emissions impacting the CO and aerosol distribution in the upper troposphere over the SWIO basin during the convective season.

Spaceborne formaldehyde (HCHO) measurements constitute an excellent proxy for the sources of non-methane volatile organic compounds (NMVOCs). Past studies suggested substantial overestimations of NMVOC emissions in state-of-the-art inventories over major source regions. Here, the QA4ECV (Quality Assurance for Essential Climate Variables) retrieval of HCHO columns from OMI (Ozone Monitoring Instrument) is evaluated against (1) FTIR (Fourier-transform infrared) column observations at 26 stations worldwide and (2) aircraft in situ HCHO concentration measurements from campaigns conducted over the USA during 2012–2013. Both validation exercises show that OMI underestimates high columns and overestimates low columns. The linear regression of OMI and aircraft-based columns gives ΩOMI=0.651Ωairc+2.95×1015 molec.cm-2, with ΩOMI and Ωairc the OMI and aircraft-derived vertical columns, whereas the regression of OMI and FTIR data gives ΩOMI=0.659ΩFTIR+2.02×1015 molec.cm-2. Inverse modelling of NMVOC emissions with a global model based on OMI columns corrected for biases based on those relationships leads to much-improved agreement against FTIR data and HCHO concentrations from 11 aircraft campaigns. The optimized global isoprene emissions (∼445Tgyr-1) are 25 % higher than those obtained without bias correction. The optimized isoprene emissions bear both striking similarities and differences with recently published emissions based on spaceborne isoprene columns from the CrIS (Cross-track Infrared Sounder) sensor. Although the interannual variability of OMI HCHO columns is well understood over regions where biogenic emissions are dominant, and the HCHO trends over China and India clearly reflect anthropogenic emission changes, the observed HCHO decline over the southeastern USA remains imperfectly elucidated.

The presented study investigates the efficiency of heterogeneous ice formation in natural clouds over Lauder, New Zealand / Aotearoa. Aerosol conditions in the middle troposphere above Lauder are subject to huge contrasts. Clean, pristine air masses from Antarctica and the Southern Ocean arrive under southerly flow conditions, while high aerosol loads can occur when air masses are advected from nearby Australia. This study assesses how these contrasts in aerosol load affect the ice formation efficiency in stratiform midlevel clouds in the heterogeneous freezing range (−40 to 0 ∘C). For this purpose, an 11-year dataset was analyzed from a dual-wavelength polarization lidar system operated by National Institute of Water and Atmospheric Research (NIWA), Taihoro Nukurangi, at Lauder in collaboration with the National Institute for Environmental Studies in Japan and the Meteorological Research Institute of the Japan Meteorological Agency. These data were used to investigate the efficiency of heterogeneous ice formation in clouds over the site as a function of cloud-top temperature as in previous studies at other locations. The Lauder cloud dataset was put into context with lidar studies from contrasting regions such as Germany and southern Chile. The ice formation efficiency found at Lauder is lower than in polluted midlatitudes (i.e., Germany) but higher than, for example, in southern Chile. Both Lauder and southern Chile are subject to generally low free-tropospheric aerosol loads, which suggests that the low ice formation efficiency at these two sites is related to low ice-nucleating-particle (INP) concentrations. However, Lauder sees episodes of continental aerosol, more than southern Chile does, which seems to lead to the moderately increased ice formation efficiency. Trajectory-based tools and aerosol model reanalyses are used to relate this cloud dataset to the aerosol load and the air mass sources. Both analyses point clearly to higher ice formation efficiency for clouds which are more strongly influenced by continental aerosol and to lower ice formation efficiency for clouds which are more influenced by Antarctic/marine aerosol and air masses.

Urban areas are responsible for more than 40 % of global energy-related carbon dioxide (CO2) emissions. The Tokyo metropolitan area (TMA), Japan, one of the most populated regions in the world, includes various emission sources, such as thermal power plants, automobile traffic, and residential facilities. In order to infer a top–down emission estimate, we conducted an intensive field campaign in the TMA from February to April 2016 to measure column-averaged dry-air mole fractions of CO2 (XCO2) with three ground-based Fourier transform spectrometers (one IFS 125HR and two EM27/SUN spectrometers). At two urban sites (Saitama and Sodegaura), measured XCO2 values were generally larger than those at a rural site (Tsukuba) by up to 9.5 ppm, and average diurnal variations increased toward evening. To simulate the XCO2 enhancement (ΔXCO2) resulting from emissions at each observation site, we used the Stochastic Time-Inverted Lagrangian Transport (STILT) model driven by meteorological fields at a horizontal resolution of ∼1 km from the Weather Research and Forecasting (WRF) model, which was coupled with anthropogenic (large point source and area source) CO2 emissions and biogenic fluxes. Although some of the diurnal variation of ΔXCO2 was not reproduced and plumes from nearby large point sources were not captured, primarily because of a transport modeling error, the WRF–STILT simulations using prior fluxes were generally in good agreement with the observations (mean bias, 0.30 ppm; standard deviation, 1.31 ppm). By combining observations with high-resolution modeling, we developed an urban-scale inversion system in which spatially resolved CO2 emission fluxes at >3 km resolution and a scaling factor of large point source emissions were estimated on a monthly basis by using Bayesian inference. The XCO2 simulation results from the posterior CO2 fluxes were improved (mean bias, −0.03 ppm; standard deviation, 1.21 ppm). The prior and posterior total CO2 emissions in the TMA are 1.026 ± 0.116 and 1.037 ± 0.054 Mt-CO2 d−1 at the 95 % confidence level, respectively. The posterior total CO2 emissions agreed with emission inventories within the posterior uncertainty, demonstrating that the EM27/SUN spectrometer data can constrain urban-scale monthly CO2 emissions.

Atmospheric carbon dioxide (CO2) accounts for the largest radiative forcing among anthropogenic greenhouse gases. There is, therefore, a pressing need to understand the rate at which CO2 accumulates in the atmosphere, including the interannual variations (IAVs) in this rate. IAV in the CO2 growth rate is a small signal relative to the long-term trend and the mean annual cycle of atmospheric CO2, and IAV is tied to climatic variations that may provide insights into long-term carbon–climate feedbacks. Observations from the Orbiting Carbon Observatory-2 (OCO-2) mission offer a new opportunity to refine our understanding of atmospheric CO2 IAV since the satellite can measure over remote terrestrial regions and the open ocean, where traditional in situ CO2 monitoring is difficult, providing better spatial coverage compared to ground-based monitoring techniques. In this study, we analyze the IAV of column-averaged dry-air CO2 mole fraction (XCO2) from OCO-2 between September 2014 and June 2021. The amplitude of the IAV, which is calculated as the standard deviation of the time series, is up to 1.2 ppm over the continents and around 0.4 ppm over the open ocean. Across all latitudes, the OCO-2-detected XCO2 IAV shows a clear relationship with El Niño–Southern Oscillation (ENSO)-driven variations that originate in the tropics and are transported poleward. Similar, but smoother, zonal patterns of OCO-2 XCO2 IAV time series compared to ground-based in situ observations and with column observations from the Total Carbon Column Observing Network (TCCON) and the Greenhouse Gases Observing Satellite (GOSAT) show that OCO-2 observations can be used reliably to estimate IAV. Furthermore, the extensive spatial coverage of the OCO-2 satellite data leads to smoother IAV time series than those from other datasets, suggesting that OCO-2 provides new capabilities for revealing small IAV signals despite sources of noise and error that are inherent to remote-sensing datasets.

Following the recent Global Carbon Project (GCP) synthesis of the decadal methane (CH4) budget over 2000-2012 (Saunois et al., 2016), we analyse here the same dataset with a focus on quasi-decadal and inter-annual variability in CH4 emissions. The GCP dataset integrates results from top-down studies (exploiting atmospheric observations within an atmospheric inverse-modelling framework) and bottom-up models (including process-based models for estimating land surface emissions and atmospheric chemistry), inventories of anthropogenic emissions, and data-driven approaches. The annual global methane emissions from top-down studies, which by construction match the observed methane growth rate within their uncertainties, all show an increase in total methane emissions over the period 2000-2012, but this increase is not linear over the 13 years. Despite differences between individual studies, the mean emission anomaly of the top-down ensemble shows no significant trend in total methane emissions over the period 2000-2006, during the plateau of atmospheric methane mole fractions, and also over the period 2008-2012, during the renewed atmospheric methane increase. However, the top-down ensemble mean produces an emission shift between 2006 and 2008, leading to 22 [16-32]Tg CH4yr-1 higher methane emissions over the period 2008-2012 compared to 2002-2006. This emission increase mostly originated from the tropics, with a smaller contribution from mid-latitudes and no significant change from boreal regions. The regional contributions remain uncertain in top-down studies. Tropical South America and South and East Asia seem to contribute the most to the emission increase in the tropics. However, these two regions have only limited atmospheric measurements and remain therefore poorly constrained. The sectorial partitioning of this emission increase between the periods 2002-2006 and 2008-2012 differs from one atmospheric inversion study to another. However, all top-down studies suggest smaller changes in fossil fuel emissions (from oil, gas, and coal industries) compared to the mean of the bottom-up inventories included in this study. This difference is partly driven by a smaller emission change in China from the top-down studies compared to the estimate in the Emission Database for Global Atmospheric Research (EDGARv4.2) inventory, which should be revised to smaller values in a near future. We apply isotopic signatures to the emission changes estimated for individual studies based on five emission sectors and find that for six individual top-down studies (out of eight) the average isotopic signature of the emission changes is not consistent with the observed change in atmospheric 13CH4. However, the partitioning in emission change derived from the ensemble mean is consistent with this isotopic constraint. At the global scale, the top-down ensemble mean suggests that the dominant contribution to the resumed atmospheric CH4 growth after 2006 comes from microbial sources (more from agriculture and waste sectors than from natural wetlands), with an uncertain but smaller contribution from fossil CH4 emissions. In addition, a decrease in biomass burning emissions (in agreement with the biomass burning emission databases) makes the balance of sources consistent with atmospheric 13CH4 observations. In most of the top-down studies included here, OH concentrations are considered constant over the years (seasonal variations but without any inter-annual variability). As a result, the methane loss (in particular through OH oxidation) varies mainly through the change in methane concentrations and not its oxidants. For these reasons, changes in the methane loss could not be properly investigated in this study, although it may play a significant role in the recent atmospheric methane changes as briefly discussed at the end of the paper.

Secondary organic aerosols (SOAs) affect human health and climate change prediction; however, the factors (e.g., temperature, acidity of pre-existing particles, and oxidants) influencing their formation are not sufficiently resolved. Using a compact chamber, the temperature and acidity dependence of SOA yields and chemical components in SOA from α-pinene ozonolysis were systematically investigated under 278, 288, and 298 K temperatures using neutral ((NH4)2SO4) and acidic (H2SO4+((NH4)2SO4)) seed aerosols. SOA components with m/z less than 400 were analyzed using negative electrospray ionization liquid-chromatography time-of-flight mass spectrometry. Based on the slightly negative temperature dependence of the SOA yields, the enthalpies of vaporization under neutral and acidic seed conditions were estimated to be 25 and 44 kJ mol−1, respectively. In addition, SOA yields increased with an increase in the acidity of seed particles (solid/near-solid state) at low SOA mass loadings, when compared with the seed particle amounts. Acidity dependence analysis of the chemical formula, molecular mass, and O:C ratio of the detected compounds indicated the enhanced formation of multiple oligomers in the wide molecular mass range with a wide range of O:C ratios under acidic seed conditions. The peak abundances of some chemical compounds increased with an increase in the acidity of seed particles (e.g., m/z 197, 311, 313, 339, 355, and 383), while decreases in the peak abundances of some chemical compounds were observed (e.g., m/z 171, 185, 215, 343, and 357). The acidity dependence could be explained by acid-catalyzed heterogeneous reactions or acid-catalyzed decomposition of hydroperoxides. In addition, organosulfate (OS) formation was observed under acidic seed conditions. Six out of the 11 detected OSs were potentially formed via the aldehyde + HSO4- pathway.

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.

We analyzed seasonality and interannual variability of tropospheric hydrogen cyanide (HCN) columns in densely populated eastern China for the first time. The results were derived from solar absorption spectra recorded with a ground-based high-spectral-resolution Fourier transform infrared (FTIR) spectrometer in Hefei (31∘54′ N, 117∘10′ E) between 2015 and 2018. The tropospheric HCN columns over Hefei, China, showed significant seasonal variations with three monthly mean peaks throughout the year. The magnitude of the tropospheric HCN column peaked in May, September, and December. The tropospheric HCN column reached a maximum monthly mean of (9.8±0.78)×1015 molecules cm−2 in May and a minimum monthly mean of (7.16±0.75)×1015 molecules cm−2 in November. In most cases, the tropospheric HCN columns in Hefei (32∘ N) are higher than the FTIR observations in Ny-Ålesund (79∘ N), Kiruna (68∘ N), Bremen (53∘ N), Jungfraujoch (47∘ N), Toronto (44∘ N), Rikubetsu (43∘ N), Izana (28∘ N), Mauna Loa (20∘ N), La Reunion Maido (21∘ S), Lauder (45∘ S), and Arrival Heights (78∘ S) that are affiliated with the Network for Detection of Atmospheric Composition Change (NDACC). Enhancements of tropospheric HCN column were observed between September 2015 and July 2016 compared to the same period of measurements in other years. The magnitude of the enhancement ranges from 5 % to 46 % with an average of 22 %. Enhancement of tropospheric HCN (ΔHCN) is correlated with the concurrent enhancement of tropospheric CO (ΔCO), indicating that enhancements of tropospheric CO and HCN were due to the same sources. The GEOS-Chem tagged CO simulation, the global fire maps, and the potential source contribution function (PSCF) values calculated using back trajectories revealed that the seasonal maxima in May are largely due to the influence of biomass burning in Southeast Asia (SEAS) (41±13.1 %), Europe and boreal Asia (EUBA) (21±9.3 %), and Africa (AF) (22±4.7 %). The seasonal maxima in September are largely due to the influence of biomass burnings in EUBA (38±11.3 %), AF (26±6.7 %), SEAS (14±3.3 %), and North America (NA) (13.8±8.4 %). For the seasonal maxima in December, dominant contributions are from AF (36±7.1 %), EUBA (21±5.2 %), and NA (18.7±5.2 %). The tropospheric HCN enhancement between September 2015 and July 2016 at Hefei (32∘ N) was attributed to an elevated influence of biomass burnings in SEAS, EUBA, and Oceania (OCE) in this period. In particular, an elevated number of fires in OCE in the second half of 2015 dominated the tropospheric HCN enhancement between September and December 2015. An elevated number of fires in SEAS in the first half of 2016 dominated the tropospheric HCN enhancement between January and July 2016.

Great efforts have been made to simulate atmospheric pollutants, but their spatial and temporal distributions are still highly uncertain. Observations can measure their concentrations with high accuracy but cannot estimate their spatial distributions due to the sporadic locations of sites. Here, we propose an ensemble method by applying a linear minimum variance estimation (LMVE) between multi-model ensemble (MME) simulations and measurements to derive a more realistic distribution of atmospheric pollutants. The LMVE is a classical and basic version of data assimilation, although the estimation itself is still useful for obtaining the best estimates by combining simulations and observations without a large amount of computer resources, even for high-resolution models. In this study, we adopt the proposed methodology for atmospheric radioactive caesium (Cs-137) in atmospheric particles emitted from the Fukushima Daiichi Nuclear Power Station (FDNPS) accident in March 2011. The uniqueness of this approach includes (1) the availability of observed Cs-137 concentrations near the surface at approximately 100 sites, thus providing dense coverage over eastern Japan; (2) the simplicity of identifying the emission source of Cs-137 due to the point source of FDNPS; (3) the novelty of MME with the high-resolution model (3 km horizontal grid) over complex terrain in eastern Japan; and (4) the strong need to better estimate the Cs-137 distribution due to its inhalation exposure among residents in Japan. The ensemble size is six, including two atmospheric transport models: the Weather Research and Forecasting – Community Multi-scale Air Quality (WRF-CMAQ) model and non-hydrostatic icosahedral atmospheric model (NICAM). The results showed that the MME that estimated Cs-137 concentrations using all available sites had the lowest geometric mean bias (GMB) against the observations (GMB =1.53), the lowest uncertainties based on the root mean square error (RMSE) against the observations (RMSE =9.12 Bq m−3), the highest Pearson correlation coefficient (PCC) with the observations (PCC =0.59) and the highest fraction of data within a factor of 2 (FAC2) with the observations (FAC2 =54 %) compared to the single-model members, which provided higher biases (GMB =1.83–4.29, except for 1.20 obtained from one member), higher uncertainties (RMSE =19.2–51.2 Bq m−3), lower correlation coefficients (PCC =0.29–0.45) and lower precision (FAC2 =10 %–29 %). At the model grid, excluding the measurements, the MME-estimated Cs-137 concentration was estimated by a spatial interpolation of the variance used in the LMVE equation using the inverse distance weights between the nearest two sites. To test this assumption, the available measurements were divided into two categories, i.e. learning and validation data; thus, the assumption for the spatial interpolation was found to guarantee a moderate PCC value (> 0.4) within an approximate distance of at least 70 km. Extra sensitivity tests for several parameters, i.e. the site number and the weighting coefficients in the spatial interpolation, the time window in the LMVE and the ensemble size, were performed. In conclusion, the important assumptions were the time window and the ensemble size; i.e. a shorter time window (the minimum in this study was 1 h, which is the observation interval) and a larger ensemble size (the maximum in this study was six, but five is also acceptable if the members are effectively selected) generated better results.

Studies of the volatility distribution of secondary organic aerosol (SOA) from aromatic compounds are limited compared with SOA from biogenic monoterpenes. In this study, the volatility distribution was investigated by composition, heating, and dilution measurements for SOA formed from the photooxidation of 1,3,5-trimethylbenzene in the presence of NOx. Composition studies revealed that highly oxygenated monomers (C9H14Ox, x = 4–7) and dimers (C18H26Ox, x = 8–12) are the major products in SOA particles. Highly oxygenated molecules (HOMs) with five or more oxygens were formed during photochemical aging, whereas dimers degraded during photochemical aging. HOMs with five or more oxygens may be produced from the photooxidation of phenol-type gaseous products, whereas dimers in the particle phase may be photolyzed to smaller molecules during photochemical aging. The results of composition, heating, and dilution measurements showed that fresh SOA that formed from 1,3,5-trimethylbenzene (TMB) photooxidation includes low-volatility compounds with  µg m−3 saturation concentrations, which are attributed to dimers. Similar results were reported for α-pinene SOA in previous studies. Low-volatility compounds with  µg m−3 saturation concentrations are not included in the volatility distributions employed in the standard volatility basis-set (VBS) approach. Improvements in the organic aerosol model will be necessary for the study of anthropogenic SOA as well as biogenic SOA.

Traditional yield curve analysis shows that semi-volatile organic compounds are a major component of secondary organic aerosols (SOAs). We investigated the volatility distribution of SOAs from α-pinene ozonolysis using positive electrospray ionization mass analysis and dilution- and heat-induced evaporation measurements. Laboratory chamber experiments were conducted on α-pinene ozonolysis, in the presence and absence of OH scavengers. Among these, we identified not only semi-volatile products, but also less volatile highly oxygenated molecules (HOMs) and dimers. Ozonolysis products were further exposed to OH radicals to check the effects of photochemical aging. HOMs were also formed during OH-initiated photochemical aging. Most HOMs that formed from ozonolysis and photochemical aging had 10 or fewer carbons. SOA particle evaporation after instantaneous dilution was measured at

Following the recent Global Carbon Project (GCP) synthesis of the decadal methane (CH4) budget over 2000–2012 (Saunois et al., 2016), we analyse here the same dataset with a focus on quasi-decadal and inter-annual variability in CH4 emissions. The GCP dataset integrates results from top-down studies (exploiting atmospheric observations within an atmospheric inverse-modelling framework) and bottom-up models (including process-based models for estimating land surface emissions and atmospheric chemistry), inventories of anthropogenic emissions, and data-driven approaches. The annual global methane emissions from top-down studies, which by construction match the observed methane growth rate within their uncertainties, all show an increase in total methane emissions over the period 2000–2012, but this increase is not linear over the 13 years. Despite differences between individual studies, the mean emission anomaly of the top-down ensemble shows no significant trend in total methane emissions over the period 2000–2006, during the plateau of atmospheric methane mole fractions, and also over the period 2008–2012, during the renewed atmospheric methane increase. However, the top-down ensemble mean produces an emission shift between 2006 and 2008, leading to 22 [16–32] Tg CH4 yr−1 higher methane emissions over the period 2008–2012 compared to 2002–2006. This emission increase mostly originated from the tropics, with a smaller contribution from mid-latitudes and no significant change from boreal regions. The regional contributions remain uncertain in top-down studies. Tropical South America and South and East Asia seem to contribute the most to the emission increase in the tropics. However, these two regions have only limited atmospheric measurements and remain therefore poorly constrained. The sectorial partitioning of this emission increase between the periods 2002–2006 and 2008–2012 differs from one atmospheric inversion study to another. However, all top-down studies suggest smaller changes in fossil fuel emissions (from oil, gas, and coal industries) compared to the mean of the bottom-up inventories included in this study. This difference is partly driven by a smaller emission change in China from the top-down studies compared to the estimate in the Emission Database for Global Atmospheric Research (EDGARv4.2) inventory, which should be revised to smaller values in a near future. We apply isotopic signatures to the emission changes estimated for individual studies based on five emission sectors and find that for six individual top-down studies (out of eight) the average isotopic signature of the emission changes is not consistent with the observed change in atmospheric 13CH4. However, the partitioning in emission change derived from the ensemble mean is consistent with this isotopic constraint. At the global scale, the top-down ensemble mean suggests that the dominant contribution to the resumed atmospheric CH4 growth after 2006 comes from microbial sources (more from agriculture and waste sectors than from natural wetlands), with an uncertain but smaller contribution from fossil CH4 emissions. In addition, a decrease in biomass burning emissions (in agreement with the biomass burning emission databases) makes the balance of sources consistent with atmospheric 13CH4 observations. In most of the top-down studies included here, OH concentrations are considered constant over the years (seasonal variations but without any inter-annual variability). As a result, the methane loss (in particular through OH oxidation) varies mainly through the change in methane concentrations and not its oxidants. For these reasons, changes in the methane loss could not be properly investigated in this study, although it may play a significant role in the recent atmospheric methane changes as briefly discussed at the end of the paper.

We use the GEOS-Chem global 3-D model of atmospheric chemistry and transport and an ensemble Kalman filter to simultaneously infer regional fluxes of methane (CH4) and carbon dioxide (CO2) directly from GOSAT retrievals of XCH4 : XCO2, using sparse ground-based CH4 and CO2 mole fraction data to anchor the ratio. This work builds on the previously reported theory that takes into account that (1) these ratios are less prone to systematic error than either the full-physics data products or the proxy CH4 data products; and (2) the resulting CH4 and CO2 fluxes are self-consistent. We show that a posteriori fluxes inferred from the GOSAT data generally outperform the fluxes inferred only from in situ data, as expected. GOSAT CH4 and CO2 fluxes are consistent with global growth rates for CO2 and CH4 reported by NOAA and have a range of independent data including new profile measurements (0–7 km) over the Amazon Basin that were collected specifically to help validate GOSAT over this geographical region. We find that large-scale multi-year annual a posteriori CO2 fluxes inferred from GOSAT data are similar to those inferred from the in situ surface data but with smaller uncertainties, particularly over the tropics. GOSAT data are consistent with smaller peak-to-peak seasonal amplitudes of CO2 than either the a priori or in situ inversion, particularly over the tropics and the southern extratropics. Over the northern extratropics, GOSAT data show larger uptake than the a priori but less than the in situ inversion, resulting in small net emissions over the year. We also find evidence that the carbon balance of tropical South America was perturbed following the droughts of 2010 and 2012 with net annual fluxes not returning to an approximate annual balance until 2013. In contrast, GOSAT data significantly changed the a priori spatial distribution of CH4 emission with a 40 % increase over tropical South America and tropical Asia and a smaller decrease over Eurasia and temperate South America. We find no evidence from GOSAT that tropical South American CH4 fluxes were dramatically affected by the two large-scale Amazon droughts. However, we find that GOSAT data are consistent with double seasonal peaks in Amazonian fluxes that are reproduced over the 5 years we studied: a small peak from January to April and a larger peak from June to October, which are likely due to superimposed emissions from different geographical regions.

To validate products of the Greenhouse gases Observing SATellite (GOSAT), we observed vertical profiles of aerosols, thin cirrus clouds, and tropospheric ozone with a mobile-lidar system that consisted of a two-wavelength (532 and 1064 nm) polarization lidar and a tropospheric ozone differential absorption lidar (DIAL). We used these lidars to make continuous measurements over Saga (33.24° N, 130.29° E) during 20–31 March 2015. High ozone and high aerosol concentrations were observed almost simultaneously in the altitude range 0.5–1.5 km from 03:00 to 20:00 Japan Standard Time (JST) on 22 March 2015. The maximum ozone volume mixing ratio was ∼ 110 ppbv. The maxima of the aerosol extinction coefficient and optical depth at 532 nm were 1.2 km−1 and 2.1, respectively. Backward trajectory analysis and the simulations by the Model of Aerosol Species IN the Global AtmospheRe (MASINGAR) mk-2 and the Meteorological Research Institute Chemistry-Climate Model, version 2 (MRI-CCM2), indicated that mineral dust particles from the Gobi Desert and an air mass with high ozone and aerosol (mainly sulfate) concentrations that originated from the North China Plain could have been transported over the measurement site within about 2 days. These high ozone and aerosol concentrations impacted surface air quality substantially in the afternoon of 22 March 2015. After some modifications of its physical and chemical parameters, MRI-CCM2 approximately reproduced the high ozone volume mixing ratio. MASINGAR mk-2 successfully predicted high aerosol concentrations, but the predicted peak aerosol optical thickness was about one-third of the observed value.

Changes of atmospheric methane total columns (CH4) since 2005 have been evaluated using Fourier transform infrared (FTIR) solar observations carried out at 10 ground-based sites, affiliated to the Network for Detection of Atmospheric Composition Change (NDACC). From this, we find an increase of atmospheric methane total columns of 0.31 ± 0.03 % year−1 (2σ level of uncertainty) for the 2005–2014 period. Comparisons with in situ methane measurements at both local and global scales show good agreement. We used the GEOS-Chem chemical transport model tagged simulation, which accounts for the contribution of each emission source and one sink in the total methane, simulated over 2005–2012. After regridding according to NDACC vertical layering using a conservative regridding scheme and smoothing by convolving with respective FTIR seasonal averaging kernels, the GEOS-Chem simulation shows an increase of atmospheric methane total columns of 0.35 ± 0.03 % year−1 between 2005 and 2012, which is in agreement with NDACC measurements over the same time period (0.30 ± 0.04 % year−1, averaged over 10 stations). Analysis of the GEOS-Chem-tagged simulation allows us to quantify the contribution of each tracer to the global methane change since 2005. We find that natural sources such as wetlands and biomass burning contribute to the interannual variability of methane. However, anthropogenic emissions, such as coal mining, and gas and oil transport and exploration, which are mainly emitted in the Northern Hemisphere and act as secondary contributors to the global budget of methane, have played a major role in the increase of atmospheric methane observed since 2005. Based on the GEOS-Chem-tagged simulation, we discuss possible cause(s) for the increase of methane since 2005, which is still unexplained.

The Total Carbon Column Observing Network (TCCON) is a network of ground-based Fourier transform spectrometers (FTSs) that record near-infrared (NIR) spectra of the sun. From these spectra, accurate and precise observations of CO2 column-averaged dry-air mole fractions (denoted XCO2) are retrieved. TCCON FTS observations have previously been used to validate satellite estimations of XCO2; however, our knowledge of the short-term spatial and temporal variations in XCO2 surrounding the TCCON sites is limited. In this work, we use the National Institute for Environmental Studies (NIES) Eulerian three-dimensional transport model and the FLEXPART (FLEXible PARTicle dispersion model) Lagrangian particle dispersion model (LPDM) to determine the footprints of short-term variations in XCO2 observed by operational, past, future and possible TCCON sites. We propose a footprint-based method for the collocation of satellite and TCCON XCO2 observations and estimate the performance of the method using the NIES model and five GOSAT (Greenhouse Gases Observing Satellite) XCO2 product data sets. Comparison of the proposed approach with a standard geographic method shows a higher number of collocation points and an average bias reduction up to 0.15 ppm for a subset of 16 stations for the period from January 2010 to January 2014. Case studies of the Darwin and Reunion Island sites reveal that when the footprint area is rather curved, non-uniform and significantly different from a geographical rectangular area, the differences between these approaches are more noticeable. This emphasises that the collocation is sensitive to local meteorological conditions and flux distributions.

Global distributions of atmospheric ammonia (NH3) measured with satellite instruments such as the Infrared Atmospheric Sounding Interferometer (IASI) contain valuable information on NH3 concentrations and variability in regions not yet covered by ground-based instruments. Due to their large spatial coverage and (bi-)daily overpasses, the satellite observations have the potential to increase our knowledge of the distribution of NH3 emissions and associated seasonal cycles. However the observations remain poorly validated, with only a handful of available studies often using only surface measurements without any vertical information. In this study, we present the first validation of the IASI-NH3 product using ground-based Fourier transform infrared spectroscopy (FTIR) observations. Using a recently developed consistent retrieval strategy, NH3 concentration profiles have been retrieved using observations from nine Network for the Detection of Atmospheric Composition Change (NDACC) stations around the world between 2008 and 2015. We demonstrate the importance of strict spatio-temporal collocation criteria for the comparison. Large differences in the regression results are observed for changing intervals of spatial criteria, mostly due to terrain characteristics and the short lifetime of NH3 in the atmosphere. The seasonal variations of both datasets are consistent for most sites. Correlations are found to be high at sites in areas with considerable NH3 levels, whereas correlations are lower at sites with low atmospheric NH3 levels close to the detection limit of the IASI instrument. A combination of the observations from all sites (Nobs = 547) give a mean relative difference of −32.4 ± (56.3) %, a correlation r of 0.8 with a slope of 0.73. These results give an improved estimate of the IASI-NH3 product performance compared to the previous upper-bound estimates (−50 to +100 %).

Extremely high levels of column-averaged dry-air mole fractions of atmospheric methane (XCH4) were detected in August and September 2013 over northeast Asia (∼ 20 ppb above the averaged summertime XCH4 over 2009–2012, after removing a long-term trend), as being retrieved from the Short-Wavelength InfraRed (SWIR) spectral data observed with the Thermal And Near-infrared Sensor for carbon Observation – Fourier Transform Spectrometer (TANSO-FTS) onboard Greenhouse Gases Observing Satellite (GOSAT). Similar enhancements of XCH4 were also observed by the ground-based measurements at two Total Carbon Column Observing Network (TCCON) sites in Japan. The analysis of surface CH4 concentrations observed at three monitoring sites around the Japan archipelago suggest that the extreme increase of XCH4 has occurred in a limited area. The model analysis was conducted to investigate this anomalously high XCH4 event, using an atmospheric transport model. The results indicate that the extreme increase of XCH4 is attributed to the anomalous atmospheric pressure pattern over East Asia during the summer of 2013, which effectively transported the CH4-rich air to Japan from the strong CH4 source areas in east China. The two Japanese TCCON sites, ∼ 1000 km east–west apart each other, coincidentally located along the substantially CH4-rich air flow from east China. This analysis demonstrates the capability of GOSAT to monitor an XCH4 event on a synoptic scale. We anticipate that the synoptic information of XCH4 from GOSAT data contributes to improve our understanding of regional carbon cycle and the regional flux estimation.

Estimates of the natural CO2 flux over Europe inferred from in situ measurements of atmospheric CO2 mole fraction have been used previously to check top-down flux estimates inferred from space-borne dry-air CO2 column (XCO2) retrievals. Several recent studies have shown that CO2 fluxes inferred from XCO2 data from the Japanese Greenhouse gases Observing SATellite (GOSAT) and the Scanning Imaging Absorption Spectrometer for Atmospheric CHartographY (SCIAMACHY) have larger seasonal amplitudes and a more negative annual net CO2 balance than those inferred from the in situ data. The cause of this elevated European uptake of CO2 is still unclear, but some recent studies have suggested that this is a genuine scientific phenomenon. Here, we put forward an alternative hypothesis and show that realistic levels of bias in GOSAT data can result in an erroneous estimate of elevated uptake over Europe. We use a global flux inversion system to examine the relationship between measurement biases and estimates of CO2 uptake from Europe. We establish a reference in situ inversion that uses an Ensemble Kalman Filter (EnKF) to assimilate conventional surface mole fraction observations and XCO2 retrievals from the surface-based Total Carbon Column Observing Network (TCCON). We use the same EnKF system to assimilate two independent versions of GOSAT XCO2 data. We find that the GOSAT-inferred European terrestrial biosphere uptake peaks during the summer, similar to the reference inversion, but the net annual flux is 1.40 ± 0.19 GtC a−1 compared to a value of 0.58 ± 0.14 GtC a−1 for our control inversion that uses only in situ data. To reconcile these two estimates, we perform a series of numerical experiments that assimilate observations with added biases or assimilate synthetic observations for which part or all of the GOSAT XCO2 data are replaced with model data. We find that for our global flux inversions, a large portion (60–90 %) of the elevated European uptake inferred from GOSAT data in 2010 is due to retrievals outside the immediate European region, while the remainder can largely be explained by a sub-ppm retrieval bias over Europe. We use a data assimilation approach to estimate monthly GOSAT XCO2 biases from the joint assimilation of in situ observations and GOSAT XCO2 retrievals. The inferred biases represent an estimate of systematic differences between GOSAT XCO2 retrievals and the inversion system at regional or sub-regional scales. We find that a monthly varying bias of up to 0.5 ppm can explain an overestimate of the annual sink of up to 0.20 GtC a−1. Our results highlight the sensitivity of CO2 flux estimates to regional observation biases, which have not been fully characterized by the current observation network. Without further dedicated measurements we cannot prove or disprove that European ecosystems are taking up a larger-than-expected amount of CO2. More robust inversion systems are also needed to infer consistent fluxes from multiple observation types.

The seasonal cycle accounts for a dominant mode of total column CO2 (XCO2) annual variability and is connected to CO2 uptake and release; it thus represents an important quantity to test the accuracy of the measurements from space. We quantitatively evaluate the XCO2 seasonal cycle of the Greenhouse Gases Observing Satellite (GOSAT) observations from the Atmospheric CO2 Observations from Space (ACOS) retrieval system and compare average regional seasonal cycle features to those directly measured by the Total Carbon Column Observing Network (TCCON). We analyse the mean seasonal cycle amplitude, dates of maximum and minimum XCO2, as well as the regional growth rates in XCO2 through the fitted trend over several years. We find that GOSAT/ACOS captures the seasonal cycle amplitude within 1.0 ppm accuracy compared to TCCON, except in Europe, where the difference exceeds 1.0 ppm at two sites, and the amplitude captured by GOSAT/ACOS is generally shallower compared to TCCON. This bias over Europe is not as large for the other GOSAT retrieval algorithms (NIES v02.21, RemoTeC v2.35, UoL v5.1, and NIES PPDF-S v.02.11), although they have significant biases at other sites. We find that the ACOS bias correction partially explains the shallow amplitude over Europe. The impact of the co-location method and aerosol changes in the ACOS algorithm were also tested and found to be few tenths of a ppm and mostly non-systematic. We find generally good agreement in the date of minimum XCO2 between ACOS and TCCON, but ACOS generally infers a date of maximum XCO2 2–3 weeks later than TCCON. We further analyse the latitudinal dependence of the seasonal cycle amplitude throughout the Northern Hemisphere and compare the dependence to that predicted by current optimized models that assimilate in situ measurements of CO2. In the zonal averages, models are consistent with the GOSAT amplitude to within 1.4 ppm, depending on the model and latitude. We also show that the seasonal cycle of XCO2 depends on longitude especially at the mid-latitudes: the amplitude of GOSAT XCO2 doubles from western USA to East Asia at 45–50° N, which is only partially shown by the models. In general, we find that model-to-model differences can be larger than GOSAT-to-model differences. These results suggest that GOSAT/ACOS retrievals of the XCO2 seasonal cycle may be sufficiently accurate to evaluate land surface models in regions with significant discrepancies between the models.

On 4 June 2011, the Puyehue-Cordón Caulle volcanic complex (40.6° S, 72.1° W) in Chile erupted violently and injected volcanic aerosols into the atmosphere. For the safety of civil aviation, continuous lidar observations were made at Lauder, New Zealand (45.0° S, 169.7° E), from 11 June through 6 July 2011. The purpose of our study is to quantify the influence of the volcanic ejections from large eruptions, and we use the data from the ground-based lidar observation. We analyzed lidar data at a wavelength of 532 nm and derived the backscattering ratio and depolarization ratio profiles. During June and July, within the altitude range of 10–15 km, the volcanic aerosols had high depolarization ratios (20–35%), an indication of non-spherical volcanic ash particles. The time series of the backscattering ratio during continuous observations had three peaks occurring at about 12-day intervals: 26.7 at 11.2 km on 11 June, 18.1 at 12.0 km on 23 June, and 5.3 at 11.1 km on 6 July. The optical depth of the volcanic aerosols was 0.45 on 11 June, when the continuous lidar observation started, 0.31 on 23 June, and 0.12 on 6 July. The depolarization ratio values remained high up to a month after the eruption, and the small wavelength exponent calculated from the backscattering coefficients at 532 nm and 1064 nm suggests that a major constituent of the volcanic aerosols was large, non-spherical particles. The presence of volcanic ash in the stratosphere might affect the error in Greenhouse gases Observing SATellite (GOSAT) XCO2 retrieval using the 1.6 μm band. We briefly discuss the influence of the increased aerosols on GOSAT products.

This study investigates the use of total column CH4 (XCH4) retrievals from the SCIAMACHY satellite instrument for quantifying large-scale emissions of methane. A unique data set from SCIAMACHY is available spanning almost a decade of measurements, covering a period when the global CH4 growth rate showed a marked transition from stable to increasing mixing ratios. The TM5 4DVAR inverse modelling system has been used to infer CH4 emissions from a combination of satellite and surface measurements for the period 2003–2010. In contrast to earlier inverse modelling studies, the SCIAMACHY retrievals have been corrected for systematic errors using the TCCON network of ground-based Fourier transform spectrometers. The aim is to further investigate the role of bias correction of satellite data in inversions. Methods for bias correction are discussed, and the sensitivity of the optimized emissions to alternative bias correction functions is quantified. It is found that the use of SCIAMACHY retrievals in TM5 4DVAR increases the estimated inter-annual variability of large-scale fluxes by 22% compared with the use of only surface observations. The difference in global methane emissions between 2-year periods before and after July 2006 is estimated at 27–35 Tg yr−1. The use of SCIAMACHY retrievals causes a shift in the emissions from the extra-tropics to the tropics of 50 ± 25 Tg yr−1. The large uncertainty in this value arises from the uncertainty in the bias correction functions. Using measurements from the HIPPO and BARCA aircraft campaigns, we show that systematic errors in the SCIAMACHY measurements are a main factor limiting the performance of the inversions. To further constrain tropical emissions of methane using current and future satellite missions, extended validation capabilities in the tropics are of critical importance.

Column-averaged dry air mole fractions of carbon dioxide (XCO2) retrieved from Greenhouse gases Observing SATellite (GOSAT) Short-Wavelength InfraRed (SWIR) observations were validated with aircraft measurements by the Comprehensive Observation Network for TRace gases by AIrLiner (CONTRAIL) project, the National Oceanic and Atmospheric Administration (NOAA), the US Department of Energy (DOE), the National Institute for Environmental Studies (NIES), the HIAPER Pole-to-Pole Observations (HIPPO) program, and the GOSAT validation aircraft observation campaign over Japan. To calculate XCO2 based on aircraft measurements (aircraft-based XCO2), tower measurements and model outputs were used for additional information near the surface and above the tropopause, respectively. Before validation, we investigated the impacts of GOSAT SWIR column averaging kernels (CAKs) and the shape of a priori profiles on the aircraft-based XCO2 calculation. The differences between aircraft-based XCO2 with and without the application of GOSAT CAK were evaluated to be less than ±0.4 ppm at most, and less than ±0.1 ppm on average. Therefore, we concluded that the GOSAT CAK produces only a minor effect on the aircraft-based XCO2 calculation in terms of the overall uncertainty of GOSAT XCO2. We compared GOSAT data retrieved within ±2 or ±5° latitude/longitude boxes centered at each aircraft measurement site to aircraft-based data measured on a GOSAT overpass day. The results indicated that GOSAT XCO2 over land regions agreed with aircraft-based XCO2, except that the former is biased by −0.68 ppm (−0.99 ppm) with a standard deviation of 2.56 ppm (2.51 ppm), whereas the averages of the differences between the GOSAT XCO2 over ocean and the aircraft-based XCO2 were −1.82 ppm (−2.27 ppm) with a standard deviation of 1.04 ppm (1.79 ppm) for ±2° (±5°) boxes.

We present the application of a global carbon cycle modeling system to the estimation of monthly regional CO2 fluxes from the column-averaged mole fractions of CO2 (XCO2) retrieved from spectral observations made by the Greenhouse gases Observing SATellite (GOSAT). The regional flux estimates are to be publicly disseminated as the GOSAT Level 4 data product. The forward modeling components of the system include an atmospheric tracer transport model, an anthropogenic emissions inventory, a terrestrial biosphere exchange model, and an oceanic flux model. The atmospheric tracer transport was simulated using isentropic coordinates in the stratosphere and was tuned to reproduce the age of air. We used a fossil fuel emission inventory based on large point source data and observations of nighttime lights. The terrestrial biospheric model was optimized by fitting model parameters to observed atmospheric CO2 seasonal cycle, net primary production data, and a biomass distribution map. The oceanic surface pCO2 distribution was estimated with a 4-D variational data assimilation system based on reanalyzed ocean currents. Monthly CO2 fluxes of 64 sub-continental regions, between June 2009 and May 2010, were estimated from GOSAT FTS SWIR Level 2 XCO2 retrievals (ver. 02.00) gridded to 5° × 5° cells and averaged on a monthly basis and monthly-mean GLOBALVIEW-CO2 data. Our result indicated that adding the GOSAT XCO2 retrievals to the GLOBALVIEW data in the flux estimation brings changes to fluxes of tropics and other remote regions where the surface-based data are sparse. The uncertainties of these remote fluxes were reduced by as much as 60% through such addition. Optimized fluxes estimated for many of these regions, were brought closer to the prior fluxes by the addition of the GOSAT retrievals. In most of the regions and seasons considered here, the estimated fluxes fell within the range of natural flux variabilities estimated with the component models.

No abstract available.

Atmospheric column-averaged mole fractions of carbon dioxide (XCO2) at 53 locations around the world were derived from aircraft measurements covering the altitude range of about 1–10 km. We used CO2 vertical profile measurements from three major carbon cycle programs, a global climatological data set of air number density profiles and tropopause height for calculating XCO2 for the period of 2007–2009. Vertical profiles of the CO2 mixing ratio are complemented by tall tower data up to 400 m from the earth's surface and by simulated profiles in the stratosphere from a chemistry-transport model. The amplitude of the seasonal cycle of calculated XCO2 values shows clear latitudinal dependence, and the amplitude decreases from about 10 ppm at high latitudes in the Northern Hemisphere to at most 2 ppm in the tropics and the Southern Hemisphere. The uncertainties of XCO2 were estimated from assumptions about CO2 profiles for each flight. Typically, uncertainties were less than 1 ppm; thus, this data set is within the level of uncertainty needed for primary validation of XCO2 measurements by the Greenhouse gases Observing SATellite (GOSAT) and by future satellite missions for monitoring greenhouse gases.

We have developed an improved version of the National Institute for Environmental Studies (NIES) three-dimensional chemical transport model (TM) designed for accurate tracer transport simulations in the stratosphere, using a hybrid sigma-isentropic (σ-θ) vertical coordinate that employs both terrain-following and isentropic parts switched smoothly around the tropopause. The air-ascending rate was derived from the effective heating rate and was used to simulate vertical motion in the isentropic part of the grid (above level 350 K), which was adjusted to fit to the observed age of the air in the stratosphere. Multi-annual simulations were conducted using the NIES TM to evaluate vertical profiles and dry-air column-averaged mole fractions of CO2 and CH4. Comparisons with balloon-borne observations over Sanriku (Japan) in 2000–2007 revealed that the tracer transport simulations in the upper troposphere and lower stratosphere are performed with accuracies of ~5% for CH4 and SF6, and ~1% for CO2 compared with the observed volume-mixing ratios. The simulated column-averaged dry air mole fractions of atmospheric carbon dioxide (XCO2) and methane (XCH4) were evaluated against daily ground-based high-resolution Fourier Transform Spectrometer (FTS) observations measured at twelve sites of the Total Carbon Column Observing Network (TCCON) (Bialystok, Bremen, Darwin, Garmisch, Izaña, Lamont, Lauder, Orleans, Park Falls, Sodankylä, Tsukuba, and Wollongong) between January 2009 and January 2011. The comparison shows the model's ability to reproduce the site-dependent seasonal cycles as observed by TCCON, with correlation coefficients typically on the order 0.8–0.9 and 0.4–0.8 for XCO2 and XCH4, respectively, and mean model biases of ±0.2% and ±0.5%, excluding Sodankylä, where strong biases are found. The ability of the model to capture the tracer total column mole fractions is strongly dependent on the model's ability to reproduce seasonal variations in tracer concentrations in the planetary boundary layer (PBL). We found a marked difference in the model's ability to reproduce near-surface concentrations at sites located some distance from multiple emission sources and where high emissions play a notable role in the tracer's budget. Comparisons with aircraft observations over Surgut (West Siberia), in an area with high emissions of methane from wetlands, show contrasting model performance in the PBL and in the free troposphere. Thus, the PBL is another critical region for simulating the tracer total column mole fractions.

An increase in stratospheric aerosols caused by the volcanic eruption of Mt. Nabro (13.37° N, 41.70° E) on 12 June 2011 was detected by lidar at Tsukuba (36.05° N, 140.13° E) and Saga (33.24° N, 130.29° E) in Japan. The maximum backscattering ratios at a wavelength of 532 nm were 2.0 at 17.0 km on 10 July 2011 at Tsukuba and 3.6 at 18.2 km on 23 June 2011 at Saga. The maximum integrated backscattering coefficients (IBCs) at 532 nm above the first tropopause height were 4.18×10−4 sr−1 on 11 February 2012 at Tsukuba and 4.19×10−4 sr−1 on 23 June 2011 at Saga, respectively. A time series of lidar observational results at Tsukuba have also been reported from January 2008 through May 2012. Increases in stratospheric aerosols were observed after the volcanic eruptions of Mt. Kasatochi (52.18° N, 175.51° E) in August 2008 and Mt. Sarychev Peak (48.09° N, 153.20° E) in June 2009. The yearly averaged IBCs at Tsukuba were 2.54×10−4 sr−1, 2.48×10−4 sr−1, 2.45×10−4 sr−1, and 2.20×10−4 sr−1 for 2008, 2009, 2010, and 2011, respectively. These values were about twice the IBC background level (1.21×10−4 sr−1) from 1997 to 2001 at Tsukuba. We briefly discuss the influence of the increased aerosols on climate and the implications for analysis of satellite data.

We present a comparison of an atmospheric general circulation model (AGCM)-based chemistry-transport model (ACTM) simulation with total column measurements of CO2, CH4 and N2O from the Total Carbon Column Observing Network (TCCON). The model is able to capture observed trends, seasonal cycles and inter hemispheric gradients at most sampled locations for all three species. The model-observation agreements are best for CO2, because the simulation uses fossil fuel inventories and an inverse model estimate of non-fossil fuel fluxes. The ACTM captures much of the observed seasonal variability in CO2 and N2O total columns (~81 % variance, R>0.9 between ACTM and TCCON for 19 out of 22 cases). These results suggest that the transport processes in troposphere and stratosphere are well represented in ACTM. Thus the poor correlation between simulated and observed CH4 total columns, particularly at tropical and extra-tropical sites, have been attributed to the uncertainties in surface emissions and loss by hydroxyl radicals. While the upward-looking total column measurements of CO2 contains surface flux signals at various spatial and temporal scales, the N2O measurements are strongly affected by the concentration variations in the upper troposphere and stratosphere.

Lidar observations of vertical profiles of aerosols and thin cirrus clouds were made at Tsukuba (36.05° N, 140.12° E), Japan, to investigate the influence of aerosols and thin cirrus clouds on the column-averaged dry-air mole fraction of carbon dioxide (XCO2) retrieved from observation data of the Thermal And Near-infrared Sensor for carbon Observation Fourier Transform Spectrometer, measured in the Short-Wavelength InfraRed band (TANSO-FTS SWIR), onboard the Greenhouse gases Observing SATellite (GOSAT). The lidar system measured the backscattering ratio, depolarization ratio, and/or the wavelength exponent of atmospheric particles. The lidar observations and ground-based high-resolution FTS measurements at the Tsukuba Total Carbon Column Observing Network (Tsukuba TCCON) site were recorded simultaneously during passages of GOSAT over Tsukuba. GOSAT SWIR XCO2 data (Version 01.xx) released in August 2010 were compared with the lidar and Tsukuba TCCON data. High-altitude aerosols and thin cirrus clouds had a large impact on the GOSAT SWIR XCO2 results. By taking into account the observed aerosol/cirrus vertical profiles and using a more adequate solar irradiance database in the GOSAT SWIR retrieval, the difference between the GOSAT SWIR XCO2 data and the Tsukuba TCCON data was reduced. The 3-band retrieval approach where the aerosol and cirrus profiles were retrieved gave us the best results and the retrieved XCO2 data followed the seasonal cycle of ~8 ppm observed at Tsukuba TCCON site.

Time series of total column abundances of hydrogen chloride (HCl), chlorine nitrate (ClONO2), and hydrogen fluoride (HF) were determined from ground-based Fourier transform infrared (FTIR) spectra recorded at 17 sites belonging to the Network for the Detection of Atmospheric Composition Change (NDACC) and located between 80.05° N and 77.82° S. By providing such a near-global overview on ground-based measurements of the two major stratospheric chlorine reservoir species, HCl and ClONO2, the present study is able to confirm the decrease of the atmospheric inorganic chlorine abundance during the last few years. This decrease is expected following the 1987 Montreal Protocol and its amendments and adjustments, where restrictions and a subsequent phase-out of the prominent anthropogenic chlorine source gases (solvents, chlorofluorocarbons) were agreed upon to enable a stabilisation and recovery of the stratospheric ozone layer. The atmospheric fluorine content is expected to be influenced by the Montreal Protocol, too, because most of the banned anthropogenic gases also represent important fluorine sources. But many of the substitutes to the banned gases also contain fluorine so that the HF total column abundance is expected to have continued to increase during the last few years. The measurements are compared with calculations from five different models: the two-dimensional Bremen model, the two chemistry-transport models KASIMA and SLIMCAT, and the two chemistry-climate models EMAC and SOCOL. Thereby, the ability of the models to reproduce the absolute total column amounts, the seasonal cycles, and the temporal evolution found in the FTIR measurements is investigated and inter-compared. This is especially interesting because the models have different architectures. The overall agreement between the measurements and models for the total column abundances and the seasonal cycles is good. Linear trends of HCl, ClONO2, and HF are calculated from both measurement and model time series data, with a focus on the time range 2000–2009. This period is chosen because from most of the measurement sites taking part in this study, data are available during these years. The precision of the trends is estimated with the bootstrap resampling method. The sensitivity of the trend results with respect to the fitting function, the time of year chosen and time series length is investigated, as well as a bias due to the irregular sampling of the measurements. The measurements and model results investigated here agree qualitatively on a decrease of the chlorine species by around 1% yr−1. The models simulate an increase of HF of around 1% yr−1. This also agrees well with most of the measurements, but some of the FTIR series in the Northern Hemisphere show a stabilisation or even a decrease in the last few years. In general, for all three gases, the measured trends vary more strongly with latitude and hemisphere than the modelled trends. Relative to the FTIR measurements, the models tend to underestimate the decreasing chlorine trends and to overestimate the fluorine increase in the Northern Hemisphere. At most sites, the models simulate a stronger decrease of ClONO2 than of HCl. In the FTIR measurements, this difference between the trends of HCl and ClONO2 depends strongly on latitude, especially in the Northern Hemisphere.

We describe a method of evaluating systematic errors in measurements of total column dry-air mole fractions of CO2 (XCO2) from space, and we illustrate the method by applying it to the v2.8 Atmospheric CO2 Observations from Space retrievals of the Greenhouse Gases Observing Satellite (ACOS-GOSAT) measurements over land. The approach exploits the lack of large gradients in XCO2 south of 25° S to identify large-scale offsets and other biases in the ACOS-GOSAT data with several retrieval parameters and errors in instrument calibration. We demonstrate the effectiveness of the method by comparing the ACOS-GOSAT data in the Northern Hemisphere with ground truth provided by the Total Carbon Column Observing Network (TCCON). We use the observed correlation between free-tropospheric potential temperature and XCO2 in the Northern Hemisphere to define a dynamically informed coincidence criterion between the ground-based TCCON measurements and the ACOS-GOSAT measurements. We illustrate that this approach provides larger sample sizes, hence giving a more robust comparison than one that simply uses time, latitude and longitude criteria. Our results show that the agreement with the TCCON data improves after accounting for the systematic errors, but that extrapolation to conditions found outside the region south of 25° S may be problematic (e.g., high airmasses, large surface pressure biases, M-gain, measurements made over ocean). A preliminary evaluation of the improved v2.9 ACOS-GOSAT data is also discussed.

Column-averaged volume mixing ratios of carbon dioxide (XCO2) during the period from January 2007 to May 2008 over Tsukuba, Japan, were derived using CO2 concentrations measured by Continuous CO2 Measuring Equipment (CME). The CMEs were installed on Japan Airlines Corporation (JAL) commercial airliners, which frequently fly to and from Narita Airport. It was assumed that CO2 profiles over Tsukuba and Narita are the same. CO2 profile data for 493 flights on clear-sky days were analyzed in order to calculate XCO2 with one of two ancillary datasets: "Tsukuba observational" data (rawinsonde and meteorological tower), or "global" forecast/reanalysis and climatological data (NCEP and CIRA-86). The amplitude of the seasonal variation of XCO2 using the ancillary data measured in Tsukuba (XCO2 (Tsukuba observational)) was determined by a least squares fit using a harmonic function to roughly evaluate the seasonal variation over Tsukuba. The highest and lowest values of the obtained fitted curve in 2007 for XCO2 (Tsukuba observational) were 386.4 ± 1.0 and 381.7 ± 1.0 ppm in May and September, respectively, where the errors represent 1 standard deviation of the fit residuals. The dependence of XCO2 on the type of ancillary dataset was evaluated. The average difference between XCO2 from global climatological data, XCO2 (global), and XCO2 (Tsukuba observational), i.e., the bias of XCO2 (global) based on XCO2 (Tsukuba observational), was found to be −0.621 ppm with a standard deviation of 0.682 ppm. The uncertainty of XCO2 (global) based on XCO2 (Tsukuba observational) was estimated to be 0.922 ppm. This small uncertainty relative to the GOSAT precision suggests that calculating XCO2 using data from airliners and global climatological data can be applied to the validation of GOSAT products for XCO2 over airports worldwide.

No abstract available.