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.

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 (AGR_TCCON) 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 AGR_TCCON potential error, due to the irregular data sampling is relatively low (2.4–17.9%). In 2006–2019, global AGR_TCCON 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 AGR_TCCON magnitude is similar to the reference AGR from satellite data (AGR_SAT = 1.57–2.94 ppm) and the surface-based estimates of Global Carbon Budget (AGR_GCB = 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 (AGR_TCCON = 3.27 ± 0.82 ppm vs. AGR_SAT = 3.23 ± 0.50 ppm). The overall agreement between global AGR_TCCON 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 AGR_TCCON uncertainties were excessively high (AGR_TCCON = 2.64 ppm with 3.92 ppm or 148% uncertainty). Moreover, in 2008 and 2015, the ENSO-driven bias between global AGR_TCCON 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 AGR_TCCON 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.