Stakes at 2 km intervals were installed in January 1997 and remeasured in February 1998, January 1999, January 2005 and during the 2007/08 austral summer along a 1248 km traverse route from Zhongshan station to Dome A, East Antarctica. Based on topographical parameters, meteorological features and the records of ∼650 stakes and six stake arrays, the route is divided into five zones. We find that the snow accumulation rate decreases with increasing altitude as one progresses inland, except in the zone 800–1128 km from the coast, where the average annual accumulation rate is higher than in the zone 524–800 km from the coast. The Dome A zone (1128–1248 km) has the lowest accumulation rate (35 kg m⁻² a⁻¹, 2005–08) due to having the highest elevation and being furthest from the coast. The surface mass balance in the region 202–1128 km from the coast exhibits no temporal change from 1999–2005 to 2005–08, but there is a change in the accumulation distribution. The zone from 202 to 524 km shows a decrease in surface mass balance from 84 kg m⁻² a⁻¹ in 1999–2005 to 67 kg m⁻² a⁻¹ in 2005–08, while the zone between 800 and 1128 km shows an increase from 67 kg m⁻² a⁻¹ in 1999–2005 to 75 kg m⁻² a⁻¹ in 2005–08.

A new meteorological dataset derived from records of Antarctic automatic weather stations (here called the AntAWS dataset) at 3 h, daily and monthly resolutions including quality control information is presented here. This dataset integrates the measurements of air temperature, air pressure, relative humidity, and wind speed and direction from 267 Antarctic AWSs obtained from 1980 to 2021. The AWS spatial distribution remains heterogeneous, with the majority of instruments located in near-coastal areas and only a few inland on the East Antarctic Plateau. Among these 267 AWSs, 63 have been operating for more than 20 years and 27 of them in excess of more than 30 years. Of the five meteorological parameters, the measurements of air temperature have the best continuity and the highest data integrity. The overarching aim of this comprehensive compilation of AWS observations is to make these data easily and widely accessible for efficient use in local, regional and continental studies; it may be accessed at https://doi.org/10.48567/key7-ch19 (Wang et al., 2022). This dataset is invaluable for improved characterization of the surface climatology across the Antarctic continent, to improve our understanding of Antarctic surface snow–atmosphere interactions including precipitation events associated with atmospheric rivers and to evaluate regional climate models or meteorological reanalysis products.

Rain-on-snow (ROS) events can accelerate the surface ablation of sea ice, thus greatly influencing the ice–albedo feedback. However, the variability of ROS events over the Arctic Ocean is poorly understood due to limited historical station data in this region. In this study early melt season ROS events were investigated based on four widely used reanalysis products (ERA-Interim, JRA-55, MERRA, and ERA5) in conjunction with available observations at Arctic coastal stations. The performance of the reanalysis products in representing the timing of ROS events and the phase change of precipitation was assessed. Our results show that ERA-Interim better represents the onset date of ROS events in spring, and ERA5 better represents the phase change of precipitation associated with ROS events. All reanalyses indicate that ROS event timing has shifted to earlier dates in recent decades (with maximum trends up to −4 to −6 d per decade in some regions in ERA-Interim) and that sea ice melt onset in the Pacific sector and most of the Eurasian marginal seas is correlated with this shift. There has been a clear transition from solid to liquid precipitation, leading to more ROS events in spring, although large discrepancies were found between different reanalysis products. In ERA5, the shift from solid to liquid precipitation phase during the early melt season has directly contributed to a reduction in spring snow depth on sea ice by more than −0.5 cm per decade averaged over the Arctic Ocean since 1980, with the largest contribution (about −2.0 cm per decade) in the Kara–Barents seas and Canadian Arctic Archipelago.

This paper introduces a unique multiyear dataset and the monitoring capability of the PANDA automatic weather station network, which includes 11 automatic weather stations (AWSs) across the Prydz Bay–Amery Ice Shelf–Dome A area from the coast to the summit of the East Antarctic Ice Sheet. The ∼ 1460 km transect from Zhongshan to Panda S follows roughly along ∼ 77^∘ E longitude and covers all geographic units of East Antarctica. Initial inland observations, near the coast, started in the 1996/97 austral summer. All AWSs in this network measure air temperature, relative humidity, air pressure, wind speed and wind direction at 1 h intervals, and some of them can also measure firn temperature and shortwave/longwave radiation. Data are relayed in near real time via the Argos system. The data quality is generally very reliable, and the data have been used widely. In this paper, we firstly present a detailed overview of the AWSs, including the sensor characteristics, installation procedure, data quality control protocol and the basic analysis of each variable. We then give an example of a short-term atmospheric event that shows the monitoring capacity of the PANDA AWS network. This dataset, which is publicly available, is planned to be updated on a near-real-time basis and should be valuable for climate change estimation, extreme weather events diagnosis, data assimilation, weather forecasting, etc. The dataset is available at https://doi.org/10.11888/Atmos.tpdc.272721 (Ding et al., 2022b).

In this study, 2-m or near-surface air temperature (T2m) products from atmospheric reanalysis ERA5 and the regional climate model RACMO2.3p2 over Greenland are compared with observations from staffed stations and Automated Weather Stations (AWS). The results show the following: 1) Greenland experienced decadal periods of both cooling and warming during 1958–2020, with an inflection point around the mid-1990s, and no significant warming after ∼2005 except in the north and northeast. 2) In the full time series, the magnitude of the warming increases gradually from south to north, with peak warming found along the northeastern coast. 3) The most intense warming occurred in autumn and winter, notably in the northeast. 4) The correlations of T2m with the large-scale circulation indices NAO and GBI are highly significant, but they gradually weaken from southwestern to northeastern Greenland. Under the background of Greenland rapidly warming, the shift from positive to negative NAO (negative to positive GBI) is critical to the sudden warming in Greenland since the mid-1990s.

To identify the atmospheric controls of the summertime glacier surface energy balance in the Himalayas, in situ meteorological data collected at 6,523 m above sea level during May–July 2005 were obtained and analyzed. Our results showed that net shortwave radiation (103 W m⁻²) and turbulent sensible heat flux (12 W m⁻²) acted as energy sources, and net longwave radiation (−62 W m⁻²) and turbulent latent heat flux (−20 W m⁻²) represented heat sinks. Cloud cover controlled the summer surface energy balance. During the active period of the South Asian Summer Monsoon, the frequent cloud coverage increased the incoming longwave radiation more than it decreased the incident solar radiation. Intensification (weakening) of the South Asian Summer Monsoon strengthened (suppressed) surface melting. The melt energy measured during the nonmonsoon period was small due to the energy consumption associated with glacier volume warming, energy loss from sublimation, and large heat loss through net longwave radiation due to the low amount of incoming longwave radiation caused by the low cloudiness. The comparison of glacier surface energy balances on the Tibetan Plateau shows that on continental glaciers, net radiation is lower and accounts for a smaller contribution to energy sources, and the dominant energy sinks are sublimation and evaporation, rather than melting, which is the primary energy sink for maritime/subcontinental glaciers. This implies an important spatial variability in glacial sensitivity to different climatic conditions on the Tibetan Plateau.