Search

In this study, we present the assimilation of data from the Orbiting Carbon Observatory-2 (OCO-2) (land nadir and glint data, version 9) to estimate the Australian carbon surface fluxes for the year 2015. To perform this estimation, we used both a regional-scale atmospheric transport–dispersion model and a four-dimensional variational assimilation scheme. Our results suggest that Australia was a carbon sink of −0.41 ± 0.08 PgC yr−1 compared to the prior estimate 0.09 ± 0.20 PgC yr−1 (excluding fossil fuel emissions). Most of the carbon uptake occurred in northern Australia over the savanna ecotype and in the western region over areas with sparse vegetation. Analysis of the enhanced vegetation index (EVI) suggests that the majority of the carbon uptake over the savanna ecosystem was due to an increase of vegetation productivity (positive EVI anomalies) amplified by an anomalous increase of rainfall in summer. Further from this, a slight increase of carbon uptake in Western Australia over areas with sparse vegetation (the largest ecosystem in Australia) was noted due to increased land productivity in the area caused by positive rainfall anomalies. The stronger carbon uptake estimate in this ecosystem was partially due to the land surface model (CABLE-BIOS3) underestimating the gross primary productivity of the ecosystem. To evaluate the accuracy of our carbon flux estimates from OCO-2 retrievals, we compare our posterior concentration fields against the column-averaged carbon retrievals from the Total Carbon Column Observing Network (TCCON) and ground-based in situ monitoring sites located around our domain. The validation analysis against TCCON shows that our system is able to reduce bias mainly in the summer season. Comparison with surface in situ observations was less successful, particularly over oceanic monitoring sites that are strongly affected by oceanic fluxes and subject to less freedom by the inversion. For stations located far from the coast, the comparison with in situ data was more variable, suggesting difficulties matching the column-integrated and surface data by the inversion, most likely linked to model vertical transport. Comparison of our fluxes against the OCO-2 model intercomparison (MIP) was encouraging. The annual carbon uptake estimated by our inversion falls within the ensemble of the OCO-2 MIP global inversions and presents a similar seasonal pattern.

Australian fires are a primary driver of variability in Australian atmospheric composition and contribute significantly to regional and global carbon budgets. However, biomass burning emissions fro...

In Australia, bushfires are a natural part of the country’s landscape and essential for the regeneration of plant species, however, the 2019–20 bushfires were unprecedented in their extent and intensity. This paper is focused on the 2019–20 Australian bushfires and the resulting surface and column atmospheric carbon monoxide (CO) anomalies around Wollongong. Column CO data from the ground-based Total Carbon Column Observing Network (TCCON) and Network for the Detection of Atmospheric Composition Change (NDACC) site in Wollongong are used together with surface in situ measurements. A systematic comparison was performed between the surface in situ and column measurements of CO to better understand whether column measurements can be used as an estimate of the surface concentrations. If so, satellite column measurements of CO could be used to estimate the exposure of humans to CO and other fire-related pollutants. We find that the enhancements in the column measurements are not always significantly evident in the corresponding surface measurements. Topographical features play a key role in determining the surface exposures from column abundance especially in a coastal city like Wollongong. The topography at Wollongong, combined with meteorological effects, potentially exacerbates differences in the column and surface. Hence, satellite column amounts are unlikely to provide an accurate reflection of exposure at the ground during major events like the 2019–2020 bushfires.

Quantitative understanding of the sources and sinks of greenhouse gases is essential for predicting greenhouse gas-climate feedback processes and their impacts on climate variability and change. Australia plays a significant role in driving variability in global carbon cycling, but the budgets of carbon gases in Australia remain highly uncertain. Here, shipborne Fourier Transform Infrared Spectrometer measurements collected around Australia are used together with a global chemical transport model (GEOS-Chem) to identify and quantify the sources of three direct and indirect carbon greenhouse gases: carbon dioxide (CO2), methane (CH4) and carbon monoxide (CO). Using these measurements, we provide an updated distribution of these gases and their sources and sinks. We find that for all three gases, the east Australian coast is largely influenced by local anthropogenic sources, which can be transported as far as 400 km off the coast. The south and west coasts are characterised by a mixture of anthropogenic sources and biomass burning. Tropical northern regions are dominated by biomass burning emissions, with significant contribution from fossil fuel for CO2 and wetlands for CH4. Averaged across Australia, fossil fuels followed by biomass burning contribute the most to total CO2 and to both its background value and short-term enhancements. Wetlands provide the largest background CH4 source, followed by livestock, oil, gas and waste emissions, with short-term enhancements mainly driven by anthropogenic sources. For CO, secondary production from oxidation of CH4 and non-methane volatile organic compounds contributes most to the background and total CO burdens, while enhancements are driven by biomass burning and anthropogenic sources. Clean air characteristic of the tropospheric background was observed away from the coast in the Indian Ocean, Coral Sea, and Tasman Sea. From the measurements in the Indian Ocean, we found that the background values of all three gases increase towards the tropics with latitudinal gradients of 0.019 ± 0.003 ppm deg−1 for CO2, 0.34 ± 0.02 ppb deg−1 for CH4 and 0.82 ± 0.05 ppb deg−1 for CO. Comparing coincident and co-located enhancements in the three carbon gases highlighted several common sources from the Australian continent. We found evidence for 17 events with similar enhancement patterns indicative of co-emission and calculated enhancements ratios and modelled source contributions for each event. We found that anthropogenic co-enhancement events are common along the east coast, while co-enhancement events in the tropics primarily derive from biomass burning sources. Few co-enhancement events were observed along the south and west coasts. While the GEOS-Chem model generally reproduced the timing of co-enhancement events, it was less able to reproduce the magnitude of enhancements. We found model overestimates of CH4 from coal burning and underestimates of all three gases from biomass burning with overestimates for CO during some events. We identified missing sources from fossil fuel, biofuel, oil, gas, coal, livestock, biomass burning and the biosphere in the model, pointing to the need to further develop and evaluate greenhouse gas emission inventories for the Australian continent.

Top-down estimates of CO2 fluxes are typically constrained by either surface-based or space-based CO2 observations. Both of these measurement types have spatial and temporal gaps in observational coverage that can lead to differences in inferred fluxes. Assimilating both surface-based and space-based measurements concurrently in a flux inversion framework improves observational coverage and reduces sampling related artifacts. This study examines the consistency of flux constraints provided by these different observations and the potential to combine them by performing a series of 6-year (2010?2015) CO2 flux inversions. Flux inversions are performed assimilating surface-based measurements from the in situ and flask network, measurements from the Total Carbon Column Observing Network (TCCON), and space-based measurements from the Greenhouse Gases Observing Satellite (GOSAT), or all three data sets combined. Combining the data sets results in more precise flux estimates for subcontinental regions relative to any of the data sets alone. Combining the data sets also improves the accuracy of the posterior fluxes, based on reduced root-mean-square differences between posterior flux-simulated CO2 and aircraft-based CO2 over midlatitude regions (0.33?0.56?ppm) in comparison to GOSAT (0.37?0.61?ppm), TCCON (0.50?0.68?ppm), or in situ and flask measurements (0.46?0.56?ppm) alone. These results suggest that surface-based and GOSAT measurements give complementary constraints on CO2 fluxes in the northern extratropics and can be combined in flux inversions to improve constraints on regional fluxes. This stands in contrast with many earlier attempts to combine these data sets and suggests that improvements in the NASA Atmospheric CO2 Observations from Space (ACOS) retrieval algorithm have significantly improved the consistency of space-based and surface-based flux constraints.

We characterize the magnitude of seasonally and spatially varying biases in the National Aeronautics and Space Administration (NASA) Orbiting Carbon Observatory-2 (OCO-2) Version 8 (v8) and the Atmospheric CO2 Observations from Space (ACOS) Greenhouse Gas Observing SATellite (GOSAT) version 7.3 (v7.3) satellite CO2 retrievals by comparisons to measurements collected by the Total Carbon Column Observing Network (TCCON), Atmospheric Tomography (ATom) experiment, and National Oceanic and Atmospheric Administration (NOAA) Earth System Research Laboratory (ESRL) and U. S. Department of Energy (DOE) aircraft, and surface stations. Although the ACOS-GOSAT estimates of the column averaged carbon dioxide (CO2) dry air mole fraction (XCO2) have larger random errors than the OCO-2 XCO2 estimates, and the space-based estimates over land have larger random errors than those over ocean, the systematic errors are similar across both satellites and surface types, 0.6 ± 0.1 ppm. We find similar estimates of systematic error whether dynamic versus geometric coincidences or ESRL/DOE aircraft versus TCCON are used for validation (over land), once validation and co-location errors are accounted for. We also find that areas with sparse throughput of good quality data (due to quality flags and preprocessor selection) over land have ~double the error of regions of high-throughput of good quality data. We characterize both raw and bias-corrected results, finding that bias correction improves systematic errors by a factor of 2 for land observations and improves errors by ~ 0.2 ppm for ocean. We validate the lowermost tropospheric (LMT) product for OCO-2 and ACOS-GOSAT by comparison to aircraft and surface sites, finding systematic errors of ~ 1.1 ppm, while having 2–3 times the variability of XCO2. We characterize the time and distance scales of correlations for OCO-2 XCO2 errors, and find error correlations on scales of 0.3 degrees, 5–10 degrees, and 60 days. We find comparable scale lengths for the bias correction term. Assimilation of the OCO-2 bias correction term is used to estimate flux errors resulting from OCO-2 seasonal biases, finding annual flux errors on the order of 0.3 and 0.4 PgC/yr for Transcom-3 ocean and land regions, respectively.

Western wildfires have a major impact on air quality in the US. In the fall of 2016, 107 test fires were burned in the large-scale combustion facility at the US Forest Service Missoula Fire Sciences Laboratory as part of the Fire Influence on Regional and Global Environments Experiment (FIREX). Canopy, litter, duff, dead wood, and other fuel components were burned in combinations that represented realistic fuel complexes for several important western US coniferous and chaparral ecosystems including ponderosa pine, Douglas fir, Engelmann spruce, lodgepole pine, subalpine fir, chamise, and manzanita. In addition, dung, Indonesian peat, and individual coniferous ecosystem fuel components were burned alone to investigate the effects of individual components (e.g., duff) and fuel chemistry on emissions. The smoke emissions were characterized by a large suite of state-of-the-art instruments. In this study we report emission factor (EF, grams of compound emitted per kilogram of fuel burned) measurements in fresh smoke of a diverse suite of critically important trace gases measured using open-path Fourier transform infrared spectroscopy (OP-FTIR). We also report aerosol optical properties (absorption EF; single-scattering albedo, SSA; and Ångström absorption exponent, AAE) as well as black carbon (BC) EF measured by photoacoustic extinctiometers (PAXs) at 870 and 401 nm. The average trace gas emissions were similar across the coniferous ecosystems tested and most of the variability observed in emissions could be attributed to differences in the consumption of components such as duff and litter, rather than the dominant tree species. Chaparral fuels produced lower EFs than mixed coniferous fuels for most trace gases except for NOx and acetylene. A careful comparison with available field measurements of wildfires confirms that several methods can be used to extract data representative of real wildfires from the FIREX laboratory fire data. This is especially valuable for species rarely or not yet measured in the field. For instance, the OP-FTIR data alone show that ammonia (1.62 g kg−1), acetic acid (2.41 g kg−1), nitrous acid (HONO, 0.61 g kg−1), and other trace gases such as glycolaldehyde (0.90 g kg−1) and formic acid (0.36 g kg−1) are significant emissions that were poorly characterized or not characterized for US wildfires in previous work. The PAX measurements show that the ratio of brown carbon (BrC) absorption to BC absorption is strongly dependent on modified combustion efficiency (MCE) and that BrC absorption is most dominant for combustion of duff (AAE 7.13) and rotten wood (AAE 4.60): fuels that are consumed in greater amounts during wildfires than prescribed fires. Coupling our laboratory data with field data suggests that fresh wildfire smoke typically has an EF for BC near 0.2 g kg−1, an SSA of ∼ 0.91, and an AAE of ∼ 3.50, with the latter implying that about 86 % of the aerosol absorption at 401 nm is due to BrC.

The SAFIRED (Savannah Fires in the Early Dry Season) campaign took place from 29 May until 30 June 2014 at the Australian Tropical Atmospheric Research Station (ATARS) in the Northern Territory, Australia. The purpose of this campaign was to investigate emissions from fires in the early dry season in northern Australia. Measurements were made of biomass burning aerosols, volatile organic compounds, polycyclic aromatic carbons, greenhouse gases, radon, speciated atmospheric mercury and trace metals. Aspects of the biomass burning aerosol emissions investigated included; emission factors of various species, physical and chemical aerosol properties, aerosol aging, micronutrient supply to the ocean, nucleation, and aerosol water uptake. Over the course of the month-long campaign, biomass burning signals were prevalent and emissions from several large single burning events were observed at ATARS.Biomass burning emissions dominated the gas and aerosol concentrations in this region. Dry season fires are extremely frequent and widespread across the northern region of Australia, which suggests that the measured aerosol and gaseous emissions at ATARS are likely representative of signals across the entire region of north Australia. Air mass forward trajectories show that these biomass burning emissions are carried north-west over the Timor Sea and could influence the atmosphere over Indonesia and the tropical atmosphere over the Indian Ocean. Here we present characteristics of the biomass burning observed at the sampling site and provide an overview of the more specific outcomes of the SAFIRED campaign.

The powerful El Niño event of 2015–2016 – the third most intense since the 1950s – has exerted a large impact on the Earth’s natural climate system. The column-averaged CO2 dry-air mole fraction (XCO2) observations from satellites and ground-based networks are analyzed together with in situ observations for the period of September 2014 to October 2016. From the differences between satellite (OCO-2) observations and simulations using an atmospheric chemistry-transport model, we estimate that, relative to the mean annual fluxes for 2014, the most recent El Niño has contributed to an excess CO2 emission from the Earth’s surface (land + ocean) to the atmosphere in the range of 2.4 ± 0.2 PgC (1 Pg = 1015 g) over the period of July 2015 to June 2016. The excess CO2 flux is resulted primarily from reduction in vegetation uptake due to drought, and to a lesser degree from increased biomass burning. It is about the half of the CO2 flux anomaly (range: 4.4–6.7 PgC) estimated for the 1997/1998 El Niño. The annual total sink is estimated to be 3.9 ± 0.2 PgC for the assumed fossil fuel emission of 10.1 PgC. The major uncertainty in attribution arise from error in anthropogenic emission trends, satellite data and atmospheric transport.

Savanna fires contribute significantly to global aerosol loading and hence to the earth's radiative budget. Modelling of the climatic impact of these aerosols is made difficult due to a lack of knowledge of their size distribution. Australia is the third largest source of global carbon emissions from biomass burning, with emissions dominated by tropical savanna fires. Despite this, only a few previous studies have reported emission factors of trace gases from this important ecosystem and there are no previous published emission factors for the aerosol properties reported here for Australian savanna fires. In June 2014, the SAFIRED campaign (Savanna Fires in the Early Dry season) took place in the northern territory of Australia, with the purpose of investigating emissions and aging of aerosols from Australian savanna fires. This paper presents observed enhancement ratios and inferred emission factors of trace gases (CO2, CO, CH4, N2O and gaseous elemental mercury), particles over different size modes (Aitken and accumulation) and speciated aerosols components (organics, sulfate, nitrate, ammonium and chloride). Nine smoke events were identified from the data using large enhancements in CO and/or aerosol data to indicate biomass burning event. The results reported in this paper include the first emission factors for Aitken and accumulation mode aerosols from savanna fires, providing useful size information to enable better modelling of the climatic impact of this important source of global aerosols.

Well-drained, aerated soils are important sinks for atmospheric methane (CH4) via the process of CH4 oxidation by methane-oxidising bacteria (MOB). This terrestrial CH4 sink may contribute towards climate change mitigation, but the impact of changing soil moisture and temperature regimes on CH4 uptake is not well understood in all ecosystems. Soils in temperate forest ecosystems are the greatest terrestrial CH4 sink globally. Under predicted climate change scenarios, temperate eucalypt forests in south-eastern Australia are predicted to experience rapid and extreme changes in rainfall patterns, temperatures and wild fires. To investigate the influence of environmental drivers on seasonal and inter-annual variation of soil–atmosphere CH4 exchange, we measured soil–atmosphere CH4 exchange at high-temporal resolution (−1) and in a wet temperature eucalypt forest in Tasmania (Warra Long-Term Ecological Research site, 1700 mm yr−1). Both forest soil systems were continuous CH4 sinks of −1.79 kg CH4 ha−1 yr−1 in Victoria and −3.83 kg CH4 ha−1 yr−1 in Tasmania. Soil CH4 uptake showed substantial temporal variation and was strongly controlled by soil moisture at both forest sites. Soil CH4 uptake increased when soil moisture decreased and this relationship explained up to 90 % of the temporal variability. Furthermore, the relationship between soil moisture and soil CH4 flux was near-identical at both forest sites when soil moisture was expressed as soil air-filled porosity (AFP). Soil temperature only had a minor influence on soil CH4 uptake. Soil nitrogen concentrations were generally low and fluctuations in nitrogen availability did not influence soil CH4 uptake at either forest site. Our data suggest that soil MOB activity in the two forests was similar and that differences in soil CH4 exchange between the two forests were related to differences in soil moisture and thereby soil gas diffusivity. The differences between forest sites and the variation in soil CH4 exchange over time could be explained by soil AFP as an indicator of soil moisture status.

Global and regional methane budgets are markedly uncertain. Conventionally, estimates of methane sources are derived by bridging emissions inventories with atmospheric observations employing chemical transport models. The accuracy of this approach requires correctly simulating advection and chemical loss such that modeled methane concentrations scale with surface fluxes. When total column measurements are assimilated into this framework, modeled stratospheric methane introduces additional potential for error. To evaluate the impact of such errors, we compare Total Carbon Column Observing Network (TCCON) and GEOS-Chem total and tropospheric column-averaged dry-air mole fractions of methane. We find that the model's stratospheric contribution to the total column is insensitive to perturbations to the seasonality or distribution of tropospheric emissions or loss. In the Northern Hemisphere, we identify disagreement between the measured and modeled stratospheric contribution, which increases as the tropopause altitude decreases, and a temporal phase lag in the model's tropospheric seasonality driven by transport errors. Within the context of GEOS-Chem, we find that the errors in tropospheric advection partially compensate for the stratospheric methane errors, masking inconsistencies between the modeled and measured tropospheric methane. These seasonally varying errors alias into source attributions resulting from model inversions. In particular, we suggest that the tropospheric phase lag error leads to large misdiagnoses of wetland emissions in the high latitudes of the Northern Hemisphere.

In this study, we present ground-based measurements of column-averaged dry-air mole fractions (DMFs) of CO2 (or XCO2) from an EM27/SUN portable spectrometer, equipped with an automated clam shell cover, taken in a semi-arid region of Australia. We compared these measurements to space-based XCO2 retrievals from the Greenhouse Gases Observing Satellite (GOSAT). Side-by-side measurements of EM27/SUN with the Total Carbon Column Observing Network (TCCON) instrument at the University of Wollongong were conducted in 2015–2016 to derive an XCO2 calibration factor of 0.9954 relative to TCCON. Although we found a slight drift of .0125 % per month in the calibration curve of the EM27/SUN vs TCCON XCO2, the alignment of the EM27/SUN proved stable enough for a campaign, keeping the retrieved Xair values, a measure of stability, to within 0.5 % and the modulation efficiency to within 2 %. From the measurements in Alice Springs, we confirm a small bias of around 2 ppm in the GOSAT M-gain to H-gain XCO2 retrievals, as reported by the NIES GOSAT validation team. Based on the reported random errors from GOSAT, we estimate the required duration of a future campaign in order to improve the estimated bias between the EM27/SUN and GOSAT. The dataset from the Alice Springs measurements is accessible at http://dx.doi.org/10.4225/48/5b21f16ce69bc (Velazco et al., 2018).

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

Wollongong, Australia is an urban site at the intersection of anthropogenic, biomass burning, biogenic and marine sources of atmospheric trace gases. The location offers a valuable opportunity to study drivers of atmospheric composition in the Southern Hemisphere. Here, a record of surface carbon monoxide (CO), methane (CH4) and carbon dioxide (CO2) was measured with an in situ Fourier transform infrared trace gas analyser between April 2011 and August 2014. Clean air was found to arrive at Wollongong in approximately 10% of air masses. Biomass burning influence was evident in the average annual cycle of clean air CO during austral spring. A significant negative short-term trend was found in clean air CO (−1.5 nmol mol−1 a−1), driven by a reduction in northern Australian biomass burning. Significant short-term positive trends in clean air CH4 (5.4 nmol mol−1 a−1) and CO2 (1.9 μmol mol−1 a−1) were consistent with the long-term global average trends. Polluted Wollongong air was investigated using wind-direction/wind-speed clustering, which revealed major influence from local urban and industrial sources from the south. High values of CH4, with anthropogenic ΔCH4/ΔCO2 enhancement ratio signatures, originated from the northwest, in the direction of local coal mining. A pollution climatology was developed for the region using back trajectory analysis and ΔO3/ΔCO enhancement ratios. Ozone production environments in austral spring and summer were associated with anticyclonic meteorology on the east coast of Australia, while ozone depletion environments in autumn and winter were associated with continental transport, or fast moving trajectories from southern latitudes. This implies the need to consider meteorological conditions when developing policies for controlling air quality.

The proxy method, using the ratio of total column CH4 to CO2 to reduce the effects of common biases, has been used to retrieve column-averaged dry-air mole fraction of CH4 from satellite data. The present study characterizes the remaining scattering effects in the CH4/CO2 ratio component of the Greenhouse gases Observing SATellite (GOSAT) retrieval and uses them for bias correction. The variation of bias between the GOSAT and Total Carbon Column Observing Network (TCCON) ratio component with GOSAT data-derived variables was investigated. Then, it was revealed that the variability of the bias could be reduced by using four variables for the bias correction&mdash, namely, airmass, 2 &mu, m band radiance normalized with its noise level, the ratio between the partial column-averaged dry-air mole fraction of CH4 for the lower atmosphere and that for the upper atmosphere, and the difference in surface albedo between the CH4 and CO2 bands. The ratio of partial column CH4 reduced the dependence of bias on the cloud fraction and the difference between hemispheres. In addition to the reduction of bias (from 0.43% to 0%), the precision (standard deviation of the difference between GOSAT and TCCON) was reduced from 0.61% to 0.55% by the correction. The bias and its temporal variation were reduced for each site: the mean and standard deviation of the mean bias for individual seasons were within 0.2% for most of the sites.

We investigate the impact of biogenic emissions on carbon monoxide (CO) and formaldehyde (HCHO) in the Southern Hemisphere (SH), with simulations using two different biogenic emission inventories for isoprene and monoterpenes. Results from four atmospheric chemistry models are compared to continuous long-term ground-based CO and HCHO column measurements at the SH Network for the Detection of Atmospheric Composition Change (NDACC) sites, the satellite measurement of tropospheric CO columns from the Measurement of Pollution in the Troposphere (MOPITT), and in situ surface CO measurements from across the SH, representing a subset of the National Oceanic and Atmospheric Administration's Global Monitoring Division (NOAA GMD) network. Simulated mean model CO using the Model of Emissions of Gases and Aerosols from Nature (v2.1) computed in the frame work of the Land Community Model (CLM-MEGANv2.1) inventory is in better agreement with both column and surface observations than simulations adopting the emission inventory generated from the LPJ-GUESS dynamical vegetation model framework, which markedly underestimate measured column and surface CO at most sites. Differences in biogenic emissions cause large differences in CO in the source regions which propagate to the remote SH. Significant inter-model differences exist in modelled column and surface CO, and secondary production of CO dominates these inter-model differences, due mainly to differences in the models' oxidation schemes for volatile organic compounds, predominantly isoprene oxidation. While biogenic emissions are a significant factor in modelling SH CO, inter-model differences pose an additional challenge to constrain these emissions. Corresponding comparisons of HCHO columns at two SH mid-latitude sites reveal that all models significantly underestimate the observed values by approximately a factor of 2. There is a much smaller impact on HCHO of the significantly different biogenic emissions in remote regions, compared to the source regions. Decreased biogenic emissions cause decreased CO export to remote regions, which leads to increased OH; this in turn results in increased HCHO production through methane oxidation. In agreement with earlier studies, we corroborate that significant HCHO sources are likely missing in the models in the remote SH.

Biomass burning releases trace gases and aerosol particles that significantly affect the composition and chemistry of the atmosphere. Australia contributes approximately 8% of gross global carbon emissions from biomass burning, yet there are few previous measurements of emissions from Australian forest fires available in the literature. This paper describes the results of field measurements of trace gases emitted during hazard reduction burns in Australian temperate forests using open-path Fourier transform infrared spectroscopy. In a companion paper, similar techniques are used to characterise the emissions from hazard reduction burns in the savanna regions of the Northern Territory. Details of the experimental methods are explained, including both the measurement set-up and the analysis techniques employed. The advantages and disadvantages of different ways to estimate whole-fire emission factors are discussed and a measurement uncertainty budget is developed. Emission factors for Australian temperate forest fires are measured locally for the first time for many trace gases. Where ecosystem-relevant data are required, we recommend the following emission factors for Australian temperate forest fires (in grams of gas emitted per kilogram of dry fuel burned) which are our mean measured values: 1620 ± 160 g kg−1 of carbon dioxide; 120 ± 20 g kg−1 of carbon monoxide; 3.6 ± 1.1 g kg−1 of methane; 1.3 ± 0.3 g kg−1 of ethylene; 1.7 ± 0.4 g kg−1 of formaldehyde; 2.4 ± 1.2 g kg−1 of methanol; 3.8 ± 1.3 g kg−1 of acetic acid; 0.4 ± 0.2 g kg−1 of formic acid; 1.6 ± 0.6 g kg−1 of ammonia; 0.15 ± 0.09 g kg−1 of nitrous oxide and 0.5 ± 0.2 g kg−1 of ethane.

Since the late 1990s, the meteorological observatory established in Anmyeondo (36.5382˚ N, 126.3311˚ E, and 30 m above mean sea level), has been monitoring several greenhouse gases such as CO2, CH4, N2O, CFCs, and SF6, as part of the Global Atmosphere Watch (GAW) Program. A high resolution ground-based (g-b) Fourier Transform Spectrometer (FTS, IFS-125HR model) was installed at such observation site in 2013, and has been fully operated within the frame work of the Total Carbon Column Observing Network (TCCON) since August, 2014. The solar spectra recorded by the g-b FTS are covered in the range between 3,800 and 16,000 cm−1 at the spectral resolution of 0.02 cm−1 during the measurement period between 2013 and 2016. In this work, the GGG2014 version of the TCCON standard retrieval algorithm was used to retrieve XCO2 concentrations from the FTS spectra. Two spectral bands (at 6220.0 and 6339.5 cm−1 center wavenumbers) were used to derive the XCO2 concentration within the spectral residual of +0.01 %. All sources of errors were thoroughly analyzed. In this paper, we introduced a new home made OASIS (Operational Automatic System for Intensity of Sunray) system to our g-b FTS instrument and that allows reducing the solar intensity variations (SIV) below 2 %. A comparison of the XCO2 concentration in g-b FTS and OCO-2 (Orbiting Carbon Observatory) satellite observations were presented only for the measurement period between 2014 and 2015. Nine coincident observations were selected on a daily mean basis. It was obtained that OCO-2 exhibited low bias with respect to the g-b FTS, which is about −0.065 ppm with the standard deviation of 1.66 ppm, and revealed a strong correlation (R = 0.85). Based on seasonal cycle comparisons, both instruments were generally agreed in capturing seasonal variations of the target species with its maximum and minimum levels in spring and late summer respectively. In the future, it is planned to exert further works in utilizing the FTS measurements for the evaluation of satellite observations such as Greenhouse Gases Observing Satellite (GOSAT) at observation sites. This is the first report of the g-b FTS observations of XCO2 species over the Anmyeondo station.

The Total Carbon Column Observing Network (TCCON) is a global ground-based network of Fourier transform spectrometers that produce precise measurements of column-averaged dry-air mole fractions of atmospheric methane (CH4). Temporal variability in the total column of CH4 due to stratospheric dynamics obscures fluctuations and trends driven by tropospheric transport and local surface fluxes that are critical for understanding CH4 sources and sinks. We reduce the contribution of stratospheric variability from the total column average by subtracting an estimate of the stratospheric CH4 derived from simultaneous measurements of hydrogen fluoride (HF). HF provides a proxy for stratospheric CH4 because it is strongly correlated to CH4 in the stratosphere, has an accurately known tropospheric abundance (of zero), and is measured at most TCCON stations. The stratospheric partial column of CH4 is calculated as a function of the zonal and annual trends in the relationship between CH4 and HF in the stratosphere, which we determine from ACE-FTS satellite data. We also explicitly take into account the CH4 column averaging kernel to estimate the contribution of stratospheric CH4 to the total column. The resulting tropospheric CH4 columns are consistent with in situ aircraft measurements and augment existing observations in the troposphere.

We report trace-gas emission factors from three pine-understory prescribed fires in South Carolina, US measured during the fall of 2011. The fires were more intense than many prescribed burns because the fuels included mature pine stands not subjected to prescribed fire in decades that were lit following an extended drought. The emission factors were measured with a fixed open-path Fourier transform infrared (OP-FTIR) system that was deployed on the fire control lines. We compare these emission factors to those measured with a roving, point sampling, land-based FTIR and an airborne FTIR that were deployed on the same fires. We also compare to emission factors measured by a similar OP-FTIR system deployed on savanna fires in Africa. The data suggest that the method used to sample smoke can strongly influence the relative abundance of the emissions that are observed. The majority of the fire emissions were lofted in the convection column and they were sampled by the airborne FTIR along with the downwind chemistry. The roving, ground-based, point sampling FTIR measured the contribution of actively located individual residual smoldering combustion fuel elements scattered throughout the burn site. The OP-FTIR provided a ~30 m path-integrated sample of emissions transported to the fixed path via complex ground-level circulation. The OP-FTIR typically probed two distinct combustion regimes, "flaming-like" (immediately after adjacent ignition and before the adjacent plume achieved significant vertical development) and "smoldering-like." These two regimes are denoted "early" and "late", respectively. The emission factors from all three systems were plotted versus modified combustion efficiency and for some species (e.g. CH4 and CH3OH) they fit a single trend suggesting that the different emission factors for these species were mainly due to the specific mix of flaming and smoldering that each system sampled. For other species, the different fuels sampled also likely contributed to platform differences in emission factors. The path-integrated sample of the ground-level smoke layer adjacent to the fire provided by the OP-FTIR also provided our best estimate of fire-line exposure to smoke for wildland fire personnel. We provide a table of estimated fire-line exposures for numerous known air toxics based on synthesizing results from several studies. Our data suggest that peak exposures are more likely to challenge permissible exposure limits for wildland fire personnel than shift-average (8 h) exposures.

The abundance of chlorine in the Earth's atmosphere increased considerably during the 1970s to 1990s, following large emissions of anthropogenic long-lived chlorine-containing source gases, notably the chlorofluorocarbons. The chemical inertness of chlorofluorocarbons allows their transport and mixing throughout the troposphere on a global scale, before they reach the stratosphere where they release chlorine atoms that cause ozone depletion. The large ozone loss over Antarctica was the key observation that stimulated the definition and signing in 1987 of the Montreal Protocol, an international treaty establishing a schedule to reduce the production of the major chlorine- and bromine-containing halocarbons. Owing to its implementation, the near-surface total chlorine concentration showed a maximum in 1993, followed by a decrease of half a per cent to one per cent per year, in line with expectations. Remote-sensing data have revealed a peak in stratospheric chlorine after 1996, then a decrease of close to one per cent per year, in agreement with the surface observations of the chlorine source gases and model calculations. Here we present ground-based and satellite data that show a recent and significant increase, at the 2σ level, in hydrogen chloride (HCl), the main stratospheric chlorine reservoir, starting around 2007 in the lower stratosphere of the Northern Hemisphere, in contrast with the ongoing monotonic decrease of near-surface source gases. Using model simulations, we attribute this trend anomaly to a slowdown in the Northern Hemisphere atmospheric circulation, occurring over several consecutive years, transporting more aged air to the lower stratosphere, and characterized by a larger relative conversion of source gases to HCl. This short-term dynamical variability will also affect other stratospheric tracers and needs to be accounted for when studying the evolution of the stratospheric ozone layer.

Micrometeorological techniques are effective in measuring methane (CH4) emission rates at the herd scale, but their suitability as verification tools for emissions mitigation depends on the uncertainty with which they can detect a treatment difference. An experiment was designed to test for a range of techniques whether they could detect a change in weekly mean emission rate from a herd of cattle, in response to a controlled change in feed supply. The cattle were kept in an enclosure and fed pasture baleage, of amounts increasing from one week to the next. Methane emission rates were measured at the herd scale by the following techniques: (1) an external tracer-ratio technique, releasing nitrous oxide (N2O) from canisters on the animals’ necks and measuring line-averaged CH4 and N2O mole fractions with Fourier-transform infra-red (FTIR) spectrometers deployed upwind and downwind of the cattle, (2) a mass-budget technique using vertical profiles of wind speed and CH4 mole fraction, (3) a dispersion model, applied separately to CH4 mole fraction data from the FTIR spectrometers, the vertical profile, and a laser system measuring along four paths surrounding the enclosure. For reference, enteric CH4 emissions were also measured at the animal scale on a daily basis, using an enteric tracer-ratio technique (with SF6 as the tracer). The animal-scale technique showed that mean CH4 emissions increased less than linearly with increasing feed intake. The herd-scale techniques showed that the emission rates followed a diurnal pattern, with the maximum about 2 h after the feed was offered. The herd-scale techniques could detect the weekly changes in emission levels, except that the two vertical-profile techniques (mass-budget technique and dispersion model applied to profile) failed to resolve the first step change. The weekly emission rates from the external tracer-ratio technique and the dispersion model, applied to data from either the two FTIR paths or the four laser paths, agreed within ±10% with the enteric tracer-ratio technique. By contrast, the two vertical-profile techniques gave 33–68% higher weekly emission rates. It is shown with a sensitivity study that systematically uneven animal distribution within the enclosure could explain some of this discrepancy. Another cause for bias was the data yield of the vertical-profile techniques being higher at day-time than at night-time, thus giving more weight to times of larger emission rates. The techniques using line-averaged mole fractions were less sensitive to the exact locations of emission sources and less prone to data loss from unsuitable wind directions; these advantages outweighed the lack of a method to calibrate CH4 mole fractions in situ.

We report a new shortwave infrared (SWIR) retrieval of the column-averaged HDO/H2O ratio from the Japanese Greenhouse Gases Observing Satellite (GOSAT). From synthetic simulation studies, we have estimated that the inferred δD values will typically have random errors between 20‰ (desert surface and 30° solar zenith angle) and 120‰ (conifer surface and 60° solar zenith angle). We find that the retrieval will have a small but significant sensitivity to the presence of cirrus clouds, the HDO a priori profile shape and atmospheric temperature, which has the potential of introducing some regional-scale biases in the retrieval. From comparisons to ground-based column observations from the Total Carbon Column Observing Network (TCCON), we find differences between δD from GOSAT and TCCON of around −30‰ for northern hemispheric sites which increase up to −70‰ for Australian sites. The bias for the Australian sites significantly reduces when decreasing the spatial co-location criteria, which shows that spatial averaging contributes to the observed differences over Australia. The GOSAT retrievals allow mapping the global distribution of δD and its variations with season, and we find in our global GOSAT retrievals the expected strong latitudinal gradients with significant enhancements over the tropics. The comparisons to the ground-based TCCON network and the results of the global retrieval are very encouraging, and they show that δD retrieved from GOSAT should be a useful product that can be used to complement datasets from thermal-infrared sounder and ground-based networks and to extend the δD dataset from SWIR retrievals established from the recently ended SCIAMACHY mission.

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

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

The distribution of methane (CH4) in the stratosphere can be a major driver of spatial variability in the dry-air column-averaged CH4 mixing ratio (XCH4), which is being measured increasingly for the assessment of CH4 surface emissions. Chemistry-transport models (CTMs) therefore need to simulate the tropospheric and stratospheric fractional columns of XCH4 accurately for estimating surface emissions from XCH4. Simulations from three CTMs are tested against XCH4 observations from the Total Carbon Column Network (TCCON). We analyze how the model-TCCON agreement in XCH4 depends on the model representation of stratospheric CH4 distributions. Model equivalents of TCCON XCH4 are computed with stratospheric CH4 fields from both the model simulations and from satellite-based CH4 distributions from MIPAS (Michelson Interferometer for Passive Atmospheric Sounding) and MIPAS CH4 fields adjusted to ACE-FTS (Atmospheric Chemistry Experiment Fourier Transform Spectrometer) observations. In comparison to simulated model fields we find an improved model-TCCON XCH4 agreement for all models with MIPAS-based stratospheric CH4 fields. For the Atmospheric Chemistry Transport Model (ACTM) the average XCH4 bias is significantly reduced from 38.1 ppb to 13.7 ppb, whereas small improvements are found for the models TM5 (Transport Model, version 5; from 8.7 ppb to 4.3 ppb), and LMDz (Laboratoire de Météorologie Dynamique model with Zooming capability; from 6.8 ppb to 4.3 ppb), respectively. MIPAS stratospheric CH4 fields adjusted to ACE-FTS reduce the average XCH4 bias for ACTM (3.3 ppb), but increase the average XCH4 bias for TM5 (10.8 ppb) and LMDz (20.0 ppb). These findings imply that the range of satellite-based stratospheric CH4 is insufficient to resolve a possible stratospheric contribution to differences in total column CH4 between TCCON and TM5 or LMDz. Applying transport diagnostics to the models indicates that model-to-model differences in the simulation of stratospheric transport, notably the age of stratospheric air, can largely explain the inter-model spread in stratospheric CH4 and, hence, its contribution to XCH4. This implies that there is a need to better understand the impact of individual model transport components (e.g., physical parameterization, meteorological data sets, model horizontal/vertical resolution) on modeled stratospheric CH4.

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.

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.

The TROPOspheric Monitoring Instrument (TROPOMI) will be part of ESA's Sentinel-5 Precursor (S5P) satellite platform scheduled for launch in 2015. TROPOMI will monitor methane and carbon monoxide concentrations in the Earth's atmosphere by measuring spectra of back-scattered sunlight in the short-wave infrared (SWIR). S5P will be the first satellite mission to rely uniquely on the spectral window at 4190–4340 cm−1 (2.3 μm) to retrieve CH4 and CO. In this study, we investigated if the absorption features of the three relevant molecules CH4, CO, and H2O are adequately known. To this end, we retrieved total columns of CH4, CO, and H2O from absorption spectra measured by two ground-based Fourier transform spectrometers that are part of the Total Carbon Column Observing Network (TCCON). The retrieval results from the 4190–4340 cm−1 range at the TROPOMI resolution (0.45 cm−1) were then compared to the CH4 results obtained from the 6000 cm−1 region, and the CO results obtained from the 4190–4340 cm−1 region at the higher TCCON resolution (0.02 cm−1). For TROPOMI-like settings, we were able to reproduce the CH4 columns to an accuracy of 0.3% apart from a constant bias of 1%. The CO retrieval accuracy was, through interference, systematically influenced by the shortcomings of the CH4 and H2O spectroscopy. In contrast to CH4, the CO column error also varied significantly with atmospheric H2O content. Unaddressed, this would introduce seasonal and latitudinal biases to the CO columns retrieved from TROPOMI measurements. We therefore recommend further effort from the spectroscopic community to be directed at the H2O and CH4 spectroscopy in the 4190–4340 cm−1 region.

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.

Here, we report preliminary estimates of the column averaged carbon dioxide (CO2) dry air mole fraction, XCO2, retrieved from spectra recorded over land by the Greenhouse gases Observing Satellite, GOSAT (nicknamed "Ibuki"), using retrieval methods originally developed for the NASA Orbiting Carbon Observatory (OCO) mission. After screening for clouds and other known error sources, these retrievals reproduce much of the expected structure in the global XCO2 field, including its variation with latitude and season. However, low yields of retrieved XCO2 over persistently cloudy areas and ice covered surfaces at high latitudes limit the coverage of some geographic regions, even on seasonal time scales. Comparisons of early GOSAT XCO2 retrievals with XCO2 estimates from the Total Carbon Column Observing Network (TCCON) revealed a global, −2% (7–8 parts per million, ppm, with respect to dry air) XCO2 bias and 2 to 3 times more variance in the GOSAT retrievals. About half of the global XCO2 bias is associated with a systematic, 1% overestimate in the retrieved air mass, first identified as a global +10 hPa bias in the retrieved surface pressure. This error has been attributed to errors in the O2 A-band absorption cross sections. Much of the remaining bias and spurious variance in the GOSAT XCO2 retrievals has been traced to uncertainties in the instrument's calibration, oversimplified methods for generating O2 and CO2 absorption cross sections, and other subtle errors in the implementation of the retrieval algorithm. Many of these deficiencies have been addressed in the most recent version (Build 2.9) of the retrieval algorithm, which produces negligible bias in XCO2 on global scales as well as a ~30% reduction in variance. Comparisons with TCCON measurements indicate that regional scale biases remain, but these could be reduced by applying empirical corrections like those described by Wunch et al. (2011b). We recommend that such corrections be applied before these data are used in source sink inversion studies to minimize spurious fluxes associated with known biases. These and other lessons learned from the analysis of GOSAT data are expected to accelerate the delivery of high quality data products from the Orbiting Carbon Observatory-2 (OCO-2), once that satellite is successfully launched and inserted into orbit.

This paper presents extensive validation 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. The ACE satellite instruments operate in the mid-infrared and ultraviolet-visible-near-infrared spectral regions using the solar occultation technique. In order to continue the long-standing record of solar occultation measurements from space, a detailed quality assessment is required to evaluate the ACE data and validate their use for scientific purposes. Here we compare the latest ozone data products from ACE-FTS and ACE-MAESTRO with coincident observations from 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 mean differences range generally between 0 and +10% with a slight but systematic positive bias (typically +5%). At higher altitudes (45-60 km), the ACE-FTS ozone amounts are significantly larger than those of the comparison instruments by up to ~40% (typically +20%). For the ACE-MAESTRO version 1.2 ozone data product, agreement within ±10% (generally better than ±5%) is found between 18 and 40 km for the sunrise and sunset measurements. At higher altitudes (45-55 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 (by as much as -10%), the sunset occultation profiles for ACE-MAESTRO show results that are qualitatively similar to ACE-FTS and indicate a large positive bias (+10 to +30%) in this altitude range. In contrast, there is no significant difference in bias found for the ACE-FTS sunrise and sunset measurements. These systematic effects in the ozone profiles retrieved from the measurements of ACE-FTS and ACE-MAESTRO are being investigated. This work shows that the ACE instruments provide reliable, high quality measurements from the tropopause to the upper stratosphere and can be used with confidence in this vertical domain

Vertical profiles of NO2 and NO have been obtained from solar occultation measurements by the Atmospheric Chemistry Experiment (ACE), using an infrared Fourier Transform Spectrometer (ACE-FTS) and (for NO2) an ultraviolet-visible-near-infrared spectrometer, MAESTRO (Measurement of Aerosol Extinction in the Stratosphere and Troposphere Retrieved by Occultation). In this paper, the quality of the ACE-FTS version 2.2 NO2 and NO and the MAESTRO version 1.2 NO2 data are assessed using other solar occultation measurements (HALOE, SAGE II, SAGE III, POAM III, SCIAMACHY), stellar occultation measurements (GOMOS), limb measurements (MIPAS, OSIRIS), nadir measurements (SCIAMACHY), balloon-borne measurements (SPIRALE, SAOZ) and ground-based measurements (UV-VIS, FTIR). Time differences between the comparison measurements were reduced using either a tight coincidence criterion, or where possible, chemical box models. ACE-FTS NO2 and NO and the MAESTRO NO2 are generally consistent with the correlative data. The ACE-FTS and MAESTRO NO2 volume mixing ratio (VMR) profiles agree with the profiles from other satellite data sets to within about 20% between 25 and 40 km, with the exception of MIPAS ESA (for ACE-FTS) and SAGE II (for ACE-FTS (sunrise) and MAESTRO) and suggest a negative bias between 23 and 40 km of about 10%. MAESTRO reports larger VMR values than the ACE-FTS. In comparisons with HALOE, ACE-FTS NO VMRs typically (on average) agree to ±8% from 22 to 64 km and to +10% from 93 to 105 km, with maxima of 21% and 36%, respectively. Partial column comparisons for NO2 show that there is quite good agreement between the ACE instruments and the FTIRs, with a mean difference of +7.3% for ACE-FTS and +12.8% for MAESTRO.

The Atmospheric Chemistry Experiment (ACE), also known as SCISAT, was launched on 12 August 2003, carrying two instruments that measure vertical profiles of atmospheric constituents using the solar occultation technique. One of these instruments, the ACE Fourier Transform Spectrometer (ACE-FTS), is measuring volume mixing ratio (VMR) profiles of nitrous oxide (N 2 O) from the upper troposphere to the lower mesosphere at a vertical resolution of about 3–4 km. In this study, the quality of the ACE-FTS version 2.2 N 2 O data is assessed through comparisons with coincident measurements made by other satellite, balloon-borne, aircraft, and ground-based instruments. These consist of vertical profile comparisons with the SMR, MLS, and MIPAS satellite instruments, multiple aircraft flights of ASUR, and single balloon flights of SPIRALE and FIRS-2, and partial column comparisons with a network of ground-based Fourier Transform InfraRed spectrometers (FTIRs). Overall, the quality of the ACE-FTS version 2.2 N 2 O VMR profiles is good over the entire altitude range from 5 to 60 km. Between 6 and 30 km, the mean absolute differences for the satellite comparisons lie between -42 ppbv and +17 ppbv, with most within ±20 ppbv. This corresponds to relative deviations from the mean that are within ±15%, except for comparisons with MIPAS near 30 km, for which they are as large as 22.5%. Between 18 and 30 km, the mean absolute differences are generally within ±10 ppbv, again excluding the aircraft and balloon comparisons. From 30 to 60 km, the mean absolute differences are within ±4 ppbv, and are mostly between -2 and +1 ppbv. Given the small N 2 O VMR in this region, the relative deviations from the mean are therefore large at these altitudes, with most suggesting a negative bias in the ACE-FTS data between 30 and 50 km. In the comparisons with the FTIRs, the mean relative differences between the ACE-FTS and FTIR partial columns are within ±6.6% for eleven of the twelve contributing stations. This mean relative difference is negative at ten stations, suggesting a small negative bias in the ACE-FTS partial columns over the altitude regions compared. Excellent correlation ( R =0.964) is observed between the ACE-FTS and FTIR partial columns, with a slope of 1.01 and an intercept of -0.20 on the line fitted to the data.

The Atmospheric Chemistry Experiment (ACE) satellite was launched on 12 August 2003. Its two instruments measure vertical profiles of over 30 atmospheric trace gases by analyzing solar occultation spectra in the ultraviolet/visible and infrared wavelength regions. The reservoir gases HNO3, ClONO2, and N2O5 are three of the key species provided by the primary instrument, the ACE Fourier Transform Spectrometer (ACE-FTS). This paper describes the ACE-FTS version 2.2 data products, including the N2O5 update, for the three species and presents validation comparisons with available observations. We have compared volume mixing ratio (VMR) profiles of HNO3, ClONO2, and N2O5 with measurements by other satellite instruments (SMR, MLS, MIPAS), aircraft measurements (ASUR), and single balloon-flights (SPIRALE, FIRS-2). Partial columns of HNO3 and ClONO2 were also compared with measurements by ground-based Fourier Transform Infrared (FTIR) spectrometers. Overall the quality of the ACE-FTS v2.2 HNO3 VMR profiles is good from 18 to 35 km. For the statistical satellite comparisons, the mean absolute differences are generally within ±1 ppbv ±20%) from 18 to 35 km. For MIPAS and MLS comparisons only, mean relative differences lie within±10% between 10 and 36 km. ACE-FTS HNO3 partial columns (~15–30 km) show a slight negative bias of −1.3% relative to the ground-based FTIRs at latitudes ranging from 77.8° S–76.5° N. Good agreement between ACE-FTS ClONO2 and MIPAS, using the Institut für Meteorologie und Klimaforschung and Instituto de Astrofísica de Andalucía (IMK-IAA) data processor is seen. Mean absolute differences are typically within ±0.01 ppbv between 16 and 27 km and less than +0.09 ppbv between 27 and 34 km. The ClONO2 partial column comparisons show varying degrees of agreement, depending on the location and the quality of the FTIR measurements. Good agreement was found for the comparisons with the midlatitude Jungfraujoch partial columns for which the mean relative difference is 4.7%. ACE-FTS N2O5 has a low bias relative to MIPAS IMK-IAA, reaching −0.25 ppbv at the altitude of the N2O5 maximum (around 30 km). Mean absolute differences at lower altitudes (16–27 km) are typically −0.05 ppbv for MIPAS nighttime and ±0.02 ppbv for MIPAS daytime measurements.

The ACE-FTS (Atmospheric Chemistry Experiment – Fourier Transform Spectrometer) solar occultation instrument that was launched onboard the Canadian SCISAT-1 satellite in August 2003 is measuring vertical profiles from the upper troposphere to the lower mesosphere for a large number of atmospheric constituents. Methane is one of the key species. The version v2.2 data of the ACE-FTS CH4 data have been compared to correlative satellite, balloon-borne and ground-based Fourier transform infrared remote sensing data to assess their quality. The comparison results indicate that the accuracy of the data is within 10% in the upper troposphere – lower stratosphere, and within 25% in the middle and higher stratosphere up to the lower mesosphere (

Accurate measurements of methane (CH4) emission rates from livestock in their undisturbed natural environments are required to assess their impacts on radiative forcing (i.e., enhanced greenhouse effect) and the environment. Here we compare results from two nonintrusive techniques for the measurement of CH4 emissions from cattle. The cows were kept in an outdoor feeding strip that allowed them to follow natural behavioral patterns but contained them within a well defined space. In the first technique, nitrous oxide (N2O) was released as a tracer at the upwind edge of the feeding strip, and the downwind concentrations of N2O and CH4 were measured simultaneously using Fourier transform infrared (FTIR) spectroscopy. Average CH4 emission per cow was calculated each half-hour on three separate days from the correlation between the two gases. The second technique was the integrated horizontal flux (IHF) or 1-D mass-balance method, in which we used the measured vertical profiles of CH4 concentration and windspeed downwind of the cows to determine the total CH4 emission. Comparing the IHF results to the known release rate of N2O allowed us to test the IHF technique independently. We found agreement within 10% for all comparisons on all days. The daily CH4 emission rate averaged over all tracer and IHF measurements was 342 g CH4 head(-1) d(-1). This is within the range of previous measurements for mature lactating dairy cattle (200-430 g CH4 head(-1) d(-1)) but higher than expected for yearling cattle. The high CH4 emissions are accompanied by high CO2 emissions determined from the FTIR measurements. The bias is most likely due to the measurements being made during and after supplementary feeding of the cattle.

Altitude profiles of ClONO2 retrieved with the IMK (Institut für Meteorologie und Klimaforschung) science-oriented data processor from MIPAS/Envisat (Michelson Interferometer for Passive Atmospheric Sounding on Envisat) mid-infrared limb emission measurements between July 2002 and March 2004 have been validated by comparison with balloon-borne (Mark IV, FIRS2, MIPAS-B), airborne (MIPAS-STR), ground-based (Spitsbergen, Thule, Kiruna, Harestua, Jungfraujoch, Izaña, Wollongong, Lauder), and spaceborne (ACE-FTS) observations. With few exceptions we found very good agreement between these instruments and MIPAS with no evidence for any bias in most cases and altitude regions. For balloon-borne measurements typical absolute mean differences are below 0.05 ppbv over the whole altitude range from 10 to 39 km. In case of ACE-FTS observations mean differences are below 0.03 ppbv for observations below 26 km. Above this altitude the comparison with ACE-FTS is affected by the photochemically induced diurnal variation of ClONO2. Correction for this by use of a chemical transport model led to an overcompensation of the photochemical effect by up to 0.1 ppbv at altitudes of 30–35 km in case of MIPAS-ACE-FTS comparisons while for the balloon-borne observations no such inconsistency has been detected. The comparison of MIPAS derived total column amounts with ground-based observations revealed no significant bias in the MIPAS data. Mean differences between MIPAS and FTIR column abundances are 0.11±0.12×1014 cm−2 (1.0±1.1%) and −0.09±0.19×1014 cm−2 (−0.8±1.7%), depending on the coincidence criterion applied. χ2 tests have been performed to assess the combined precision estimates of MIPAS and the related instruments. When no exact coincidences were available as in case of MIPAS – FTIR or MIPAS – ACE-FTS comparisons it has been necessary to take into consideration a coincidence error term to account for χ2 deviations. From the resulting χ2 profiles there is no evidence for a systematic over/underestimation of the MIPAS random error analysis.

New Zealand's largest industrial sector is pastoral agriculture, giving rise to a large fraction of the country's emissions of methane (CH4) and nitrous oxide (N2O). We designed a system to continuously measure CH4 and N2O fluxes at the field scale on two adjacent pastures that differed with respect to management. At the core of this system was a closed-cell Fourier-transform infrared spectrometer (FTIR), measuring the mole fractions of CH4, N2O and carbon dioxide (CO2) at two heights at each site. In parallel, CO2 fluxes were measured using eddy-covariance instrumentation. We applied two different micrometeorological ratio methods to infer the CH4 and N2O fluxes from their respective mole fractions and the CO2 fluxes. The first is a variant of the flux-gradient method, where it is assumed that the turbulent diffusivities of CH4 and N2O equal that of CO2. This method was reliable when the CO2 mole-fraction difference between heights was at least 4 times greater than the FTIR's resolution of differences. For the second method, the temporal increases of mole fractions in the stable nocturnal boundary layer, which are correlated for concurrently-emitted gases, are used to infer the unknown fluxes of CH4 and N2O from the known flux of CO2. This method was sensitive to "contamination" from trace gas sources other than the pasture of interest and therefore required careful filtering. With both methods combined, estimates of mean daily CH4 and N2O fluxes were obtained for 60 % of days at one site and 77 % at the other. Both methods indicated both sites as net sources of CH4 and N2O. Mean emission rates for one year at the unfertilised, winter-grazed site were 8.2 (± 0.91) nmol CH4 m−2 s−1 and 0.40 (± 0.018) nmol N2O m−2 s−1. During the same year, mean emission rates at the irrigated, fertilised and rotationally-grazed site were 7.0 (± 0.89) nmol CH4 m−2 s−1 and 0.57 (± 0.019) nmol N2O m−2 s−1. At this site, the N2O emissions amounted to 1.19 (± 0.15) % of the nitrogen inputs from animal excreta and fertiliser application.

We investigate the impact of biogenic emissions on carbon monoxide (CO) and formaldehyde (HCHO) in the Southern Hemisphere (SH), with simulations using two different biogenic emission inventories for isoprene and monoterpenes. Results from four atmospheric chemistry models are compared to continous long-term ground-based CO and HCHO column measurements at SH NDACC sites, and to in situ surface CO measurements from across the SH, representing a subset of the NOAA GMD network. Simulated mean model CO using the CLM-MEGANv2.1 inventory is in good agreement with both column and surface observations, whereas simulations adopting LPJ-GUESS emissions markedly underestimate measured column and surface CO at most sites. Differences in biogenic emissions cause large differences in CO in the source regions which propagate to the remote SH. Significant inter-model differences exist in modelled column and surface CO, due mainly to differences in the models' oxidation schemes for volatile organic compounds; secondary production of CO dominates these inter-model differences. While biogenic emissions are a significant factor in modelling SH CO, inter-model differences pose an additional challenge to constrain these emissions. Corresponding comparisons of HCHO columns at two SH mid-latitude sites reveal that all models significantly underestimate the observed values by approximately a factor of 2. There is a much smaller impact on HCHO of the significantly different biogenic emissions in remote regions, compared to the source regions. Decreased biogenic emissions cause decreased CO export to remote regions, which leads to increased OH; this in turn results in increased HCHO production through methane oxidation. In agreement with earlier studies, we corroborate that significant HCHO sources are likely missing in the models in the remote SH.

Near infrared absorption of solar radiation is the basis for both ground- and space-based remote sensing of atmospheric composition. We describe the instruments and networks which provide global mapping of atmospheric greenhouse gases.

Total column amounts of CO, CH4, CO2 and N2O retrieved from SCIAMACHY nadir observations in its 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.

The tropospheric mixing ratio profile of ethylene (C2H4) has been retrieved from a high spectral resolution ground-based infrared solar absorption spectrum. The spectrum was recorded during intense fires in New South Wales, Australia on January 1, 2002, and was analyzed with the C2H4 spectral parameters added to the 2000 HITRAN compilation. Absorption by C2H4 in the smoke-affected spectrum extends over a broad spectral range in a region with observable rotational fine structure. The fine structure occurs in addition to the 949.5 cm(-1) v(7) band Q branch that is traditionally used to quantify C2H4 amounts from infrared atmospheric measurements assuming room temperature laboratory absorption coefficients. The measured spectrum is fitted to near the noise level with a retrieved vertical column of (3.8 +/- 0.2) x 10(17) molecules per square centimeter. The retrieved vertical C2H4 profile increases with altitude near the surface reaching a maximum of 37 parts per billion (10(-9)) near 1 km, decreasing above. (C) 2005 Elsevier Ltd. All rights reserved. (Less)

Micrometeorological flux-gradient and nocturnal boundary layer methods were combined with Fourier transform infrared (FTIR) spectroscopy for high-precision trace gas analysis to measure fluxes of the trace gases CO2, CH4 and N2O between agricultural fields and the atmosphere. The FTIR measurements were fully automated and routinely obtained a precision of 0.1–0.2% for several weeks during a measurement campaign in October 1995. In flux-gradient measurements, vertical profiles of the trace gases were measured every 30 min from the ground to 22 m. When combined with independent micrometeorological measurements of water vapour fluxes, trace gas fluxes from the underlying surface could be determined. In the nocturnal boundary layer method the rate of change in mass storage in the 0–22 m layer was combined with fluxes measured at 22 m to estimate surface fluxes. Daytime fluxes for CO2 were −0.78±0.40 (1σ) mg CO2 m−2 s−1. Daytime fluxes of N2O and CH4 were very small and difficult to measure reliably using the flux-gradient technique, despite the high precision of the concentration measurements. Mean daytime flux for N2O was 17±48 ng N m−2 s−1, while the corresponding flux for CH4 was 47±410 ng CH4 m−2 s−1. The mean nighttime flux of CO2 estimated using the nocturnal boundary layer method was +0.15±0.05 mg CO2 m−2 s−1, in good agreement with chamber measurements of respiration rates. Nighttime fluxes of CH4 and N2O from the nocturnal boundary layer method were 109±69 ng CH4 m−2 s−1 and 2±3.2 ng N m−2 s−1, respectively, in good agreement with chamber measurements and inventory estimates based on the sheep and cattle stocking rates in the region. The suitability of FTIR-based methods for long term monitoring of spatially and temporally averaged flux measurements is discussed.

A mass balance method is described for calculating gas production from a surface or volume source in a small test plot from measurements of differences in the horizontal fluxes of the gas across upwind and downwind boundaries. It employs a square plot, 24 m×24 m, with measurements of gas concentration at four heights (up to 3.5 m) along each of the four boundaries. Gas concentrations are multiplied by the appropriate vector winds to yield the horizontal fluxes at each height on each boundary. The difference between these fluxes integrated over downwind and upwind boundaries represents production. Illustrations of the method, which involve exchanges of methane and carbon dioxide, are drawn from experiments with landfills, pastures and grazing animals. Tests included calculation of recovery rates from known gas releases and comparisons with a conventional micrometeorological approach and a backward dispersion model. The method performed satisfactorily in all cases. Its sensitivity for measuring exchanges of CO2, CH4 and N2O in various scenarios was examined. As employed by us, the mass balance method can suffer from errors arising from the large number of gas analyses required for a flux determination, and becomes unreliable when there are light winds and variable wind directions. On the other hand, it is non-disturbing, has a simple theoretical basis, is independent of atmospheric stability or the shape of the wind profile, and is appropriate for flux measurement in situations where conventional micrometeorological methods can not be used, e.g. for small plots, elevated point sources, and heterogeneous surface sources.

Total column abundances of CHClF2 (HCFC-22) have been retrieved from high-resolution infrared solar absorption spectra recorded at the Network for the Detection of Stratospheric Change (NDSC) station in Lauder, New Zealand (370 m altitude, 45.04°S latitude, 169.68°E longitude). The analysis, based on nonlinear least squares fittings to the unresolved 2v6 band Q branch of CH35ClF2 at 829.05 cm−1, has been applied to a time series of 670 spectra recorded on 394 days between May 1985 and November 1994. The measurements indicate exponential and linear (referenced to the beginning of 1994) increase rates of (7.5±0.3)% yr−1 and (5.9±0.2)% yr−1, 1 σ, corresponding to a doubling of the total column abundance over the 9.5-year measurement period. Of the two models the exponential increase model yields a slightly better fit to the data than the linear model. A HCFC-22 south/north hemispheric ratio of 0.83 ±0.04, 1σ, is derived by comparing the Lauder column measurements with column measurements from the International Scientific Station of the Jungfraujoch (46.5°N, 8.0°E), after correction for the altitude difference between the two sites. Using a second, independent method in which the N2O column serves as a surrogate air mass, we have used the Lauder measurements and similar measurements from Table Mountain (34.4°N) to calculate a south/north ratio of 0.91±0.10.

The soil in a drained fjord area, reclaimed for arable farming, produced N2O mainly at 75–105 cm depth, just above the ground water level. Surface emissions of N2O were measured from discrete small areas by closed and open-flow chamber methods, using gas chromatographic analysis and over larger areas by integrative methods: flux gradient (analysis by FTIR), conditional sampling (analysis by TDLAS), and eddy covariance (analysis by TDLAS). The mean emission of N2O as determined by chamber procedures during a 9-day campaign was 162–202 μg N2ONm−2h−1 from a wheat stubble and 328–467 μg N2ONm−2 h−1 from a carrot field. The integrative approaches gave N2O emissions of 149–495 μg N2ONm−2 h−1, i.e. a range similar to those determined with the chamber methods. Wind direction affected the comparison of chamber and integrative methods because of patchiness of the N2O emission over the area. When a uniform area with a single type of vegetation had a dominant effect on the N2O gradient at the sampling mast, the temporal variation in N2O emission determined by the flux gradient/FTIR method and chamber methods was very similar, with differences of only 18% or less in mean N2O emission, well below the variation encountered with the chamber methods themselves. A detailed comparison of FTIR gradient and chamber data taking into account the precise emission footprint showed good agreement. It is concluded that there was no bias between the different approaches used to measure the N2O emission and that the precision of the measurements was determined by the spatial variability of the N2O emission at the site and the variability inherent in the individual techniques. These results confirm that measurements of N2O emissions from different ecosystems obtained by the different methods can be meaningfully compared.

The spatial variability of N2O emission from soil makes extrapolation to the field scale very difficult using; conventional chamber techniques ( < 1 m2). Micrometeorological techniques, which integrate N2O fluxes over areas of 0.1 to 1 km2 were therefore developed and compared with chamber methods over arable cropland. Measurements of N2O emission from an unfertilised organic soil (reclaimed from the sea in 1879) were made over a 10 d period at Lammefjord, Denmark. Flux-gradient and conditional sampling techniques were applied using two tunable diode laser spectrometers (TDLs), a Fourier transform infra-red spectrometer (FTIR) and a gas chromatograph (GC). Eddy covariance measurements were also made by the TDLs. Over the 10 d campaign approximately 5 d of continuous fluxes by the different methods were, obtained. Fluxes determined by eddy covariance were in reasonable agreement, showing a mean flux of 269 μg N m2 h−1. Flux-gradient techniques measured a mean flux of 226 μg N m−2 h−1. The mean flux measured by conditional sampling was 379 μg N m−2 h−1. The maximum annual emission of N2O from this soil system was estimated to be 23.5 kg N ha−1.

[1] This report describes a validation study of Greenhouse gases Observing Satellite (GOSAT) data processing using ground-based measurements of the Total Carbon Column Observing Network (TCCON) as reference data for column-averaged dry air mole fractions of atmospheric carbon dioxide (XCO2). We applied the photon path length probability density function method to validate XCO2retrievals from GOSAT data obtained during 22 months starting from June 2009. This method permitted direct evaluation of optical path modifications due to atmospheric light scattering that would have a negligible impact on ground-based TCCON measurements but could significantly affect gas retrievals when observing reflected sunlight from space. Our results reveal effects of optical path lengthening over Northern Hemispheric stations, essentially from May–September of each year, and of optical path shortening for sun-glint observations in tropical regions. These effects are supported by seasonal trends in aerosol optical depth derived from an offline three-dimensional aerosol transport model and by cirrus optical depth derived from space-based measurements of the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. Removal of observations that were highly contaminated by aerosol and cloud from the GOSAT data set resulted in acceptable agreement in the seasonal variability of XCO2 over each station as compared with TCCON measurements. Statistical comparisons between GOSAT and TCCON coincident measurements of CO2column abundance show a correlation coefficient of 0.85, standard deviation of 1.80 ppm, and a sub-ppm negative bias of −0.43 ppm for all TCCON stations. Global distributions of monthly mean retrieved XCO2 with a spatial resolution of 2.5° latitude × 2.5° longitude show agreement within ∼2.5 ppm with those predicted by the atmospheric tracer transport model.