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The primary instrument on the Greenhouse gases Observing SATellite (GOSAT) is the Thermal And Near infrared Sensor for carbon Observations (TANSO) Fourier transform spectrometer (FTS). TANSO-FTS uses three short-wave infrared (SWIR) bands to retrieve total columns of CO2 and CH4 along its optical line of sight and one thermal infrared (TIR) channel to retrieve vertical profiles of CO2 and CH4 volume mixing ratios (VMRs) in the troposphere. We examine version 1 of the TANSO-FTS TIR CH4 product by comparing co-located CH4 VMR vertical profiles from two other remote-sensing FTS systems: the Canadian Space Agency's Atmospheric Chemistry Experiment FTS (ACE-FTS) on SCISAT (version 3.5) and the European Space Agency's Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) on Envisat (ESA ML2PP version 6 and IMK-IAA reduced-resolution version V5R-CH4-224/225), as well as 16 ground stations with the Network for the Detection of Atmospheric Composition Change (NDACC). This work follows an initial inter-comparison study over the Arctic, which incorporated a ground-based FTS at the Polar Environment Atmospheric Research Laboratory (PEARL) at Eureka, Canada, and focuses on tropospheric and lower-stratospheric measurements made at middle and tropical latitudes between 2009 and 2013 (mid-2012 for MIPAS). For comparison, vertical profiles from all instruments are interpolated onto a common pressure grid, and smoothing is applied to ACE-FTS, MIPAS, and NDACC vertical profiles. Smoothing is needed to account for differences between the vertical resolution of each instrument and differences in the dependence on a priori profiles. The smoothing operators use the TANSO-FTS a priori and averaging kernels in all cases. We present zonally averaged mean CH4 differences between each instrument and TANSO-FTS with and without smoothing, and we examine their information content, their sensitive altitude range, their correlation, their a priori dependence, and the variability within each data set. Partial columns are calculated from the VMR vertical profiles, and their correlations are examined. We find that the TANSO-FTS vertical profiles agree with the ACE-FTS and both MIPAS retrievals' vertical profiles within 4% (± ~40ppbv) below 15km when smoothing is applied to the profiles from instruments with finer vertical resolution but that the relative differences can increase to on the order of 25% when no smoothing is applied. Computed partial columns are tightly correlated for each pair of data sets. We investigate whether the difference between TANSO-FTS and other CH4 VMR data products varies with latitude. Our study reveals a small dependence of around 0.1% per 10 degrees latitude, with smaller differences over the tropics and greater differences towards the poles.

Ground-based Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) measurements of nitrous acid (HONO) and its precursor NO2 (nitrogen dioxide) as well as aerosols have been performed daily in Beijing city centre (39.98° N, 116.38° E) from July 2008 to April 2009 and at the suburban site of Xianghe (39.75° N, 116.96° E) located ~60 km east of Beijing from March 2010 to December 2012. This extensive dataset allowed for the first time the investigation of the seasonal cycle of HONO as well as its diurnal variation in and in the vicinity of a megacity. Our study was focused on the HONO and NO2 near-surface concentrations (0–200 m layer) and total vertical column densities (VCDs) and also aerosol optical depths (AODs) and extinction coefficients retrieved by applying the Optimal Estimation Method to the MAX-DOAS observations. Monthly averaged HONO near-surface concentrations at local noon display a strong seasonal cycle with a maximum in late fall/winter (~0.8 and 0.7 ppb at Beijing and Xianghe, respectively) and a minimum in summer (~0.1 ppb at Beijing and 0.03 ppb at Xianghe). The seasonal cycles of HONO and NO2 appear to be highly correlated, with correlation coefficients in the 0.7–0.9 and 0.5–0.8 ranges at Beijing and Xianghe, respectively. The stronger correlation of HONO with NO2 and also with aerosols observed in Beijing suggests possibly larger role of NO2 conversion into HONO in the Beijing city center than at Xianghe. The observed diurnal cycle of HONO near-surface concentration shows a maximum in the early morning (about 1 ppb at both sites) likely resulting from night-time accumulation, followed by a decrease to values of about 0.1–0.4 ppb around local noon. The HONO / NO2 ratio shows a similar pattern with a maximum in the early morning (values up to 0.08) and a decrease to ~0.01–0.02 around local noon. The seasonal and diurnal cycles of the HONO near-surface concentration are found to be similar in shape and in relative amplitude to the corresponding cycles of the HONO total VCD and are therefore likely driven mainly by the balance between HONO sources and the photolytic sink, whereas dilution effects appear to play only a minor role. The estimation of OH radical production from HONO and O3 photolysis based on retrieved HONO near-surface concentrations and calculated photolysis rates indicate that in the 0–200 m altitude range, HONO is by far the largest source of OH radicals in winter as well as in the early morning at all seasons, while the contribution of O3 dominates in summer from mid-morning until mid-afternoon.

The variability of stratospheric chemical composition occurs on a broad spectrum of timescales, ranging from day to decades. A large part of the variability appears to be driven by external forcings such as volcanic aerosols, solar activity, halogen loading, levels of greenhouse gases (GHG), and modes of climate variability (quasi-biennial oscillation (QBO), El Niño-Southern Oscillation (ENSO)). We estimate the contributions of different external forcings to the interannual variability of stratospheric chemical composition and evaluate how well 3-D chemistry-climate models (CCMs) can reproduce the observed response-forcing relationships. We carry out multivariate regression analyses on long time series of observed and simulated time series of several traces gases in order to estimate the contributions of individual forcings and unforced variability to their internannual variability. The observations are typically decadal time series of ground-based data from the international Network for the Detection of Atmospheric Composition Change (NDACC) and the CCM simulations are taken from the CCMVal-2 REF-B1 simulations database. The chemical species considered are column O3, HCl, NO2, and N2O. We check the consistency between observations and model simulations in terms of the forced and internal components of the total interannual variability (externally forced variability and internal variability) and identify the driving factors in the interannual variations of stratospheric chemical composition over NDACC measurement sites. Overall, there is a reasonably good agreement between regression results from models and observations regarding the externally forced interannual variability. A much larger fraction of the observed and modelled interannual variability is explained by external forcings in the tropics than in the extratropics, notably in polar regions. CCMs are able to reproduce the amplitudes of responses in chemical composition to specific external forcings. However, CCMs tend to underestimate very substantially the internal variability and hence the total interannual variability for almost all species considered. This lack of internal variability in CCMs might partly originate from the surface forcing of these CCMs by analysed SSTs. The results illustrate the potential of NDACC ground-based observations for evaluating CCMs.

The trend in stratospheric NO2 column at the NDACC (Network for the Detection of Atmospheric Composition Change) station of Jungfraujoch (46.5° N, 8.0° E) is assessed using ground-based FTIR and zenith-scattered visible sunlight SAOZ measurements over the period 1990 to 2009 as well as a composite satellite nadir data set constructed from ERS-2/GOME, ENVISAT/SCIAMACHY, and METOP-A/GOME-2 observations over the 1996–2009 period. To calculate the trends, a linear least squares regression model including explanatory variables for a linear trend, the mean annual cycle, the quasi-biennial oscillation (QBO), solar activity, and stratospheric aerosol loading is used. For the 1990–2009 period, statistically indistinguishable trends of −3.7 ± 1.1% decade−1 and −3.6 ± 0.9% decade−1 are derived for the SAOZ and FTIR NO2 column time series, respectively. SAOZ, FTIR, and satellite nadir data sets show a similar decrease over the 1996–2009 period, with trends of −2.4 ± 1.1% decade−1, −4.3 ± 1.4% decade−1, and −3.6 ± 2.2% decade−1, respectively. The fact that these declines are opposite in sign to the globally observed +2.5% decade−1 trend in N2O, suggests that factors other than N2O are driving the evolution of stratospheric NO2 at northern mid-latitudes. Possible causes of the decrease in stratospheric NO2 columns have been investigated. The most likely cause is a change in the NO2/NO partitioning in favor of NO, due to a possible stratospheric cooling and a decrease in stratospheric chlorine content, the latter being further confirmed by the negative trend in the ClONO2 column derived from FTIR observations at Jungfraujoch. Decreasing ClO concentrations slows the NO + ClO → NO2 + Cl reaction and a stratospheric cooling slows the NO + O3 → NO2 + O2 reaction, leaving more NOx in the form of NO. The slightly positive trends in ozone estimated from ground- and satellite-based data sets are also consistent with the decrease of NO2 through the NO2 + O3 → NO3 + O2 reaction. Finally, we cannot rule out the possibility that a strengthening of the Dobson-Brewer circulation, which reduces the time available for N2O photolysis in the stratosphere, could also contribute to the observed decline in stratospheric NO2 above Jungfraujoch.

Model simulations of column-averaged methane mixing ratios (XCH4) are extensively used for inverse estimates of methane (CH4) emissions from atmospheric measurements. Our study shows that virtually all chemical transport models (CTM) used for this purpose are affected by stratospheric model-transport errors. We quantify the impact of such model transport errors on the simulation of stratospheric CH4 concentrations via an a posteriori correction method. This approach compares measurements of the mean age of air with modeled age and expresses the difference in terms of a correction to modeled stratospheric CH4 mixing ratios. We find age differences up to ~ 3 years yield to a bias in simulated CH4 of up to 250 parts per billion (ppb). Comparisons between model simulations and ground-based XCH4 observations from the Total Carbon Column Network (TCCON) reveal that stratospheric model-transport errors cause biases in XCH4 of ~ 20 ppb in the midlatitudes and ~ 27 ppb in the arctic region. Improved overall as well as seasonal model-observation agreement in XCH4 suggests that the proposed, age-of-air-based stratospheric correction is reasonable. The latitudinal model bias in XCH4 is supposed to reduce the accuracy of inverse estimates using satellite-derived XCH4 data. Therefore, we provide an estimate of the impact of stratospheric model-transport errors in terms of CH4 flux errors. Using a one-box approximation, we show that average model errors in stratospheric transport correspond to an overestimation of CH4 emissions by ~ 40 % (~ 7 Tg yr−1) for the arctic, ~ 5 % (~ 7 Tg yr−1) for the northern, and ~ 60 % (~ 7 Tg yr−1) for the southern hemispheric mid-latitude region. We conclude that an improved modeling of stratospheric transport is highly desirable for the joint use with atmospheric XCH4 observations in atmospheric inversions.

Trends in the CO and C2H6 partial columns (~0–15 km) have been estimated from four European groundbasedsolar FTIR (Fourier Transform InfraRed) stations for the 1996–2006 time period. The CO trends from the four stations Jungfraujoch, Zugspitze, Harestua and Kiruna have been estimated to −0.45±0.16%yr−1, −1.00 ± 0.24%yr−1, −0.62±0.19%yr−1 and −0.61±0.16%yr−1, respectively. The corresponding trends for C2H6 are−1.51±0.23%yr−1, −2.11±0.30%yr−1, −1.09±0.25%yr−1 and −1.14±0.18%yr−1. All trends are presented with their 2-σ confidence intervals. To find possible reasons for the CO trends, the global-scale EMEP MSC-W chemical transport model has been used in a series of sensitivity scenarios. It is shown that the trends are consistent with the combination of a 20% decrease in the anthropogenic CO emissions seen in Europe and North America during the 1996–2006 period and a 20% increase in the anthropogenic CO emissions in East Asia, during the same time period. The possible impacts of CH4 and biogenic volatile organic compounds (BVOCs) are also considered. The European and global-scale EMEP models have been evaluated against the measured CO and C2H6 partial columns from Jungfraujoch, Zugspitze, Bremen, Harestua, Kiruna and Ny-Ålesund. The European model reproduces, on average the measurements at the different sites fairly well and within 10–22% deviation for CO and 14–31% deviation for C2H6. Their seasonal amplitude is captured within 6–35% and 9–124% for CO and C2H6, respectively. However, 61–98% of the CO and C2H6 partial columns in the European model are shown to arise from the boundary conditions, making the globalscale model a more suitable alternative when modeling these two species. In the evaluation of the global model the average partial columns for 2006 are shown to be within 1–9% and 37–50% of the measurements for CO and C2H6, respectively. The global model sensitivity for assumptions made in this paper is also analyzed.

We report on airborne Differential Optical Absorption Spectroscopy (DOAS) measurements of aerosol extinction and NO2 tropospheric profiles performed off the North coast of Norway in April 2008. The DOAS instrument was installed on the Safire ATR-42 aircraft during the POLARCAT-France spring campaign and recorded scattered light spectra in near-limb geometry using a scanning telescope. We use O4 slant column measurements to derive the aerosol extinction at 360 nm. Regularization is based on the maximum a posteriori solution, for which we compare a linear and a logarithmic approach. The latter inherently constrains the solution to positive values and yields aerosol extinction profiles more consistent with independently measured size distributions. We present results from two soundings performed on 8 April 2008 above 71° N, 22° E and on 9 April 2008 above 70° N, 17.8° E. The first profile shows aerosol extinction and NO2 in the marine boundary layer with respective values of 0.04 ± 0.005 km−1 and 1.9 ± 0.3 × 109 molec cm−3. A second extinction layer of 0.01 ± 0.003 km−1 is found at 4 km altitude where the NO2 concentration is 0.32 ± 0.2 × 109 molec cm−3. During the second sounding, clouds prevent retrieval of profile parts under 3 km altitude but a layer with enhanced extinction (0.025 ± 0.005 km−1) and NO2 (1.95 ± 0.2 × 109 molec cm−3) is clearly detected at 4 km altitude. From CO and ozone in-situ measurements complemented by back-trajectories, we interpret the measurements in the free troposphere as, for the first sounding, a mix between stratospheric and polluted air from Northern Europe and for the second sounding, polluted air from Central Europe containing NO2. Considering the boundary layer measurements of the first flight, modeled source regions indicate closer sources, especially the Kola Peninsula smelters, which can explain the NO2 enhancement not correlated with a CO increase at the same altitude.

International audience; It is indispensable to monitor the atmospheric composition and its variability and trends, in order to identify and understand its long-term changes and evaluate the possible impacts on our environmental conditions. This monitoring of the Earth atmosphere on a global scale is the major objective of the European project GEOmon (http://www.geomon.eu). GEOmon includes a study of Stratospheric Ozone and Climate, including 64 ground-based instruments at 32 stations world-wide that make regular observations of stratospheric ozone, nitrogen dioxide, bromine oxide, halogenated reservoir species, water vapour, temperature, aerosol and polar stratospheric clouds. The resulting preliminary data are submitted rapidly to the GEOmon data center where they are publicly accessible. Most of the instruments are contributing to NDACC and have historical time series in the NDACC archive. Hence quality-controlled data are mostly archived at the NDACC Data Handling Facility (ftp://ftp.cpc.ncep.noaa.gov/ndacc/) Some time series are also being revised to improve the homogeneity of the network, and to enable long-term trend studies and synergistic use with satellite data. The latter aspect requires a good characterisation of the information content of the ground-based and satellite data, work that is also ongoing in this GEOmon activity. This presentation will give an overview of the data set and highlight some recent results concerning long-term time series and trends, and their implications for stratospheric ozone recovery and the connections with climate parameters like the Brewer Dobson circulation.

Part of the abstract: The Michelson Interferometer for Passive AtmosphericSounding (MIPAS), on-board the European ENVIronmentalSATellite (ENVISAT) launched on 1 March 2002,is a middle infrared Fourier Transform spectrometer measuringthe atmospheric emission spectrum in limb sounding geometry.The instrument is capable to retrieve the vertical distributionMIPAS data were re-processed by ESA using updated versions ofthe Instrument Processing Facility (IPF v4.61 and v4.62) andprovided a complete set of level-2 operational products (geolocatedvertical profiles of temperature and volume mixingratio of H2O, O3, HNO3, CH4, N2O and NO2). MIPAS operated in its standard observation mode for approximately two years, from July 2002 to March 2004. MIPAS data were re-processed by ESA using updated versions of the Instrument Processing Facility (IPF v4.61 and v4.62) and provided a complete set of level-2 operational products (geolocated vertical profiles of temperature and volume mixing ratio of H2O, O3, HNO3, CH4, N2O and NO2). MIPAS operated in its standard observation mode from July 2002 to March 2004, covering the altitude range from the mesosphere to the upper troposphere with relatively high vertical resolution (about 3 km in the stratosphere). In this paper, we report a detailed description of the validation of MIPAS-ENVISAT operational ozone data, that was based on the comparison between MIPAS v4.61 (and, to a lesser extent, v4.62) O3 VMR profilesand a comprehensive set of correlative data, including observations from ozone sondes, ground-based lidar, FTIR and microwave radiometers, remote-sensing and in situ instruments on-board stratospheric aircraft and balloons, concurrent satellite sensors and ozone fields assimilated by theEuropean Center for Medium-range Weather Forecasting. A clear indication of the validity of MIPAS O3 vertical profiles is obtained for most of the stratosphere, where the mean relative difference with the individual correlative data sets is always lower than ±10%. Furthermore, these differences always fall within the combined systematic error (from1 hPa to 50 hPa) and the standard deviation is fully consistent with the random error of the comparison (from 1 hPa to 3040 hPa).

Total column amounts of CO, CH4, CO2 and N2O retrieved from SCIAMACHY nadir observations in ist near-infrared channels have been compared to data from a ground-based quasi-global network of Fourier-transform infrared (FTIR) spectrometers. The SCIAMACHY data considered here have been produced by three different retrieval algorithms, WFM-DOAS (version 0.5 for CO and CH4 and version 0.4 for CO2 and N2O), IMAP-DOAS (version 1.1 and 0.9 (for CO)) and IMLM (version 6.3) and cover the January to December 2003 time period. Comparisons have been made for individual data, as well as for monthly averages. To maximize the number of reliable coincidences that satisfy the temporal and spatial collocation criteria, the SCIAMACHY data have been compared with a temporal 3rd order polynomial interpolation of the ground-based data. Particular attention has been given to the question whether SCIAMACHY observes correctly the seasonal and latitudinal variability of the target species. The present results indicate that the individual SCIAMACHY data obtained with the actual versions of the algorithms have been significantly improved, but that the quality requirements, for estimating emissions on regional scales, are not yet met. Nevertheless, possible directions for further algorithm upgrades have been identified which should result in more reliable data products in a near future.

Consistent validation of satellite CO2 estimates is a prerequisite for using multiple satellite CO2 measurements for joint flux inversion, and for establishing an accurate long-term atmospheric CO2 data record. Harmonizing satellite CO2 measurements is particularly important since the differences in instruments, observing geometries, sampling strategies, etc. imbue different measurement characteristics in the various satellite CO2 data products. We focus on validating model and satellite observation attributes that impact flux estimates and CO2 assimilation, including accurate error estimates, correlated and random errors, overall biases, biases by season and latitude, the impact of coincidence criteria, validation of seasonal cycle phase and amplitude, yearly growth, and daily variability. We evaluate dry-air mole fraction (XCO2) for Greenhouse gases Observing SATellite (GOSAT) (Atmospheric CO2 Observations from Space, ACOS b3.5) and SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) (Bremen Optimal Estimation DOAS, BESD v2.00.08) as well as the CarbonTracker (CT2013b) simulated CO2 mole fraction fields and the Monitoring Atmospheric Composition and Climate (MACC) CO2 inversion system (v13.1) and compare these to Total Carbon Column Observing Network (TCCON) observations (GGG2012/2014). We find standard deviations of 0.9, 0.9, 1.7, and 2.1 ppm vs. TCCON for CT2013b, MACC, GOSAT, and SCIAMACHY, respectively, with the single observation errors 1.9 and 0.9 times the predicted errors for GOSAT and SCIAMACHY, respectively. We quantify how satellite error drops with data averaging by interpreting according to error2 = a2 + b2/n (with n being the number of observations averaged, a the systematic (correlated) errors, and b the random (uncorrelated) errors). a and b are estimated by satellites, coincidence criteria, and hemisphere. Biases at individual stations have year-to-year variability of ∼ 0.3 ppm, with biases larger than the TCCON-predicted bias uncertainty of 0.4 ppm at many stations. We find that GOSAT and CT2013b underpredict the seasonal cycle amplitude in the Northern Hemisphere (NH) between 46 and 53° N, MACC overpredicts between 26 and 37° N, and CT2013b underpredicts the seasonal cycle amplitude in the Southern Hemisphere (SH). The seasonal cycle phase indicates whether a data set or model lags another data set in time. We find that the GOSAT measurements improve the seasonal cycle phase substantially over the prior while SCIAMACHY measurements improve the phase significantly for just two of seven sites. The models reproduce the measured seasonal cycle phase well except for at Lauder_125HR (CT2013b) and Darwin (MACC). We compare the variability within 1 day between TCCON and models in JJA; there is correlation between 0.2 and 0.8 in the NH, with models showing 10–50 % the variability of TCCON at different stations and CT2013b showing more variability than MACC. This paper highlights findings that provide inputs to estimate flux errors in model assimilations, and places where models and satellites need further investigation, e.g., the SH for models and 45–67° N for GOSAT and CT2013b.

This paper presents extensive {bias determination} analyses of ozone observations from the Atmospheric Chemistry Experiment (ACE) satellite instruments: the ACE Fourier Transform Spectrometer (ACE-FTS) and the Measurement of Aerosol Extinction in the Stratosphere and Troposphere Retrieved by Occultation (ACE-MAESTRO) instrument. Here we compare the latest ozone data products from ACE-FTS and ACE-MAESTRO with coincident observations from nearly 20 satellite-borne, airborne, balloon-borne and ground-based instruments, by analysing volume mixing ratio profiles and partial column densities. The ACE-FTS version 2.2 Ozone Update product reports more ozone than most correlative measurements from the upper troposphere to the lower mesosphere. At altitude levels from 16 to 44 km, the average values of the mean relative differences are nearly all within +1 to +8%. At higher altitudes (45–60 km), the ACE-FTS ozone amounts are significantly larger than those of the comparison instruments, with mean relative differences of up to +40% (about +20% on average). For the ACE-MAESTRO version 1.2 ozone data product, mean relative differences are within ±10% (average values within ±6%) between 18 and 40 km for both the sunrise and sunset measurements. At higher altitudes (~35–55 km), systematic biases of opposite sign are found between the ACE-MAESTRO sunrise and sunset observations. While ozone amounts derived from the ACE-MAESTRO sunrise occultation data are often smaller than the coincident observations (with mean relative differences down to −10%), the sunset occultation profiles for ACE-MAESTRO show results that are qualitatively similar to ACE-FTS, indicating a large positive bias (mean relative differences within +10 to +30%) in the 45–55 km altitude range. In contrast, there is no significant systematic difference in bias found for the ACE-FTS sunrise and sunset measurements.