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Atmospheric CO2 growth is the primary driver of the global warming and the rate of this growth is a valuable indicator of the interannual changes in carbon cycle. Despite atmospheric CO2 growth rate had been considered as the well-known quantity, the latest findings indicated that CO2 models can considerably disagree in reproducing this rate. This study is aimed to advance our knowledge about temporal and spatial variations of annual CO2 growth rate (AGR) by using CO2 observations from the Total Column Observing Network (TCCON), CO2 simulations from Carbon Tracker (CT) and Copernicus Atmospheric Monitoring System (CAMS) models being compared with the previously-reported global references of AGR from Global Carbon Budget (GCB) and satellite observations (SAT) for 2004–2019 years. TCCON and the CO2 models revealed temporal AGR variations (AGRTCCON = 1.71–3.35 ppm, AGRCT = 1.64–3.15 ppm, AGRCAMS = 1.66–3.13 ppm) of very similar magnitude to the global CO2 growth references (AGRGCB = 1.59–3.23 ppm, AGRSAT = 1.55–2.92 ppm). However, AGRTCCON estimates agree well with the references only during the 2010s (correlation coefficient, r = 0.68 vs GCB and r = 0.75 vs SAT) as the TCCON observational coverage has been substantially expanded since 2009. Moreover, AGRTCCON reasonably agrees (r = 0.67) with the strength of El-Nino Southern Oscillations (ENSO) in the 2010s. The highest atmospheric CO2 growth (2015–2016) driven by the very strong El-Nino was accurately reproduced by TCCON which provided AGR of 2015–2016 years (3.29 ± 0.98 ppm) in very close agreement to the AGRSAT reference (3.23 ± 0.50 ppm). We further validated AGR simulations (CT and CAMS) versus the newly-acquired AGRTCCON (as point-location reference) for every TCCON site and found low agreement between the models and TCCON (r < 0.50) only at 3 out of 20 stations. This minor caveat has not affected the accuracy of global AGR simulations as they showed high agreement with SAT (r ≈ 0.76–0.78) and GCB (r ≈ 0.72–0.78) and reasonable agreement with TCCON (r = 0.65) global-scale references. The spatial correlation between CT and CAMS in simulating AGR (applied for every 3°×2° grid cell) is perfect (r = 0.99) for the modeling period (2004–2016). Similarly, land-wise intercomparison between CAMS and CT simulations of AGR yielded in perfect correlation for most MODIS land classes (median of land-dependent r > 0.98). From spatial perspective, the highest AGR estimates (> 20 % from the median) were observed in the regions of intense fossil fuel combustion (East Asia) or biomass burning (Amazon, Central Africa). Lack of ideal correlation and small disagreement between CT and CAMS (< 3.9 % difference between medians of global AGR estimates) are likely driven by the slight spatial disagreement between CT and CAMS in the aforementioned regions. To validate this statement, a sensitivity experiment is needed where in CO2 inverse model, alongside with the current setup of a priori biomass burning fluxes, an alternative setup is assembled (multiple independent estimates of burned area and fire-dependent emission factors for various type of tropical fires can be used). In overall, our study showed that the current estimates of global atmospheric growth rate of CO2 are consistent across a wide range of the different data sources and strengthening of carbon observational infrastructure (like covering more developing countries with ground-based CO2 observations and providing more satellite CO2 observations from cloudy and hazy regions) should improve the accuracy of CO2 growth rate estimates on both local and global scales. [ABSTRACT FROM AUTHOR]

It is evident that evaluating the measurement of greenhouse gases (GHGs) obtained from multi-platform instruments against accurate and precise instrument such as aircraft in-situ is very essential when using remote sensing GHGs results for source/sink estimations with inverse modeling. The results of the inverse models are very sensitive even to small biases in the data (Rayner and O'Brien 2001). In this work, we have evaluated ground-based high resolution Fourier Transform Spectrometer (g-b FTS) and the Greenhouse gases Observing SATellite (GOSAT) column-averaged dry air mole fraction of methane (XCH4) through aircraft in-situ observations over Anmyeondo station (36.538o N, 126.331o E, 30 m above sea level). The impact of the spatial coincidence criteria was assessed by comparing GOSAT data against g-b FTS. We noticed there was no any systematic difference based on the given coincidence criteria. GOSAT exhibited a bias ranging from 0.10 to 3.37 ppb, with the standard deviation from 4.92 to 12.54 ppb, against g-b FTS with the spatial coincidence criteria of ±1, ±3, ±5 degrees of latitude and longitude and ± 1 h time window. Data observed during ascent and descent of the aircraft is considered as vertical profiles within an altitude range of 0.2 to a maximum of 9.0 km so that some assumptions were applied for the construction of the profiles below 0.2 and above 9.0 km. In addition, the suitability of aircraft data for evaluation of remote sensing instruments was confirmed based on the assessment of uncertainties. The spatial coincidence criteria is ±1o latitude and ± 2o longitude and for temporal difference is ±1 h of the satellite observation overpass time were applied, whereas g-b FTS data are the mean values measured within ±30 min of the aircraft observation time. Furthermore, the sensitivity differences of the instruments were taken into account. With respect to aircraft, the g-b FTS data were biased by −0.19 ± 0.69%, while GOSAT data were biased by −0.42 ± 0.84%. These results confirm that both g-b FTS and GOSAT are consistent aircraft observations and assure the reliability of the datasets for inverse estimate of CH4. [ABSTRACT FROM AUTHOR]

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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. [ABSTRACT FROM AUTHOR]

Understanding the temporal variability of atmospheric methane (CH4) and its potential drivers can advance the progress toward mitigating changes to the climate. To comprehend interannual variability and spatial characteristics of anomalous CH4 mole fractions and its drivers, we used integrated data from different platforms such as in situ measurements and satellites (TROPOspheric Monitoring Instrument (TROPOMI) and Greenhouse Gases Observing SATellite (GOSAT)) retrievals. A pronounced change of annual growth rate was detected at Anmyeondo (AMY), Republic of Korea, ranging from −16.8 to 31.3 ppb yr−1 as captured in situ through 2015–2020 and 3.9 to 16.4 ppb yr−1 detected by GOSAT through 2014–2019, respectively. High growth rates were discerned in 2016 (31.3 ppb yr−1 and 13.4 ppb yr−1 from in situ and GOSAT, respectively) and 2019 (27.4 ppb yr−1 and 16.4 ppb yr−1 from in situ and GOSAT, respectively). The high growth in 2016 was essentially explained by the strong El Niño event in 2015–2016, whereas the large growth rate in 2019 was not related to ENSO. We suggest that the growth rate that appeared in 2019 was related to soil temperature according to the Noah Land Surface Model. The stable isotopic composition of 13C/12C in CH4 (δ13-CH4) collected by flask-air sampling at AMY during 2014–2019 supported the soil methane hypothesis. The intercept of the Keeling plot for summer and autumn were found to be −53.3‰ and −52.9‰, respectively, which suggested isotopic signature of biogenic emissions. The isotopic values in 2019 exhibited the strongest depletion compared to other periods, which suggests even a stronger biogenic signal. Such changes in the biogenic signal were affected by the variations of soil temperature and soil moisture. We looked more closely at the variability of XCH4 and the relationship with soil properties. The result indicated a spatial distribution of interannual variability, as well as the captured elevated anomaly over the southwest of the domain in autumn 2019, up to 70 ppb, which was largely explained by the combined effect of soil temperature and soil moisture changes, indicating a pixel-wise correlation of XCH4 anomaly with those parameters in the range of 0.5–0.8 with a statistical significance (p < 0.05). This implies that the soil-associated drivers are able to exert a large-scale influence on the regional distribution of CH4 in Korea. [ABSTRACT FROM AUTHOR]