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The radiogenic isotope compositions of strontium (Sr) and neodymium (Nd) on the surface of the Earth are powerful tools for tracing dust sources and sinks on the Earth's surface. To differentiate between the spatial variabilities in eolian dust sources in key cryospheric regions at the three poles (the Arctic; Antarctica; and the "third pole", covering the high mountainous area in Asia), a dataset of Sr–Nd isotopic compositions from extremely cold or arid terrestrial environments was compiled, similar to the method of Blanchet (2019). The database includes Holocene and Quaternary snow, ice, sand, soil (loess), sediment, and rock samples from the three poles based on 90 different references and our own measurement data, with a total of 1989 data points, comprising 206 data points with different grain sizes and 212 data points with fraction measurements. There are 485 data points from the third pole, 727 data points from the Arctic, and 777 data points from Antarctica. The sampling and measurement methods of these data are introduced. For each pole, geographical coordinates and other information are provided. The main scientific purpose of this dataset is to provide a Sr–Nd dataset based on collective documentation and our own measurements, which will be useful for determining the sources and transport pathways of dust in snow, ice, rivers, and oceans at or near the three poles as well as to investigate whether multiple dust sources are present at each of the poles. This dataset provides exhaustive detailed documentation of the isotopic signatures at the three poles during specific time intervals in the Quaternary period, which are useful for understanding the sources or sinks of eolian dust and sediments at the three poles. The dataset is available from the National Tibetan Plateau Data Center (10.11888/Cryos.tpdc.272100, Du, 2022). [ABSTRACT FROM AUTHOR]

The meltwater scavenging coefficient (MSC) of black carbon (BC) is a crucial parameter in snow and sea ice models, as it determines the BC enrichment in the surface layer of melting snow over sea ice and therefore modulates the BC–snow–albedo feedbacks. We present a new method for MSC estimation by sampling the melt–refreeze ice layer that is produced from refreezing of the meltwater within snowpack and its overlying snow and measuring their physical characteristics in Elson Lagoon northeast of Utqiaġvik (formerly Barrow), Alaska, during the melting season. The bias of estimated MSC ranges from -5.4 % to 7.3 %, which is not exactly dependent on the degree of ablation. The average MSC value calculated with this proposed method is slightly lower than that derived from the repeating sampling (RS) method in Elson Lagoon while still being within its best estimate range. Further estimation demonstrates that the MSC in the Canada Basin (23.6%±2.1%) is close to that in Greenland (23.0%±12.5%) and larger than that in the Chukchi Sea (17.9%±5.0%) in the northwest of Utqiaġvik. Elson Lagoon has the lowest MSC (14.5%±2.6%) in the study areas. The method suggested in this study provides a possible approach for large-scale measurements of MSC over the sea ice area in the Arctic. Of course, this method depends on the presence of a melt–refreeze ice layer in the observation area. [ABSTRACT FROM AUTHOR]

Snow plays an important role in the Arctic climate system, modulating heat transfer in terrestrial and marine environments and controlling feedbacks. Changes in snow depth over Arctic sea ice, particularly in spring, have a strong impact on the surface energy budget, influencing ocean heat loss, ice growth and surface ponding. Snow conditions are sensitive to the phase (solid or liquid) of deposited precipitation. However, variability and potential trends of rain-on-snow events over Arctic sea ice and their role in sea-ice losses are poorly understood. Time series of surface observations at Utqiaġvik, Alaska, reveal rapid reduction in snow depth linked to late-spring rain-on-snow events. Liquid precipitation is key in preconditioning and triggering snow ablation through reduction in surface albedo as well as latent heat release determined by rainfall amount, supported by field observations beginning in 2000 and model results. Rainfall was found to accelerate warming and ripening of the snowpack, with even small amounts (such as 0.3 mm recorded on 24 May 2017) triggering the transition from the warming phase into the ripening phase. Subsequently, direct heat input drives snowmelt, with water content of the snowpack increasing until meltwater output occurs, with an associated rapid decrease in snow depth. Rainfall during the ripening phase can further raise water content in the snow layer, prompting onset of the meltwater output phase in the snowpack. First spring rainfall in Utqiaġvik has been observed to shift to earlier dates since the 1970s, in particular after the mid-1990s. Early melt season rainfall and its fraction of total annual precipitation also exhibit an increasing trend. These changes of precipitation over sea ice may have profound impacts on ice melt through feedbacks involving earlier onset of surface melt. [ABSTRACT FROM AUTHOR]