A long-term dataset of 38 years (1982–2019) from the Advanced Very High Resolution Radiometer (AVHRR) satellite observations is applied to investigate the spatio-temporal seasonal trends in cloud fraction, surface downwelling longwave flux, and surface upwelling longwave flux over the Arctic seas (60~90° N) by the non-parametric methods. The results presented here provide a further contribution to understand the cloud cover and longwave surface radiation trends over the Arctic seas, and their correlations to the shrinking sea ice. Our results suggest that the cloud fraction shows a positive trend for all seasons since 2008. Both surface downwelling and upwelling longwave fluxes present significant positive trends since 1982 with higher magnitudes in autumn and winter. The spatial distribution of the trends is nearly consistent between the cloud fraction and the surface longwave radiation, except for spring over the Chukchi and Beaufort Seas. We further obtained a significant negative correlation between cloud fraction (surface downwelling/upwelling longwave fluxes) and sea-ice concentration during autumn, which is largest in magnitude for regions with substantial sea ice retreat. We found that the negative correlation between cloud fraction and sea-ice concentration is not as strong as that for the surface downwelling longwave flux. It indicates the increase in cloudiness may result in positive anomalies in surface downwelling longwave flux which is highly correlated with the sea-ice retreat in autumn.

Low tropospheric temperature inversion is very common in the Arctic region. Based on the hyperspectral Atmospheric Infrared Sounder (AIRS) profiles from 2002 to 2020, this study provides a comprehensive analysis of the characteristics and anomalies for low tropospheric inversions in the entire Arctic, especially during the summer period. Three types of inversion are classified here, representing the inversions under the clear-sky condition (“clear” inversion), under the cloudy condition with clouds under the inversion layer top (“cloud-I” inversion), and without clouds under the inversion layer top (“cloud-II” inversion). Obvious seasonality is revealed in these three types of inversion, which is stronger in winter than in summer, as per previous studies. We further found that a “summer” peak of inversions occurs in the Arctic, notably in July. Averaged over the study region (60−90° N, 180° W−180° E), the frequencies of “cloud-I” and “cloud-II” inversions peak in July with values of about 22.1% and 34.6%, respectively. Moreover, the three inversion types all display a small “July” peak of inversion strength, ranging from 2.14 to 3.19 K. The result reveals that when the frequency and strength of summer inversions are both with high positive anomalies, there would be a drop in sea ice concentration in September. This implied that the high positive anomalies, both in inversion frequency and strength in summer, might be a predicted signal for the extreme low sea ice event in September. It is also noted that during the extreme low sea ice events in 2007 and 2020, the summer inversion has a strong positive anomaly. However, the summer inversion in 2012, when the sea ice extent also broke the low record, was not extreme as in 2007 and 2020. Further study needs to be supported by follow-up models and observations to evaluate the impact of the inversions on the sea ice.

The Tibetan Plateau vortex (TPV), as an α-scale mesoscale weather system, often brings severe weather conditions like torrential rain and severe convective storms. Based on the detections from the Global Precipitation Measurement (GPM) Core Observatory’s Dual-frequency Precipitation Radar (DPR) and the FY-4A satellite’s Advanced Geostationary Radiation Imager (AGRI), combined with ERA5 reanalysis data, the precipitation characteristics of a TPV moving eastward during 8–13 July 2021 at different developmental stages are explored in this study. It was clear that the near-surface precipitation rate of the TPV during the initial stage at the eastern Tibetan Plateau (TP) was below 1 mm·h⁻¹, implying overall weak precipitation dominated by stratiform clouds. After moving out of the TP, the radar reflectivity factor (Ze), precipitation rate, and normalized intercept parameter (dBNw) significantly increased, while the proportion of convective clouds gradually rose. Following the TPV movement, the distribution range and vertical thickness of Ze, mass-weighted mean diameter (Dm), and dBNw tended to increase. The high-frequency region of Ze appeared at 15–20 dBZ, while Dm and dBNw occurred at around 1 mm and 33 mm⁻¹·m⁻³, respectively. Near the melting layer, Ze was characterized by a significant increase due to the aggregation and melting of ice crystals. The precipitation rate of convective clouds was generally greater than that of stratiform clouds, whilst both of them increased during the movement of the TPV. Particularly, at 01:00 on 12 July, there was a significant increase in the precipitation rate and Dm of convective clouds, while dBNw noticeably decreased. These findings could provide valuable insights into the three-dimensional structure and microphysical characteristics of the precipitation during the movement of the TPV, contributing to a better understanding of cloud precipitation mechanisms.

Mid-level clouds play a crucial role in the Arctic. Due to observational limitations, there is scarce research on the long-term evolution of Arctic mid-level clouds. From a satellite perspective, this study attempts to analyze the seasonal variations in Arctic mid-level clouds and explore the possible relationships with sea ice changes using observations from the hyperspectral Atmospheric Infrared Sounder (AIRS) over the past two decades. For mid-level clouds of three layers (648, 548, and 447 hPa) involved in AIRS, high values of effective cloud fraction (ECF) occur in summer, and low values primarily occur in early spring, while the seasonal variations are different. The ECF anomalies are notably larger at 648 hPa than those at 548 and 447 hPa. Meanwhile, the ECF values at 648 hPa show a clear reduced seasonal variability for the regions north of 80°N, which has its minimum coefficient of variation (CV) during 2019 to 2020. The seasonal CV is relatively lower in the regions dominated by Greenland and sea areas with less sea ice coverage. Analysis indicates that the decline in mid-level ECF’s seasonal mean CV is closely correlated to the retreat of Arctic sea ice during September. Singular value decomposition (SVD) analysis reveals a reverse spatial pattern in the seasonal CV anomaly of mid-level clouds and leads anomaly. However, it is worth noting that this pattern varies by region. In the Greenland Sea and areas near the Canadian Arctic Archipelago, both CV and leads demonstrate negative (positive) anomalies, probably attributed to the stronger influence of atmospheric and oceanic circulations or the presence of land on the sea ice in these areas.