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The Total Carbon Column Observing Network (TCCON) measures column-average mole fractions of several greenhouse gases (GHGs), beginning in 2004, from over 30 current or past measurement sites around the world using solar absorption spectroscopy in the near-infrared (near-IR) region. TCCON GHG data have been used extensively for multiple purposes, including in studies of the carbon cycle and anthropogenic emissions, as well as to validate and improve observations from space-based sensors. Here, we describe an update to the retrieval algorithm used to process the TCCON near-IR solar spectra and to generate the associated data products. This version, called GGG2020, was initially released in April 2022. It includes updates and improvements to all steps of the retrieval, including but not limited to the conversion of the original interferograms into spectra, the spectroscopic information used in the column retrieval, post hoc air mass dependence correction, and scaling to align with the calibration scales of in situ GHG measurements. All TCCON data are available through https://tccondata.org/ (last access: 22 April 2024) and are hosted on CaltechDATA (https://data.caltech.edu/, last access: 22 April 2024). Each TCCON site has a unique DOI for its data record. An archive of all the sites' data is also available with the DOI https://doi.org/10.14291/TCCON.GGG2020 (Total Carbon Column Observing Network (TCCON) Team, 2022). The hosted files are updated approximately monthly, and TCCON sites are required to deliver data to the archive no later than 1 year after acquisition. Full details of data locations are provided in the “Code and data availability” section.

Observations of nitrogen dioxide (NO2) and nitrogen oxide (NO) in the stratosphere are relevant to understand long-term changes and variabilities in stratospheric nitrogen oxide (NOx) and ozone (O3) concentrations. Due to the versatile role of NO2 and NO in stratospheric O3 photochemistry, they are important for recovery and build-up of O3 holes in the stratosphere and therefore can indirectly affect human life. Thus, we present in this work the evaluation of NO2 and NO stratospheric partial columns (> 16 km altitude) retrieved from ground-based Fourier-transform infrared (FTIR) measurements of over 25 years at Zugspitze (47.42° N, 10.98° E; 2964 m a.s.l.) and 18 years at Garmisch (47.47° N, 11.06° E; 745 m a.s.l.), Germany. The obtained stratospheric columns are only weakly influenced by tropospheric pollution and show only a very small bias of 2.5 ± 0.2 % when comparing NO2 above Zugspitze and Garmisch. Stratospheric columns of both NO2 and NO show a diurnal increase that depends on local solar time (LST). We quantified this behavior by calculating diurnal increasing rates. Here, we find mean values for the NO2 diurnal increasing rate of (0.89 ± 0.14) × 1014 and (0.94 ± 0.14) × 1014 cm−2 h−1 at Zugspitze and Garmisch, respectively. The mean NO morning diurnal increasing rate above Zugspitze is found to be (1.42 ± 0.12) × 1014 cm−2 h−1. Regarding the seasonal dependency of these increasing rates, for the first time, we were able to experimentally detect a significant seasonal variation in both NO2 diurnal increasing rates and NO morning diurnal increasing rates with a maximum of (1.13 ± 0.04) × 1014 cm−1 h−1 for NO2 and (1.76 ± 0.25) × 1014 cm−1 h−1 for NO in September and a minimum of (0.71 ± 0.18) × 1014 cm−1 h−1 in December for NO2 and a minimum of (1.18 ± 0.41) × 1014 cm−1 h−1 in November for NO. This similar behavior may be explained by the interconnection of both species in stratospheric photochemistry. The outcome of this work is a retrieval and analysis strategy of FTIR data for NOx stratospheric columns, which can help to further validate photochemical models or improve satellite validations. The first use of this data set is shown in the companion paper (Nürnberg et al., 2023) wherein experiment-based NOx scaling factors describing the diurnal increase in the retrieved partial columns are extracted and recently published model-based scaling factors are validated.

The Far-Infrared Radiation Mobile Observation System (FIRMOS) is a Fourier transform spectroradiometer developed to support the Far-infrared Outgoing Radiation Understanding and Monitoring (FORUM) satellite mission by validating measurement methods and instrument design concepts, both in the laboratory and in field campaigns. FIRMOS is capable of measuring the downwelling spectral radiance emitted by the atmosphere in the spectral band from 100 to 1000 cm−1 (10–100 µm in wavelength), with a maximum spectral resolution of 0.25 cm−1. We describe the instrument design and its characterization and discuss the geophysical products obtained by inverting the atmospheric spectral radiance measured during a campaign from the high-altitude location of Mount Zugspitze in Germany, beside the Extended-range Atmospheric Emitted Radiance Interferometer (E-AERI), which is permanently installed at the site. Following the selection of clear-sky scenes, using a specific algorithm, the water vapour and temperature profiles were retrieved from the FIRMOS spectra by applying the Kyoto protocol and Informed Management of the Adaptation (KLIMA) code. The profiles were found in very good agreement with those provided by radiosondes and by the Raman lidar operating from the Zugspitze Schneefernerhaus station. In addition, the retrieval products were validated by comparing the retrieved integrated water vapour values with those obtained from the E-AERI spectra.

We show new results from an updated version of the Fast atmOspheric traCe gAs retrievaL (FOCAL) retrieval method applied to measurements of the Greenhouse gases Observing SATellite (GOSAT) and its successor GOSAT-2. FOCAL was originally developed for estimating the total column carbon dioxide mixing ratio (XCO2) from spectral measurements made by the Orbiting Carbon Observatory-2 (OCO-2). However, depending on the available spectral windows, FOCAL also successfully retrieves total column amounts for other atmospheric species and their uncertainties within one single retrieval. The main focus of the current paper is on methane (XCH4; full-physics and proxy product), water vapour (XH2O) and the relative ratio of semi-heavy water (HDO) to water vapour (δD). Due to the extended spectral range of GOSAT-2, it is also possible to derive information on carbon monoxide (XCO) and nitrous oxide (XN2O) for which we also show first results. We also present an update on XCO2 from both instruments. For XCO2, the new FOCAL retrieval (v3.0) significantly increases the number of valid data compared with the previous FOCAL retrieval version (v1) by 50 % for GOSAT and about a factor of 2 for GOSAT-2 due to relaxed pre-screening and improved post-processing. All v3.0 FOCAL data products show reasonable spatial distribution and temporal variations. Comparisons with the Total Carbon Column Observing Network (TCCON) result in station-to-station biases which are generally in line with the reported TCCON uncertainties. With this updated version of the GOSAT-2 FOCAL data, we provide a first total column average XN2O product. Global XN2O maps show a gradient from the tropics to higher latitudes on the order of 15 ppb, which can be explained by variations in tropopause height. The new GOSAT-2 XN2O product compares well with TCCON. Its station-to-station variability is lower than 2 ppb, which is about the magnitude of the typical N2O variations close to the surface. However, both GOSAT-2 and TCCON measurements show that the seasonal variations in the total column average XN2O are on the order of 8 ppb peak-to-peak, which can be easily resolved by the GOSAT-2 FOCAL data. Noting that only few XN2O measurements from satellites exist so far, the GOSAT-2 FOCAL product will be a valuable contribution in this context.

Abstract We validate formaldehyde (HCHO) vertical column densities (VCDs) from Ozone Mapping and Profiler Suite Nadir Mapper (OMPS‐NM) instruments onboard the Suomi National Polar‐orbiting Partnership (Suomi NPP) satellite for 2012–2020 and National Oceanic and Atmospheric Administration‐20 (NOAA‐20) satellite for 2018–2020, hereafter referred to as OMPS‐NPP and OMPS‐N20, with ground‐based Fourier‐Transform Infrared (FTIR) observations of the Network for the Detection of Atmospheric Composition Change (NDACC). OMPS‐NPP/N20 HCHO products reproduce seasonal variability at 24 FTIR sites. Monthly variability of OMPS‐NPP/N20 has a very good agreement with FTIR, showing correlation coefficients of 0.83 and 0.88, respectively. OMPS‐NPP (N20) biases averaged over all sites are −0.9 (4) ± 3 (6)%. However, at clean sites (with VCDs 4.0 × 1015 molecules cm−2, negative biases of −15% ± 4% appear for OMPS‐NPP, but OMPS‐N20 shows smaller bias of 0.5% ± 6% due to its smaller ground pixel footprints. Therefore, smaller satellite footprint sizes are important in distinguishing small‐scale plumes. In addition, we discuss a bias correction and provide lower limit for the monthly uncertainty of OMPS‐NPP/N20 HCHO products. The total uncertainty for OMPS‐NPP (N20) at clean sites is 0.7 (0.8) × 1015 molecules cm−2, corresponding to a relative uncertainty of 32 (30)%. In the case of HCHO VCDs > 4.0 × 1015 molecules cm−2, however, the relative uncertainty in HCHO VCDs for OMPS‐NPP (N20) decreases to 31 (18)%.

The Thermal And Near infrared Sensor for carbon Observation – Fourier Transform Spectrometer (TANSO-FTS) on the Japanese Greenhouse gases Observing SATellite (GOSAT) has been returning data since April 2009. The version 9 (v9) Atmospheric Carbon Observations from Space (ACOS) Level 2 Full Physics (L2FP) retrieval algorithm (Kiel et al., 2019) was used to derive estimates of carbon dioxide (CO2) dry air mole fraction (XCO2) from the TANSO-FTS measurements collected over its first 11 years of operation. The bias correction and quality filtering of the L2FP XCO2 product were evaluated using estimates derived from the Total Carbon Column Observing Network (TCCON) as well as values simulated from a suite of global atmospheric inversion systems (models) which do not assimilate satellite-derived CO2. In addition, the v9 ACOS GOSAT XCO2 results were compared with collocated XCO2 estimates derived from NASA's Orbiting Carbon Observatory-2 (OCO-2), using the version 10 (v10) ACOS L2FP algorithm. These tests indicate that the v9 ACOS GOSAT XCO2 product has improved throughput, scatter, and bias, when compared to the earlier v7.3 ACOS GOSAT product, which extended through mid 2016. Of the 37 million soundings collected by GOSAT through June 2020, approximately 20 % were selected for processing by the v9 L2FP algorithm after screening for clouds and other artifacts. After post-processing, 5.4 % of the soundings (2×106 out of 37×106) were assigned a “good” XCO2 quality flag, as compared to 3.9 % in v7.3 ( ×106 out of 24×106). After quality filtering and bias correction, the differences in XCO2 between ACOS GOSAT v9 and both TCCON and models have a scatter (1σ) of approximately 1 ppm for ocean-glint observations and 1 to 1.5 ppm for land observations. Global mean biases against TCCON and models are less than approximately 0.2 ppm. Seasonal mean biases relative to the v10 OCO-2 XCO2 product are of the order of 0.1 ppm for observations over land. However, for ocean-glint observations, seasonal mean biases relative to OCO-2 range from 0.2 to 0.6 ppm, with substantial variation in time and latitude. The ACOS GOSAT v9 XCO2 data are available on the NASA Goddard Earth Science Data and Information Services Center (GES-DISC) in both the per-orbit full format (https://doi.org/10.5067/OSGTIL9OV0PN, OCO-2 Science Team et al., 2019b) and in the per-day lite format (https://doi.org/10.5067/VWSABTO7ZII4, OCO-2 Science Team et al., 2019a). In addition, a new set of monthly super-lite files, containing only the most essential variables for each satellite observation, has been generated to provide entry level users with a light-weight satellite product for initial exploration (CaltechDATA, https://doi.org/10.22002/D1.2178, Eldering, 2021). The v9 ACOS Data User's Guide (DUG) describes best-use practices for the GOSAT data (O'Dell et al., 2020). The GOSAT v9 data set should be especially useful for studies of carbon cycle phenomena that span a full decade or more and may serve as a useful complement to the shorter OCO-2 v10 data set, which begins in September 2014.

Measurements of the spectrum of the atmospheric emission in the far-infrared (FIR) range, between 100 and 667 cm−1 (100–15 µm) are scarce because of the detection complexity and of the strong absorption of air at ground level preventing the sounding of the FIR from low altitude. Consequently, FIR measurements need to be made from high-altitude sites or on board airborne platforms or satellites. This paper describes the dataset of FIR spectral radiances of the atmosphere and snow surface emission measured in the 100–1000 cm−1 range by the Far-Infrared Radiation Mobile Observation System (FIRMOS) instrument during a 2-month campaign carried out from the ground at about 3000 m of altitude on the top of Mt. Zugspitze in the German Alps in 2018–2019. This campaign is part of the preparatory activity of a new space FIR mission, named Far-infrared Outgoing Radiation Understanding and Monitoring (FORUM), which is under development by the European Space Agency (ESA). The dataset acquired during the campaign also includes all the additional measurements needed to provide a full characterisation of the observed atmospheric state and the local atmospheric and surface conditions. It includes co-located spectral measurements in the infrared range from 400 to 1800 cm−1; lidar backscatter profiles; radio soundings of temperature, humidity and aerosol backscatter profiles; local weather parameters; and snow/ice microphysical properties. These measurements provide a unique dataset that can be used to perform radiative closure experiments to improve modelling parameters in the FIR that are not well-characterised, such as water vapour spectroscopy, scattering properties of cirrus clouds, and the FIR emissivity of the surface covered by snow. The consolidated dataset is freely available via the ESA campaign dataset website at https://doi.org/10.5270/ESA-38034ee (Palchetti et al., 2020a).

The Sentinel-5 Precursor (S5P) mission with the TROPOspheric Monitoring Instrument (TROPOMI) on board has been measuring solar radiation backscattered by the Earth's atmosphere and surface since its launch on 13 October 2017. In this paper, we present for the first time the S5P operational methane (CH4) and carbon monoxide (CO) products' validation results covering a period of about 3 years using global Total Carbon Column Observing Network (TCCON) and Infrared Working Group of the Network for the Detection of Atmospheric Composition Change (NDACC-IRWG) network data, accounting for a priori alignment and smoothing uncertainties in the validation, and testing the sensitivity of validation results towards the application of advanced co-location criteria. We found that the S5P standard and bias-corrected CH4 data over land surface for the recommended quality filtering fulfil the mission requirements. The systematic difference of the bias-corrected total column-averaged dry air mole fraction of methane (XCH4) data with respect to TCCON data is -0.26±0.56 % in comparison to -0.68±0.74 % for the standard XCH4 data, with a correlation of 0.6 for most stations. The bias shows a seasonal dependence. We found that the S5P CO data over all surfaces for the recommended quality filtering generally fulfil the missions requirements, with a few exceptions, which are mostly due to co-location mismatches and limited availability of data. The systematic difference between the S5P total column-averaged dry air mole fraction of carbon monoxide (XCO) and the TCCON data is on average 9.22±3.45 % (standard TCCON XCO) and 2.45±3.38 % (unscaled TCCON XCO). We found that the systematic difference between the S5P CO column and NDACC CO column (excluding two outlier stations) is on average 6.5±3.54 %. We found a correlation of above 0.9 for most TCCON and NDACC stations. The study shows the high quality of S5P CH4 and CO data by validating the products against reference global TCCON and NDACC stations covering a wide range of latitudinal bands, atmospheric conditions and surface conditions.

Although optical components in Fourier transform infrared (FTIR) spectrometers are preferably wedged, in practice, infrared spectra typically suffer from the effects of optical resonances (“channeling”) affecting the retrieval of weakly absorbing gases. This study investigates the level of channeling of each FTIR spectrometer within the Network for the Detection of Atmospheric Composition Change (NDACC). Dedicated spectra were recorded by more than 20 NDACC FTIR spectrometers using a laboratory mid-infrared source and two detectors. In the indium antimonide (InSb) detector domain (1900–5000 cm−1), we found that the amplitude of the most pronounced channeling frequency amounts to 0.1 ‰ to 2.0 ‰ of the spectral background level, with a mean of (0.68±0.48) ‰ and a median of 0.60 ‰. In the mercury cadmium telluride (HgCdTe) detector domain (700–1300 cm−1), we find even stronger effects, with the largest amplitude ranging from 0.3 ‰ to 21 ‰ with a mean of (2.45±4.50) ‰ and a median of 1.2 ‰. For both detectors, the leading channeling frequencies are 0.9 and 0.11 or 0.23 cm−1 in most spectrometers. The observed spectral frequencies of 0.11 and 0.23 cm−1 correspond to the optical thickness of the beam splitter substrate. The 0.9 cm−1 channeling is caused by the air gap in between the beam splitter and compensator plate. Since the air gap is a significant source of channeling and the corresponding amplitude differs strongly between spectrometers, we propose new beam splitters with the wedge of the air gap increased to at least 0.8∘. We tested the insertion of spacers in a beam splitter's air gap to demonstrate that increasing the wedge of the air gap decreases the 0.9 cm−1 channeling amplitude significantly. A wedge of the air gap of 0.8∘ reduces the channeling amplitude by about 50 %, while a wedge of about 2∘ removes the 0.9 cm−1 channeling completely. This study shows the potential for reducing channeling in the FTIR spectrometers operated by the NDACC, thereby increasing the quality of recorded spectra across the network.

Verifying anthropogenic carbon dioxide (CO2) emissions globally is essential to inform about the progress of institutional efforts to mitigate anthropogenic climate forcing. To monitor localized emission sources, spectroscopic satellite sensors have been proposed that operate on the CO2 absorption bands in the shortwave-infrared (SWIR) spectral range with ground resolution as fine as a few tens of meters to about a hundred meters. When designing such sensors, fine ground resolution requires a trade-off towards coarse spectral resolution in order to achieve sufficient noise performance. Since fine ground resolution also implies limited ground coverage, such sensors are envisioned to fly in fleets of satellites, requiring low-cost and simple design, e.g., by restricting the spectrometer to a single spectral band. Here, we use measurements of the Greenhouse Gases Observing Satellite (GOSAT) to evaluate the spectral resolution and spectral band selection of a prospective satellite sensor with fine ground resolution. To this end, we degrade GOSAT SWIR spectra of the CO2 bands at 1.6 (SWIR-1) and 2.0 µm (SWIR-2) to coarse spectral resolution, without a further addition of noise, and we evaluate single-band retrievals of the column-averaged dry-air mole fractions of CO2 (XCO2) by comparison to ground truth provided by the Total Carbon Column Observing Network (TCCON) and by comparison to global “native” GOSAT retrievals with native spectral resolution and spectral band selection. Coarsening spectral resolution from GOSAT's native resolving power of >20 000 to the range of 700 to a few thousand makes the scatter of differences between the SWIR-1 and SWIR-2 retrievals and TCCON increase moderately. For resolving powers of 1200 (SWIR-1) and 1600 (SWIR-2), the scatter increases from 2.4 (native) to 3.0 ppm for SWIR-1 and 3.3 ppm for SWIR-2. Coarser spectral resolution yields only marginally worse performance than the native GOSAT configuration in terms of station-to-station variability and geophysical parameter correlations for the GOSAT–TCCON differences. Comparing the SWIR-1 and SWIR-2 configurations to native GOSAT retrievals on the global scale, however, reveals that the coarse-resolution SWIR-1 and SWIR-2 configurations suffer from some spurious correlations with geophysical parameters that characterize the light-scattering properties of the scene such as particle amount, size, height and surface albedo. Overall, the SWIR-1 and SWIR-2 configurations with resolving powers of 1200 and 1600 show promising performance for future sensor design in terms of random error sources while residual errors induced by light scattering along the light path need to be investigated further. Due to the stronger CO2 absorption bands in SWIR-2 than in SWIR-1, the former has the advantage that measurement noise propagates less into the retrieved XCO2 and that some retrieval information on particle scattering properties is accessible.

On 13 October 2017, the Tropospheric Monitoring Instrument (TROPOMI) was launched on the Copernicus Sentinel-5 Precursor satellite in a sun-synchronous orbit. One of the mission's operational data products is the total column concentration of carbon monoxide (CO), which was released to the public in July 2018. The current TROPOMI CO processing uses the HITRAN 2008 spectroscopic data with updated water vapor spectroscopy and produces a CO data product compliant with the mission requirement of 10 % precision and 15 % accuracy for single soundings. Comparison with ground-based CO observations of the Total Carbon Column Observing Network (TCCON) show systematic differences of about 6.2 ppb and single-orbit observations are superimposed by a significant striping pattern along the flight path exceeding 5 ppb. In this study, we discuss possible improvements of the CO data product. We found that the molecular spectroscopic data used in the retrieval plays a key role for the data quality where the use of the Scientific Exploitation of Operational Missions – Improved Atmospheric Spectroscopy Databases (SEOM-IAS) and the HITRAN 2012 and 2016 releases reduce the bias between TROPOMI and TCCON due to improved CH4 spectroscopy. SEOM-IAS achieves the best spectral fit quality (root-mean-square, rms, differences between the simulated and measured spectrum) of 1.5×10-10 mol s−1 m−2 nm−1 sr−1 and reduces the bias between TROPOMI and TCCON to 3.4 ppb, while HITRAN 2012 and HITRAN 2016 decrease the bias even further below 1 ppb. HITRAN 2012 shows the worst fit quality (rms = 2.5×10-10 mol s−1 m−2 nm−1 sr−1) of the tested cross sections and furthermore introduces an artificial bias of about -1.5×1017 molec cm−2 between TROPOMI CO and the CAMS-IFS model in the Tropics caused by the H2O spectroscopic data. Moreover, analyzing 1 year of TROPOMI CO observations, we identified increased striping patterns by about 16 % percent from November 2017 to November 2018. For that, we defined a measure γ, quantifying the relative pixel-to-pixel variation in CO in the cross-track and along-track directions. To mitigate this effect, we discuss two destriping methods applied to the CO data a posteriori. A destriping mask calculated per orbit by median filtering of the data in the cross-track direction significantly reduced the stripe pattern from γ=2.1 to γ=1.6. However, the destriping can be further improved, achieving γ=1.2 by deploying a Fourier analysis and filtering of the data, which not only corrects for stripe patterns in the cross-track direction but also accounts for the variability of stripes along the flight path.

Observations of carbon monoxide (CO) from the Measurements Of Pollution In The Troposphere (MOPITT) instrument aboard the Terra spacecraft were expected to have an accuracy of 10 % prior to the launch in 1999. Here we evaluate MOPITT Version 7 joint (V7J) thermal-infrared and near-infrared (TIR–NIR) retrieval accuracy and precision and suggest ways to further improve the accuracy of the observations. We take five steps involving filtering or bias corrections to reduce scatter and bias in the data relative to other MOPITT soundings and ground-based measurements. (1) We apply a preliminary filtering scheme in which measurements over snow and ice are removed. (2) We find a systematic pairwise bias among the four MOPITT along-track detectors (pixels) on the order of 3–4 ppb with a small temporal trend, which we remove on a global scale using a temporally trended bias correction. (3) Using a small-region approximation (SRA), a new filtering scheme is developed and applied based on additional quality indicators such as the signal-to-noise ratio (SNR). After applying these new filters, the root-mean-squared error computed using the local median from the SRA over 16 years of global observations decreases from 3.84 to 2.55 ppb. (4) We also use the SRA to find variability in MOPITT retrieval anomalies that relates to retrieval parameters. We apply a bias correction to one parameter from this analysis. (5) After applying the previous bias corrections and filtering, we compare the MOPITT results with the GGG2014 ground-based Total Carbon Column Observing Network (TCCON) observations to obtain an overall global bias correction. These comparisons show that MOPITT V7J is biased high by about 6 %–8 %, which is similar to past studies using independent validation datasets on V6J. When using TCCON spectrometric column retrievals without the standard airmass correction or scaling to aircraft (WMO scale), the ground- and satellite-based observations overall agree to better than 0.5 %. GEOS-Chem data assimilations are used to estimate the influence of filtering and scaling to TCCON on global CO and tend to pull concentrations away from the prior fluxes and closer to the truth. We conclude with suggestions for further improving the MOPITT data products.

On 13 October 2017, the European Space Agency (ESA) successfully launched the Sentinel-5 Precursor satellite with the Tropospheric Monitoring Instrument (TROPOMI) as its single payload. TROPOMI is the first of ESA's atmospheric composition Sentinel missions, which will provide complete long-term records of atmospheric trace gases for the coming 30 years as a contribution to the European Union's Earth Observing program Copernicus. One of TROPOMI's primary products is atmospheric carbon monoxide (CO). It is observed with daily global coverage and a high spatial resolution of 7×7 km2. The moderate atmospheric resistance time and the low background concentration leads to localized pollution hotspots of CO and allows the tracking of the atmospheric transport of pollution on regional to global scales. In this contribution, we demonstrate the groundbreaking performance of the TROPOMI CO product, sensing CO enhancements above cities and industrial areas and tracking, with daily coverage, the atmospheric transport of pollution from biomass burning regions. The CO data product is validated with two months of Fourier-transform spectroscopy (FTS) measurements at nine ground-based stations operated by the Total Carbon Column Observing Network (TCCON). We found a good agreement between both datasets with a mean bias of 6 ppb (average of individual station biases) for both clear-sky and cloudy TROPOMI CO retrievals. Together with the corresponding standard deviation of the individual station biases of 3.8 ppb for clear-sky and 4.0 ppb for cloudy sky, it indicates that the CO data product is already well within the mission requirement.

Among the more than 20 ground-based FTIR (Fourier transform infrared) stations currently operating around the globe, only a few have provided formaldehyde (HCHO) total column time series until now. Although several independent studies have shown that the FTIR measurements can provide formaldehyde total columns with good precision, the spatial coverage has not been optimal for providing good diagnostics for satellite or model validation. Furthermore, these past studies used different retrieval settings, and biases as large as 50 % can be observed in the HCHO total columns depending on these retrieval choices, which is also a weakness for validation studies combining data from different ground-based stations.For the present work, the HCHO retrieval settings have been optimized based on experience gained from past studies and have been applied consistently at the 21 participating stations. Most of them are either part of the Network for the Detection of Atmospheric Composition Change (NDACC) or under consideration for membership. We provide the harmonized settings and a characterization of the HCHO FTIR products. Depending on the station, the total systematic and random uncertainties of an individual HCHO total column measurement lie between 12 % and 27 % and between 1 and 11×1014 molec cm−2, respectively. The median values among all stations are 13 % and 2.9×1014 molec cm−2 for the total systematic and random uncertainties.This unprecedented harmonized formaldehyde data set from 21 ground-based FTIR stations is presented and its comparison with a global chemistry transport model shows consistency in absolute values as well as in seasonal cycles. The network covers very different concentration levels of formaldehyde, from very clean levels at the limit of detection (few 1013 molec cm−2) to highly polluted levels (7×1016 molec cm−2). Because the measurements can be made at any time during daylight, the diurnal cycle can be observed and is found to be significant at many stations. These HCHO time series, some of them starting in the 1990s, are crucial for past and present satellite validation and will be extended in the coming years for the next generation of satellite missions.

NASA's Orbiting Carbon Observatory-2 (OCO-2) has been measuring carbon dioxide column-averaged dry-air mole fraction, XCO2, in the Earth's atmosphere for over 2 years. In this paper, we describe the comparisons between the first major release of the OCO-2 retrieval algorithm (B7r) and XCO2 from OCO-2's primary ground-based validation network: the Total Carbon Column Observing Network (TCCON). The OCO-2 XCO2 retrievals, after filtering and bias correction, agree well when aggregated around and coincident with TCCON data in nadir, glint, and target observation modes, with absolute median differences less than 0.4 ppm and RMS differences less than 1.5 ppm. After bias correction, residual biases remain. These biases appear to depend on latitude, surface properties, and scattering by aerosols. It is thus crucial to continue measurement comparisons with TCCON to monitor and evaluate the OCO-2 XCO2 data quality throughout its mission.

Quantitative knowledge of water vapor absorption is crucial for accurate climate simulations. An open science question in this context concerns the strength of the water vapor continuum in the near infrared (NIR) at atmospheric temperatures, which is still to be quantified by measurements. This issue can be addressed with radiative closure experiments using solar absorption spectra. However, the spectra used for water vapor continuum quantification have to be radiometrically calibrated. We present for the first time a method that yields sufficient calibration accuracy for NIR water vapor continuum quantification in an atmospheric closure experiment. Our method combines the Langley method with spectral radiance measurements of a high-temperature blackbody calibration source (−1, but minor modifications to the method enable calibration also throughout the remainder of the NIR spectral range. The resulting uncertainty (2σ) excluding the contribution due to inaccuracies in the extra-atmospheric solar spectrum (ESS) is below 1 % in window regions and up to 1.7 % within absorption bands. The overall radiometric accuracy of the calibration depends on the ESS uncertainty, on which at present no firm consensus has been reached in the NIR. However, as is shown in the companion publication Reichert and Sussmann (2016), ESS uncertainty is only of minor importance for the specific aim of this study, i.e., the quantification of the water vapor continuum in a closure experiment. The calibration uncertainty estimate is substantiated by the investigation of calibration self-consistency, which yields compatible results within the estimated errors for 91.1 % of the 2500 to 7800 cm−1 range. Additionally, a comparison of a set of calibrated spectra to radiative transfer model calculations yields consistent results within the estimated errors for 97.7 % of the spectral range.

Quantitative knowledge of water vapor radiative processes in the atmosphere throughout the terrestrial and solar infrared spectrum is still incomplete even though this is crucial input to the radiation codes forming the core of both remote sensing methods and climate simulations. Beside laboratory spectroscopy, ground-based remote sensing field studies in the context of so-called radiative closure experiments are a powerful approach because this is the only way to quantify water absorption under cold atmospheric conditions. For this purpose, we have set up at the Zugspitze (47.42° N, 10.98° E; 2964 m a.s.l.) a long-term radiative closure experiment designed to cover the infrared spectrum between 400 and 7800 cm−1 (1.28–25 µm). As a benefit for such experiments, the atmospheric states at the Zugspitze frequently comprise very low integrated water vapor (IWV; minimum = 0.1 mm, median = 2.3 mm) and very low aerosol optical depth (AOD = 0.0024–0.0032 at 7800 cm−1 at air mass 1). All instruments for radiance measurements and atmospheric-state measurements are described along with their measurement uncertainties. Based on all parameter uncertainties and the corresponding radiance Jacobians, a systematic residual radiance uncertainty budget has been set up to characterize the sensitivity of the radiative closure over the whole infrared spectral range. The dominant uncertainty contribution in the spectral windows used for far-infrared (FIR) continuum quantification is from IWV uncertainties, while T profile uncertainties dominate in the mid-infrared (MIR). Uncertainty contributions to near-infrared (NIR) radiance residuals are dominated by water vapor line parameters in the vicinity of the strong water vapor bands. The window regions in between these bands are dominated by solar Fourier transform infrared (FTIR) calibration uncertainties at low NIR wavenumbers, while uncertainties due to AOD become an increasing and dominant contribution towards higher NIR wavenumbers. Exceptions are methane or nitrous oxide bands in the NIR, where the associated line parameter uncertainties dominate the overall uncertainty. As a first demonstration of the Zugspitze closure experiment, a water vapor continuum quantification in the FIR spectral region (400–580 cm−1) has been performed. The resulting FIR foreign-continuum coefficients are consistent with the MT_CKD 2.5.2 continuum model and also agree with the most recent atmospheric closure study carried out in Antarctica. Results from the first determination of the NIR water vapor continuum in a field experiment are detailed in a companion paper (Reichert and Sussmann, 2016) while a novel NIR calibration scheme for the underlying FTIR measurements of incoming solar radiance is presented in another companion paper (Reichert et al., 2016).

We describe a method for removing systematic biases of column-averaged dry air mole fractions of CO2 (XCO2) and CH4 (XCH4) derived from short-wavelength infrared (SWIR) spectra of the Greenhouse gases Observing SATellite (GOSAT). We conduct correlation analyses between the GOSAT biases and simultaneously retrieved auxiliary parameters. We use these correlations to bias correct the GOSAT data, removing these spurious correlations. Data from the Total Carbon Column Observing Network (TCCON) were used as reference values for this regression analysis. To evaluate the effectiveness of this correction method, the uncorrected/corrected GOSAT data were compared to independent XCO2 and XCH4 data derived from aircraft measurements taken for 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 Japan Meteorological Agency (JMA), the HIAPER Pole-to-Pole observations (HIPPO) program, and the GOSAT validation aircraft observation campaign over Japan. These comparisons demonstrate that the empirically derived bias correction improves the agreement between GOSAT XCO2/XCH4 and the aircraft data. Finally, we present spatial distributions and temporal variations of the derived GOSAT biases.

Dry-air column-averaged mole fractions of methane (XCH4) retrieved from ground-based solar Fourier transform infrared (FTIR) measurements provide valuable information for satellite validation, evaluation of chemical-transport models, and source-sink-inversions. In this context, Sussmann et al. (2013) have shown that midinfrared (MIR) soundings from the Network for the Detection of Atmospheric Composition Change (NDACC) can be combined with near-infrared (NIR) soundings from the Total Carbon Column Observing Network (TCCON) without the need to apply an overall intercalibration factor. However, in spite of efforts to reduce a priori impact, some residual seasonal biases were identified, and the reasons behind remained unclear. In extension to this previous work, which was based on multiannual quasi-coincident MIR and NIR measurements from the stations Garmisch (47.48° N, 11.06° E, 743 m a.s.l.) and Wollongong (34.41° S, 150.88° E, 30 m a.s.l.), we now investigate upgraded retrievals with longer temporal coverage and include three additional stations (Ny-Ålesund, 78.92° N, 11.93° E, 20 m a.s.l.; Karlsruhe, 49.08° N, 8.43° E, 110 m a.s.l.; Izaña, 28.31° N, 16.45° W, 2.370 m a.s.l.). Our intercomparison results (except for Ny-Ålesund) confirm that there is no overall bias between MIR and NIR XCH4 retrievals, and all MIR and NIR time series reveal a quasi-periodic seasonal bias for all stations, except for Izaña. We find that dynamical variability causes MIR–NIR differences of up to ~ 30 ppb (parts per billion) for Ny-Ålesund, ~ 20 ppb for Wollongong, ~ 18 ppb for Garmisch, and ~ 12 ppb for Karlsruhe. The mechanisms behind this variability are elaborated via two case studies, one dealing with stratospheric subsidence induced by the polar vortex at Ny-Ålesund and the other with a deep stratospheric intrusion event at Garmisch. Smoothing effects caused by the dynamical variability during these events are different for MIR and NIR retrievals depending on the altitude of the perturbation area. MIR retrievals appear to be more realistic in the case of stratospheric subsidence, while NIR retrievals are more accurate in the case of stratosphere–troposphere exchange (STE) in the upper troposphere/lower stratosphere (UTLS) region. About 35% of the FTIR measurement days at Garmisch are impacted by STE, and about 23% of the measurement days at Ny-Ålesund are influenced by polar vortex subsidence. The exclusion of data affected by these dynamical situations resulted in improved agreement of MIR and NIR seasonal cycles for Ny-Ålesund and Garmisch. We found that dynamical variability is a key factor in constraining the accuracy of MIR and NIR seasonal cycles. To mitigate this impact it is necessary to use more realistic a priori profiles that take these dynamical events into account (e.g., via improved models), and/or to improve the FTIR retrievals to achieve a more uniform sensitivity at all altitudes (possibly including profile retrievals for the TCCON data).

The column-averaged dry-air mole fractions of carbon dioxide and methane (XCO2 and XCH4) have been retrieved from Greenhouse gases Observing SATellite (GOSAT) Short-Wavelength InfraRed (SWIR) observations and released as a SWIR L2 product from the National Institute for Environmental Studies (NIES). XCO2 and XCH4 retrieved using the version 01.xx retrieval algorithm showed large negative biases and standard deviations (−8.85 and 4.75 ppm for XCO2 and −20.4 and 18.9 ppb for XCH4, respectively) compared with data of the Total Carbon Column Observing Network (TCCON). Multiple reasons for these error characteristics (e.g., solar irradiance database, handling of aerosol scattering) are identified and corrected in a revised version of the retrieval algorithm (version 02.xx). The improved retrieval algorithm shows much smaller biases and standard deviations (−1.48 and 2.09 ppm for XCO2 and −5.9 and 12.6 ppb for XCH4, respectively) than the version 01.xx. Also, the number of post-screened measurements is increased, especially at northern mid- and high-latitudinal areas.

We present the first intercalibration of dry-air column-averaged mole fractions of methane (XCH4) retrieved from solar Fourier transform infrared (FTIR) measurements of the Network for the Detection of Atmospheric Composition Change (NDACC) in the mid-infrared (MIR) versus near-infrared (NIR) soundings from the Total Carbon Column Observing Network (TCCON). The study uses multi-annual quasi-coincident MIR and NIR measurements from the stations Garmisch, Germany (47.48° N, 11.06° E, 743 m a.s.l.), and Wollongong, Australia (34.41° S, 150.88° E, 30 m a.s.l.). Direct comparison of the retrieved MIR and NIR XCH4 time series for Garmisch shows a quasi-periodic seasonal bias leading to a standard deviation (stdv) of the difference time series (NIR–MIR) of 7.2 ppb. After reducing time-dependent a priori impact by using realistic site- and time-dependent ACTM-simulated profiles as a common prior, the seasonal bias is reduced (stdv = 5.2 ppb). A linear fit to the MIR/NIR scatter plot of monthly means based on same-day coincidences does not show a y-intercept that is statistically different from zero, and the MIR/NIR intercalibration factor is found to be close to ideal within 2-σ uncertainty, i.e. 0.9996(8). The difference time series (NIR–MIR) do not show a significant trend. The same basic findings hold for Wollongong. In particular an overall MIR/NIR intercalibration factor close to the ideal 1 is found within 2-σ uncertainty. At Wollongong the seasonal cycle of methane is less pronounced and corresponding smoothing errors are not as significant, enabling standard MIR and NIR retrievals to be used directly, without correction to a common a priori. Our results suggest that it is possible to set up a harmonized NDACC and TCCON XCH4 data set which can be exploited for joint trend studies, satellite validation, or the inverse modeling of sources and sinks.

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.

Global observations of column-averaged dry air mole fractions of carbon dioxide (CO2), denoted by XCO2 , retrieved from SCIAMACHY on-board ENVISAT can provide important and missing global information on the distribution and magnitude of regional CO2 surface fluxes. This application has challenging precision and accuracy requirements. In a previous publication (Heymann et al., 2012), it has been shown by analysing seven years of SCIAMACHY WFM-DOAS XCO2 (WFMDv2.1) that unaccounted thin cirrus clouds can result in significant errors. In order to enhance the quality of the SCIAMACHY XCO2 data product, we have developed a new version of the retrieval algorithm (WFMDv2.2), which is described in this manuscript. It is based on an improved cloud filtering and correction method using the 1.4 μm strong water vapour absorption and 0.76 μm O2-A bands. The new algorithm has been used to generate a SCIAMACHY XCO2 data set covering the years 2003–2009. The new XCO2 data set has been validated using ground-based observations from the Total Carbon Column Observing Network (TCCON). The validation shows a significant improvement of the new product (v2.2) in comparison to the previous product (v2.1). For example, the standard deviation of the difference to TCCON at Darwin, Australia, has been reduced from 4 ppm to 2 ppm. The monthly regional-scale scatter of the data (defined as the mean intra-monthly standard deviation of all quality filtered XCO2 retrievals within a radius of 350 km around various locations) has also been reduced, typically by a factor of about 1.5. Overall, the validation of the new WFMDv2.2 XCO2 data product can be summarised by a single measurement precision of 3.8 ppm, an estimated regional-scale (radius of 500 km) precision of monthly averages of 1.6 ppm and an estimated regional-scale relative accuracy of 0.8 ppm. In addition to the comparison with the limited number of TCCON sites, we also present a comparison with NOAA's global CO2 modelling and assimilation system CarbonTracker. This comparison also shows significant improvements especially over the Southern Hemisphere.

In September/October 2009, six European ground-based Fourier Transform Spectrometers (FTS) of the Total Carbon Column Observation Network (TCCON) were calibrated for the first time using aircraft measurements. The campaign was part of the Infrastructure for Measurement of the European Carbon Cycle (IMECC) project. During this campaign, altitude profiles of several trace gases and meteorological parameters were taken close to the FTS sites (typically within 1–2 km distance for flight altitudes below 5000 m). Profiles of CO2, CH4, CO and H2O were measured continuously. N2O, H2, and SF6 were later derived from flask measurements. The aircraft data had a vertical coverage ranging from approximately 300 to 13 000 m, corresponding to ~80% of the total atmospheric column seen by the FTS. This study summarizes the calibration results for CH4. The resulting calibration factor of 0.978 ± 0.002 (±1 σ) from the IMECC campaign agreed very well with the results that Wunch et al. (2010) had derived for TCCON instruments in North America, Australia, New Zealand, and Japan using similar methods. By combining our results with the data of Wunch et al. (2010), the uncertainty of the calibration factor could be reduced by a factor of three (compared to using only IMECC or only Wunch et al. (2010) data). A careful analysis of the calibration method used by Wunch et al. (2010) revealed that the incomplete vertical coverage of the aircraft profiles can lead to a bias in the calibration factor. This bias can be compensated with a new iterative approach that we developed. Using this improved method, we derived a significantly lower calibration factor of 0.974 ± 0.002 (±1 σ). This corresponds to a correction of all TCCON CH4 measurements by roughly −7 ppb.

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.

Trends of column-averaged methane for the time period (1996, Sep 2011) are derived from the mid-infrared (mid-IR) solar FTIR time series at the Zugspitze (47.42° N, 10.98° E, 2964 m a.s.l.) and Garmisch (47.48° N, 11.06° E, 743 m a.s.l.). Trend analysis comprises a fit to the de-seasonalized time series along with bootstrap resampling for quantifying trend uncertainties. We find a positive trend during [1996, 1998] of 9.0 [3.2, 14.7] ppb yr−1 for Zugspitze (95% confidence interval), an insignificant growth during [1999, mid 2006] of 0.8 [−0.1, 1.7] ppb yr−1 (Zugspitze), and a significant renewed increase during [mid 2006, Sep 2011] of 5.1 [4.2, 6.0] ppb yr−1 for Garmisch, which is in agreement with 4.8 [3.8, 5.9] ppb yr−1 for Zugspitze. The agreement of methane trends at the two closely neighboring FTIR sites Zugspitze and Garmisch within the uncertainties indicates a good station-to-station consistency as a basis for future trend analyses by the ground-based mid-IR FTIR network on the global scale. Furthermore, the Zugspitze FTIR trend for the time interval [Jul 2006, Jun 2009] is found to agree with the trend derived from SCIAMACHY (WFM-DOAS v2.0.2) data within the 95% confidence intervals. In case a 1000-km pixel selection radius around the Zugspitze is used, the confidence interval is narrower for the FTIR trend (6.9 [4.2, 9.5] ppb yr−1) compared to SCIAMACHY (7.1 [5.1, 8.6] ppb yr−1). If, however, a loosened pixel selection is used (≈1000-km half-width latitudinal band), the SCIAMACHY trend significance interval is narrower (6.8 [5.1, 8.6] ppb yr−1) compared to Zugspitze FTIR (5.7 [3.0, 8.3] ppb yr−1). While earlier studies using surface network data revealed changes of 8.0 ± 0.6 ppb in 2007, 6.4 ± 0.6 ppb in 2008, and 4.7 ± 0.6 ppb in 2009 (Dlugokencky et al., 2011), our updated result proves that the renewed methane increase meanwhile has been persisting for >5 years [mid 2006, Sep 2011]. This is either the longest and largest positive trend anomaly since the beginning of systematic observations more than 25 years ago or the onset of a new period of strongly increasing CH4 levels in the atmosphere. Several scenarios have been developed to explain the persistent increase observed, mainly invoking an increase in emissions from natural wetlands, an increase in fossil fuel-related emissions or a decrease in OH concentrations. However, more work is needed to fully attribute this increase to a particular source or sink.

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.

SCIAMACHY onboard ENVISAT (launched in 2002) enables the retrieval of global long-term column-averaged dry air mole fractions of the two most important anthropogenic greenhouse gases carbon dioxide and methane (denoted XCO2 and XCH4). In order to assess the quality of the greenhouse gas data obtained with the recently introduced v2 of the scientific retrieval algorithm WFM-DOAS, we present validations with ground-based Fourier Transform Spectrometer (FTS) measurements and comparisons with model results at eight Total Carbon Column Observing Network (TCCON) sites providing realistic error estimates of the satellite data. Such validation is a prerequisite to assess the suitability of data sets for their use in inverse modelling. It is shown that there are generally no significant differences between the carbon dioxide annual increases of SCIAMACHY and the assimilation system CarbonTracker (2.00 ± 0.16 ppm yr−1 compared to 1.94 ± 0.03 ppm yr−1 on global average). The XCO2 seasonal cycle amplitudes derived from SCIAMACHY are typically larger than those from TCCON which are in turn larger than those from CarbonTracker. The absolute values of the northern hemispheric TCCON seasonal cycle amplitudes are closer to SCIAMACHY than to CarbonTracker and the corresponding differences are not significant when compared with SCIAMACHY, whereas they can be significant for a subset of the analysed TCCON sites when compared with CarbonTracker. At Darwin we find discrepancies of the seasonal cycle derived from SCIAMACHY compared to the other data sets which can probably be ascribed to occurrences of undetected thin clouds. Based on the comparison with the reference data, we conclude that the carbon dioxide data set can be characterised by a regional relative precision (mean standard deviation of the differences) of about 2.2 ppm and a relative accuracy (standard deviation of the mean differences) of 1.1–1.2 ppm for monthly average composites within a radius of 500 km. For methane, prior to November 2005, the regional relative precision amounts to 12 ppb and the relative accuracy is about 3 ppb for monthly composite averages within the same radius. The loss of some spectral detector pixels results in a degradation of performance thereafter in the spectral range currently used for the methane column retrieval. This leads to larger scatter and lower XCH4 values are retrieved in the tropics for the subsequent time period degrading the relative accuracy. As a result, the overall relative precision is estimated to be 17 ppb and the relative accuracy is in the range of about 10–20 ppb for monthly averages within a radius of 500 km. The derived estimates show that the SCIAMACHY XCH4 data set before November 2005 is suitable for regional source/sink determination and regional-scale flux uncertainty reduction via inverse modelling worldwide. In addition, the XCO2 monthly data potentially provide valuable information in continental regions, where there is sparse sampling by surface flask measurements.

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.

The Total Carbon Column Observing Network (TCCON) is a ground-based network of Fourier Transform Spectrometer (FTS) sites around the globe, where the column abundances of CO2, CH4, N2O, CO and O2 are measured. CO2 is constrained with a precision better than 0.25% (1-σ). To achieve a similarly high accuracy, calibration to World Meteorological Organization (WMO) standards is required. This paper introduces the first aircraft calibration campaign of five European TCCON sites and a mobile FTS instrument. A series of WMO standards in-situ profiles were obtained over European TCCON sites via aircraft and compared with retrievals of CO2 column amounts from the TCCON instruments. The results of the campaign show that the FTS measurements are consistently biased 1.1% ± 0.2% low with respect to WMO standards, in agreement with previous TCCON calibration campaigns. The standard a priori profile for the TCCON FTS retrievals is shown to not add a bias. The same calibration factor is generated using aircraft profiles as a priori and with the TCCON standard a priori. With a calibration to WMO standards, the highly precise TCCON CO2 measurements of total column concentrations provide a suitable database for the calibration and validation of nadir-viewing satellites.

We present a strategy (MIR-GBM v1.0) for the retrieval of column-averaged dry-air mole fractions of methane (XCH4) with a precision MIR-GBM v1.0 is using HITRAN 2000 (including the 2001 update release) and 3 spectral micro windows (2613.70–2615.40 cm−1, 2835.50–2835.80 cm−1, 2921.00–2921.60 cm−1). A first-order Tikhonov constraint is applied to the state vector given in units of per cent of volume mixing ratio. It is tuned to achieve minimum diurnal variation without damping seasonality. Final quality selection of the retrievals uses a threshold for the goodness of fit (χ2 < 1) as well as for the ratio of root-mean-square spectral noise and information content ( MIR-GBM v1.0 is the optimum of 24 tested retrieval strategies (8 different spectral micro-window selections, 3 spectroscopic line lists: HITRAN 2000, 2004, 2008). Dominant errors of the non-optimum retrieval strategies are systematic HDO/H2O-CH4 interference errors leading to a seasonal bias up to ≈5%. Therefore interference errors have been quantified at 3 test sites covering clear-sky integrated water vapor levels representative for all NDACC sites (Wollongong maximum = 44.9 mm, Garmisch mean = 14.9 mm, Zugspitze minimum = 0.2 mm). The same quality ranking of the 24 strategies was found for all 3 test sites with one optimum, i.e. MIR-GBM v1.0. Seasonality of XCH4 above the Zugspitze (47° N) shows a minus-sine shape with a minimum in March/April, a maximum in September, and an amplitude of 16.2 ± 2.9 ppb (0.94 ± 0.17%). This agrees well with the WFM-DOAS v2.0 scientific XCH4 retrieval product. A conclusion from this paper is that improved spectroscopic parameters for CH4, HDO, and H2O in the 2613–2922 cm−1 spectral domain are urgently needed. If such become available with sufficient accuracy, at least two more spectral micro windows could be utilized leading to another improvement in precision. The absolute inter-calibration of NDACC MIR-GBM v1.0 XCH4 to TCCON data is subject of ongoing work.

Column-averaged volume mixing ratios of carbon dioxide and methane retrieved from the Greenhouse gases Observing SATellite (GOSAT) Short-Wavelength InfraRed observation (GOSAT SWIR XCO2 and XCH4) were compared with the reference calibrated data obtained by ground-based high-resolution Fourier Transform Spectrometers (g-b FTSs) participating in the Total Carbon Column Observing Network (TCCON). Preliminary results are as follows: the GOSAT SWIR XCO2 and XCH4 (Version 01.xx) are biased low by 8.85 ± 4.75 ppm (2.3 ± 1.2 %) and 20.4 ± 18.9 ppb (1.2 ± 1.1 %), respectively. The standard deviation of the GOSAT SWIR XCO2 and XCH4 is about 1 % (1 σ) after correcting the negative biases of XCO2 and XCH4 by 8.85 ppm and 20.4 ppb, respectively. The latitudinal distributions of zonal means of the GOSAT SWIR XCO2 and XCH4 show similar features to those of the g-b FTS data except for the negative biases in the GOSAT data.

This paper presents a validation study of SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) carbon monoxide (CO) total column measurements from the Iterative Maximum Likelihood Method (IMLM) algorithm using ground-based spectrometer observations from twenty surface stations for the five year time period of 2003–2007. Overall we find a good agreement between SCIAMACHY and ground-based observations for both mean values as well as seasonal variations. For high-latitude Northern Hemisphere stations absolute differences between SCIAMACHY and ground-based measurements are close to or fall within the SCIAMACHY CO 2σ precision of 0.2 × 1018 molecules/cm2 (∼10%) indicating that SCIAMACHY can observe CO accurately at high Northern Hemisphere latitudes. For Northern Hemisphere mid-latitude stations the validation is complicated due to the vicinity of emission sources for almost all stations, leading to higher ground-based measurements compared to SCIAMACHY CO within its typical sampling area of 8° × 8°. Comparisons with Northern Hemisphere mountain stations are hampered by elevation effects. After accounting for these effects, the validation provides satisfactory results. At Southern Hemisphere mid- to high latitudes SCIAMACHY is systematically lower than the ground-based measurements for 2003 and 2004, but for 2005 and later years the differences between SCIAMACHY and ground-based measurements fall within the SCIAMACHY precision. The 2003–2004 bias is consistent with previously reported results although its origin remains under investigation. No other systematic spatial or temporal biases could be identified based on the validation presented in this paper. Validation results are robust with regard to the choices of the instrument-noise error filter, sampling area, and time averaging required for the validation of SCIAMACHY CO total column measurements. Finally, our results show that the spatial coverage of the ground-based measurements available for the validation of the 2003–2007 SCIAMACHY CO columns is sub-optimal for validation purposes, and that the recent and ongoing expansion of the ground-based network by carefully selecting new locations may be very beneficial for SCIAMACHY CO and other satellite trace gas measurements validation efforts.

We present a method for harmonized retrieval of integrated water vapor (IWV) from existing, long-term, measurement records at the ground-based mid-infrared solar FTIR spectrometry stations of the Network for the Detection of Atmospheric Composition Change (NDACC). Correlation of IWV from FTIR with radiosondes shows an ideal slope of 1.00(3). This optimum matching is achieved via tuning one FTIR retrieval parameter, i.e., the strength of a Tikhonov regularization constraining the derivative (with respect to height) of retrieved water profiles given in per cent difference relative to an a priori profile. All other FTIR-sonde correlation parameters (intercept=0.02(12) mm, bias=0.02(5) mm, standard deviation of coincident IWV differences (stdv)=0.27 mm, R=0.99) are comparable to or better than results for all other ground-based IWV sounding techniques given in the literature. An FTIR-FTIR side-by-side intercomparison reveals a strong exponential increase in stdv as a function of increasing temporal mismatch starting at Δt≈1 min. This is due to atmospheric water vapor variability. Based on this result we derive an upper limit for the precision of the FTIR IWV retrieval for the smallest Δt(=3.75 min) still giving a statistically sufficient sample (32 coincidences), i.e., precision(IWVFTIR) As first application examples long-term IWV data sets are retrieved from the FTIR records of the Zugspitze (47.4° N, 11.0° E, 2964 m a.s.l.) and Jungfraujoch (46.5° N, 8.0° E, 3580 m a.s.l.) NDACC sites. Station-trend analysis comprises a linear fit after subtracting an intra-annual model (3 Fourier components) and constructing an uncertainty interval [95% confidence] via bootstrap resampling. For the Zugspitze a significant trend of 0.79 [0.65, 0.92] mm/decade is found for the time interval [1996–2008], whereas for the Jungfraujoch no significant trend is found. This confirms recent findings that strong variations of IWV trends do occur above land on the local to regional scale (≈250 km) in spite of homogeneous surface temperature trends. This paper provides a basis for future exploitation of more than a dozen existing, multi-decadal FTIR measurement records around the globe for climate studies.

Columnar NO2 retrievals from solar FTIR measurements at the Zugspitze (47.42° N, 10.98° E, 2964 m a.s.l.), Germany were investigated synergistically with columnar NO2 retrieved from SCIAMACHY data by the University of Bremen scientific algorithm UB1.5 for the time span July 2002-October 2004. A new concept to match FTIR data to the time of satellite overpass makes use of the NO2 daytime increasing rate retrieved from the FTIR data set itself [+1.02(6)E+14 cm-2/h]. This measured increasing rate shows no significant seasonal variation. SCIAMACHY data within a 200-km radius around Zugspitze were considered, and a pollution-clearing scheme was developed to select only pixels corresponding to clean background (free) tropospheric conditions, and exclude local pollution hot spots. The resulting difference between SCIAMACHY and FTIR columns (without correcting for the different sensitivities of the instruments) varies between 0.60-1.24E+15 cm-2 with an average of 0.83E+15 cm-2. A day-to-day scatter of daily means of ≈7-10% could be retrieved in mutual agreement from FTIR and SCIAMACHY. Both data sets are showing sufficient precisions to make this assessment. Analysis of the averaging kernels gives proof that at high-mountain-site FTIR is a highly accurate measure for the pure stratospheric column, while SCIAMACHY shows significant tropospheric sensitivity. Based on this finding, we set up a combined a posteriori FTIR-SCIAMACHY retrieval for tropospheric NO2, based upon the averaging kernels. It yields an annual cycle of the clean background (free) tropospheric column (-2, an average of 1.09E+15 cm-2, and an intermediate phase between that of the well known boundary layer and stratospheric annual cycles. The outcome is a concept for an integrated global observing system for tropospheric NO2 that comprises DOAS nadir satellite measurements and a set of latitudinally distributed mountain-site or clean-air FTIR stations.