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An 11-year dataset from a polarization lidar operated by NIWA at Lauder was used to investigate heterogeneous ice formation in clouds as a function of temperature for a site on the South Island of New Zealand. Trajectory-based tools and model data were used to relate this cloud dataset to the aerosol load and air mass sources, as aerosol conditions in the middle troposphere above Lauder are subject to contrasts. Clean, pristine airmasses from Antarctica and the Southern Ocean arrive under southerly flow conditions while elevated aerosol loads occur when air masses are advected from nearby Australia.The Lauder cloud dataset was put into context with lidar studies from contrasting regions such as Germany and southern Chile. The ice-formation efficiency found at Lauder is lower than in polluted mid-latitudes (i.e., Germany) but higher than in southern Chile. Both, Lauder and southern Chile are subject to generally low free-tropospheric aerosol loads, which suggests that the low ice-formation efficiency at these two sites is related to low ice-nucleating particle (INP) concentrations. However, Lauder sees episodes of continental aerosol, more than does southern Chile, what seems to lead to the moderately increased ice-formation efficiency. Trajectory-based tools and aerosol model reanalyses were used to relate the cloud dataset to the aerosol load and the air mass sources. Both analyses point clearly to higher ice formation efficiency for clouds which are more strongly influenced by continental aerosol, and to lower ice formation efficiency for clouds which are more influenced by Antarctic/marine aerosol and air masses., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

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

The practical usefulness of a desktop optical spectrum analyzer (OSA) for measuring atmospheric CO2 and CH4 column densities at surface sites was examined in two separate measurement campaigns. The first comparison involved operating the OSA in parallel with a high resolution Fourier transform spectroscopy (FTS) situated at the University of Wollongong in Australia. Scale factors for the OSA were assigned for the column average volume mixing ratios of xCO2 and xCH4 by comparing with the well-studied FTS. The second method is a calibration against aircraft CO2 profiles in situ over Tsukuba in Japan obtained during a GOSAT validation campaign carried out from 28 January to 7 February 2011. The xCO2 values in the campaign, deduced by use of a derived OSA scale factor, were in excellent agreement with the integrated aircraft profiles.

Solar absorption spectra in the near-infrared region have been continuously acquired with a ground-based (g-b) high-resolution Fourier transform spectrometer (FTS) at Saga, Japan, since July 2011. Column-averaged dry-air mole fractions of greenhouse gases were retrieved from the measured spectra for the period from July 2011 to December 2014. Aircraft measurements of CO2 and CH4 for calibrating the g-b FTS data were performed in January 2012 and 2013, and it is found that the g-b FTS and aircraft data agree to within ± 0.2 %. The column-averaged dry-air mole fractions of CO2 and CH4 (XCO2 and XCH4) show increasing trends, with average growth rates of 2.3 and 9.5 ppb yr−1, respectively, during the ∼ 3.5 yr of observation. We compared the g-b FTS XCO2 and XCH4 data with those derived from backscattered solar spectra in the short-wavelength infrared (SWIR) region measured with Thermal And Near-infrared Sensor for carbon Observation–Fourier Transform Spectrometer (TANSO-FTS) onboard the Greenhouse gases Observing SATellite (GOSAT): NIES SWIR Level 2 products (versions 02.xx). Average differences between TANSO-FTS and g-b FTS data (TANSO-FTS minus g-b FTS) are 0.40 ± 2.51 and −7.6 ± 13.7 ppb for XCO2 and XCH4, respectively. Using aerosol information measured with a sky radiometer at Saga, we found that the differences between the TANSO-FTS and g-b FTS XCO2 data are moderately negatively correlated with aerosol optical thickness and do not depend explicitly on aerosol size. In addition, from several aerosol profiles measured with lidar located right by the g-b FTS, we were able to show that the presence of cirrus clouds tends to cause an overestimation in the TANSO-FTS XCO2 retrieval, while high aerosol loading in the lower troposphere tends to cause an underestimation.

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

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

The column-averaged dry-air mole fractions of carbon dioxide and methane (XCO2 and XCH4) have been retrieved from Greenhouse gases Observing SATellite (GOSAT) Short-Wavelength InfraRed (SWIR) observations and released as a SWIR L2 product from the National Institute for Environmental Studies (NIES). XCO2 and XCH4 retrieved using the version 01.xx retrieval algorithm showed large negative biases and standard deviations (-8.85 and 4.75 ppm for XCO2 and -20.4 and 18.9 ppb for XCH 4, respectively) compared with data of the Total Carbon Column Observing Network (TCCON). Multiple reasons for these error characteristics (e.g., solar irradiance database, handling of aerosol scattering) are identified and corrected in a revised version of the retrieval algorithm (version 02.xx). The improved retrieval algorithm shows much smaller biases and standard deviations (-1.48 and 2.09 ppm for XCO2 and -5.9 and 12.6 ppb for XCH4, respectively) than the version 01.xx. Also, the number of post-screened measurements is increased, especially at northern mid- and high-latitudinal areas. © Author(s) 2013.

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%± 17.7%, reasonable consistency over different locations (station bias of -3.1%±9.5%) and very good correlation with TCCON (R = 0.95). We identified two potential sources of discrepancy between the NIES and TCCON retrievals over land. While the TCCON XH$_{2}$O amounts can reach 6000–6500ppm 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.

This paper presents an improved photon path length probability density function method that permits simultaneous retrievals of column-average greenhouse gas mole fractions and light path modifications through the atmosphere when processing high-resolution radiance spectra acquired from space. We primarily describe the methodology and retrieval setup and then apply them to the processing of spectra measured by the Greenhouse gases Observing SATellite (GOSAT). We have demonstrated substantial improvements of the data processing with simultaneous carbon dioxide and light path retrievals and reasonable agreement of the satellite-based retrievals against ground-based Fourier transform spectrometer measurements provided by the Total Carbon Column Observing Network (TCCON).

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 (X CO2). We applied the photon path length probability density function method to validate X CO2 retrievals from GOSAT data obtained during 22months 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 threedimensional 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 X CO2 over each station as compared with TCCON measurements. Statistical comparisons between GOSAT and TCCON coincident measurements of CO 2 column abundance show a correlation coefficient of 0.85, standard deviation of 1.80ppm, and a sub-ppm negative bias of -0.43ppm for all TCCON stations. Global distributions of monthly mean retrieved X CO2 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.

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

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Using a set of selected surface ozone (nine stations) and ozone vertical profile measurements (from six stations), we have documented changes in tropospheric ozone at a number of locations. From two stations at high northern hemisphere (NH) latitudes there has been a significant decline in ozone amounts throughout the troposphere since the early 1980s. At midlatitudes of the NH where data are the most abundant, on the other hand, important regional differences prevail. The two stations in the eastern United States show that changes in ozone concentrations since the early 1970s have been relatively small. At the two sites in Europe, however, ozone amounts increased rapidly into the mid-1980s, but have increased less rapidly (or in some places not at all) since then. Increases at the Japanese ozonesonde station have been largest in the lower troposphere, but have slowed in the recent decade. The tropics are sparsely sampled but do not show significant changes. Small increases are suggested at southern hemisphere (SH) midlatitudes by the two surface data records. In Antarctica large declines in the ozone concentration are noted in the South Pole data, and like those at high latitudes of the NH, seem to parallel the large decreases in the stratosphere.