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The microstructure of the uppermost portions of a melting Arctic sea ice cover has a disproportionately large influence on how incident sunlight is reflected and absorbed in the ice/ocean system. The surface scattering layer (SSL) effectively backscatters solar radiation and keeps the surface albedo of melting ice relatively high compared to ice with the SSL manually removed. Measurements of albedo provide information on how incoming shortwave radiation is partitioned by the SSL and have been pivotal to improving climate model parameterizations. However, the relationship between the physical and optical properties of the SSL is still poorly constrained. Until now, radiative transfer models have been the only way to infer the microstructure of the SSL. During the MOSAiC expedition of 2019–2020, we took samples and, for the first time, directly measured the microstructure of the SSL on bare sea ice using X-ray micro-computed tomography. We show that the SSL has a highly anisotropic, coarse, and porous structure, with a small optical diameter and density at the surface, increasing with depth. As the melting surface ablates, the SSL regenerates, maintaining some aspects of its microstructure throughout the melt season. We used the microstructure measurements with a radiative transfer model to improve our understanding of the relationship between physical properties and optical properties at 850 nm wavelength. When the microstructure is used as model input, we see a 10–15% overestimation of the reflectance at 850 nm. This comparison suggests that either a) spatial variability at the meter scale is important for the two in situ optical measurements and therefore a larger sample size is needed to represent the microstructure or b) future work should investigate either i) using a ray-tracing approach instead of explicitly solving the radiative transfer equation or ii) using a more appropriate radiative transfer model.

Repeated transects have become the backbone of spatially distributed ice and snow thickness measurements crucial for understanding of ice mass balance. Here we detail the transects at the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) 2019–2020, which represent the first such measurements collected across an entire season. Compared with similar historical transects, the snow at MOSAiC was thin (mean depths of approximately 0.1–0.3 m), while the sea ice was relatively thick first-year ice (FYI) and second-year ice (SYI). SYI was of two distinct types: relatively thin level ice formed from surfaces with extensive melt pond cover, and relatively thick deformed ice. On level SYI, spatial signatures of refrozen melt ponds remained detectable in January. At the beginning of winter the thinnest ice also had the thinnest snow, with winter growth rates of thin ice (0.33 m month−1 for FYI, 0.24 m month−1 for previously ponded SYI) exceeding that of thick ice (0.2 m month−1). By January, FYI already had a greater modal ice thickness (1.1 m) than previously ponded SYI (0.9 m). By February, modal thickness of all SYI and FYI became indistinguishable at about 1.4 m. The largest modal thicknesses were measured in May at 1.7 m. Transects included deformed ice, where largest volumes of snow accumulated by April. The remaining snow on level ice exhibited typical spatial heterogeneity in the form of snow dunes. Spatial correlation length scales for snow and sea ice ranged from 20 to 40 m or 60 to 90 m, depending on the sampling direction, which suggests that the known anisotropy of snow dunes also manifests in spatial patterns in sea ice thickness. The diverse snow and ice thickness data obtained from the MOSAiC transects represent an invaluable resource for model and remote sensing product development.

We assess the influence of snow on sea ice in experiments using the Community Earth System Model version 2 for a preindustrial and a 2xCO2 climate state. In the preindustrial climate, we find that increasing simulated snow accumulation on sea ice results in thicker sea ice and a cooler climate in both hemispheres. The sea ice mass budget response differs fundamentally between the two hemispheres. In the Arctic, increasing snow results in a decrease in both congelation sea ice growth and surface sea ice melt due to the snow's impact on conductive heat transfer and albedo, respectively. These factors dominate in regions of perennial ice but have a smaller influence in seasonal ice areas. Overall, the mass budget changes lead to a reduced amplitude in the annual cycle of ice thickness. In the Antarctic, with increasing snow, ice growth increases due to snow–ice formation and is balanced by larger basal ice melt, which primarily occurs in regions of seasonal ice. In a warmer 2xCO2 climate, the Arctic sea ice sensitivity to snow depth is small and reduced relative to that of the preindustrial climate. In contrast, in the Antarctic, the sensitivity to snow on sea ice in the 2xCO2 climate is qualitatively similar to the sensitivity in the preindustrial climate. These results underscore the importance of accurately representing snow accumulation on sea ice in coupled Earth system models due to its impact on a number of competing processes and feedbacks that affect the melt and growth of sea ice.

On Arctic sea ice, the melt of snow and sea ice generate a summertime flux of fresh water to the upper ocean. The partitioning of this meltwater to storage in melt ponds and deposition in the ocean has consequences for the surface heat budget, the sea ice mass balance, and primary productivity. Synthesizing results from the 1997–1998 SHEBA field experiment, we calculate the sources and sinks of meltwater produced on a multiyear floe during summer melt. The total meltwater input to the system from snowmelt, ice melt, and precipitation from 1 June to 9 August was equivalent to a layer of water 80 cm thick over the ice-covered and open ocean. A total of 85 % of this meltwater was deposited in the ocean, and only 15 % of this meltwater was stored in ponds. The cumulative contributions of meltwater input to the ocean from drainage from the ice surface and bottom melting were roughly equal.

Precipitation is a major component of the hydrologic cycle and plays a significant role in the sea ice mass balance in the polar regions. Over the Southern Ocean, precipitation is particularly uncertain due to the lack of direct observations in this remote and harsh environment. Here we demonstrate that precipitation estimates from eight global reanalyses produce similar spatial patterns between 2000 and 2010, although their annual means vary by about 250 mm yr−1 (or 26% of the median values) and there is little similarity in their representation of interannual variability. ERA-Interim produces the smallest and CFSR produces the largest amount of precipitation overall. Rainfall and snowfall are partitioned in five reanalyses; snowfall suffers from the same issues as the total precipitation comparison, with ERA-Interim producing about 128 mm less snowfall and JRA-55 about 103 mm more rainfall compared to the other reanalyses. When compared to CloudSat-derived snowfall, these five reanalyses indicate similar spatial patterns, but differ in their magnitude. All reanalyses indicate precipitation on nearly every day of the year, with spurious values occurring on an average of about 60 days yr−1, resulting in an accumulation of about 4.5 mm yr−1. While similarities in spatial patterns among the reanalyses suggest a convergence, the large spread in magnitudes points to issues with the background models in adequately reproducing precipitation rates, and the differences in the model physics employed. Further improvements to model physics are required to achieve confidence in precipitation rate, as well as the phase and frequency of precipitation in these products.

In recent decades, Arctic sea ice has shifted toward a younger, thinner, seasonal ice regime. Studying and understanding this “new” Arctic will be the focus of a year-long ship campaign beginning in autumn 2019. Lagrangian tracking of sea ice floes in the Community Earth System Model Large Ensemble (CESM-LE) during representative “perennial” and “seasonal” time periods allows for understanding of the conditions that a floe could experience throughout the calendar year. These model tracks, put into context a single year of observations, provide guidance on how observations can optimally shape model development, and how climate models could be used in future campaign planning. The modeled floe tracks show a range of possible trajectories, though a Transpolar Drift trajectory is most likely. There is also a small but emerging possibility of high-risk tracks, including possible melt of the floe before the end of a calendar year. We find that a Lagrangian approach is essential in order to correctly compare the seasonal cycle of sea ice conditions between point-based observations and a model. Because of high variability in the melt season sea ice conditions, we recommend in situ sampling over a large range of ice conditions for a more complete understanding of how ice type and surface conditions affect the observed processes. We find that sea ice predictability emerges rapidly during the autumn freeze-up and anticipate that process-based observations during this period may help elucidate the processes leading to this change in predictability.

Sea ice thickness is a key parameter in the polar climate and ecosystem. Thermodynamic and dynamic processes alter the sea ice thickness. The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition provided a unique opportunity to study seasonal sea ice thickness changes of the same sea ice. We analyzed 11 large-scale (∼50 km) airborne electromagnetic sea thickness and surface roughness surveys from October 2019 to September 2020. Data from ice mass balance and position buoys provided additional information. We found that thermodynamic growth and decay dominated the seasonal cycle with a total mean sea ice thickness increase of 1.4 m (October 2019 to June 2020) and decay of 1.2 m (June 2020 to September 2020). Ice dynamics and deformation-related processes, such as thin ice formation in leads and subsequent ridging, broadened the ice thickness distribution and contributed 30% to the increase in mean thickness. These processes caused a 1-month delay between maximum thermodynamic sea ice thickness and maximum mean ice thickness. The airborne EM measurements bridged the scales from local floe-scale measurements to Arctic-wide satellite observations and model grid cells. The spatial differences in mean sea ice thickness between the Central Observatory (

Our understanding of Arctic sea ice and its wide-ranging influence is deeply rooted in observation. Advancing technologies have profoundly improved our ability to observe Arctic sea ice, document its processes and properties, and describe atmosphere-ice-ocean interactions with unprecedented detail. Yet, our progress toward better understanding the Arctic sea ice system is mired by the stark disparities between observations that tend to be siloed by method, scientific discipline, and application. This article presents a review and philosophical design for observing sea ice and accelerating our understanding of the Arctic sea ice system. We give a brief history of Arctic sea ice observations and showcase the 2018 melt season within the context of five observational themes: spatial heterogeneity, temporal variability, cross-disciplinary science, scalability, and retrieval uncertainty. We synthesize buoy data, optical imagery, satellite retrievals, and airborne measurements to demonstrate how disparate data sets can be woven together to transcend observational-scale issues. The results show that there are limitations to interpreting any single data set alone. However, many of these limitations can be surmounted by combining observations that cross spatial and temporal scales. We conclude the article with pathways toward coordinating across observational platforms in order to: (1) optimize the scientific, operational, and community return on observational investments, and (2) facilitate a richer understanding of Arctic sea ice and its role in the climate system.

Since the early 2000s, sea ice has experienced an increased rate of decline in thickness and extent and transitioned to a seasonal ice cover. This shift to thinner, seasonal ice in the 'New Arctic' is accompanied by a reshuffling of energy flows at the surface. Understanding the magnitude and nature of this reshuffling and the feedbacks therein remains limited. A novel database is presented that combines satellite observations, model output, and reanalysis data with daily sea ice parcel drift tracks produced in a Lagrangian framework. This dataset consists of daily time series of sea ice parcel locations, sea ice and snow conditions, and atmospheric states. Building on previous work, this dataset includes remotely sensed radiative and turbulent fluxes from which the surface energy budget can be calculated. Additionally, flags indicate when sea ice parcels travel within cyclones, recording distance and direction from the cyclone center. The database drift track was evaluated by comparison with sea ice mass balance buoys. Results show ice parcels generally remain within 100km of the corresponding buoy, with a mean distance of 82.6 km and median distance of 54 km. The sea ice mass balance buoys also provide recordings of sea ice thickness, snow depth, and air temperature and pressure which were compared to this database. Ice thickness and snow depth typically are less accurate than air temperature and pressure due to the high spatial variability of the former two quantities when compared to a point measurement. The correlations between the ice parcel and buoy data are high, which highlights the accuracy of this Lagrangian database in capturing the seasonal changes and evolution of sea ice. This database has multiple applications for the scientific community; it can be used to study the processes that influence individual sea ice parcel time series, or to explore generalized summary statistics and trends across the Arctic. Applications such as these may shed light on the atmosphere-snow-sea ice interactions in the changing Arctic environment.

This study isolates the influence of sea ice mean state on pre-industrial climate and transient 1850-2100 climate change within a fully coupled global model: The Community Earth System Model versio...

Pond bathymetry and average pond depth on Arctic sea ice are important for characterizing and quantifying the distribution of surface melt water volume. Melt pond models that take depth into account used to be based on manual in situ measurements; however, the capability of measuring pond depth through other means have increased substantially in recent years .We take advantage of the extensive sampling and data recorded during the 2019-2020 MOSAiC campaign to compare different melt pond depth retrievals from a unique case study involving a melt pond in the center of the MOSAiC floe. Thus, we are able to present the most recent upscaling cascade of pond depth measurement methods. The methods we examine in our contribution include in-situ echo sounder and hyperspectral measurements, airborne hyperspectral and photogrammetry-based measurements, as well as spaceborne multispectral measurements. Each method is assessed regarding its spatial resolution, retrieval accuracy, technical prerequisites and limitations.

Sea ice albedo is both a driver and a consequence of summer melt evolution. The ability to collect comprehensive observations and develop accurate, general, and consistent physics-based models is central to our quantitative understanding of sea ice mass and heat budgets and a variety of associated feedback processes. Sea ice albedos recorded during the MOSAiC field campaign have extended our knowledge of the optical properties of specific ice types as well as their seasonal evolution. This new dataset complements and extends observations made during the Surface Heat Budget of the Arctic Ocean (SHEBA) campaign in the Beaufort Sea in 1998. It also presents an opportunity to improve numerical treatment of shortwave radiation partitioning by sea ice covers in climate models. Specifically, the observations include spectral and broadband albedo measurements made by observers on the surface for two classes of measurement: 1) individual ice types including snow covered ice (prior to and during melt), bare melting ice, ponded ice, and sediment-laden ice, and 2) time series measured over the full seasonal cycles. The MOSAiC and SHEBA data sets show remarkable similarity with respect to the steady spectral albedo of bare, melting summer ice and the seasonal evolution measured over representative survey lines. Both data sets include coordinated physical property characterization, which is key to the development and refinement of radiative transfer treatment in climate modeling.In this work, we compare the observational record with results generated from runs of the CESM2 model. The Community Earth System Model (CESM2) is a coupled climate model that includes a physics-based radiative transfer treatment for sea ice. This model relies on a 2-stream delta-Eddington solution with prescribed ice-type-specific inherent optical properties. Specifically, we consider newly available sub-gridscale diagnostics in the model that detail the radiative partitioning for individual surface types and thickness categories. Comparisons between observed and modeled values are considered for the albedo of individual surface types, aggregate albedo estimates, and their seasonal progression. In particular, we use these comparisons to derive a quantitative picture of the overall partitioning of shortwave radiation by the ice cover, and how it has changed over past decades. These results can help pinpoint where the most substantial model upgrades can be accomplished as well as where the best observational investments should be made.

Earth system models are valuable tools for understanding how the Arctic snow‐ice system and the feedbacks therein may respond to a warming climate. In this analysis, we investigate snow on Arctic sea ice to better understand how snow conditions may change under different forcing scenarios. First, we use in situ, airborne, and satellite observations to assess the realism of the Community Earth System Model (CESM) in simulating snow on Arctic sea ice. CESM versions one and two are evaluated, with V1 being the Large Ensemble experiment (CESM1‐LE) and V2 being configured with low‐ and high‐top atmospheric components. The assessment shows CESM2 underestimates snow depth and produces overly uniform snow distributions, whereas CESM1‐LE produces a highly variable, excessively‐thick snow cover. Observations indicate that snow in CESM2 accumulates too slowly in autumn, too quickly in winter‐spring, and melts too soon and rapidly in late spring. The 1950–2050 trends in annual mean snow depths are markedly smaller in CESM2 (−0.8 cm decade−1) than in CESM1‐LE (−3.6 cm decade−1) due to CESM2 having less snow overall. A perennial, thick sea‐ice cover, cool summers, and excessive summer snowfall facilitate a thicker, longer‐lasting snow cover in CESM1‐LE. Under the SSP5‐8.5 forcing scenario, CESM2 shows that, compared to present‐day, snow on Arctic sea ice will: (1) undergo enhanced, earlier spring melt, (2) accumulate less in summer‐autumn, (3) sublimate more, and (4) facilitate marginally more snow‐ice formation. CESM2 also reveals that summers with snow‐free ice can occur ∼30–60 years before an ice‐free central Arctic, which may promote faster sea‐ice melt., Key Points CESM2 (CESM1‐LE) snow depths on Arctic sea ice are thinner (thicker) than observed depthsThe 1950–2050 trends in annual mean snow depth on Arctic sea ice are −0.8 and −3.6 cm decade−1 in CESM2 and CESM1‐LE, respectivelyCESM2 shows enhanced earlier snowmelt, less snow accumulation, more sublimation, and slightly more snow‐ice formation in future decades

The NASA Eulerian Snow On Sea Ice Model (NESOSIM) is a new, open-source snow budget model that is currently configured to produce daily estimates of the depth and density of snow on sea ice across the Arctic Ocean through the accumulation season. NESOSIM has been developed in a three-dimensional Eulerian framework and includes two (vertical) snow layers and several simple parameterizations (accumulation, wind packing, advection–divergence, blowing snow lost to leads) to represent key sources and sinks of snow on sea ice. The model is forced with daily inputs of snowfall and near-surface winds (from reanalyses), sea ice concentration (from satellite passive microwave data) and sea ice drift (from satellite feature tracking) during the accumulation season (August through April). In this study, we present the NESOSIM formulation, calibration efforts, sensitivity studies and validation efforts across an Arctic Ocean domain (100 km horizontal resolution). The simulated snow depth and density are calibrated with in situ data collected on drifting ice stations during the 1980s. NESOSIM shows strong agreement with the in situ seasonal cycles of snow depth and density, and shows good (moderate) agreement with the regional snow depth (density) distributions. NESOSIM is run for a contemporary period (2000 to 2015), with the results showing strong sensitivity to the reanalysis-derived snowfall forcing data, with the Modern-Era Retrospective analysis for Research and Applications (MERRA) and the Japanese Meteorological Agency 55-year reanalysis (JRA-55) forced snow depths generally higher than ERA-Interim, and the Arctic System Reanalysis (ASR) generally lower. We also generate and force NESOSIM with a consensus median daily snowfall dataset from these reanalyses. The results are compared against snow depth estimates derived from NASA's Operation IceBridge (OIB) snow radar data from 2009 to 2015, showing moderate–strong correlations and root mean squared errors of ∼ 10 cm depending on the OIB snow depth product analyzed, similar to the comparisons between OIB snow depths and the commonly used modified Warren snow depth climatology. Potential improvements to this initial NESOSIM formulation are discussed in the hopes of improving the accuracy and reliability of these simulated snow depths and densities.

A melt pond (MP) distribution equation has been developed and incorporated into the Marginal Ice-Zone Modeling and Assimilation System to simulate Arctic MPs and sea ice over 1979-2016. The equation differs from previous MP models and yet benefits from previous studies for MP parameterizations as well as a range of observations for model calibration. Model results show higher magnitude of MP volume per unit ice area and area fraction in most of the Canada Basin and the East Siberian Sea and lower magnitude in the central Arctic. This is consistent with Moderate Resolution Imaging Spectroradiometer observations, evaluated with Measurements of Earth Data for Environmental Analysis (MEDEA) data, and closely related to top ice melt per unit ice area. The model simulates a decrease in the total Arctic sea ice volume and area, owing to a strong increase in bottom and lateral ice melt. The sea ice decline leads to a strong decrease in the total MP volume and area. However, the Arctic-averaged MP volume per unit ice area and area fraction show weak, statistically insignificant downward trends, which is linked to the fact that MP water drainage per unit ice area is increasing. It is also linked to the fact that MP volume and area decrease relatively faster than ice area. This suggests that overall the actual MP conditions on ice have changed little in the past decades as the ice cover is retreating in response to Arctic warming, thus consistent with the Moderate Resolution Imaging Spectroradiometer observations that show no clear trend in MP area fraction over 2000-2011.

Precipitation over the Arctic Ocean has a significant impact on the basin-scale freshwater and energy budgets but is one of the most poorly constrained variables in atmospheric reanalyses. Precipitation controls the snow cover on sea ice, which impedes the exchange of energy between the ocean and atmosphere, inhibiting sea ice growth. Thus, accurate precipitation amounts are needed to inform sea ice modeling, especially for the production of thickness estimates from satellite altimetry freeboard data. However, obtaining a quantitative estimate of the precipitation distribution in the Arctic is notoriously difficult because of a number of factors, including a lack of reliable, long-term in situ observations; difficulties in remote sensing over sea ice; and model biases in temperature and moisture fields and associated uncertainty of modeled cloud microphysical processes in the polar regions. Here, we compare precipitation estimates over the Arctic Ocean from eight widely used atmospheric reanalyses over the period 2000–16 (nominally the “new Arctic”). We find that the magnitude, frequency, and phase of precipitation vary drastically, although interannual variability is similar. Reanalysis-derived precipitation does not increase with time as expected; however, an increasing trend of higher fractions of liquid precipitation (rainfall) is found. When compared with drifting ice mass balance buoys, three reanalyses (ERA-Interim, MERRA, and NCEP R2) produce realistic magnitudes and temporal agreement with observed precipitation events, while two products [MERRA, version 2 (MERRA-2), and CFSR] show large, implausible magnitudes in precipitation events. All the reanalyses tend to produce overly frequent Arctic precipitation. Future work needs to be undertaken to determine the specific factors in reanalyses that contribute to these discrepancies in the new Arctic.

Snow on Arctic sea ice is a poorly observed variable that plays an important role in the Arctic climate system and impacts the remote sensing systems that monitor Arctic sea ice. We present and validate a reconstruction of Arctic snow depth based on observed sea ice motion and snowfall derived from reanalysis data. Overall, the reconstruction is in good agreement with direct measurements of snow depth from Operation IceBridge, slightly underestimating mean IceBridge snow depth. At the local scale (10 km), the reconstruction is more skilled than a weighted climatology over first year ice, but underestimates deeper snow over multiyear ice. Reconstructions of single buoy snow depths are unskilled, but the reconstruction shows skill in simulating the mean snow depth across all buoys. Spring snow depths show a lowtohigh crossArctic gradient and tend to be greatest in the Atlantic sector of the eastern Arctic. The relationship between ice type (multiyear or firstyear ice) and snow depth previously documented in the western Arctic is not evident in the eastern Arctic. Using ice type to weight snow depths for satellite ice thickness retrievals may not be justifiable in the eastern Arctic. Reconstructed snow depth across the Arctic shows significant interannual variability, suggesting that use of a fixed snow depth climatology may lead to biases in retrieved ice thickness and its variability. However, interannual variability in panArctic mean snow depth is comparable or smaller than the uncertainty in both the reconstruction and IceBridge, highlighting the need for high accuracy snow depth products and reconstructions.

Since 2009, the ultra-wideband snow radar on Operation IceBridge (OIB; a NASA airborne mission to survey the polar ice covers) has acquired data in annual campaigns conducted during the Arctic and Antarctic springs. Progressive improvements in radar hardware and data processing methodologies have led to improved data quality for subsequent retrieval of snow depth. Existing retrieval algorithms differ in the way the air–snow (a–s) and snow–ice (s–i) interfaces are detected and localized in the radar returns and in how the system limitations are addressed (e.g., noise, resolution). In 2014, the Snow Thickness On Sea Ice Working Group (STOSIWG) was formed and tasked with investigating how radar data quality affects snow depth retrievals and how retrievals from the various algorithms differ. The goal is to understand the limitations of the estimates and to produce a well-documented, long-term record that can be used for understanding broader changes in the Arctic climate system. Here, we assess five retrieval algorithms by comparisons with field measurements from two ground-based campaigns, including the BRomine, Ozone, and Mercury EXperiment (BROMEX) at Barrow, Alaska; a field program by Environment and Climate Change Canada at Eureka, Nunavut; and available climatology and snowfall from ERA-Interim reanalysis. The aim is to examine available algorithms and to use the assessment results to inform the development of future approaches. We present results from these assessments and highlight key considerations for the production of a long-term, calibrated geophysical record of springtime snow thickness over Arctic sea ice.

Identifying the mechanisms controlling the timing and magnitude of snow accumulation on sea ice is crucial for understanding snow’s net effect on the surface energy budget and sea-ice mass balance. Here, we analyze the role of cyclone activity on the seasonal buildup of snow on Arctic sea ice using model, satellite, and in situ data over 1979–2016. On average, 44% of the variability in monthly snow accumulation was controlled by cyclone snowfall and 29% by sea-ice freeze-up. However, there were strong spatio-temporal differences. Cyclone snowfall comprised ~50% of total snowfall in the Pacific compared to 83% in the Atlantic. While cyclones are stronger in the Atlantic, Pacific snow accumulation is more sensitive to cyclone strength. These findings highlight the heterogeneity in atmosphere-snow-ice interactions across the Arctic, and emphasize the need to scrutinize mechanisms governing cyclone activity to better understand their effects on the Arctic snow-ice system with anthropogenic warming., Snow cover can affect the Arctic sea-ice system in different ways. Here authors study the relationship between cyclone activity and the seasonal build-up of snow on Arctic sea ice at a multi-decadal and basin-wide scale and find that 44% of the variability in monthly snow accumulation was controlled by cyclone snowfall and 29% by sea-ice freeze-up with strong spatio-temporal differences.

Snow is the most reflective, and also the most insulative, natural material on Earth. Consequently, it is an integral part of the sea-ice and climate systems. However, the spatial and temporal heterogeneities of snow pose challenges for observing, understanding and modelling those systems under anthropogenic warming. Here, we survey the snow–ice system, then provide recommendations for overcoming present challenges. These include: collecting process-oriented observations for model diagnostics and understanding snow–ice feedbacks, and improving our remote sensing capabilities of snow for monitoring large-scale changes in snow on sea ice. These efforts could be achieved through stronger coordination between the observational, remote sensing and modelling communities, and would pay dividends through distinct improvements in predictions of polar environments.

The NASA Eulerian Snow On Sea Ice Model (NESOSIM) is a new open source model that produces daily estimates of the depth and density of snow on sea ice across the polar oceans. NESOSIM has been developed in a three-dimensional Eulerian framework and includes two (vertical) snow layers and several simple parameterizations to represent the key sources and sinks of snow on sea ice. The model is forced with daily inputs of snowfall and near-surface winds (from reanalyses), sea ice concentration (from satellite passive microwave data) and sea ice drift (from satellite feature tracking), during the accumulation season (August through April). In this study, we present the NESOSIM formulation, initial calibration efforts, sensitivity studies and validation efforts across an Arctic Ocean domain (100 km horizontal resolution). The simulated snow depth and density are calibrated with in-situ data collected on drifting ice stations during the 1980s. NESOSIM demonstrates very strong agreement with the in-situ seasonal cycles of snow depth and density, and shows good (moderate) agreement with the regional snow depth (density) distributions. The results exhibit strong sensitivity to the reanalysis-derived snowfall forcing data, with the MERRA/JRA-55 (ASR) derived snow depths generally higher (lower) than ERA-Interim. We derive a new median daily snowfall dataset from these three reanalysis datasets to improve reliability in our input snowfall data. NESOSIM is run for a contemporary period (2000 to 2015) and compared against snow depth estimates derived from NASA's Operation IceBridge (OIB) snow radar data from 2009–2015, showing moderate/strong agreement, especially in the 2012–2015 comparisons.

The albedo and transmittance of melting, first-year Arctic sea ice were measured during two cruises of the Impacts of Climate on the Eco-Systems and Chemistry of the Arctic Pacific Environment (ICESCAPE) project during the summers of 2010 and 2011. Spectral measurements were made for both bare and ponded ice types at a total of 19 ice stations in the Chukchi and Beaufort Seas. These data, along with irradiance profiles taken within boreholes, laboratory measurements of the optical properties of core samples, ice physical property observations, and radiative transfer model simulations are employed to describe representative optical properties for melting first-year Arctic sea ice. Ponded ice was found to transmit roughly 4.4 times more total energy into the ocean, relative to nearby bare ice. The ubiquitous surface-scattering layer and drained layer present on bare, melting sea ice are responsible for its relatively high albedo and relatively low transmittance. Light transmittance through ponded ice depends on the physical thickness of the ice and the magnitude of the scattering coefficient in the ice interior. Bare ice reflects nearly three-quarters of the incident sunlight, enhancing its resiliency to absorption by solar insolation. In contrast, ponded ice absorbs or transmits to the ocean more than three-quarters of the incident sunlight. Characterization of the heat balance of a summertime ice cover is largely dictated by its pond coverage, and light transmittance through ponded ice shows strong contrast between first-year and multiyear Arctic ice covers.

The seasonal evolution of melt ponds has been well documented on multiyear and landfast first-year sea ice, but is critically lacking on drifting, first-year sea ice, which is becoming increasingly prevalent in the Arctic. Using 1 m resolution panchromatic satellite imagery paired with airborne and in situ data, we evaluated melt pond evolution for an entire melt season on drifting first-year and multiyear sea ice near the 2011 Applied Physics Laboratory Ice Station (APLIS) site in the Beaufort and Chukchi seas. A new algorithm was developed to classify the imagery into sea ice, thin ice, melt pond, and open water classes on two contrasting ice types: first-year and multiyear sea ice. Surprisingly, melt ponds formed ∼3 weeks earlier on multiyear ice. Both ice types had comparable mean snow depths, but multiyear ice had 0–5 cm deep snow covering ∼37% of its surveyed area, which may have facilitated earlier melt due to its low surface albedo compared to thicker snow. Maximum pond fractions were 53 ± 3% and 38 ± 3% on first-year and multiyear ice, respectively. APLIS pond fractions were compared with those from the Surface Heat Budget of the Arctic Ocean (SHEBA) field campaign. APLIS exhibited earlier melt and double the maximum pond fraction, which was in part due to the greater presence of thin snow and first-year ice at APLIS. These results reveal considerable differences in pond formation between ice types, and underscore the importance of snow depth distributions in the timing and progression of melt pond formation.

Physical and morphological properties of sea ice in the Chukchi and western Beaufort seas were measured during the 2010 and 2011 June−July ICESCAPE (Impacts of Climate on the Eco-Systems and Chemistry of the Arctic Pacific Environment) missions aboard the USCGC Healy. Observations of ice conditions, including ice thicknesses, types, and concentrations of primary, secondary, and tertiary categories were reported at 2-h intervals while the ship was in transit using the Antarctic Sea ice Processes and Climate (ASPeCt) protocol. On-ice surveys of ice thickness, melt-pond depth, and ice properties (including profiles of internal temperature, salinity, and isotopic composition) were conducted at 21 total ice stations (12 in 2010 and 9 in 2011). Comparison to historical ship-based observations confirms a multi-decadal transition from a multiyear to thinner, first year -dominated seasonal sea-ice pack in the region, with much earlier ice retreat than in past decades. The ice encountered was predominantly (>98%) first year, with un-deformed ice thicknesses ranging from 0.73 to 1.2 m. Pond coverage was extensive, averaging 29% in 2010 and 19% in 2011, resulting in considerable light absorption in the ice and transmission of light to the ocean. Enhanced melting near the ice edge is consistent with transport of Pacific-origin heat and/or ice-albedo feedbacks. Sediment entrainment was visible in 7.5% and 10.9% of the ice in 2010 and 2011, respectively. Overall, the results indicate a regime shift in the characteristics of the ice cover has occurred in the region over recent decades, with substantive implications for thermodynamic forcing, light availability in the upper ocean, and biological and biogeochemical processes in the ice and water column beneath. The results presented have applications for the interpretation of optical and biological measurements in the Chukchi Sea and serve as record of ice conditions for assessing long-term change.

Snow plays a key role in the growth and decay of Arctic sea ice. In winter, it insulates sea ice from cold air temperatures, slowing sea ice growth. From spring to summer, the albedo of snow determines how much insolation is absorbed by the sea ice and underlying ocean, impacting ice melt processes. Knowledge of the contemporary snow depth distribution is essential for estimating sea ice thickness and volume, and for understanding and modeling sea ice thermodynamics in the changing Arctic. This study assesses spring snow depth distribution on Arctic sea ice using airborne radar observations from Operation IceBridge for 2009–2013. Data were validated using coordinated in situ measurements taken in March 2012 during the Bromine, Ozone, and Mercury Experiment (BROMEX) field campaign. We find a correlation of 0.59 and root-mean-square error of 5.8 cm between the airborne and in situ data. Using this relationship and IceBridge snow thickness products, we compared the recent results with data from the 1937, 1954–1991 Soviet drifting ice stations. The comparison shows thinning of the snowpack, from 35.1 ± 9.4 to 22.2 ± 1.9 cm in the western Arctic, and from 32.8 ± 9.4 to 14.5 ± 1.9 cm in the Beaufort and Chukchi seas. These changes suggest a snow depth decline of 37 ± 29% in the western Arctic and 56 ± 33% in the Beaufort and Chukchi seas. Thinning is negatively correlated with the delayed onset of sea ice freezeup during autumn.