The Total Carbon Column Observing Network's GGG2020 Data Version

Laughner, Joshua L.; Toon, Geoffrey C.; Mendonca, Joseph; Petri, Christof; Roche, Sébastien; Wunch, Debra; Blavier, Jean-Francois; Griffith, David W. T.; Heikkinen, Pauli; Keeling, Ralph F.; Kiel, Matthäus; Kivi, Rigel; Roehl, Coleen M.; Stephens, Britton B.; Baier, Bianca C.; Chen, Huilin; Choi, Yonghoon; Deutscher, Nicholas M.; DiGangi, Joshua P.; Gross, Jochen; Herkommer, Benedikt; Jeseck, Pascal; Laemmel, Thomas; Lan, Xin; McGee, Erin; McKain, Kathryn; Miller, John; Morino, Isamu; Notholt, Justus; Ohyama, Hirofumi; Pollard, David F.; Rettinger, Markus; Riris, Haris; Rousogenous, Constantina; Sha, Mahesh Kumar; Shiomi, Kei; Strong, Kimberly; Sussmann, Ralf; Té, Yao; Velazco, Voltaire A.; Wofsy, Steven C.; Zhou, Minqiang; Wennberg, Paul O.

doi:10.5194/essd-2023-331

Cited by 9 publications

(6 citation statements)

References 47 publications

(74 reference statements)

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“…Using the retrieved O 2 column can reduce common uncertainties, such as instrument and light path errors [10,25]. The uncertainty of TCCON XCO 2 is proved to be below 0.15% (~0.6 ppm) [29], and the TCCON CO 2 retrievals have good sensitivity in the troposphere (Figure 4c).…”

Section: Tcconmentioning

confidence: 95%

Optimizing the Atmospheric CO2 Retrieval Based on the NDACC-Type FTIR Mid-Infrared Spectra at Xianghe, China

Wang,

Zhou,

Langerock

et al. 2024

Remote Sensing

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Carbon dioxide (CO2) is the most important long-lived greenhouse gas and can be retrieved using solar absorption spectra recorded by a ground-based Fourier-transform infrared spectrometer (FTIR). In this study, we investigate the CO2 retrieval strategy using the Network for the Detection of Atmospheric Composition Change–Infrared Working Group (NDACC–IRWG) type spectra between August 2018 and April 2022 (~4 years) at Xianghe, China, aiming to find the optimal observed spectra, retrieval window, and spectroscopy. Two spectral regions, near 2600 and 4800 cm−1, are analyzed. The differences in column-averaged dry-air mole fraction of CO2 (XCO2) derived from spectroscopies (ATM18, ATM20, HITRAN2016, and HITRAN2020) can be up to 1.65 ± 0.95 ppm and 7.96 ± 2.02 ppm for NDACC-type 2600 cm−1 and 4800 cm−1 retrievals, respectively, which is mainly due to the CO2 differences in air-broadened Lorentzian HWHM coefficient (γair) and line intensity (S). HITRAN2020 provides the best fitting, and the retrieved CO2 columns and profiles from both 2600 and 4800 cm−1 are compared to the co-located Total Column Carbon Observing Network (TCCON) measurements and the greenhouse gas reanalysis dataset from the Copernicus Atmosphere Monitoring Service (CAMS). The amplitude of XCO2 seasonal variation derived from the NDACC-type (4800 cm−1) is closer to the TCCON measurements than that from the NDACC-type (2600 cm−1). Moreover, the NDACC-type (2600 cm−1) retrievals are strongly affected by the a priori profile. For tropospheric XCO2, the correlation coefficient between NDACC-type (4800 cm−1) and CAMS model is 0.73, which is higher than that between NDACC-type (2600 cm−1) and CAMS model (R = 0.56).

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Section: Tcconmentioning

confidence: 95%

Optimizing the Atmospheric CO2 Retrieval Based on the NDACC-Type FTIR Mid-Infrared Spectra at Xianghe, China

Wang,

Zhou,

Langerock

et al. 2024

Remote Sensing

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“…The Total Carbon Column Observing Network (TCCON) is a globally ground-based network of Fourier transform spectrometers that records NIR solar absorption spectra, crucial for validating space-based XCO 2 estimates (Laughner et al, 2023;Wunch et al, 2011Wunch et al, , 2017. In this study, we used the latest TCCON GGG2020 data (Total Carbon Column Observing Network (TCCON) Team, 2022) from 24 stations spanning 2009-2020 (Table S2 in Supporting Information S1).…”

Section: Tccon Datamentioning

confidence: 99%

Improved Consistency of Satellite XCO₂ Retrievals Based on Machine Learning

Huang,

Deng,

Jiang

et al. 2024

Geophysical Research Letters

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Quantifying atmospheric CO2 over long periods from space is crucial in understanding the carbon cycle's response to climate change. However, a single satellite offers limited spatiotemporal coverage, making comprehensive monitoring challenging. Moreover, biases among various satellite retrievals hinder their direct integration. This study proposed a machine learning framework for fusing the column‐averaged dry‐air mole fraction of CO2 (XCO2) retrievals from Greenhouse Gases Observing Satellite (GOSAT) and OCO‐2 satellites. The best model (R2 = 0.85) presented improved consistency of GOSAT retrievals by reducing 71.5% of the average monthly bias while using OCO‐2 retrievals as a benchmark, indicating the fusion data set's potential to enhance observation coverage. Incorporating the adjusted GOSAT XCO2 retrievals into the OCO‐2 data set added an average of 84.7 thousand observations annually, enhancing the yearly temporal coverage by 53.6% (from 14 to 21.5 days per grid). This method can be adapted to other satellites, maximizing satellite resources for a more robust carbon flux inversion.

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“…As a technical quantity it is created to deliver a value near unity for a spectrometer correctly set up and aligned. According to Laughner et al (2023b) for the TCCON the expected value is 0.999 due to imperfections in the O 2 spectroscopy.…”

Section: Ils Evaluationmentioning

confidence: 99%

Using a portable FTIR spectrometer to evaluate the consistency of TCCON measurements on a global scale: The COCCON Travel Standard

Herkommer,

Alberti,

Castracane

et al. 2024

Preprint

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Abstract. To fight climate change it is crucial to have a precise knowledge of Greenhouse Gas (GHG) concentrations in the atmosphere and to monitor sources and sinks of GHGs. On global scales, satellites are an appropriate monitoring tool. For the validation of the satellite measurements, and to tie them to the World Meteorological Organization (WMO) trace gas scale, ground based Fourier Transform Infrared (FTIR) networks are used, which provide reference data. To ensure the highest quality validation data, the network must be scaled to the WMO trace gas scale and have a very small site-to-site bias. Currently, the Total Carbon Column Observing Network (TCCON) is the de-facto standard FTIR network for providing reference data. To ensure a small site-to-site bias is a major challenge for the TCCON. In this work we describe the development and application of a new method to evaluate the site-to-site bias by using a remotely controlled portable FTIR spectrometer as a Travel Standard (TS) for evaluating the consistency of columnar GHG measurements performed at different TCCON stations and we describe campaign results for the TCCON sites in Tsukuba (Japan), East Trout Lake (Canada) and Wollongong (Australia). The TS is based on a characterized portable EM27/SUN FTIR spectrometer equipped with an accurate pressure sensor which is operated in an automated enclosure. The EM27/SUN is the standard instrument of the Collaborative Carbon Column Observing Network (COCCON). The COCCON is designed such that all spectrometers are referenced to a common reference unit located in Karlsruhe, Germany. To evaluate the long-term stability of the TS instrument, it is placed side-by-side with the TCCON instrument in Karlsruhe and the COCCON reference unit (the EM27/SUN spectrometer SN37, which is operated permanently next to the TCCON-KA site) between deployments to collect comparing measurements. At each of the visited TCCON sites, the TCCON spectrometers collected low-resolution (LR) (0.5 cm-1) and high-resolution (HR) (0.02 cm-1) measurements in an alternating manner. In East Trout Lake (ETL), the TCCON spectrometer broke down while the TS was en route to the station. Hence, no side-by-side comparison was possible there. For Tsukuba and Wollongong the agreement found for XCO2 is on the 0.1 % level. For XCH4 the agreement is at the 0.2 % level, with the low-resolution measurements showing a low bias at both sites and for both gases. For XCO the deviations are up to 7 %. The reason for this is likely to be an known issue with the CO a priori profiles used by TCCON over source regions. The pressure analysis reveals excellent agreement (0.027 hPa, 0.135 hPa, and 0.094 hPa) for the Tsukuba, ETL, and Wollongong sites.

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The Total Carbon Column Observing Network's GGG2020 Data Version

Cited by 9 publications

References 47 publications

Optimizing the Atmospheric CO2 Retrieval Based on the NDACC-Type FTIR Mid-Infrared Spectra at Xianghe, China

Optimizing the Atmospheric CO2 Retrieval Based on the NDACC-Type FTIR Mid-Infrared Spectra at Xianghe, China

Improved Consistency of Satellite XCO₂ Retrievals Based on Machine Learning

Using a portable FTIR spectrometer to evaluate the consistency of TCCON measurements on a global scale: The COCCON Travel Standard

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The Total Carbon Column Observing Network's GGG2020 Data Version

Cited by 9 publications

References 47 publications

Optimizing the Atmospheric CO2 Retrieval Based on the NDACC-Type FTIR Mid-Infrared Spectra at Xianghe, China

Optimizing the Atmospheric CO2 Retrieval Based on the NDACC-Type FTIR Mid-Infrared Spectra at Xianghe, China

Improved Consistency of Satellite XCO2 Retrievals Based on Machine Learning

Using a portable FTIR spectrometer to evaluate the consistency of TCCON measurements on a global scale: The COCCON Travel Standard

Contact Info

Product

Resources

About

Improved Consistency of Satellite XCO₂ Retrievals Based on Machine Learning