Year-round observations of the physical snow and ice properties and processes that govern the ice pack evolution and its interaction with the atmosphere and the ocean were conducted during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition of the research vessel Polarstern in the Arctic Ocean from October 2019 to September 2020. This work was embedded into the interdisciplinary design of the 5 MOSAiC teams, studying the atmosphere, the sea ice, the ocean, the ecosystem, and biogeochemical processes. The overall aim of the snow and sea ice observations during MOSAiC was to characterize the physical properties of the snow and ice cover comprehensively in the central Arctic over an entire annual cycle. This objective was achieved by detailed observations of physical properties and of energy and mass balance of snow and ice. By studying snow and sea ice dynamics over nested spatial scales from centimeters to tens of kilometers, the variability across scales can be considered. On-ice observations of in situ and remote sensing properties of the different surface types over all seasons will help to improve numerical process and climate models and to establish and validate novel satellite remote sensing methods; the linkages to accompanying airborne measurements, satellite observations, and results of numerical models are discussed. We found large spatial variabilities of snow metamorphism and thermal regimes impacting sea ice growth. We conclude that the highly variable snow cover needs to be considered in more detail (in observations, remote sensing, and models) to better understand snow-related feedback processes. The ice pack revealed rapid transformations and motions along the drift in all seasons. The number of coupled ice–ocean interface processes observed in detail are expected to guide upcoming research with respect to the changing Arctic sea ice.

The Arctic climate system includes numerous highly interactive small-scale physical processes in the atmosphere, sea ice, and ocean. During and since the International Polar Year 2007–2009, significant advances have been made in understanding these processes. Here, these recent advances are reviewed, synthesized, and discussed. In atmospheric physics, the primary advances have been in cloud physics, radiative transfer, mesoscale cyclones, coastal, and fjordic processes as well as in boundary layer processes and surface fluxes. In sea ice and its snow cover, advances have been made in understanding of the surface albedo and its relationships with snow properties, the internal structure of sea ice, the heat and salt transfer in ice, the formation of superimposed ice and snow ice, and the small-scale dynamics of sea ice. For the ocean, significant advances have been related to exchange processes at the ice–ocean interface, diapycnal mixing, double-diffusive convection, tidal currents and diurnal resonance. Despite this recent progress, some of these small-scale physical processes are still not sufficiently understood: these include wave–turbulence interactions in the atmosphere and ocean, the exchange of heat and salt at the ice–ocean interface, and the mechanical weakening of sea ice. Many other processes are reasonably well understood as stand-alone processes but the challenge is to understand their interactions with and impacts and feedbacks on other processes. Uncertainty in the parameterization of small-scale processes continues to be among the greatest challenges facing climate modelling, particularly in high latitudes. Further improvements in parameterization require new year-round field campaigns on the Arctic sea ice, closely combined with satellite remote sensing studies and numerical model experiments.

Snow and ice thermodynamics over the Arctic Ocean were simulated applying a one‐dimensional model. A number of numerical experiments in synoptic (10 days in early autumn) and seasonal (May–September) scales were carried out to investigate the impact of external forcing, snow physics, and the model resolution: the number of layers in both snow and ice ranged from 3 to 40. The model forcing was based on in situ observations carried out in 2003 during the Chinese National Arctic Research Expedition (CHINARE) as well as on forecasts and analyses of the European Centre for Medium‐Range Weather Forecasts (ECMWF) and the National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR). The model results were compared against the results of the ECMWF and NCEP/NCAR sea ice schemes. The ECMWF operational precipitation forecasts yielded realistic seasonal snowfall, while the precipitation in NCEP/NCAR reanalysis was unrealistically large. A good result on snow thickness evolution also strongly depended on the accuracy of modeled snowmelt. A time‐dependent surface albedo parameterization was critical for the seasonal evolution of snow and ice thickness. Application of 15–20 model levels in snow and ice is recommended as it (1) ensured good reproduction of the vertical snow/ice temperature profile also when solar radiation was large, (2) decreased the sensitivity of snow and ice mass balance to changes in surface albedo, (3) enabled the calculation of subsurface melting of snow and ice, and (4) reasonably reproduced the superimposed ice formation and onset of ice melt. In autumn, however, the accuracy of atmospheric forcing was more important than the model resolution.

Sea ice physical properties were determined at 21 first‐year ice (FYI) and 20 multiyear ice (MYI) stations in the Pacific sector of the Arctic during summer in 2008–2018. The bulk ice temperature was between −2.7 and −0.3 °C for FYI and between −1.7 and −0.2 °C for MYI. The bulk salinity was 0.4–3.2 practical salinity unit (psu) for FYI and 0.4–2.4 psu for MYI. A low‐salinity layer and an almost fresh layer were always present at the top of FYI and MYI, respectively. The bulk density was 600.3–900.1 kg/m³ for FYI and 686.1–903.3 kg/m³ for MYI. The upper layer density was less than the lower layer density due to higher gas content. Salinity and density were less than previously reported values of summer sea ice, and their parameterization formulae were updated. The brine and gas volume fractions were determined based on the measured ice temperature, salinity, and density. The average bulk brine and gas volume fractions were 11.3 ± 6.2% and 14.9 ± 7.9% for FYI and 9.5 ± 11.0% and 13.5 ± 12.7% for MYI. Typical brine and gas volume fraction profiles were identified and parameterized using regression analysis. The large brine and gas content of sea ice affects the sea ice melting. The energy used to melt a unit volume of summer Arctic sea ice was estimated as (2.26 ± 0.29) × 10⁵ kJ for FYI and (2.36 ± 0.24) × 10⁵ kJ for MYI. This study shows that the current physical state of the Arctic summer sea ice is different from earlier sea ice climatology, and the results will guide further research and modeling of Arctic sea ice.

An ice mass balance buoy (IMB) monitors the evolution of snow and ice cover on seas, ice caps and lakes through the measurement of various variables. The crucial measurement of snow and ice thickness has been achieved using acoustic sounders in early devices but a more recently developed IMB called the Snow and Ice Mass Balance Array (SIMBA) measures vertical temperature profiles through the air-snow-ice-water column using a thermistor string. The determination of snow depth and ice thickness from SIMBA temperature profiles is presently a manual process. We present an automated algorithm to perform this task. The algorithm is based on heat flux continuation, limit ratio between thermal heat conductivity of snow and ice, and minimum resolution (±0.0625°C) of the temperature sensors. The algorithm results are compared with manual analyses, in situ borehole measurements and numerical model simulation. The bias and root mean square error between algorithm and other methods ranged from 1 to 9 cm for ice thickness counting 2%–7% of the mean observed values. The algorithm works well in cold condition but becomes less reliable in warmer conditions where the vertical temperature gradient is reduced.

The seasonal evolution of snow and ice on Lake Orajärvi, northern Finland, was investigated for three consecutive winter seasons. Material consisting of numerical weather prediction model (HIRLAM) output, weather station observations, manual snow and ice observations, high spatial resolution snow and ice temperatures from ice mass balance buoys (SIMB), and Moderate Resolution Imaging Spectroradiometer (MODIS) lake ice surface temperature observations was gathered. A snow/ice model (HIGHTSI) was applied to simulate the evolution of the snow and ice surface energy balance, temperature profiles and thickness. The weather conditions in early winter were found critical in determining the seasonal evolution of the thickness of lake ice and snow. During the winter season (Nov.–Apr.), precipitation, longwave radiative flux and air temperature showed large inter-annual variations. The uncertainty in snow/ice model simulations originating from precipitation was investigated. The contribution of snow to ice transfor...