Comparison of XCO abundances from the Total Carbon Column Observing Network and the Network for the Detection of Atmospheric Composition Change measured in Karlsruhe

We present a comparison of Karlsruhe XCO records (April 2010–December 2014) from the Total Carbon Column Observing Network (TCCON) and from the spectral region covered by the Network for the Detection of Atmospheric Composition Change (NDACC). The Karlsruhe TCCON Fourier transform infrared (FTIR) spectrometer allows us to record spectra in the mid-infrared (MIR) and near-infrared (NIR) spectral region simultaneously, which makes Karlsruhe a favourable FTIR site to directly compare measurements from both spectral regions. We compare XCO retrieved from the fundamental absorption band at 4.7 µm (as used by NDACC) and first overtone absorption band at 2.3 µm (TCCON-style measurements). We observe a bias of (4.47 ± 0.17) ppb between both data sets with a standard deviation of 2.39 ppb in seasonal variation. This corresponds to a relative bias of (4.76 ± 0.18) % and a standard deviation of 2.28 %. We identify different sources which contribute to the observed bias (air-mass-independent correction factor, air-mass-dependent correction factor, isotopic identities, differing a priori volume mixing ratio profiles) and quantify their contributions. We show that the seasonality in the residual of NDACC and TCCON XCO can be largely explained by the smoothing effect caused by differing averaging kernel sensitivities between the MIR and NIR spectral region. This study aims to improve the comparability of NDACC and TCCON XCO validation data sets as desired for potential future satellite missions and model studies

An eleven year record of XCO2 estimates derived from GOSAT measurements using the NASA ACOS version 9 retrieval algorithm

The Thermal And Near infrared Sensor for carbon Observation – Fourier Transform Spectrometer (TANSO-FTS) on the Japanese Greenhouse gases Observing SATellite (GOSAT) has been returning data since April 2009. The version 9 (v9) Atmospheric Carbon Observations from Space (ACOS) Level 2 Full Physics (L2FP) retrieval algorithm (Kiel et al., 2019) was used to derive estimates of carbon dioxide (CO2) dry air mole fraction (XCO2) from the TANSO-FTS measurements collected over it's first eleven years of operation. The bias correction and quality filtering of the L2FP XCO2 product were evaluated using estimates derived from the Total Carbon Column Observing Network (TCCON) as well as values simulated from a suite of global atmospheric inverse modeling systems (models). In addition, the v9 ACOS GOSAT XCO2 results were compared with collocated XCO2 estimates derived from NASA's Orbiting Carbon Observatory-2 (OCO-2), using the version 10 (v10) ACOS L2FP algorithm. These tests indicate that the v9 ACOS GOSAT XCO2 product has improved throughput, scatter and bias, when compared to the earlier v7.3 ACOS GOSAT product, which extended through mid 2016. Of the 37 million (M) soundings collected by GOSAT through June 2020, approximately 20 % were selected for processing by the v9 L2FP algorithm after screening for clouds and other artifacts. After post-processing, 5.4 % of the soundings (2M out of 37M) were assigned a “good” XCO2 quality flag, as compared to 3.9 % in v7.3 (< 1M out of 24M). After quality filtering and bias correction, the differences in XCO2 between ACOS GOSAT v9 and both TCCON and models have a scatter (one sigma) of approximately 1 ppm for ocean-glint observations and 1 to 1.5 ppm for land observations. Similarly, global mean biases are less than approximately 0.2 ppm. Seasonal mean biases relative to the v10 OCO-2 XCO2 product are of order 0.1 ppm for observations over land. However, for ocean-glint observations, seasonal mean biases relative to OCO-2 range from 0.2 to 0.6 ppm, with substantial variation in time and latitude. The ACOS GOSAT v9 XCO2 data are available on the NASA Goddard Earth Science Data and Information Services Center (GES-DISC). The v9 ACOS Data User's Guide (DUG) describes best-use practices for the data. This dataset should be especially useful for studies of carbon cycle phenomena that span a full decade or more, and may serve as a useful complement to the shorter OCO-2 v10 dataset, which begins in September 2014

Tropospheric water vapour isotopologue data (H162O, H182O and HD16O) as obtained from NDACC/FTIR solar absorption spectra [Discussion paper]

Tropospheric water vapour isotopologue distributions have been consistently generated and quality filtered for 12 globally distributed ground-based FTIR sites. The products are provided as two data types. The first type is best-suited for tropospheric water vapour distribution studies. The second type is needed for analysing moisture pathways by means of {H2O,δD}-pair distributions. This paper describes the data types and gives recommendations for their correct usage.E. Sepúlveda is supported by the Ministerio de Economía y Competitividad from Spain under the project CGL2012-37505 (NOVIA project). The measurements in Mexico (Altzomoni) are supported by UNAM-DGAPA grants (IN109914, IN112216) and Conacyt (239618, 249374). Start-up of the measurements in Altzomoni was supported by International Bureau of BMBF under contract no. 01DN12064. 15 Special thanks to A. Bezanilla for data management and the RUOA program (www.ruoa.unam.mx) and personnel for helping maintaining the station. Measurements at Wollongong are supported by the Australian Research Council, grant DP110103118. This study has been conducted in the framework of the project MUSICA which is funded by the European Research Council under the European Community’s Seventh Framework Programme (FP7/2007-2013) / ERC Grant agreement number 256961

Improved Retrievals of Carbon Dioxide from the Orbiting Carbon Observatory-2 with the version 8 ACOS algorithm [Discussion paper]

Since September 2014, NASA’s Orbiting Carbon Observatory-2 (OCO-2) satellite has been taking measurements of reflected solar spectra and using them to infer atmospheric carbon dioxide levels. This work provides details of the OCO-2 retrieval algorithm, versions 7 and 8, used to derive the column-averaged dry air mole fraction of atmospheric CO2 (XCO2) for the roughly 100,000 cloud-free measurements recorded by OCO-2 each day. The algorithm is based on the Atmospheric Carbon Observations from Space (ACOS) algorithm which has been applied to observations from the Greenhouse Gases Observing SATellite (GOSAT) since 2009, with modifications necessary for OCO-2. Because high accuracy, better than 0.25%, is required in order to accurately infer carbon sources and sinks from XCO2, significant errors and regional-scale biases in the measurements must be minimized. We discuss efforts to filter out poor quality measurements, and correct the remaining good quality measurements to minimize regional-scale biases. Updates to the radiance calibration and retrieval forward model in version 8 have improved many aspects of the retrieved data products. The version 8 data appear to have reduced regionalscale biases overall, and demonstrate a clear improvement over the version 7 data. In particular, error variance with respect to TCCON was reduced by 20% over land and 40% over ocean between versions 7 and 8, and nadir and glint observations over land are now more consistent. While this paper documents the significant improvements in the ACOS algorithm, it will continue to evolve and improve as the CO2 data record continues to expand.Part of this work was conducted at the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration (NASA) for the Orbiting Carbon Observatory-2 Project. Work at Colorado State University and the Geology and Planetary Sciences Department at the California Institute of Technology was supported by subcontracts from the OCO-2 Project

Comparisons of the Orbiting Carbon Observatory-2 (OCO-2) XCO2 measurements with TCCON [Discussion paper]

This paper describes the comparisons between NASA's Orbiting Carbon Observatory (OCO-2) column-averaged dry-air mole fractions of CO2 with its primary ground-based validation network, the Total Carbon Column Observing Network (TCCON). The paper shows that while the standard bias correction does a good job of reducing spurious variability globally in the satellite measurements, local biases remain