Search

Satellite-based observations of atmospheric carbon dioxide (CO 2 ) provide measurements in remote regions, such as the biologically sensitive but undersampled northern high latitudes, and are progressing toward true global data coverage. Recent improvements in satellite retrievals of total column-averaged dry air mole fractions of CO 2 ( X CO 2 ) from the NASA Orbiting Carbon Observatory 2 (OCO-2) have allowed for unprecedented data coverage of northern high-latitude regions, while maintaining acceptable accuracy and consistency relative to ground-based observations, and finally providing sufficient data in spring and autumn for analysis of satellite-observed X CO 2 seasonal cycles across a majority of terrestrial northern high-latitude regions. Here, we present an analysis of X CO 2 seasonal cycles calculated from OCO-2 data for temperate, boreal, and tundra regions, subdivided into 5 ∘ latitude by 20 ∘ longitude zones. We quantify the seasonal cycle amplitudes (SCAs) and the annual half drawdown day (HDD). OCO-2 SCAs are in good agreement with ground-based observations at five high-latitude sites, and OCO-2 SCAs show very close agreement with SCAs calculated for model estimates of X CO 2 from the Copernicus Atmosphere Monitoring Services (CAMS) global inversion-optimized greenhouse gas flux model v19r1 and the CarbonTracker2019 model (CT2019B). Model estimates of X CO 2 from the GEOS-Chem CO 2 simulation version 12.7.2 with underlying biospheric fluxes from CarbonTracker2019 (GC-CT2019) yield SCAs of larger magnitude and spread over a larger range than those from CAMS, CT2019B, or OCO-2; however, GC-CT2019 SCAs still exhibit a very similar spatial distribution across northern high-latitude regions to that from CAMS, CT2019B, and OCO-2. Zones in the Asian boreal forest were found to have exceptionally large SCA and early HDD, and both OCO-2 data and model estimates yield a distinct longitudinal gradient of increasing SCA from west to east across the Eurasian continent. In northern high-latitude regions, spanning latitudes from 47 to 72 ∘ N, longitudinal gradients in both SCA and HDD are at least as pronounced as latitudinal gradients, suggesting a role for global atmospheric transport patterns in defining spatial distributions of X CO 2 seasonality across these regions. GEOS-Chem surface contact tracers show that the largest X CO 2 SCAs occur in areas with the greatest contact with land surfaces, integrated over 15–30 d. The correlation of X CO 2 SCA with these land surface contact tracers is stronger than the correlation of X CO 2 SCA with the SCA of CO 2 fluxes or the total annual CO 2 flux within each 5 ∘ latitude by 20 ∘ longitude zone. This indicates that accumulation of terrestrial CO 2 flux during atmospheric transport is a major driver of regional variations in X CO 2 SCA.

We examined biases in the global GEOS-Chem chemical transport model for the period of February–May 2010 using weak-constraint (WC) four-dimensional variational (4D-Var) data assimilation and dry-air mole fractions of CH4 (XCH4) from the Greenhouse gases Observing SATellite (GOSAT). The ability of the observations and the WC 4D-Var method to mitigate model errors in CH4 concentrations was first investigated in a set of observing system simulation experiments (OSSEs). We then assimilated the GOSAT XCH4 retrievals and found that they were capable of providing information on the vertical structure of model errors and of removing a significant portion of biases in the modeled CH4 state. In the WC 4D-Var assimilation, corrections were added to the modeled CH4 state at each model time step to account for model errors and improve the model fit to the assimilated observations. Compared to the conventional strong-constraint (SC) 4D-Var assimilation, the WC method was able to significantly improve the model fit to independent observations. Examination of the WC state corrections suggested that a significant source of model errors was associated with discrepancies in the model CH4 in the stratosphere. The WC state corrections also suggested that the model vertical transport in the troposphere at middle and high latitudes is too weak. The problem was traced back to biases in the uplift of CH4 over the source regions in eastern China and North America. In the tropics, the WC assimilation pointed to the possibility of biased CH4 outflow from the African continent to the Atlantic in the mid-troposphere. The WC assimilation in this region would greatly benefit from glint observations over the ocean to provide additional constraints on the vertical structure of the model errors in the tropics. We also compared the WC assimilation at 4∘ × 5∘ and 2∘ × 2.5∘ horizontal resolutions and found that the WC corrections to mitigate the model errors were significantly larger at 4∘ × 5∘ than at 2∘ × 2.5∘ resolution, indicating the presence of resolution-dependent model errors. Our results illustrate the potential utility of the WC 4D-Var approach for characterizing model errors. However, a major limitation of this approach is the need to better characterize the specified model error covariance in the assimilation scheme.

This work presents the latest release (v9.0) of the University of Leicester GOSAT Proxy XCH4 dataset. Since the launch of the GOSAT satellite in 2009, these data have been produced by the UK National Centre for Earth Observation (NCEO) as part of the ESA Greenhouse Gas Climate Change Initiative (GHG-CCI) and Copernicus Climate Change Services (C3S) projects. With now over a decade of observations, we outline the many scientific studies achieved using past versions of these data in order to highlight how this latest version may be used in the future. We describe in detail how the data are generated, providing information and statistics for the entire processing chain from the L1B spectral data through to the final quality-filtered column-averaged dry-air mole fraction (XCH4) data. We show that out of the 19.5 million observations made between April 2009 and December 2019, we determine that 7.3 million of these are sufficiently cloud-free (37.6 %) to process further and ultimately obtain 4.6 million (23.5 %) high-quality XCH4 observations. We separate these totals by observation mode (land and ocean sun glint) and by month, to provide data users with the expected data coverage, including highlighting periods with reduced observations due to instrumental issues. We perform extensive validation of the data against the Total Carbon Column Observing Network (TCCON), comparing to ground-based observations at 22 locations worldwide. We find excellent agreement with TCCON, with an overall correlation coefficient of 0.92 for the 88 345 co-located measurements. The single-measurement precision is found to be 13.72 ppb, and an overall global bias of 9.06 ppb is determined and removed from the Proxy XCH4 data. Additionally, we validate the separate components of the Proxy (namely the modelled XCO2 and the XCH4∕XCO2 ratio) and find these to be in excellent agreement with TCCON. In order to show the utility of the data for future studies, we compare against simulated XCH4 from the TM5 model. We find a high degree of consistency between the model and observations throughout both space and time. When focusing on specific regions, we find average differences ranging from just 3.9 to 15.4 ppb. We find the phase and magnitude of the seasonal cycle to be in excellent agreement, with an average correlation coefficient of 0.93 and a mean seasonal cycle amplitude difference across all regions of −0.84 ppb. These data are available at https://doi.org/10.5285/18ef8247f52a4cb6a14013f8235cc1eb (Parker and Boesch, 2020).

The Total Carbon Column Observing Network (TCCON) is the baseline ground-based network of instruments that record solar absorption spectra from which accurate and precise column-averaged dry-air mole fractions of CO2 (XCO2), CH4 (XCH4), CO (XCO), and other gases are retrieved. The TCCON data have been widely used for carbon cycle science and validation of satellites measuring greenhouse gas concentrations globally. The number of stations in the network (currently about 25) is limited and has a very uneven geographical coverage: the stations in the Northern Hemisphere are distributed mostly in North America, Europe, and Japan, and only 20 % of the stations are located in the Southern Hemisphere, leaving gaps in the global coverage. A denser distribution of ground-based solar absorption measurements is needed to improve the representativeness of the measurement data for various atmospheric conditions (humid, dry, polluted, presence of aerosol), various surface conditions such as high albedo (>0.4) and very low albedo, and a larger latitudinal distribution. More stations in the Southern Hemisphere are also needed, but a further expansion of the network is limited by its costs and logistical requirements. For this reason, several groups are investigating supplemental portable low-cost instruments. The European Space Agency (ESA) funded campaign Fiducial Reference Measurements for Ground-Based Infrared Greenhouse Gas Observations (FRM4GHG) at the Sodankylä TCCON site in northern Finland aims to characterise the assessment of several low-cost portable instruments for precise solar absorption measurements of XCO2, XCH4, and XCO. The test instruments under investigation are three Fourier transform spectrometers (FTSs): a Bruker EM27/SUN, a Bruker IRcube, and a Bruker Vertex70, as well as a laser heterodyne spectroradiometer (LHR) developed by the UK Rutherford Appleton Laboratory. All four remote sensing instruments performed measurements simultaneously next to the reference TCCON instrument, a Bruker IFS 125HR, for a full year in 2017. The TCCON FTS was operated in its normal high-resolution mode (TCCON data set) and in a special low-resolution mode (HR125LR data set), similar to the portable spectrometers. The remote sensing measurements are complemented by regular AirCore launches performed from the same site. They provide in situ vertical profiles of the target gas concentrations as auxiliary reference data for the column retrievals, which are traceable to the WMO SI standards. The reference measurements performed with the Bruker IFS 125HR were found to be affected by non-linearity of the indium gallium arsenide (InGaAs) detector. Therefore, a non-linearity correction of the 125HR data was performed for the whole campaign period and compared with the test instruments and AirCore. The non-linearity-corrected data (TCCONmod data set) show a better match with the test instruments and AirCore data compared to the non-corrected reference data. The time series, the bias relative to the reference instrument and its scatter, and the seasonal and the day-to-day variations of the target gases are shown and discussed. The comparisons with the HR125LR data set gave a useful analysis of the resolution-dependent effects on the target gas retrieval. The solar zenith angle dependence of the retrievals is shown and discussed. The intercomparison results show that the LHR data have a large scatter and biases with a strong diurnal variation relative to the TCCON and other FTS instruments. The LHR is a new instrument under development, and these biases are currently being investigated and addressed. The campaign helped to characterise and identify instrumental biases and possibly retrieval biases, which are currently under investigation. Further improvements of the instrument are ongoing. The EM27/SUN, the IRcube, the modified Vertex70, and the HR125LR provided stable and precise measurements of the target gases during the campaign with quantified small biases. The bias dependence on the humidity along the measurement line of sight has been investigated and no dependence was found. These three portable low-resolution FTS instruments are suitable to be used for campaign deployment or long-term measurements from any site and offer the ability to complement the TCCON and expand the global coverage of ground-based reference measurements of the target gases.

The Total Carbon Column Observing Network (TCCON) column-averaged dry air mole fraction of CH4 ( X CH 4 ) measurements have been widely used to validate satellite observations and to estimate model simulations. The GGG2014 code is the standard TCCON retrieval software used in performing a profile scaling retrieval. In order to obtain several vertical pieces of information in addition to the total column, in this study, the SFIT4 retrieval code is applied to retrieve the CH4 mole fraction vertical profile from the Fourier transform spectrometer (FTS) spectrum at six sites (Ny-Alesund, Sodankyla, Bialystok, Bremen, Orleans and St Denis) during the time period of 2016–2017. The retrieval strategy of the CH4 profile retrieval from ground-based FTS near-infrared (NIR) spectra using the SFIT4 code (SFIT4NIR) is investigated. The degree of freedom for signal (DOFS) of the SFIT4NIR retrieval is about 2.4, with two distinct pieces of information in the troposphere and in the stratosphere. The averaging kernel and error budget of the SFIT4NIR retrieval are presented. The data accuracy and precision of the SFIT4NIR retrievals, including the total column and two partial columns (in the troposphere and stratosphere), are estimated by TCCON standard retrievals, ground-based in situ measurements, Atmospheric Chemistry Experiment – Fourier Transform Spectrometer (ACE-FTS) satellite observations, TCCON proxy data and AirCore and aircraft measurements. By comparison against TCCON standard retrievals, it is found that the retrieval uncertainty of SFIT4NIR X CH 4 is similar to that of TCCON standard retrievals with systematic uncertainty within 0.35 % and random uncertainty of about 0.5 %. The tropospheric and stratospheric X CH 4 from SFIT4NIR retrievals are assessed by comparison with AirCore and aircraft measurements, and there is a 1.0 ± 0.3 % overestimation in the SFIT4NIR tropospheric X CH 4 and a 4.0 ± 2.0 % underestimation in the SFIT4NIR stratospheric X CH 4 , which are within the systematic uncertainties of SFIT4NIR-retrieved partial columns in the troposphere and stratosphere respectively.

Satellite retrievals of XCO2 at northern high latitudes currently have sparser coverage and lower data quality than most other regions of the world. We use a neural network (NN) to filter OCO-2 B10 bias-corrected XCO2 retrievals and compare the quality of the filtered data to the quality of the data filtered with the standard B10 quality control filter. To assess the performance of the NN filter, we use Total Carbon Column Observing Network (TCCON) data at selected northern high latitude sites as a truth proxy. We found that the NN filter decreases the overall bias by 0.25 ppm (~50 %), improves the precision by 0.18 ppm (~12 %), and increases the throughput by 16 % at these sites when compared to the standard B10 quality control filter. Most of the increased throughput was due to an increase in throughput during the spring, fall, and winter seasons. There was a decrease in throughput during the summer, but as a result the bias and precision were improved during the summer months. The main drawback of using the NN filter is that it lets through fewer retrievals at the highest latitude Arctic TCCON sites compared to the B10 quality control filter, but the lower throughput improves the bias and precision.

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.

TCCON (Total Carbon Column Observing Network) column-averaged dry air mole fraction of CH4 (XCH4) measurements have been widely used to validate satellite observations and to estimate model simulations. The GGG2014 code is the standard TCCON retrieval software performing a profile scaling retrieval. In order to obtain several vertical information in addition to total column, in this study, the SFIT4 retrieval code is applied to retrieve CH4 mole fraction vertical profile using TCCON spectra (SFIT4TCCON) at six sites (Ny-Ålesund, Sodankylä, Bialystok, Bremen, Orléans and St Denis) during the time period of 2016−2017. The retrieval strategy of SFIT4TCCON is investigated. The degree of freedom for signal of the SFIT4TCCON retrieval is about 2.4, with two distinct species of information in the troposphere and in the stratosphere. The averaging kernel and error budget of the SFIT4TCCON retrieval are presented. The data accuracy and precision of the SFIT4TCCON retrievals, including the total column and two partial columns (in the troposphere and stratosphere), are estimated by TCCON standard retrievals, ground-based in situ measurements, ACE-FTS satellite observations, TCCON proxy data and AirCore measurements. By comparison against TCCON standard retrievals, it is found that the retrieval uncertainty of SFIT4TCCON XCH4 is similar to that of TCCON standard retrievals with the systematic uncertainty within 0.35 % and the random uncertainty about 0.5 %. The tropospheric and stratospheric XCH4 from SFIT4TCCON retrievals are assessed by comparing with AirCore measurements at Sodankylä, and there is a 1.2 % overestimation in the SFIT4TCCON tropospheric XCH4 and a 4.0 % underestimation in the SFIT4TCCON stratospheric XCH4, which are within the systematic uncertainties of SFIT4TCCON retrieved partial columns in the troposphere and stratosphere, respectively.

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

This study presents results from the European Centre for Medium-Range Weather Forecasts (ECMWF) carbon dioxide (CO2) analysis system where the atmospheric CO2 is controlled through the assimilation of column-averaged dry-air mole fractions of CO2 (XCO2) from the Greenhouse gases Observing Satellite (GOSAT). The analysis is compared to a free-run simulation (without assimilation of XCO2), and they are both evaluated against XCO2 data from the Total Carbon Column Observing Network (TCCON). We show that the assimilation of the GOSAT XCO2 product from the Bremen Optimal Estimation Differential Optical Absorption Spectroscopy (BESD) algorithm during the year 2013 provides XCO2 fields with an improved mean absolute error of 0.6 parts per million (ppm) and an improved station-to-station bias deviation of 0.7 ppm compared to the free run (1.1 and 1.4 ppm, respectively) and an improved estimated precision of 1 ppm compared to the GOSAT BESD data (3.3 ppm). We also show that the analysis has skill for synoptic situations in the vicinity of frontal systems, where the GOSAT retrievals are sparse due to cloud contamination. We finally computed the 10-day forecast from each analysis at 00:00 UTC, and we demonstrate that the CO2 forecast shows synoptic skill for the largest-scale weather patterns (of the order of 1000 km) even up to day 5 compared to its own analysis.

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.

Instrumental line shape (ILS) degradation can cause significant biases between global FTIR (Fourier transform infrared) networks if not properly treated. Currently, how ILS degradation influences the global NDACC (Network for Detection of Atmospheric Composition Change) gases retrieval and how much ILS deviation is acceptable for each NDACC gas are still not fully quantified. We simulated ILS degradations with respect to typical types of misalignment, and compared their influence on each NDACC gas. The sensitivities of total column, root mean square of fitting residual (RMS), total random uncertainty, total systematic uncertainty, total uncertainty, degrees of freedom for signal (DOFs), and profile with respect to different levels of ILS degradation for all current NDACC gases, i.e., O3, HNO3, HCl, HF, ClONO2, CH4, CO, N2O, C2H6, and HCN, were investigated. The influence of an imperfect ILS on NDACC gases retrieval were assessed, and the consistency under different meteorological conditions and solar zenith angles (SZA) were examined. The study concluded that the influence of ILS degradation can be approximated by the linear sum of individual modulation efficiency (ME) amplitude influence and phase error (PE) influence. The PE influence is of secondary importance compared with the ME amplitude influence. For total column retrieval, the stratospheric gases are more sensitive to ILS degradation than the tropospheric gases. For profile retrieval, the positive ME has more influence on tropospheric gases than the stratospheric gases. In contrast, the negative ME has more influence on stratospheric gases than the tropospheric gases. In order to suppress the influence on total column for ClONO2 and other NDACC gases within 10 % and 1 %, respectively, the permitted maximum ILS degradation for each NDACC gas was deduced (summarized in Table 5).

This study evaluated three bias correction methods of systematic biases in column-averaged dry-air mole fraction of water vapor (XH2O) data retrieved from Greenhouse Gases Observing Satellite (GOSAT) Short-Wavelength Infrared (SWIR) observations compared with ground-based data from the Total Carbon Column Observing Network (TCCON). They included an empirically multilinear regression method, altitude bias correction method, and combination of altitude and empirical correction for three cases defined by the temporal and spatial collocation around TCCON site. The results showed that large altitude differences between GOSAT observation points and TCCON instruments are the main cause of bias, and the altitude bias correction method is the most effective bias correction method. The lowest biases result from GOSAT SWIR XH2O data within a 0.5&deg, 0.5&deg, latitude longitude box centered at each TCCON site matched with TCCON XH2O data averaged over &plusmn, 15 min of the GOSAT overpass time. Considering land data, the global bias changed from &minus, 1.3 &plusmn, 9.3% to &minus, 2.2 &plusmn, 8.5%, and station bias from &minus, 2.3 &plusmn, 9.0% to &minus, 1.7 &plusmn, 8.4%. In mixed land and ocean data, global bias and station bias changed from &minus, 0.3 &plusmn, 7.6% and &minus, 1.9 &plusmn, 7.1% to &minus, 0.8 &plusmn, 7.2% and &minus, 6.8%, respectively, after bias correction. The results also confirmed that the fine spatial and temporal collocation criteria are necessary in bias correction methods.

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

Understanding the atmospheric distribution of water (H 2 O) is crucial for global warming studies and climate change mitigation. In this context, reliable satellite data are extremely valuable for their global and continuous coverage, once their quality has been assessed. Short-wavelength infrared spectra are acquired by the Thermal And Near-infrared Sensor for carbon Observation-Fourier Transform Spectrometer (TANSO-FTS) aboard the Greenhouse gases Observing Satellite (GOSAT). From these, column-averaged dry-air mole fractions of carbon dioxide, methane and water vapor (XH 2 O) have been retrieved at the National Institute for Environmental Studies (NIES, Japan) and are available as a Level 2 research product. We compare the NIES XH 2 O data, Version 02.21, with retrievals from the ground-based Total Carbon Column Observing Network (TCCON, Version GGG2014). The datasets are in good overall agreement, with GOSAT data showing a slight global low bias of −3.1% ± 24.0%, good consistency over different locations (station bias of −1.53% ± 10.35%) and reasonable correlation with TCCON (R = 0.89). We identified two potential sources of discrepancy between the NIES and TCCON retrievals over land. While the TCCON XH 2 O amounts can reach 6000–7000 ppm when the atmospheric water content is high, the correlated NIES values do not exceed 5500 ppm. This could be due to a dry bias of TANSO-FTS in situations of high humidity and aerosol content. We also determined that the GOSAT-TCCON differences directly depend on the altitude difference between the TANSO-FTS footprint and the TCCON site. Further analysis will account for these biases, but the NIES V02.21 XH 2 O product, after public release, can already be useful for water cycle studies.