Process-evaluation of tropospheric humidity simulated by general circulation models using water vapor isotopologues: 1. Comparison between models and observations

The goal of this study is to determine how H_2O and HDO measurements in water vapor can be used to detect and diagnose biases in the representation of processes controlling tropospheric humidity in atmospheric general circulation models (GCMs). We analyze a large number of isotopic data sets (four satellite, sixteen ground-based remote-sensing, five surface in situ and three aircraft data sets) that are sensitive to different altitudes throughout the free troposphere. Despite significant differences between data sets, we identify some observed HDO/H_2O characteristics that are robust across data sets and that can be used to evaluate models. We evaluate the isotopic GCM LMDZ, accounting for the effects of spatiotemporal sampling and instrument sensitivity. We find that LMDZ reproduces the spatial patterns in the lower and mid troposphere remarkably well. However, it underestimates the amplitude of seasonal variations in isotopic composition at all levels in the subtropics and in midlatitudes, and this bias is consistent across all data sets. LMDZ also underestimates the observed meridional isotopic gradient and the contrast between dry and convective tropical regions compared to satellite data sets. Comparison with six other isotope-enabled GCMs from the SWING2 project shows that biases exhibited by LMDZ are common to all models. The SWING2 GCMs show a very large spread in isotopic behavior that is not obviously related to that of humidity, suggesting water vapor isotopic measurements could be used to expose model shortcomings. In a companion paper, the isotopic differences between models are interpreted in terms of biases in the representation of processes controlling humidity

Effects of atmospheric light scattering on spectroscopic observations of greenhouse gases from space. Part 2: Algorithm intercomparison in the GOSAT data processing for CO_2 retrievals over TCCON sites

This report is the second in a series of companion papers describing the effects of atmospheric light scattering in observations of atmospheric carbon dioxide (CO_2) by the Greenhouse gases Observing SATellite (GOSAT), in orbit since 23 January 2009. Here we summarize the retrievals from six previously published algorithms; retrieving column‐averaged dry air mole fractions of CO_2 (X_(CO2)) during 22 months of operation of GOSAT from June 2009. First, we compare data products from each algorithm with ground‐based remote sensing observations by Total Carbon Column Observing Network (TCCON). Our GOSAT‐TCCON coincidence criteria select satellite observations within a 5° radius of 11 TCCON sites. We have compared the GOSAT‐TCCON X_(CO2) regression slope, standard deviation, correlation and determination coefficients, and global and station‐to‐station biases. The best agreements with TCCON measurements were detected for NIES 02.xx and RemoTeC. Next, the impact of atmospheric light scattering on X_(CO2) retrievals was estimated for each data product using scan by scan retrievals of light path modification with the photon path length probability density function (PPDF) method. After a cloud pre‐filtering test, approximately 25% of GOSAT soundings processed by NIES 02.xx, ACOS B2.9, and UoL‐FP: 3G and 35% processed by RemoTeC were found to be contaminated by atmospheric light scattering. This study suggests that NIES 02.xx and ACOS B2.9 algorithms tend to overestimate aerosol amounts over bright surfaces, resulting in an underestimation of X_(CO2) for GOSAT observations. Cross‐comparison between algorithms shows that ACOS B2.9 agrees best with NIES 02.xx and UoL‐FP: 3G while RemoTeC X_(CO2) retrievals are in a best agreement with NIES PPDF‐D

GFIT2: an experimental algorithm for vertical profile retrieval from near-IR spectra

An algorithm for retrieval of vertical profiles from ground-based spectra in the near IR is described and tested. Known as GFIT2, the algorithm is primarily intended for CO₂, and is used exclusively for CO₂ in this paper. Retrieval of CO₂ vertical profiles from ground-based spectra is theoretically possible, would be very beneficial for carbon cycle studies and the validation of satellite measurements, and has been the focus of much research in recent years. GFIT2 is tested by application both to synthetic spectra and to measurements at two Total Carbon Column Observing Network (TCCON) sites. We demonstrate that there are approximately 3° of freedom for the CO2 profile, and the algorithm performs as expected on synthetic spectra. We show that the accuracy of retrievals of CO₂ from measurements in the 1.61μ (6220 cm⁻¹) spectral band is limited by small uncertainties in calculation of the atmospheric spectrum. We investigate several techniques to minimize the effect of these uncertainties in calculation of the spectrum. These techniques are somewhat effective but to date have not been demonstrated to produce CO₂ profile retrievals with sufficient precision for applications to carbon dynamics. We finish by discussing ongoing research which may allow CO₂ profile retrievals with sufficient accuracy to significantly improve the scientific value of the measurements from that achieved with column retrievals

Effects of atmospheric light scattering on spectroscopic observations of greenhouse gases from space: Validation of PPDF-based CO_2 retrievals from GOSAT

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_(CO_2)). We applied the photon path length probability density function method to validate X_(CO_2) retrievals 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 CO_2 column 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 X_(CO_2) 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 Orbiting Carbon Observatory-2: first 18 months of science data products

The Orbiting Carbon Observatory-2 (OCO-2) is the first National Aeronautics and Space Administration (NASA) satellite designed to measure atmospheric carbon dioxide (CO_2) with the accuracy, resolution, and coverage needed to quantify CO_2 fluxes (sources and sinks) on regional scales. OCO-2 was successfully launched on 2 July 2014 and has gathered more than 2 years of observations. The v7/v7r operational data products from September 2014 to January 2016 are discussed here. On monthly timescales, 7 to 12 % of these measurements are sufficiently cloud and aerosol free to yield estimates of the column-averaged atmospheric CO_2 dry air mole fraction, X_(CO)_2, that pass all quality tests. During the first year of operations, the observing strategy, instrument calibration, and retrieval algorithm were optimized to improve both the data yield and the accuracy of the products. With these changes, global maps of X_(CO)_2 derived from the OCO-2 data are revealing some of the most robust features of the atmospheric carbon cycle. This includes X_(CO)_2 enhancements co-located with intense fossil fuel emissions in eastern US and eastern China, which are most obvious between October and December, when the north–south X_(CO)_2 gradient is small. Enhanced X_(CO)_2 coincident with biomass burning in the Amazon, central Africa, and Indonesia is also evident in this season. In May and June, when the north–south X_(CO)_2 gradient is largest, these sources are less apparent in global maps. During this part of the year, OCO-2 maps show a more than 10 ppm reduction in X_(CO)_2 across the Northern Hemisphere, as photosynthesis by the land biosphere rapidly absorbs CO_2. As the carbon cycle science community continues to analyze these OCO-2 data, information on regional-scale sources (emitters) and sinks (absorbers) which impart X_(CO)_2 changes on the order of 1 ppm, as well as far more subtle features, will emerge from this high-resolution global dataset

The Orbiting Carbon Observatory-2: first 18 months of science data products

The Orbiting Carbon Observatory-2 (OCO-2) is the first National Aeronautics and Space Administration (NASA) satellite designed to measure atmospheric carbon dioxide (CO_2) with the accuracy, resolution, and coverage needed to quantify CO_2 fluxes (sources and sinks) on regional scales. OCO-2 was successfully launched on 2 July 2014 and has gathered more than 2 years of observations. The v7/v7r operational data products from September 2014 to January 2016 are discussed here. On monthly timescales, 7 to 12 % of these measurements are sufficiently cloud and aerosol free to yield estimates of the column-averaged atmospheric CO_2 dry air mole fraction, X_(CO)_2, that pass all quality tests. During the first year of operations, the observing strategy, instrument calibration, and retrieval algorithm were optimized to improve both the data yield and the accuracy of the products. With these changes, global maps of X_(CO)_2 derived from the OCO-2 data are revealing some of the most robust features of the atmospheric carbon cycle. This includes X_(CO)_2 enhancements co-located with intense fossil fuel emissions in eastern US and eastern China, which are most obvious between October and December, when the north–south X_(CO)_2 gradient is small. Enhanced X_(CO)_2 coincident with biomass burning in the Amazon, central Africa, and Indonesia is also evident in this season. In May and June, when the north–south X_(CO)_2 gradient is largest, these sources are less apparent in global maps. During this part of the year, OCO-2 maps show a more than 10 ppm reduction in X_(CO)_2 across the Northern Hemisphere, as photosynthesis by the land biosphere rapidly absorbs CO_2. As the carbon cycle science community continues to analyze these OCO-2 data, information on regional-scale sources (emitters) and sinks (absorbers) which impart X_(CO)_2 changes on the order of 1 ppm, as well as far more subtle features, will emerge from this high-resolution global dataset

Quantifying the loss of processed natural gas within California's South Coast Air Basin using long-term measurements of ethane and methane

Abstract. Methane emissions inventories for Southern California's South Coast Air Basin (SoCAB) have underestimated emissions from atmospheric measurements. To provide insight into the sources of the discrepancy, we analyze records of atmospheric trace gas total column abundances in the SoCAB starting in the late 1980s to produce annual estimates of the ethane emissions from 1989 to 2015 and methane emissions from 2007 to 2015. The first decade of measurements shows a rapid decline in ethane emissions coincident with decreasing natural gas and crude oil production in the basin. Between 2010 and 2015, however, ethane emissions have grown gradually from about 13 ± 5 to about 23 ± 3 Gg yr−1, despite the steady production of natural gas and oil over that time period. The methane emissions record begins with 1 year of measurements in 2007 and continuous measurements from 2011 to 2016 and shows little trend over time, with an average emission rate of 413 ± 86 Gg yr−1. Since 2012, ethane to methane ratios in the natural gas withdrawn from a storage facility within the SoCAB have been increasing by 0.62 ± 0.05 % yr−1, consistent with the ratios measured in the delivered gas. Our atmospheric measurements also show an increase in these ratios but with a slope of 0.36 ± 0.08 % yr−1, or 58 ± 13 % of the slope calculated from the withdrawn gas. From this, we infer that more than half of the excess methane in the SoCAB between 2012 and 2015 is attributable to losses from the natural gas infrastructure

Methane observations from the Greenhouse Gases Observing SATellite: Comparison to ground‐based TCCON data and model calculations

We report new short-wave infrared (SWIR) column retrievals of atmospheric methane (X_(CH4)) from the Japanese Greenhouse Gases Observing SATellite (GOSAT) and compare observed spatial and temporal variations with correlative ground-based measurements from the Total Carbon Column Observing Network (TCCON) and with the global 3-D GEOS-Chem chemistry transport model. GOSAT X_(CH4) retrievals are compared with daily TCCON observations at six sites between April 2009 and July 2010 (Bialystok, Park Falls, Lamont, Orleans, Darwin and Wollongong). GOSAT reproduces the site-dependent seasonal cycles as observed by TCCON with correlations typically between 0.5 and 0.7 with an estimated single-sounding precision between 0.4–0.8%. We find a latitudinal-dependent difference between the X_(CH4) retrievals from GOSAT and TCCON which ranges from 17.9 ppb at the most northerly site (Bialystok) to −14.6 ppb at the site with the lowest latitude (Darwin). We estimate that the mean smoothing error difference included in the GOSAT to TCCON comparisons can account for 15.7 to 17.4 ppb for the northerly sites and for 1.1 ppb at the lowest latitude site. The GOSAT X_(CH4) retrievals agree well with the GEOS-Chem model on annual (August 2009 – July 2010) and monthly timescales, capturing over 80% of the zonal variability. Differences between model and observed X_(CH4) are found over key source regions such as Southeast Asia and central Africa which will be further investigated using a formal inverse model analysis

Improving atmospheric CO\u3csub\u3e2\u3c/sub\u3e retrievals using line mixing and speed-dependence when fitting high-resolution ground-based solar spectra

A quadratic speed-dependent Voigt spectral line shape with line mixing (qSDV + LM) has been included in atmospheric trace-gas retrievals to improve the accuracy of the calculated CO2 absorption coefficients. CO2 laboratory spectra were used to validate absorption coefficient calculations for three bands: the strong 20013 ← 00001 band centered at 4850 cm−1, and the weak 30013 ← 00001 and 30012 ← 00001 bands centered at 6220 cm−1 and 6340 cm−1 respectively, and referred to below as bands 1 and 2. Several different line lists were tested. Laboratory spectra were best reproduced for the strong CO2 band when using HITRAN 2008 spectroscopic data with air-broadened widths divided by 0.985, self-broadened widths divided by 0.978, line mixing coefficients calculated using the exponential power gap (EPG) law, and a speed-dependent parameter of 0.11 used for all lines. For the weak CO2 bands, laboratory spectra were best reproduced using spectroscopic parameters from the studies by Devi et al. in 2007 coupled with line mixing coefficients calculated using the EPG law. A total of 132,598 high-resolution ground-based solar absorption spectra were fitted using qSDV + LM to calculate CO2 absorption coefficients and compared to fits that used the Voigt line shape. For the strong CO2 band, the average root mean square (RMS) residual is 0.49 ± 0.22% when using qSDV + LM to calculate the absorption coefficients. This is an improvement over the results with the Voigt line shape, which had an average RMS residual of 0.60 ± 0.21%. When using the qSDV + LM to fit the two weak CO2 bands, the average RMS residual is 0.47 ± 0.19% and 0.51 ± 0.20% for bands 1 and 2, respectively. These values are identical to those obtained with the Voigt line shape. Finally, we find that using the qSDV + LM decreases the airmass dependence of the column averaged dry air mole fraction of CO2 retrieved from the strong and both weak CO2 bands when compared to the retrievals obtained using the Voigt line shape