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.

A cavity ring-down spectroscopy (CRDS) G-2401m analyzer onboard a Beechcraft King Air 350, a new Korean Meteorological Administration (KMA) research aircraft measurement platform since 2018, has been used to measure in situ CO₂, CH₄, and CO. We analyzed the aircraft measurements obtained in two campaigns: a within-boundary layer survey over the western Republic of Korea (hereafter Korea) for analyzing the CO₂ and CH₄ emission characteristics for each season (the climate change monitoring (CM) CM mission), and a low altitude survey over the Yellow Sea for monitoring the pollutant plumes transported into Korea from China (the environment monitoring (EM) mission). This study analyzed CO₂, CH₄, and CO data from a total of 14 flights during 2019 season. To characterize the regional combustion sources signatures of CO₂ and CH₄, we calculated the short-term (1-min slope based on one second data) regression slope of CO to CO₂ and CH₄ to CO enhancements (subtracted with background level, present as ∆CO, ∆CO₂, and ∆CH₄); slope filtered with correlation coefficients (R²) (4% when air flows from east-central China such as Hebei, Shandong, and Jiangsu provinces, etc., sustained for 1–3 days, suggesting pollutants from these regions were most likely characterized by incomplete fossil fuel combustions at the industries. Over 80% of the observations in the Western Korea missions were attributed to Korean emission sources with regression slope between ∆CO and ∆CO₂ of 0.5–1.9%. The CO₂ emissions hotspots were mainly located in the north-Western Korea of high population density and industrial activities. The higher CH₄ were observed during summer season with the increasing concentration of approximately 6% over the background level, it seems to be attributed to biogenic sources such as rice paddies, landfill, livestock, and so on. It is also noted that occurrences of high pollution episodes in North-Western Korea are more closely related to the emissions in China than in Korea.

Global ultraviolet (UV) surface reflectivity climatologies are all composed from daylight passive remote sensing observations of reflected UV light, generally integrated over a distribution of attainable reflection directions. We discovered the sensitivity of Aeolus lidar surface returns (LSR) to surface characteristics, providing the first evidence that active remote sensing can be effectively used for retrieving unidirectional UV surface reflectivity on global scales. LSR reproduces surface reflectivity monthly changes in Sahara, which are visible in the Lambertian Equivalent Reflectivity (LER) climatologies from TROPOMI and GOME-2. Very high correlations (r > 0.90) between gridded LSR and the LER climatologies are reported at global and regional scales for 36 different regions. Three clear land cover gradients are discerned from Aeolus LSR signal: (1) water/land, (2) vegetation/arid areas and (3) no snow/snow. The strongest LSR signal was retrieved over snow, while over vegetation, we found moderate negative agreement (r < − 0.60) between LSR and vegetation index proxy. Overall, the success of the first active remote sensing method for retrieving unidirectional UV surface reflectivity using Aeolus is demonstrated. Our approach can be effectively used to detect unresolved land and, especially, snow cover changes in high latitudes because, unlike passive instruments, Aeolus also provided nighttime observations.

The Atmospheric Laser Doppler Instrument (ALADIN) aboard Aeolus was the first spaceborne high-resolution lidar and measured vertical profiles of aerosol optical properties at 355 nm at an incidence angle of ∼ 35°. Although Aeolus was primarily developed to provide vertical profiles of wind speed, aerosols and cloud products, its lidar surface returns (LSRs) have been shown to contain useful information about ultraviolet (UV) surface reflectivity and have agreed well with passive remote sensing reflectance. With a focus on the process to incorporate the LSR algorithm into the Aeolus Level-2A product, we describe the methodology and evaluate the results of the adopted LSR retrieval. The algorithm combines attenuated backscattering parameters (Level-2 Aeolus Profile Processor Algorithm, L2 AEL-PRO, data) with information on the surface bin detection (Level-1 data) to produce attenuated LSR estimates (e.g., surface-integrated attenuated backscatter) for all bins where the ground was detected. The correction for producing final LSR estimates at the original Aeolus resolution is performed using the Aeolus L2 retrievals, namely, the aerosol optical depth (AOD) and Rayleigh optical depth, to ensure that LSRs are free of the effects of atmospheric attenuating features, such as optically thick clouds and thick aerosol conditions (AOD > 1.0). The evaluation shows that Aeolus LSR estimates produced using this approach agree well with the UV Lambertian-equivalent reflectivity (LER) from the Global Ozone Monitoring Experiment-2 (GOME-2; LERG) and TROPOspheric Monitoring Instrument (TROPOMI; LERT) climatologies at all spatial scales. For four reference orbits (10 September 2018, 30 November 2018, 11 January 2019 and 1 May 2019), all cloud and aerosol-free LSR estimates agree well with both LER references, with correlation coefficient (r) values varying from 0.55 to 0.71. For monthly scales, the agreement was moderate to high for the LSR–LERT comparison (r = 0.61–0.77 depending on the month) and weak to moderate for the LSR–LERG comparison (r = 0.44–0.64). Globally, the averaged 2.5° × 2.5° LSR estimates exhibit very high agreement with both the LERG (0.90) and LERT (0.92) references. With respect to reproducing the regional monthly dynamics, LSR and LER agree very well in snow- or ice-covered regions (r > 0.90), semiarid regions (r > 0.90), arid regions (r > 0.70), and some regions with mixed vegetation (like Australia; r = 0.94), whereas no agreement was found for ocean regions due to the Aeolus optical setup, which is favorable for the ocean subsurface but not for direct surface backscatter probing. We unveiled four reflectivity clusters of LSRs at the 2.5° × 2.5° grid scale, manifesting a transition from white to darker surfaces in descending LSR magnitude order: (1) ice, (2) snow, (3) surface without snow and (4) water. Regionally, the LSR–LER agreement can vary and yields the highest correlation values in regions where snow is present in winter, indicating the excellent sensitivity of Aeolus LSRs to white surfaces such as snow. This finding is corroborated by the very good agreement of LSRs with modeled snow cover that we demonstrated (r = 0.62–0.74 between these parameters in such regions), while the sensitivity to purely vegetation-driven changes in the surface is lower, as indicated by the comparison between LSRs and the normalized difference vegetation index (NDVI) without snow (r < 0.30 in the regional analysis). By demonstrating the usability of LSRs for scientific applications at non-nadir angles, our work deepens the knowledge about LSRs, which has mostly been based on nadir-looking Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) studies in the past. Using experiences from both the nadir-looking CALIPSO and the highly non-nadir Aeolus mission, a framework for the effective LSR utilization using future lidar missions such as EarthCARE and Aeolus-2 can be effectively designed.

The Atmospheric Laser Doppler Instrument (ALADIN) onboard Aeolus was the first spaceborne high-resolution lidar, measuring vertical profiles of aerosol optical properties at 355 nm at an incidence angle of ~ 35°. Although Aeolus had been primarily developed to provide vertical profiles of wind speed, aerosols, and cloud products, its lidar surface returns (LSR) were shown to contain useful information about UV surface reflectivity and agreed well with passive remote sensing reflectance. Within the process to incorporate the LSR algorithm into the Aeolus Level 2A product during the post-commissioning phase of Aeolus, we describe the methodology and evaluate the results of the adopted LSR retrieval. The algorithm combines attenuated backscattering parameters (L2 AEL-PRO data) with the information on the surface bin detection (L1 data) to produce attenuated LSR estimates (e.g. surface integrated attenuated backscatter) for all bins where the ground was detected. The correction for producing final LSR estimates at the original Aeolus resolution is performed using the Aeolus L2 retrievals, namely Aerosol Optical Depth (AOD) and Rayleigh Optical Depth to ensure that LSR is free from effects of atmospheric attenuative features such as optically thick clouds and thick aerosol conditions (AOD > 1.0). The evaluation shows that Aeolus LSR estimates produced from this approach agreed well with the UV Lambertian-Equivalent Reflectivity (LER) from GOME-2 (LER_G) and TROPOMI (LER_T) climatologies at all spatial scales. For four reference orbits (September 10, 2018; November 30, 2018; January 11, 2019; and May 1, 2019), all cloud and aerosol-free LSR estimates agree well with both LER references with correlation coefficients (r) varying from 0.55 to 0.71. For monthly scales, the agreement was moderate-to-high for LSR-LER_T (r = 0.61 – 0.77 depending on the month) and was weak-to-moderate for LSR-LER_G comparison (r = 0.44 – 0.64). Globally, the averaged 2.5 × 2.5° LSR estimates exhibit very high agreement with both LERG (0.90) and LERT (0.92) references. In reproducing regional monthly dynamics LSR and LER agree very well in snow/ice-covered regions (r > 0.90), semi-arid regions (r > 0.90), arid regions (r > 0.70), and only some regions with mixed vegetation like Australia (r = 0.94), while no agreement was found for ocean regions due to the Aeolus optical setup, favourable for ocean subsurface, not direct surface backscatter probing. We unveiled four reflectivity clusters of LSR at 2.5 × 2.5 degree grids, manifesting a transition from white to darker surfaces in descending LSR magnitude order: ice, snow, surface without snow, and water. Regionally, the LSR-LER agreement can vary and yields the highest correlation values in regions where snow is present in winter. This pattern is explained by the very good sensitivity of LSR to modelled snow cover we demonstrated (r = 0.62 – 0.74 between these parameters in such regions), while sensitivity to purely vegetation-driven changes of surface is lower, as indicated by the comparison between LSR and NDVI without snow (r < 0.30 in the regional analysis). Overall, our work complemented existing LSR studies that were mostly focused on nadir-looking CALIPSO cases by demonstrating the usability of LSR for scientific applications at non-nadir angles. By taking together CALIPSO and Aeolus experiences, a framework on effective LSR utilization using future lidar missions such as EarthCARE and Aeolus-2 can be effectively designed.

The Atmospheric Laser Doppler Instrument (ALADIN) onboard Aeolus was the first spaceborne high-resolution lidar, measuring vertical profiles of aerosol optical properties at 355 nm at an incidence angle of ~ 35°. Although Aeolus had been primarily developed to provide vertical profiles of wind speed, aerosols, and cloud products, its lidar surface returns (LSR) were shown to contain useful information about UV surface reflectivity and agreed well with passive remote sensing reflectance. Within the process to incorporate the LSR algorithm into the Aeolus Level 2A product during the post-commissioning phase of Aeolus, we describe the methodology and evaluate the results of the adopted LSR retrieval. The algorithm combines attenuated backscattering parameters (L2 AEL-PRO data) with the information on the surface bin detection (L1 data) to produce attenuated LSR estimates (e.g. surface integrated attenuated backscatter) for all bins where the ground was detected. The correction for producing final LSR estimates at the original Aeolus resolution is performed using the Aeolus L2 retrievals, namely Aerosol Optical Depth (AOD) and Rayleigh Optical Depth to ensure that LSR is free from effects of atmospheric attenuative features such as optically thick clouds and thick aerosol conditions (AOD > 1.0). The evaluation shows that Aeolus LSR estimates produced from this approach agreed well with the UV Lambertian-Equivalent Reflectivity (LER) from GOME-2 (LER_G) and TROPOMI (LER_T) climatologies at all spatial scales. For four reference orbits (September 10, 2018; November 30, 2018; January 11, 2019; and May 1, 2019), all cloud and aerosol-free LSR estimates agree well with both LER references with correlation coefficients (r) varying from 0.55 to 0.71. For monthly scales, the agreement was moderate-to-high for LSR-LER_T (r = 0.61 – 0.77 depending on the month) and was weak-to-moderate for LSR-LER_G comparison (r = 0.44 – 0.64). Globally, the averaged 2.5 × 2.5° LSR estimates exhibit very high agreement with both LERG (0.90) and LERT (0.92) references. In reproducing regional monthly dynamics LSR and LER agree very well in snow/ice-covered regions (r > 0.90), semi-arid regions (r > 0.90), arid regions (r > 0.70), and only some regions with mixed vegetation like Australia (r = 0.94), while no agreement was found for ocean regions due to the Aeolus optical setup, favourable for ocean subsurface, not direct surface backscatter probing. We unveiled four reflectivity clusters of LSR at 2.5 × 2.5 degree grids, manifesting a transition from white to darker surfaces in descending LSR magnitude order: ice, snow, surface without snow, and water. Regionally, the LSR-LER agreement can vary and yields the highest correlation values in regions where snow is present in winter. This pattern is explained by the very good sensitivity of LSR to modelled snow cover we demonstrated (r = 0.62 – 0.74 between these parameters in such regions), while sensitivity to purely vegetation-driven changes of surface is lower, as indicated by the comparison between LSR and NDVI without snow (r < 0.30 in the regional analysis). Overall, our work complemented existing LSR studies that were mostly focused on nadir-looking CALIPSO cases by demonstrating the usability of LSR for scientific applications at non-nadir angles. By taking together CALIPSO and Aeolus experiences, a framework on effective LSR utilization using future lidar missions such as EarthCARE and Aeolus-2 can be effectively designed.