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Quantitative assessment of the carbon cycle for terrestrial ecosystem is significant to improve our understanding on climate change, as it absorbs about 30% of annual global anthropogenic CO2 emission. To refine the carbon flux estimation, we construct a data-based model named CArbon Simulator from Space (CASS), motivated by the representative Light Use Efficiency (LUE) model VPRM. The model simply estimates carbon flux with the information of air temperature, relative humidity, photosynthetically active radiation (PAR), Enhanced Vegetation Index (EVI), and Land Surface Water Index (LSWI). CASS construct the hourly NEP dataset in 250m resolution for the Seoul Metropolitan Area using refined datasets, including PAR from the HIMAWARI8, geostationary satellite. Notably, CASS does not have Plant function type (PFT) dependency by replacing empirical coefficients with the machine learning regressor. The result confirms the increased ability to capture the spatiotemporal variation at local scale for NEP especially in the urban area. Our refined estimation of carbon flux is expected to help understanding the role of terrestrial ecosystem in climate crsis.

In March 2022, an unprecedented largest forest fire occurred in South Korea, burning 22,477 ha of forest for two weeks. In this study, we studied the causes of these fires based on an analysis of all meteorological data available over 100 years and also investigated their effects on air quality data obtained from ground-based observations (AirKorea and Asian Initiative for Clean Air Networks (AICAN)) and satellite data. Analysis results of meteorological data reveal changes in the climate regime from cold and wet winter conditions to hot and dry conditions. The temperature has been increased by 4 °C and precipitation has decreased by 17 mm over 100 years. The resistance level of forest fires is drastically reduced in the past few years and eventually lead to large-scale wildfires. These devastating wildfires emitted large amounts of ultrafine biomass-burning aerosols that were composed mainly of small particle sizes with diameters less than 1.0 mm. It elevated the air pollution level by more than 20 folds than usual condition. Carbon monoxide (CO) was also emitted from biomass burning that was detected as smoke paths (from 373 ppb to 1181 ppb) by the Tropospheric Monitoring Instrument (TROPOMI). This study highlights that climate change can make forests more vulnerable to fires and their effects on air quality could be more severe than expected.This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. RS-2022-00155875).

Anthropogenic emissions of greenhouse gases from fossil fuel combustion have a significant impact on the global climate. Cities, which are home to more than half of the global population, account for over 70% of anthropogenic greenhouse gas emissions and are the major sources of air pollution that we face today. However, as much as they are big emitters, cities also have great potential to be drivers of global greenhouse gas reductions. To better manage atmospheric greenhouse gases, it is necessary to accurately monitor and quantify emissions at all spatial scales from national to urban levels. In addition to using satellites which provide global coverage with high space and time resolutions for greenhouse gas monitoring, several studies have used portable ground-based remote sensing Fourier transform infrared (FTIR) spectrometers, EM27/SUNs, to measure the column-averaged concentrations of greenhouse gases. This study analyzes the column-averaged dry air mole fractions of CO2, CH4, and CO (XCO2, XCH4, XCO) in the atmosphere over Seoul using two EM27/SUNs which is the first to be done in South Korea. Moreover, we compare our measurements with satellite measurements of the Orbiting Carbon Observatory-2 (OCO-2), Orbiting Carbon Observatory-3 (OCO-3), and Sentinel-5 Precursor TROPOspheric Monitoring Instrument (TROPOMI). Together, this study aims to analyze the three-dimensional structure of greenhouse gases in Seoul which has been difficult to do due to the absence of domestic greenhouse gas observation satellites. Furthermore, this study identifies the need for more ground-based column measurements of greenhouse gases in cities and plume areas, as well as locally adaptable methods of greenhouse gas emissions monitoring in urban areas.

The bottom-up method of estimating carbon emissions from fossil fuel use based on socio-economic databases has significant uncertainty. In particular, this uncertainty increases when the spatial and temporal scales are fine, such as in cities. Therefore, an independent and complementary top-down method has been used to verify carbon emissions estimated by the bottom-up method. This method uses the atmospheric CO2 measurement, transport model, and the inverse model. In this study, carbon emissions provided by ODIAC were improved using ground and satellite CO2 observation data measured in 2019 for Seoul. We used atmospheric CO2 concentration from four observation sites located in Seoul and the column-averaged dry air mole fractions of CO2 from OCO-2. In order to quantify the footprint, which is the flux sensitivities on observation sites, STILT and X-STILT model was used for the ground observation and satellite observation, respectively. The result showed that prior carbon emissions in specific areas including power plants and airport were underestimated. The carbon emission uncertainty decreased through Bayesian inverse model, and it was found that the calculated observation and emission error covariance were appropriate through the reduced chi-square calculation. We assessed Bayesian inverse modelling of Seoul carbon emission from fossil fuel use using measurement from ground and space, which will enable effective carbon neutrality policy decisions for Seoul.This work was supported by Korea Environment Industry &Technology Institute (KEITI) through "Climate Change R&D Project for New Climate Regime", funded by Korea Ministry of Environment (MOE) (2022003560006)

Sewer systems, which consist of wastewater treatment plants (WWTPs) and sewer networks, are important urban water infrastructure that helps to improve water quality and prevent flooding. While they are recognized as a major source of methane emissions with considerable potential, accurate quantification of methane emissions from sewage systems has not yet been achieved. Here, we measured atmospheric methane from the urban sewer network using a mobile laboratory, which consists of the electric vehicle and monitoring instruments for Greenhouse Gas (GHG) such as carbon dioxide (CO2), methane (CH4), and ethane (C2H6). The mobile measurements of sewer networks are conducted in Gwanak district of Seoul in South Korea from September 2022. More than 3,000 manholes and rain gutters in commercial and residential areas were measured for the methane emissions of the combined sewer network type in Gwanak district. The methane emissions from urban sewer network were found to be significant in the study area. However, these emissions are currently not included in the national GHG inventory. Further details of our findings will be introduced at the European Geosciences Union (EGU) General Assembly 23. This work was supported by Brain Pool Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (No.2019H1D3A1A01101988)

As it is predicted that the amount of urban on-road CO2 emissions will continue to increase, it is essential to manage urban on-road CO2 concentration for effective urban CO2 mitigation. However, limited observation of on-road CO2 concentration prevents the full understanding of the variation of urban on-road CO2 concentration. Therefore, in this study, a machine learning based model that predicts on-road CO2 concentration (CO2traffic) was developed for Seoul, South Korea. This model predicts hourly CO2traffic with high precision (R2 = 0.8 and RMSE = 22.9 ppm) by utilizing CO2 observation, traffic volume, traffic speed, and wind speed data as main factors. Analyzing the CO2traffic data predicted by the model, the high spatiotemporal inhomogeneity of CO2traffic over Seoul with 14.3 ppm by time and 345.1 ppm by road was found. The large spatiotemporal variability of CO2traffic is related to different road types (major arterial road, minor arterial road, and urban highway) and land-use types (residential, commercial, bare ground, and urban vegetation) where the road belongs. The cause of the increase in CO2traffic was different by its road type, and the diurnal variation of CO2traffic was different by its land-use type. Our results demonstrate that high spatiotemporal on-road CO2 monitoring is needed to manage the urban on-road CO2 concentration showing high inhomogeneity. In addition, it suggests that a model using machine learning techniques can be an alternative for monitoring CO2 concentrations on all roads without conducting the observation.This work was supported by Korea Environment Industry &Technology Institute(KEITI) through "Climate Change R&D Project for New ClimateRegime.", funded by Korea Ministry of Environment(MOE) (2022003560006).

Airborne aerosols can alter incoming solar radiation inducing different radiative responses, yet the potential ecological effects of changes in the degree of linear polarization (DoLP) by the light alteration remain largely unknown. Light polarization is an important navigational cue for honeybee for which a threshold intensity (i.e., the DoLP) for a reliable response is known as 15%. Here, we quantify the relationship between the mass concentration of airborne fine particulate matter (PM2.5) and the DoLP by ground-based observation to provide an estimate of how the quantity of PM2.5 changes the DoLP in general and how these changes will impair navigation of honeybee by limited-visibility at the global level. We find that the PM2.5 mass concentration exponentially decreases the DoLP, reducing the average and maximum DoLP, and the size of area containing perceivable polarization information by honeybee over the sky. Applying these results to global air quality prediction models, EMAC, MPI-ESM1.2-HAM, MIROC-ES2 under a BaU (for EMAC) and SSP370 (for MPI-ESM1.2-HAM, MIROC-ES2L) scenario, we find that projected areas and the number of days of limited-visibility that honeybee experience increases globally on average. Our estimates capture almost year-round risk hotspots of limited-visibility over sub-Saharan Africa, Eastern Mediterranean, Southeast Asian regions in 2050. In particular, India is projected to experience approximately a 10 folds increase in the number of days of limited visibility. Developing countries are more vulnerable to degrading air quality than developed countries in terms of limited-visibility for honey bees. Overall, our study demonstrates degrading air quality in 2050 as a result of business-as-usual emissions of air pollutants can affect bee navigation, threatening fundamental plant-pollinator interactions. Further, warming climate will exacerbate this impact.This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. RS-2022-00155875).

International audience

The enhanced seasonal amplitude of atmospheric CO

Accurate accounting of emissions and removals of CO2 is critical for the planning and verification of emission reduction targets in support of the Paris Agreement. Here, we present a pilot dataset of country-specific net carbon exchange (NCE; fossil plus terrestrial ecosystem fluxes) and terrestrial carbon stock changes aimed at informing countries' carbon budgets. These estimates are based on “top-down” NCE outputs from the v10 Orbiting Carbon Observatory (OCO-2) modeling intercomparison project (MIP), wherein an ensemble of inverse modeling groups conducted standardized experiments assimilating OCO-2 column-averaged dry-air mole fraction (XCO2) retrievals (ACOS v10), in situ CO2 measurements or combinations of these data. The v10 OCO-2 MIP NCE estimates are combined with “bottom-up” estimates of fossil fuel emissions and lateral carbon fluxes to estimate changes in terrestrial carbon stocks, which are impacted by anthropogenic and natural drivers. These flux and stock change estimates are reported annually (2015–2020) as both a global 1∘ × 1∘ gridded dataset and a country-level dataset and are available for download from the Committee on Earth Observation Satellites' (CEOS) website: https://doi.org/10.48588/npf6-sw92 (Byrne et al., 2022). Across the v10 OCO-2 MIP experiments, we obtain increases in the ensemble median terrestrial carbon stocks of 3.29–4.58 Pg CO2 yr−1 (0.90–1.25 Pg C yr−1). This is a result of broad increases in terrestrial carbon stocks across the northern extratropics, while the tropics generally have stock losses but with considerable regional variability and differences between v10 OCO-2 MIP experiments. We discuss the state of the science for tracking emissions and removals using top-down methods, including current limitations and future developments towards top-down monitoring and verification systems.

Recent studies have reported a 9% decrease in global carbon emissions during the COVID-19 lockdown period; however, its impact on the variation of atmospheric CO2 level remains under question. Using atmospheric CO2 observed at Anmyeondo station (AMY) in South Korea, downstream of China, this study examines whether the decrease in China’s emissions due to COVID-19 can be detected from the enhancement of CO2 mole fraction (ΔCO2) relative to the background value. The Weather Research and Forecasting–Stochastic Time-Inverted Lagrangian Transport model was applied to determine when the observed mole fractions at AMY were affected by air parcels from China. Atmospheric observations at AMY showed up to a −20% (−1.92 ppm) decrease in ΔCO2 between February and March 2020 compared to the same period in 2018 and 2019, particularly with a −34% (−3.61 ppm) decrease in March. ΔCO, which was analyzed to explore the short-term effect of emission reductions, had a decrease of −43% (−80.66 ppb) during the lockdown in China. Particularly in East China, where emissions are more concentrated than in Northeast China, ΔCO2 and ΔCO decreased by −44% and −65%, respectively. The ΔCO/ΔCO2 ratio (24.8 ppb ppm−1), which is the indicator of emission characteristics, did not show a significant difference before and after the COVID-19 lockdown period (α = 0.05), suggesting that this decrease in ΔCO2 and ΔCO was associated with emission reductions rather than changes in emission sources or combustion efficiency in China. Reduced carbon emissions due to limited human activity resulted in a decrease in the short-term regional enhancement to the observed atmospheric CO2.