Comparison of Continuous In-Situ CO2 Measurements with Co-Located Column-Averaged XCO2 TCCON/Satellite Observations and CarbonTracker Model Over the Zugspitze Region

Yuan, Ye; Sussmann, Ralf; Rettinger, Markus; Ries, Ludwig; Petermeier, Hannes; Menzel, Annette

doi:10.3390/rs11242981

Cited by 10 publications

(8 citation statements)

References 36 publications

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“…Moreover, some long-term station-wise AGR TCCON estimates from this study can be compared with the AGR TCCON estimates provided in the previous studies. For instance, we discerned good agreement between our GAR (2.15 ± 1.00 ppm) and ZUG (2.54 ± 0.98 ppm) estimates and the Yuan et al [82] TCCON-based AGR estimates (GAR = 2.33 ± 0.08 ppm, ZUG = 2.48 ± 0.16 ppm), considering the uncertainties. It is to be noted that the AGR TCCON uncertainties in our study are driven by the underlying data variability and data gaps, which can indicate the statistical robustness of AGR TCCON estimates at each station.…”

Section: Estimating the Robustness Of Agr Tccon Due To Data Sampling Measurement Gaps And Irregularities In Time Seriessupporting

confidence: 79%

Towards Robust Calculation of Interannual CO2 Growth Signal from TCCON (Total Carbon Column Observing Network)

et al. 2021

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The CO2 growth rate is one of the key geophysical quantities reflecting the dynamics of climate change as atmospheric CO2 growth is the primary driver of global warming. As recent studies have shown that TCCON (Total Carbon Column Observing Network) measurement footprints embrace quasi-global coverage, we examined the sensitivity of TCCON to the global CO2 growth. To this end, we used the aggregated TCCON observations (2006-2019) to retrieve Annual Growth Rate of CO2 (AGR) at global scales. The global AGR estimates from TCCON (AGRTCCON) are robust and independent, from (a) the station-wise seasonality, from (b) the differences in time series across the TCCON stations, and from (c) the type of TCCON stations used in the calculation (“background” or “contaminated” by neighboring CO2 sources). The AGRTCCON potential error, due to the irregular data sampling is relatively low (2.4–17.9%). In 2006–2019, global AGRTCCON ranged from the minimum of 1.59 ± 2.27 ppm (2009) to the maximum of 3.27 ± 0.82 ppm (2016), whereas the uncertainties express sub-annual variability and the data gap effects. The global AGRTCCON magnitude is similar to the reference AGR from satellite data (AGRSAT = 1.57–2.94 ppm) and the surface-based estimates of Global Carbon Budget (AGRGCB = 1.57–2.85). The highest global CO2 growth rate (2015/2016), caused by the record El Niño, was nearly perfectly reproduced by the TCCON (AGRTCCON = 3.27 ± 0.82 ppm vs. AGRSAT = 3.23 ± 0.50 ppm). The overall agreement between global AGRTCCON with the AGR references was yet weakened (r = 0.37 for TCCON vs. SAT; r = 0.50 for TCCON vs. GCB) due to two years (2008, 2015). We identified the drivers of this disagreement; in 2008, when only few stations were available worldwide, the AGRTCCON uncertainties were excessively high (AGRTCCON = 2.64 ppm with 3.92 ppm or 148% uncertainty). Moreover, in 2008 and 2015, the ENSO-driven bias between global AGRTCCON and the AGR references were detected. TCCON-to-reference agreement is dramatically increased if the years with ENSO-related biases (2008, 2015) are forfeited (r = 0.67 for TCCON vs. SAT, r = 0.82 for TCCON vs. GCB). To conclude, this is the first study that showed promising ability of aggregated TCCON signal to capture global CO2 growth. As the TCCON coverage is expanding, and new versions of TCCON data are being published, multiple data sampling strategies, dynamically changing TCCON global measurement footprint, and the irregular sensitivity of AGRTCCON to strong ENSO events; all should be analyzed to transform the current efforts into a first operational algorithm for retrieving global CO2 growth from TCCON data.

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Section: Estimating the Robustness Of Agr Tccon Due To Data Sampling Measurement Gaps And Irregularities In Time Seriessupporting

confidence: 79%

Towards Robust Calculation of Interannual CO2 Growth Signal from TCCON (Total Carbon Column Observing Network)

et al. 2021

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“…The CT2017 system assimilates in situ CO 2 observations around the world to produce the optimal estimation of global CO 2 fields (CarbonTracker Team, 2018). The CT2017 CO 2 fields have been evaluated and validated by Yuan et al (2019) and Bernath et al (2019). The simulations also include anthropogenic and oceanic CO 2 emissions.…”

Section: Methodsmentioning

confidence: 99%

Terrestrial CO₂ Fluxes, Concentrations, Sources and Budget in Northeast China: Observational and Modeling Studies

Cai

et al. 2020

JGR Atmospheres

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CO2 fluxes and concentrations are not well understood in Northeast China, where dominant land surface types are mixed forest and cropland. Here, we analyzed the CO2 fluxes and concentrations using observations and the Weather Research and Forecasting model coupled with the Vegetation Photosynthesis and Respiration Model (WRF‐VPRM). We also used WRF‐VPRM outputs to examine CO2 transport/dispersion and budgets. Finally, we investigated the uncertainties of simulating CO2 fluxes related to four VPRM parameters (including maximum light use efficiency, photosynthetically active radiation half‐saturation value, and two respiration parameters) using off‐line ensemble simulations. The results indicated that mixed forests acted as a larger CO2 source and sink than rice paddies in 2016 due to a longer growth period and stronger ecosystem respiration, although measured minimum daily mean net ecosystem exchange (NEE) was smaller at rice paddy (−10 μmol·m‐2·s‐1) than at mixed forest (−6.5 μmol·m‐2·s‐1) during the growing season (May–September). The monthly fluctuation of column‐averaged CO2 concentrations (XCO2) exceeded 10 ppm in Northeast China during 2016. The large summertime biogenic sinks offset about 70% of anthropogenic contribution of XCO2 in this region. WRF‐VPRM modeling successfully captured seasonal and episodic variations of NEE and CO2 concentrations; however, NEE in mixed forest was overestimated during daytime, mainly due to the uncertainties of VPRM parameters, especially maximum light use efficiency. These findings suggest that the WRF‐VPRM modeling framework will provide greater understanding of the natural and anthropogenic contributions to the carbon cycle in China, especially after calibration of parameters that control biogenic fluxes.

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“…Ground stations and tall towers with flask sampling, such as stations within the Global Atmospheric Watch (GAW) network [8], can measure the atmospheric CO 2 concentration with high precision and can provide the mole fractions of atmospheric CO 2 at regional and global scales. However, these measurements are representative of the lower atmosphere and do not provide information about the upper atmosphere [9]. The spatial coverage of GAW network stations is limited and their measurements are insufficient for total-column CO 2 analysis, producing uncertainties in the vertical as well as the horizontal direction.…”

Section: Introductionmentioning

confidence: 99%

Validation of GOSAT and OCO-2 against In Situ Aircraft Measurements and Comparison with CarbonTracker and GEOS-Chem over Qinhuangdao, China

Mustafa

Wang

et al. 2021

Remote Sensing

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Carbon dioxide (CO2) is the most important greenhouse gas and several satellites have been launched to monitor the atmospheric CO2 at regional and global scales. Evaluation of the measurements obtained from these satellites against accurate and precise instruments is crucial. In this work, aircraft measurements of CO2 were carried out over Qinhuangdao, China (39.9354°N, 119.6005°E), on 14, 16, and 19 March 2019 to validate the Greenhous gases Observing SATellite (GOSAT) and the Orbiting Carbon Observatory 2 (OCO-2) CO2 retrievals. The airborne in situ instruments were mounted on a research aircraft and the measurements were carried out between the altitudes of ~0.5 and 8.0 km to obtain the vertical profiles of CO2. The profiles captured a decrease in CO2 concentration from the surface to maximum altitude. Moreover, the vertical profiles from GEOS-Chem and the National Oceanic and Atmospheric Administration (NOAA) CarbonTracker were also compared with in situ and satellite datasets. The satellite and the model datasets captured the vertical structure of CO2 when compared with in situ measurements, which showed good agreement among the datasets. The dry-air column-averaged CO2 mole fractions (XCO2) retrieved from OCO-2 and GOSAT showed biases of 1.33 ppm (0.32%) and −1.70 ppm (−0.41%), respectively, relative to the XCO2 derived from in situ measurements.

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Comparison of Continuous In-Situ CO2 Measurements with Co-Located Column-Averaged XCO2 TCCON/Satellite Observations and CarbonTracker Model Over the Zugspitze Region

Cited by 10 publications

References 36 publications

Towards Robust Calculation of Interannual CO2 Growth Signal from TCCON (Total Carbon Column Observing Network)

Towards Robust Calculation of Interannual CO2 Growth Signal from TCCON (Total Carbon Column Observing Network)

Terrestrial CO₂ Fluxes, Concentrations, Sources and Budget in Northeast China: Observational and Modeling Studies

Validation of GOSAT and OCO-2 against In Situ Aircraft Measurements and Comparison with CarbonTracker and GEOS-Chem over Qinhuangdao, China

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Comparison of Continuous In-Situ CO2 Measurements with Co-Located Column-Averaged XCO2 TCCON/Satellite Observations and CarbonTracker Model Over the Zugspitze Region

Cited by 10 publications

References 36 publications

Towards Robust Calculation of Interannual CO2 Growth Signal from TCCON (Total Carbon Column Observing Network)

Towards Robust Calculation of Interannual CO2 Growth Signal from TCCON (Total Carbon Column Observing Network)

Terrestrial CO2 Fluxes, Concentrations, Sources and Budget in Northeast China: Observational and Modeling Studies

Validation of GOSAT and OCO-2 against In Situ Aircraft Measurements and Comparison with CarbonTracker and GEOS-Chem over Qinhuangdao, China

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Terrestrial CO₂ Fluxes, Concentrations, Sources and Budget in Northeast China: Observational and Modeling Studies