Since May 2009, high-resolution Fourier Transform Infrared (FTIR) solar absorption spectra have been recorded at Addis Ababa (9.01° N latitude, 38.76° E longitude, 2443 m altitude above sea level), Ethiopia. The vertical profiles and total column amounts of ozone (O₃) are deduced from the spectra by using the retrieval code PROFFIT (V9.5) and regularly determined instrumental line shape (ILS). A detailed error analysis of the O₃ retrieval is performed. Averaging kernels of the target gas shows that the major contribution to the retrieved information comes from the measurement. The degrees of freedom for signals is found to be 2.1 on average for the retrieval of O₃ from the observed FTIR spectra. The ozone Volume Mixing Ratio (VMR) profiles and column amounts retrieved from FTIR spectra are compared with the coincident satellite observations of Microwave Limb Sounding (MLS), Michelson Interferometer for Passive Atmospheric Sounding (MIPAS), Tropospheric Emission Spectrometer (TES), Ozone Monitoring Instrument (OMI), Atmospheric Infrared Sounding (AIRS) and Global Ozone Monitoring Experiment (GOME-2) instruments. The mean relative differences in ozone profiles of FTIR from MLS and MIPAS are generally lower than 15% within the altitude range of 27 to 36 km, whereas difference from TES is lower than 1%. Comparisons of measurements of column amounts from the satellite and the ground-based FTIR show very good agreement as exhibited by relative differences within +0.2% to +2.8% for FTIR versus MLS and GOME-2; and −0.9 to −9.0% for FTIR versus OMI, TES and AIRS. The corresponding standard deviations are within 2.0 to 2.8% for FTIR versus MLS and GOME-2 comparisons whereas that of FTIR versus OMI, TES and AIRS are within 3.5 to 7.3%. Thus, the retrieved O₃ VMR and column amounts from a tropical site, Addis Ababa, is found to exhibit very good agreement with all coincident satellite observations over an approximate 3-yr period.

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.

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.

Drought is an extreme event that causes great economic and environmental damage. The main objective of this study is to evaluate sensitivity, characterization and propagation of drought in the Upper Blue Nile. Drought indices: standardized precipitation index (SPI) and the recently developed standardized reconnaissance drought index (RDIst) are applied for five weather stations from 1980 to 2015 to evaluate RDIst applicability in the Upper Blue Nile. From our analysis both SPI and RDIst applied for 3-, 6-, 12 month of time scales follow the same trend, but in some time steps the RDIst varies with smaller amplitude than SPI. The severity and longer duration of drought compared with others periods of meteorological drought is found in the years 1984, 2002, 2009, 2015 including five weather stations and entire Upper Blue Nile. For drought relationships the correlation analysis is made across the time scales to evaluate the relationship between meteorological drought (SPI), soil moisture drought (SMI), and hydrological drought (SRI). We found that the correlation between three indices (SPI, SMI and SRI) at different time scales the 24-month time scale is dominant and are given by 0.82, 0.63 and 0.56.

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 (36.538^o N, 126.331^o 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 ±1^o latitude and ± 2^o 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.