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Dropsonde-based sensible heat flux, latent heat flux, and buoyancy flux were estimated over the sea around the Korean Peninsula in 2021. During a preceding severe weather (SW) mission, a total of 243 dropsondes were released from a National Institute of Meteorological Sciences (NIMS) Atmospheric Research Aircraft (NARA). The heat fluxes were indirectly validated by comparison with model-based heat fluxes. The sensible heat flux calculated by the bulk transfer method depended entirely on the temperature difference between the sea level and atmosphere, whereas the latent heat flux was mainly affected by wind speed. Boundary layer heights above 800 m are closely related to buoyancy flux, which is greater in regions with higher sea surface temperatures. Furthermore, the utility of the dropsonde was confirmed in the marine atmospheric boundary layer (MABL) growth, which is difficult to observe in situ and, a relationship was proposed for estimating MABL based on mean meteorological data over the sea level. [ABSTRACT FROM AUTHOR]

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]

The Structure des Echanges Mer-Atmosphère, Propriétés Océaniques/ Recherche Expérimentale (SEMAPHORE) experiment was conducted over the oceanic Azores current located in the Azores Basin. The evolution of the marine atmospheric boundary layer (MABL) was studied based on the evaluation of mean and turbulent data using in situ measurements by a ship and two aircrafts. The sea surface temperature (SST) field was characterized by a gradient of approximately 1 °C/100 km. The SST measured by aircraft decreased at a ratio of 0.25 °C/100 m of altitude due to the divergence of the infrared radiation flux from the surface. With the exception of temperature, the mean parameters measured by the two aircrafts were in good agreement with each other. The sensible heat flux was more dispersed than the latent heat flux according to the comparisons between aircraft and aircraft, and aircraft and ship. This study demonstrates the feasibility of using two aircraft to describe the MABL and surface flux with confidence. [ABSTRACT FROM AUTHOR]

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) (<0.4 were ignored). These short-term slope analyses seem to be sensitive to aircraft measurements in which the instrument samples short-time varying mixtures of different air masses. The EM missions all of which were affected by pollutants emitted in China, show the regression slope between ∆CO and ∆CO2 with of 1.8–6% and 0.3–0.7 between ∆CH4 and ∆CO. In particular, the regression slope between ∆CO and ∆CO2 increased to >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. [ABSTRACT FROM AUTHOR]

There are still large uncertainties in the estimates of net ecosystem exchange of CO2 (NEE) with atmosphere in Asia, particularly in the boreal and eastern part of temperate Asia. To understand these uncertainties, we assessed the CarbonTracker Asia (CTA2017) estimates of the spatial and temporal distributions of NEE through a comparison with FLUXCOM and the global inversion models from the Copernicus Atmospheric Monitoring Service (CAMS), Monitoring Atmospheric Composition and Climate (MACC), and Jena CarboScope in Asia, as well as examining the impact of the nesting approach on the optimized NEE flux during the 2001–2013 period. The long-term mean carbon uptake is reduced in Asia, which is −0.32 ± 0.22 PgC yr−1, whereas −0.58 ± 0.26 PgC yr−1 is shown from CT2017 (CarbonTracker global). The domain aggregated mean carbon uptake from CTA2017 is found to be lower by 23.8%, 44.8%, and 60.5% than CAMS, MACC, and Jena CarboScope, respectively. For example, both CTA2017 and CT2017 models captured the interannual variability (IAV) of the NEE flux with a different magnitude and this leads to divergent annual aggregated results. Differences in the estimated interannual variability of NEE in response to El Niño–Southern Oscillation (ENSO) may result from differences in the transport model resolutions. These inverse models' results have a substantial difference compared to FLUXCOM, which was found to be −5.54 PgC yr−1. On the one hand, we showed that the large NEE discrepancies between both inversion models and FLUXCOM stem mostly from the tropical forests. On the other hand, CTA2017 exhibits a slightly better correlation with FLUXCOM over grass/shrub, fields/woods/savanna, and mixed forest than CT2017. The land cover inconsistency between CTA2017 and FLUXCOM is therefore one driver of the discrepancy in the NEE estimates. The diurnal averaged NEE flux between CTA2017 and FLUXCOM exhibits better agreement during the carbon uptake period than the carbon release period. Both CTA2017 and CT2017 revealed that the overall spatial patterns of the carbon sink and source are similar, but the magnitude varied with seasons and ecosystem types, which is mainly attributed to differences in the transport model resolutions. Our findings indicate that substantial inconsistencies in the inversions and FLUXCOM mainly emerge during the carbon uptake period and over tropical forests. The main problems are underrepresentation of FLUXCOM NEE estimates by limited eddy covariance flux measurements, the role of CO2 emissions from land use change not accounted for by FLUXCOM, sparseness of surface observations of CO2 concentrations used by the assimilation systems, and land cover inconsistency. This suggested that further scrutiny on the FLUXCOM and inverse estimates is most likely required. Such efforts will reduce inconsistencies across various NEE estimates over Asia, thus mitigating ecosystem-driven errors that propagate the global carbon budget. Moreover, this work also recommends further investigation on how the changes/updates made in CarbonTracker affect the interannual variability of the aggregate and spatial pattern of NEE flux in response to the ENSO effect over the region of interest. [ABSTRACT FROM AUTHOR]