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The Svalbard archipelago in the Arctic North Atlantic is experiencing rapid changes in the surface climate and sea ice distribution, with impacts for the coupled climate system and the local society. This study utilizes observational data of surface air temperature (SAT) from 1980–2016 across the whole Svalbard archipelago, and sea ice extent (SIE) from operational sea ice charts to conduct a systematic assessment of climatologies, long‐term changes and regional differences. The proximity to the warm water mass of the West Spitsbergen Current drives a markedly warmer climate in the western coastal regions compared to northern and eastern Svalbard. This imprints on the SIE climatology in southern and western Svalbard, where the annual maxima of 50–60% area ice coverage are substantially less than 80–90% in the northern and eastern fjords. Owing to winter‐amplified warming, the local climate is shifting towards more maritime conditions, and SIE reductions of between 5 and 20% per decade in particular regions are found, such that a number of fjords in the west have been virtually ice‐free in recent winters. The strongest decline comes along with SAT forcing and occurs over the most recent 1–2 decades in all regions; while in the 1980s and 1990s, enhanced northerly winds and sea ice drift can explain 30–50% of SIE variability around northern Svalbard, where they had correspondingly lead to a SIE increase. With an ongoing warming it is suggested that both the meteorological and cryospheric conditions in eastern Svalbard will become increasingly similar to what is already observed in the western fjords, namely suppressed typical Arctic climate conditions. [ABSTRACT FROM AUTHOR]

Norske Øer Ice Barrier (NØIB) is a vast region of fast ice off northeast Greenland. To understand its thermodynamics, an ice mass balance buoy (IMB) was deployed in late August 2012. The IMB operated for a full year, providing unique year-round observations of air, snow, ice, and upper ocean. Here, we examine atmospheric conditions and snow and sea ice evolution, derive the ocean heat flux at the deployment site, and investigate the effect of snow on the sea ice mass balance. Although an extremely low surface air temperature of -52°C was recorded at the IMB site, the sum of freezing-degree days (4,848 K·d) is similar to that in the central Arctic. The maximum snow depth 0.73 m (on 29 May 2013, when ice was 2.19 m thick) is thicker than typically observed on level Arctic landfast ice and drift ice, and thick snow appears common in this region. Ocean heat flux was large in autumn and early winter (>7 W m-2), attributed in part to local solar heating of surface waters in summer and advected ocean heat later in the year. The combination of high ocean heat flux and thick snow resulted in a net loss of ice at the ice bottom over the year. A one-dimensional sea ice model suggests that the thick snow cover limits bottom ice growth significantly. The heavy snow load could potentially have contributed 0.29 m snow ice; however, we cannot unambiguously prove this happened. [ABSTRACT FROM AUTHOR]

The Fram Strait sea ice volume export 1992-2014 is derived by combining sea ice thickness from upward looking sonars (ULS) with satellite observations of sea ice drift and area. Fram Strait is the main gate for sea ice export from the Arctic. The average yearly sea ice export is 2,400 ± 640km³. The mean and modal ULS ice thickness in Fram Strait decreased by 15% and 21% per decade, respectively, during 1990-2014. Combined with sea ice drift and area this leads to a decrease of the Arctic sea ice volume export of 27 ± 2% per decade between 1992 and 2014. Thus, for the given time period, changes in sea ice export do not drive the sea ice volume decrease in the Arctic Basin. However, for individual years like 2007 and 2012 the ice export likely has contributed to the loss of summer sea ice. Combined with PIOMAS model simulation we estimate that 14% of the total Arctic sea ice volume is exported every year through Fram Strait. This fraction of the total sea ice volume exported per year does not show a trend because the Arctic Basin ice volume is decreasing at a similar rate as the Fram Strait ice volume export. Compared to ice velocities from Acoustic Doppler Current Profiler (ADCP) the satellite ice drift shows good correspondence in variability but a negative bias. Ice volume transport estimates presented here thus should be considered a conservative estimate.We show, however, that the transport estimates are not sensitive to the exact flux gate location. [ABSTRACT FROM AUTHOR]

Precipitable water vapor (PWV) is an important parameter for climate research and a crucial factor to achieve high accuracy in satellite geodesy and satellite altimetry. Currently Global Navigation Satellite System (GNSS) PWV retrieval using static Precise Point Positioning is limited to ground stations. We demonstrated the PWV retrieval using kinematic Precise Point Positioning method with shipborne GNSS observations during a 20‐day experiment in 2016 in Fram Strait, the region of the Arctic Ocean between Greenland and Svalbard. The shipborne GNSS PWV shows an agreement of ~1.1 mm with numerical weather model data and radiosonde observations, and a root‐mean‐square of ~1.7 mm compared to Satellite with ARgos and ALtiKa PWV. An improvement of 10% is demonstrated with the multi‐GNSS compared to the Global Positioning System solution. The PWV retrieval was conducted under different sea state from calm water up to gale. Such shipborne GNSS PWV has the promising potential to improve numerical weather forecasts and satellite altimetry. Plain Language Summary: Atmospheric water vapor retrieval using GNSS kinematic Precise Point Positing technique is demonstrated with shipborne GNSS data collected during a 20‐day cruise in Fram Strait (~80°N), the area in the Arctic Ocean between Greenland and Svalbard. An accuracy of ~1 mm is achieved compared to ERA‐Interim precipitable water vapor (PWV) and radiosonde data, and an agreement of ~2 mm is demonstrated between shipborne GNSS PWV and satellite altimetry PWV observation. The contribution of multi‐GNSS is addressed, and efforts are also made to investigate the PWV spatial resolution in order to include more data for comparison. Static Precise Point Positioning method is widely used at ground‐based GNSS stations in climate research and weather forecasting. However, it is still limited to land. Shipborne GNSS PWV retrieval could fill in the vast ocean with high temporal/spatial resolutions. Shipborne GNSS PWV could cover open sea, coastal, and polar regions (e.g., in this study) where regular meteorological observations are rare. Shipborne GNSS PWV retrieval in real time is feasible and could provide additional information for weather predictions. Moreover, shipborne GNSS PWV retrieval provides a potential method for satellite altimetry calibration, the major technique measuring sea surface height for sea level monitoring. Key Points: Shipborne GNSS PWV retrieval is demonstrated with an accuracy of 1 mm, and an agreement of 1.7 mm with satellite altimetry PWVThe multi‐GNSS solution shows an RMS improvement of ~10% compared to the GPS solution in oceanwide PWV retrieval using kinematic PPP methodShipborne GNSS PWV retrieval could expand GNSS meteorology to ocean and improve NWM, satellite altimetry, and weather forecasting [ABSTRACT FROM AUTHOR]

Abstract: We present freeboard measurements from airborne laser scanner (ALS), the Airborne Synthetic Aperture and Interferometric Radar Altimeter System (ASIRAS), and CryoSat‐2 SIRAL radar altimeter; ice thickness measurements from both helicopter‐borne and ground‐based electromagnetic‐sounding; and point measurements of ice properties. This case study was carried out in April 2015 during the N‐ICE2015 expedition in the area of the Arctic Ocean north of Svalbard. The region is represented by deep snow up to 1.12 m and a widespread presence of negative freeboards. The main scattering surfaces from both CryoSat‐2 and ASIRAS are shown to be closer to the snow freeboard obtained by ALS than to the ice freeboard measured in situ. This case study documents the complexity of freeboard retrievals from radar altimetry. We show that even under cold (below −15°C) conditions the radar freeboard can be close to the snow freeboard on a regional scale of tens of kilometers. We derived a modal sea‐ice thickness for the study region from CryoSat‐2 of 3.9 m compared to measured total thickness 1.7 m, resulting in an overestimation of sea‐ice thickness on the order of a factor 2. Our results also highlight the importance of year‐to‐year regional scale information about the depth and density of the snowpack, as this influences the sea‐ice freeboard, the radar penetration, and is a key component of the hydrostatic balance equations used to convert radar freeboard to sea‐ice thickness. [ABSTRACT FROM AUTHOR]

Abstract: In recent years, sea‐ice conditions in the Arctic Ocean changed substantially toward a younger and thinner sea‐ice cover. To capture the scope of these changes and identify the differences between individual regions, in situ observations from expeditions are a valuable data source. We present a continuous time series of in situ measurements from the N‐ICE2015 expedition from January to June 2015 in the Arctic Basin north of Svalbard, comprising snow buoy and ice mass balance buoy data and local and regional data gained from electromagnetic induction (EM) surveys and snow probe measurements from four distinct drifts. The observed mean snow depth of 0.53 m for April to early June is 73% above the average value of 0.30 m from historical and recent observations in this region, covering the years 1955–2017. The modal total ice and snow thicknesses, of 1.6 and 1.7 m measured with ground‐based EM and airborne EM measurements in April, May, and June 2015, respectively, lie below the values ranging from 1.8 to 2.7 m, reported in historical observations from the same region and time of year. The thick snow cover slows thermodynamic growth of the underlying sea ice. In combination with a thin sea‐ice cover this leads to an imbalance between snow and ice thickness, which causes widespread negative freeboard with subsequent flooding and a potential for snow‐ice formation. With certainty, 29% of randomly located drill holes on level ice had negative freeboard. [ABSTRACT FROM AUTHOR]

Abstract: The fastest and most efficient process of gaining sea ice volume is through the mechanical redistribution of mass as a consequence of deformation events. During the ice growth season divergent motion produces leads where new ice grows thermodynamically, while convergent motion fractures the ice and either piles the resultant ice blocks into ridges or rafts one floe under the other. Here we present an exceptionally detailed airborne data set from a 9 km2 area of first year and second year ice in the Transpolar Drift north of Svalbard that allowed us to estimate the redistribution of mass from an observed deformation event. To achieve this level of detail we analyzed changes in sea ice freeboard acquired from two airborne laser scanner surveys just before and right after a deformation event brought on by a passing low‐pressure system. A linear regression model based on divergence during this storm can explain 64% of freeboard variability. Over the survey region we estimated that about 1.3% of level sea ice volume was pressed together into deformed ice and the new ice formed in leads in a week after the deformation event would increase the sea ice volume by 0.5%. As the region is impacted by about 15 storms each winter, a simple linear extrapolation would result in about 7% volume increase and 20% deformed ice fraction at the end of the season. [ABSTRACT FROM AUTHOR]

Snow is a crucial component of the Arctic sea ice system. Its thickness and thermal properties control heat conduction and radiative fluxes across the ocean, ice, and atmosphere interfaces. Hence, observations of the evolution of snow depth, density, thermal conductivity, and stratigraphy are crucial for the development of detailed snow numerical models predicting energy transfer through the snow pack. Snow depth is also a major uncertainty in predicting ice thickness using remote sensing algorithms. Here we examine the winter spatial and temporal evolution of snow physical properties on first-year (FYI) and second-year ice (SYI) in the Atlantic sector of the Arctic Ocean, during the Norwegian young sea ICE (N-ICE2015) expedition (January to March 2015). During N-ICE2015, the snow pack consisted of faceted grains (47%), depth hoar (28%), and wind slab (13%), indicating very different snow stratigraphy compared to what was observed in the Pacific sector of the Arctic Ocean during the SHEBA campaign (1997-1998). Average snow bulk density was 345 kg m−3 and it varied with ice type. Snow depth was 41 ± 19 cm in January and 56 ± 17 cm in February, which is significantly greater than earlier suggestions for this region. The snow water equivalent was 14.5 ± 5.3 cm over first-year ice and 19 ± 5.4 cm over second-year ice. [ABSTRACT FROM AUTHOR]

Snow is crucial over sea ice due to its conflicting role in reflecting the incoming solar energy and reducing the heat transfer so that its temporal and spatial variability are important to estimate. During the Norwegian Young Sea ICE (N-ICE2015) campaign, snow physical properties and variability were examined, and results from April until mid-June 2015 are presented here. Overall, the snow thickness was about 20 cm higher than the climatology for second-year ice, with an average of 55 ± 27 cm and 32 ± 20 cm on first-year ice. The average density was 350-400 kg m−3 in spring, with higher values in June due to melting. Due to flooding in March, larger variability in snow water equivalent was observed. However, the snow structure was quite homogeneous in spring due to warmer weather and lower amount of storms passing over the field camp. The snow was mostly consisted of wind slab, faceted, and depth hoar type crystals with occasional fresh snow. These observations highlight the more dynamic character of evolution of snow properties over sea ice compared to previous observations, due to more variable sea ice and weather conditions in this area. The snowpack was isothermal as early as 10 June with the first onset of melt clearly identified in early June. Based on our observations, we estimate than snow could be accurately represented by a three to four layers modeling approach, in order to better consider the high variability of snow thickness and density together with the rapid metamorphose of the snow in springtime. [ABSTRACT FROM AUTHOR]

Horizontal and vertical variability of first-year drift-ice bacterial communities was investigated along a North-South transect in the Fram Strait during the winter/spring transition. Two different developmental stages were captured along the transect based on the prevailing environmental conditions and the differences in bacterial community composition. The differences in the bacterial communities were likely driven by the changes in sea-ice algal biomass (2.6-5.6 fold differences in chl-a concentrations). Copiotrophic genera common in late spring/summer sea ice, such as Polaribacter, Octadecabacter and Glaciecola, dominated the bacterial communities, supporting the conclusion that the increase in the sea-ice algal biomass was possibly reflected in the sea-ice bacterial communities. Of the dominating bacterial genera, Polaribacter seemed to benefit the most from the increase in algal biomass, since they covered approximately 39% of the total community at the southernmost stations with higher (>6 µg l-1) chl-a concentrations and only 9% at the northernmost station with lower chl-a concentrations (<6 µg l-1). The sea-ice bacterial communities also varied between the ice horizons at all three stations and thus we recommend that for future studies multiple ice horizons be sampled to cover the variability in sea-ice bacterial communities in spring. [ABSTRACT FROM AUTHOR]

Snow thickness, duration of snow coverage and amount of ice coveringthe soil are crucial for the development of biota in the Arctic tundra environment. The snow thickness and optical properties control theamount of Photosynthetically Active Radiation (PAR) that is available for vegetation. A late snow cover may prevent birds from nesting onthe ground. Furthermore, ice at the snow/soil interface can be an obstacle for grazing of Svalbard reindeer and affect the microfauna population. Snow and ice thickness, and the physical and optical properties of snow covering Arctic tundra were measured on the Broggerhalvoya peninsula on western Svalbard in spring of 1997. Additionally, thicknesses of ground-covering ice were measured in spring of 1998. The initial maximum thickness of snow in the observed areas varied from 0.4 to 0.9 m. The snow around Ny-Alesund began to disappear by the beginning of June, with the entire snow pack disappearing within 2-3 weeks. At the bottom of the snow pack, there was a soil-covering ice layer between 0.05 and 0.1 m thick. We obtained radiation and reflectanceparameters (spectral albedo, attenuation of PAR and global radiation) as well as physical properties of snow (e.g. temperature and density) over six weeks from early May to late June. Electrolytic conductivity of melted snow samples from snow pits shows clearly different conductivity for different stratigraphic sections within the snow pack in early June. Later on, these contrasts disappeared as internal ice layers melted and the snow pack underwent percolation. The albedo maximum before melt onset exceeded 0.9, whereas in the later phase of melting snow surfaces exhibited significantly lower albedo due to metamorphosis, thinning, and blackening by soil-particle contamination. However, even an apparently clean' snow surface had about 30% lower albedo in mid-June than in mid-May. Observations from under-snow PAR measurements are verified using a physically based radiative transfer model. [ABSTRACT FROM AUTHOR]