Across High Asia, the amount, timing, and spatial patterns of snow and ice melt play key roles in providing water for downstream irrigation, hydropower generation, and general consumption. The goal of this paper is to distinguish the specific contribution of seasonal snow versus glacier ice melt in the major basins of High Mountain Asia: Ganges, Brahmaputra, Indus, Amu Darya, and Syr Darya. Our methodology involves the application of MODIS-derived remote sensing products to separately calculate daily melt outputs from snow and glacier ice. Using an automated partitioning method, we generate daily maps of (1) snow over glacier ice, (2) exposed glacier ice, and (3) snow over land. These are inputs to a temperature index model that yields melt water volumes contributing to river flow. Results for the five major High Mountain Asia basins show that the western regions are heavily reliant on snow and ice melt sources for summer dry season flow when demand is at a peak, whereas monsoon rainfall dominates runoff during the summer period in the east. While uncertainty remains in the temperature index model applied here, our approach to partitioning melt from seasonal snow and glacier ice is both innovative and systematic and more constrained than previous efforts with similar goals.

Sea ice thickness is a critical variable, both as a climate indicator and for forecasting sea ice conditions on seasonal and longer time scales. The lack of snow depth and density information is a major source of uncertainty in current thickness retrievals from laser and radar altimetry. In response to this data gap, a new Lagrangian snow evolution model (SnowModel‐LG) was developed to simulate snow depth, density, and grain size on a pan‐Arctic scale, daily from August 1980 through July 2018. In this study, we evaluate the results from this effort against various data sets, including those from Operation IceBridge, ice mass balance buoys, snow buoys, MagnaProbes, and rulers. We further compare modeled snow depths forced by two reanalysis products (Modern Era Retrospective‐Analysis for Research and Applications, Version 2 and European Centre for Medium‐Range Weather Forecasts Reanalysis, 5th Generation) with those from two historical climatologies, as well as estimates over first‐year and multiyear ice from satellite passive microwave observations. Our results highlight the ability of our SnowModel‐LG implementation to capture observed spatial and seasonal variability in Arctic snow depth and density, as well as the sensitivity to the choice of reanalysis system used to simulate snow depths. Since 1980, snow depth is found to decrease throughout most regions of the Arctic Ocean, with statistically significant trends during the cold season months in the marginal ice zones around the Arctic Ocean and slight positive trends north of Greenland and near the pole.

We evaluate fields of Arctic Ocean precipitation from six atmospheric reanalyses: NASA MERRA, NASA MERRA2, NOAA CFSR/CFSv2, ECMWF ERA‐Interim, ECMWF ERA5, and JMAO JRA55. The study is motivated by recognition that precipitation fields from reanalyses can serve as the key input into snow on sea ice accumulation models, supporting retrievals of sea ice thickness from satellite altimeter systems. Time series of annual precipitation over the central Arctic Ocean correlate well between all reanalyses, and they all capture the basic spatial and seasonal patterns of Arctic precipitation. However, they differ strongly with respect to precipitation amount: CFSR and MERRA2 are wetter than the other reanalyses. All reanalyses depict that the majority of total annual precipitation over the central Arctic Ocean comes from small events, less than 1 mm/day. Validation is challenged by sparse observations, uncertain adjustments for gauge undercatch, and other issues. However, given that correlations with data from Russian North Pole drifting station records are all roughly equal and that they depict similar interannual variability at the regional scale, all of the reanalyses appear suitable for supporting retrievals of ice thickness (provided that appropriate bias corrections can be applied). However, only CFSR, MERRA2, ERA5, and JRA55 will continue to generate output beyond 2019.

Arctic rain on snow (ROS) deposits liquid water onto existing snowpacks. Upon refreezing, this can form icy crusts at the surface or within the snowpack. By altering radar backscatter and microwave emissivity, ROS over sea ice can influence the accuracy of sea ice variables retrieved from satellite radar altimetry, scatterometers, and passive microwave radiometers. During the Arctic Ocean MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) expedition, there was an unprecedented opportunity to observe a ROS event using in situ active and passive microwave instruments similar to those deployed on satellite platforms. During liquid water accumulation in the snowpack from rain and increased melt, there was a 4-fold decrease in radar energy returned at Ku- and Ka-bands. After the snowpack refroze and ice layers formed, this decrease was followed by a 6-fold increase in returned energy. Besides altering the radar backscatter, analysis of the returned waveforms shows the waveform shape changed in response to rain and refreezing. Microwave emissivity at 19 and 89 GHz increased with increasing liquid water content and decreased as the snowpack refroze, yet subsequent ice layers altered the polarization difference. Corresponding analysis of the CryoSat-2 waveform shape and backscatter as well as AMSR2 brightness temperatures further shows that the rain and refreeze were significant enough to impact satellite returns. Our analysis provides the first detailed in situ analysis of the impacts of ROS and subsequent refreezing on both active and passive microwave observations, providing important baseline knowledge for detecting ROS over sea ice and assessing their impacts on satellite-derived sea ice variables.

The mechanisms underlying decadal variability in Arctic sea ice remain actively debated. Here, we show that variability in boreal biomass burning (BB) emissions strongly influences simulated Arctic sea ice on multidecadal time scales. In particular, we find that a strong acceleration in sea ice decline in the early 21st century in the Community Earth System Model version 2 (CESM2) is related to increased variability in prescribed BB emissions in the sixth phase of the Coupled Model Intercomparison Project (CMIP6) through summertime aerosol-cloud interactions. Furthermore, we find that more than half of the reported improvement in sea ice sensitivity to CO ₂ emissions and global warming from CMIP5 to CMIP6 can be attributed to the increased BB variability, at least in the CESM. These results highlight a new kind of uncertainty that needs to be considered when incorporating new observational data into model forcing while also raising questions about the role of BB emissions on the observed Arctic sea ice loss.

The Bering Strait oceanic heat transport influences seasonal sea ice retreat and advance in the Chukchi Sea. Monitored since 1990, it depends on water temperature and factors controlling the volume transport, assumed to be local winds in the strait and an oceanic pressure difference between the Pacific and Arctic oceans (the “pressure head”). Recent work suggests that variability in the pressure head, especially during summer, relates to the strength of the zonal wind in the East Siberian Sea that raises or drops sea surface height in this area via Ekman transport. We confirm that westward winds in the East Siberian Sea relate to a broader central Arctic pattern of high sea level pressure and note that anticyclonic winds over the central Arctic Ocean also favor low September sea ice extent for the Arctic as a whole by promoting ice convergence and positive temperature anomalies. Month‐to‐month persistence in the volume transport and atmospheric circulation patterns is low, but the period 1980–2017 had a significant summertime (June–August) trend toward higher sea level pressure over the central Arctic Ocean, favoring increased transports. Some recent large heat transports are associated with high water temperatures, consistent with persistence of open water in the Chukchi Sea into winter and early ice retreat in spring. The highest heat transport recorded, October 2016, resulted from high water temperatures and ideal wind conditions yielding a record‐high volume transport. November and December 2005, the only months with southward volume (and thus heat) transports, were associated with southward winds in the strait.