Black carbon (BC) from fossil fuel and biomass combustion darkens the snow and makes it melt sooner. The BC footprint of research activities and tourism in Antarctica has likely increased as human presence in the continent has surged in recent decades. Here, we report on measurements of the BC concentration in snow samples from 28 sites across a transect of about 2,000 km from the northern tip of Antarctica (62°S) to the southern Ellsworth Mountains (79°S). Our surveys show that BC content in snow surrounding research facilities and popular shore tourist-landing sites is considerably above background levels measured elsewhere in the continent. The resulting radiative forcing is accelerating snow melting and shrinking the snowpack on BC-impacted areas on the Antarctic Peninsula and associated archipelagos by up to 23 mm water equivalent (w.e.) every summer.

Observations from the new Japanese geostationary satellite Himawari-8 permit quasi-real-time estimation of global shortwave radiation at an unprecedented temporal resolution. However, accurate comparisons with ground-truthing observations are essential to assess their uncertainty. In this study, we evaluated the Himawari-8 global radiation product AMATERASS using observations recorded at four SKYNET stations in Japan and, for certain analyses, from the surface network of the Japanese Meteorological Agency in 2016. We found that the spatiotemporal variability of the satellite estimates was smaller than that of the ground observations; variability decreased with increases in the time step and spatial domain. Cloud variability was the main source of uncertainty in the satellite radiation estimates, followed by direct effects caused by aerosols and bright albedo. Under all-sky conditions, good agreement was found between satellite and ground-based data, with a mean bias in the range of 20–30 W m ⁻² (i.e., AMATERASS overestimated ground observations) and a root mean square error (RMSE) of approximately 70–80 W m ⁻² . However, results depended on the time step used in the validation exercise, on the spatial domain, and on the different climatological regions. In particular, the validation performed at 2.5 min showed largest deviations and RMSE values ranging from about 110 W m ⁻² for the mainland to a maximum of 150 W m ⁻² in the subtropical region. We also detected a limited overestimation in the number of clear-sky episodes, particularly at the pixel level. Overall, satellite-based estimates were higher under overcast conditions, whereas frequent episodes of cloud-induced enhanced surface radiation (i.e., measured radiation was greater than expected clear-sky radiation) tended to reduce this difference. Finally, the total mean bias was approximately 10–15 W m ⁻² under clear-sky conditions, mainly because of overall instantaneous direct aerosol forcing efficiency in the range of 120–150 W m ⁻² per unit of aerosol optical depth (AOD). A seasonal anticorrelation between AOD and global radiation differences was evident at all stations and was also observed within the diurnal cycle.

Geomagnetic activity is thought to affect ozone and, possibly, climate in polar regions via energetic particle precipitation (EPP) but observational evidence of its importance in the seasonal stratospheric ozone variation on long time scales is still lacking. Here we fill this gap by showing that at high southern latitudes, late winter ozone series, covering the 1979–2014 period, exhibit an average stratospheric depletion of about 10–15% on a monthly basis caused by EPP. Daily observations indicate that every austral winter EPP‐induced low ozone concentrations appear at about 45 km in late June and descend later to 30 km, before disappearing by September. Such stratospheric variations are coupled with mesospheric ozone changes also driven by EPP. No significant correlation between these ozone variations and solar ultraviolet irradiance has been found. This suggests the need of including the EPP forcing in both ozone model simulations and trend analysis.

Multiaxis differential optical absorption spectroscopy (MAX‐DOAS) observations were performed for the first time in Thailand at the rural site of Phimai (15.18°N, 102.56°E). Vertical profiles of eight components were retrieved separately using the Japanese MAX‐DOAS profile retrieval algorithm, version 2. Here we focused on formaldehyde (HCHO) and glyoxal (CHOCHO) and evaluated their volume mixing ratios in the lowest layer (0–1 km) of the retrieved vertical profiles. We estimated the ratio of CHOCHO to HCHO concentrations (R_GF), which has been suggested to be an important tracer indicative of changes in volatile organic compound emissions. Higher HCHO and CHOCHO concentrations were observed during the dry season, reflecting the influence of biomass burning. The mean R_GF for the Phimai site was estimated to be 0.032 ± 0.005. The estimated R_GF for the dry season (0.028 ± 0.002) was lower than that during the wet season (0.040 ± 0.003), when biogenic emissions were dominated. While Phimai can be characterized as an environment with low nitrogen dioxide (NO₂) concentrations (<1 ppbv), the potential impact of biomass burning on the lower R_GF during the dry season was investigated by the occasional enhancement of NO₂ concentrations as a tracer of biomass burning.

Observation of the spatial distribution of cloud optical thickness (COT) is useful for the prediction and diagnosis of photovoltaic power generation. However, there is not a one-to-one relationship between transmitted radiance and COT (so-called COT ambiguity), and it is difficult to estimate COT because of three-dimensional (3D) radiative transfer effects. We propose a method to train a convolutional neural network (CNN) based on a 3D radiative transfer model, which enables the quick estimation of the slant-column COT (SCOT) distribution from the image of a ground-mounted radiometrically calibrated digital camera. The CNN retrieves the SCOT spatial distribution using spectral features and spatial contexts. An evaluation of the method using synthetic data shows a high accuracy with a mean absolute percentage error of 18% in the SCOT range of 1–100, greatly reducing the influence of the 3D radiative effect. As an initial analysis result, COT is estimated from a sky image taken by a digital camera, and a high correlation is shown with the effective COT estimated using a pyranometer. The discrepancy between the two is reasonable, considering the difference in the size of the field of view, the space–time averaging method, and the 3D radiative effect.

In this study, we explored the connection between anomalies in springtime Antarctic ozone and all-year precipitation in the Southern Hemisphere by using observations from 1960–2018 and coupled simulations for 1960–2050. The observations showed that this correlation was enhanced during the last several decades, when a simultaneously increased coupling between ozone and Southern Annular Mode (SAM) anomalies became broader, covering most of the following summer and part of the previous winter. For eastern Australia, the ozone–precipitation connection shows a greater persistence toward the following summer than for other regions. On the other hand, for South America, the ozone–precipitation correlation seems more robust, especially in the early summer. There, the correlation also covers part of the previous winter, suggesting that winter planetary waves could affect both parameters. Further, we estimated the sensitivity of precipitation to changes in Antarctic ozone. In both observations and simulations, we found comparable sensitivity values during the spring–summer period. Overall, our results indicate that ozone anomalies can be understood as a tracer of stratospheric circulation. However, simulations indicate that stratospheric ozone chemistry still contributes to strengthening the interannual relationship between ozone and surface climate. Because simulations reproduced most of the observed connections, we suggest that including ozone variability in seasonal forecasting systems can potentially improve predictions.