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Melt ponds are a core component of the summer sea ice system in the Arctic, increasing the uptake of solar energy and impacting the ice-associated ecosystem. They were thus one of the key topics during the one-year drift campaign MOSAiC in the Transpolar Drift 2019/2020. Pond depth is a dominating factor in the description of the surface meltwater volume, necessary to estimate budgets, and used in model parametrization to simulate pond coverage evolution. However, observational data on pond depth is spatially and temporally strongly limited to a few in situ measurements. Pond bathymetry, which is pond depth spatially fully resolved, remains entirely unexplored. Here, we present a newly developed method to derive pond bathymetry from aerial images. We determine it from a photogrammetric multi-view reconstruction of the summer ice surface topography. Based on images recorded on dedicated grid flights and facilitated assumptions, we were able to obtain pond depth with a mean deviation of 3.5 cm compared to manual in situ observations. The method is independent of pond color and sky conditions, which is an advantage over recently developed radiometric airborne retrieval methods. It can furthermore be implemented in any typical photogrammetry workflow. We present the retrieval algorithm, including requirements for the data recording and survey planning, and a correction method for refraction at the air—pond interface. In addition, we show how the retrieved surface topography model synergizes with the initial image data to retrieve the water level of individual ponds from the visually determined pond margins. We use the method to give a profound overview of the pond coverage on the MOSAiC floe, on which we found unexpected steady pond coverage and volume. We were able to derive individual pond properties of more than 1600 ponds on the floe, including their size, bathymetry, volume, surface elevation above sea level, and temporal evolution. We present a scaling factor for single in situ depth measurements, discuss the representativeness of in situ pond measurements, and show indications for non-rigid pond bottoms. The study points out the great potential to derive geometric properties of the summer sea-ice surface emerging from the increasingly available visual image data recorded from UAVs or aircraft, allowing for an integrated understanding and improved formulation of the thermodynamic and hydrological pond system in models. [ABSTRACT FROM AUTHOR]

Melt ponds forming on Arctic sea ice in summer significantly reduce the surface albedo and impact the heat and mass balance of the sea ice. Therefore, their areal coverage, which can undergo rapid change, is crucial to monitor. We present a revised method to extract melt pond fraction (MPF) from Sentinel‐2 satellite imagery, which is evaluated by MPF products from higher‐resolution satellite and helicopter‐borne imagery. The analysis of melt pond evolution during the MOSAiC campaign in summer 2020, shows a split of the Central Observatory (CO) into a level ice and a highly deformed ice part, the latter of which exhibits exceptional early melt pond formation compared to the vicinity. Average CO MPFs are 17% before and 23% after the major drainage. Arctic‐wide analysis of MPF for years 2017–2021 shows a consistent seasonal cycle in all regions and years. Plain Language Summary: In the Arctic summer, puddles of surface melt water, called melt ponds, form on the sea ice. These melt ponds reduce the ability of the surface to reflect the sunlight. Instead, they absorb more solar energy and pave the way into the ocean beneath where the energy is also absorbed. Thus, it is important to know where these melt ponds develop and what fraction of the surface they cover. To investigate this, we present a classification algorithm that is used to extract the areal fraction of melt ponds from satellite measurements. The special focus of this study is the MOSAiC campaign in summer 2020, where the research vessel Polarstern drifted with an ice floe for 1 year. We can see a separation of this floe into two parts. One of them shows melt pond formation much earlier than the other. This is because of different ice age and surface properties. Additionally, we use the classification algorithm to analyze the differences of melt pond fraction between different dates and regions in the Arctic. Key Points: Algorithm to extract melt pond and open water areas from Sentinel‐2 imagery with maximum uncertainty of 6%Exceptional early melt pond formation on MOSAiC Central Observatory, summer 2020, compared to broader vicinityHigh spatial and temporal variability of melt pond fraction on local and regional scales [ABSTRACT FROM AUTHOR]

Comparing helicopter‐borne surface temperature maps in winter and optical orthomosaics in summer from the year‐long Multidisciplinary drifting Observatory for the Study of Arctic Climate expedition, we find a strong geometric correlation between warm anomalies in winter and melt pond location the following summer. Warm anomalies are associated with thinner snow and ice, that is, surface depression and refrozen leads, that allow for water accumulation during melt. Warm surface temperature anomalies in January were 0.3–2.5 K warmer on sea ice that later formed melt ponds. A one‐dimensional steady‐state thermodynamic model shows that the observed surface temperature differences are in line with the observed ice thickness and snow depth. We demonstrate the potential of seasonal prediction of summer melt pond location and coverage from winter surface temperature observations. A threshold‐based classification achieves a correct classification for 41% of the melt ponds. Plain Language Summary: We compare winter surface temperatures from an infrared camera with summer photographs of sea ice with melt ponds. The datasets were recorded from a helicopter during the Multidisciplinary drifting Observatory for the Study of Arctic Climate expedition. Melt ponds form on sea ice in summer when the snow melts and water accumulates in the lower locations on the ice floes. Melt ponds are very important for the Arctic energy budget because they strongly change the sea ice brightness and thus the amount of solar energy absorbed by the ice. We find surface characteristics with similar size and location between warmer areas in winter and the location of melt ponds in summer. For a better process understanding, we calculate the surface temperature with a simple model and find that the warm temperature anomalies are due to thinner ice and snow. Stronger warm temperature anomalies appear in new cracks in the ice which are covered with newly formed, thin ice. With a temperature‐based classification, we are able to estimate the summer melt pond fraction. Key Points: Winter warm surface temperature anomalies are co‐located with melt pond locations in the following summerWarm anomalies appear in refrozen leads, in refrozen melt ponds, and in troughs between ridges, due to thinner snow and iceWe show the potential for prediction of summer melt pond fraction from winter surface temperatures [ABSTRACT FROM AUTHOR]

Changes in Arctic sea ice thickness are the result of complex interactions of the dynamic and variable ice cover with atmosphere and ocean. Most of the sea ice exiting the Arctic Ocean does so through Fram Strait, which is why long-term measurements of ice thickness at the end of the Transpolar Drift provide insight into the integrated signals of thermodynamic and dynamic influences along the pathways of Arctic sea ice. We present an updated summer (July–August) time series of extensive ice thickness surveys carried out at the end of the Transpolar Drift between 2001 and 2020. Overall, we see a more than 20 % thinning of modal ice thickness since 2001. A comparison of this time series with first preliminary results from the international Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) shows that the modal summer thickness of the MOSAiC floe and its wider vicinity are consistent with measurements from previous years at the end of the Transpolar Drift. By combining this unique time series with the Lagrangian sea ice tracking tool, ICETrack, and a simple thermodynamic sea ice growth model, we link the observed interannual ice thickness variability north of Fram Strait to increased drift speeds along the Transpolar Drift and the consequential variations in sea ice age. We also show that the increased influence of upward-directed ocean heat flux in the eastern marginal ice zones, termed Atlantification, is not only responsible for sea ice thinning in and around the Laptev Sea but also that the induced thickness anomalies persist beyond the Russian shelves and are potentially still measurable at the end of the Transpolar Drift after more than a year. With a tendency towards an even faster Transpolar Drift, winter sea ice growth will have less time to compensate for the impact processes, such as Atlantification, have on sea ice thickness in the eastern marginal ice zone, which will increasingly be felt in other parts of the sea-ice-covered Arctic. [ABSTRACT FROM AUTHOR]

The surface reflection of solar radiation comprises an important boundary condition for solar radiative transfer simulations. In polar regions above snow surfaces, the surface reflection is particularly anisotropic due to low Sun elevations and the highly anisotropic scattering phase function of the snow crystals. The characterization of this surface reflection anisotropy is essential for satellite remote sensing over both the Arctic and Antarctica. To quantify the angular snow reflection properties, the hemispherical-directional reflectance factor (HDRF) of snow surfaces was derived from airborne measurements in Antarctica during austral summer in 2013/14. For this purpose, a digital 180 ∘ fish-eye camera (green channel, 490–585 nm wavelength band) was used. The HDRF was measured for different surface roughness conditions, optical-equivalent snow grain sizes, and solar zenith angles. The airborne observations covered an area of around 1000 km × 1000 km in the vicinity of Kohnen Station (75 ∘ 0 ′ S, 0 ∘ 4 ′ E) at the outer part of the East Antarctic Plateau. The observations include regions with higher (coastal areas) and lower (inner Antarctica) precipitation amounts and frequencies. The digital camera provided upward, angular-dependent radiance measurements from the lower hemisphere. The comparison of the measured HDRF derived for smooth and rough snow surfaces (sastrugi) showed significant differences, which are superimposed on the diurnal cycle. By inverting a semi-empirical kernel-driven bidirectional reflectance distribution function (BRDF) model, the measured HDRF of snow surfaces was parameterized as a function of solar zenith angle, surface roughness, and optical-equivalent snow grain size. This allows a direct comparison of the HDRF measurements with the BRDF derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite product MCD43. For the analyzed cases, MODIS observations (545–565 nm wavelength band) generally underestimated the anisotropy of the surface reflection. The largest deviations were found for the volumetric model weight fvol (average underestimation by a factor of 10). These deviations are likely linked to short-term changes in snow properties. [ABSTRACT FROM AUTHOR]

The Arctic atmospheric boundary layer (AABL) in the central Arctic was characterized by dropsonde, lidar, ice thickness and airborne in situ measurements during the international Polar Airborne Measurements and Arctic Regional Climate Model Simulation Project (PAMARCMiP) in April 2009. We discuss AABL observations in the lowermost 500 m above (A) open water, (B) sea ice with many open/refrozen leads (C) sea ice with few leads, and (D) closed sea ice with a front modifying the AABL. Above water, the AABL had near-neutral stratification and contained a high water vapor concentration. Above sea ice, a low AABL top, low near-surface temperatures, strong surface-based temperature inversions and an increase of moisture with altitude were observed. AABL properties and particle concentrations were modified by a frontal system, allowing vertical mixing with the free atmosphere. Above areas with many leads, the potential temperature decreased with height in the lowest 50 m and was nearly constant above, up to an altitude of 100-200 m, indicating vertical mixing. The increase of the backscatter coefficient towards the surface was high. Above sea ice with few refrozen leads, the stably stratified boundary layer extended up to 200-300 m altitude. It was characterized by low specific humidity and a smaller increase of the backscatter coefficient towards the surface. [ABSTRACT FROM AUTHOR]

The size and shape of snow grains directly impacts the reflection by a snowpack. In this article, different approaches to retrieve the optical-equivalent snow grain size ( r opt ) or, alternatively, the specific surface area (SSA) using satellite, airborne, and ground-based observations are compared and used to evaluate ICON-ART (ICOsahedral Nonhydrostatic—Aerosols and Reactive Trace gases) simulations. The retrieval methods are based on optical measurements and rely on the r opt -dependent absorption of solar radiation in snow. The measurement data were taken during a three-week campaign that was conducted in the North of Greenland in March/April 2018, such that the retrieval methods and radiation measurements are affected by enhanced uncertainties under these low-Sun conditions. An adjusted airborne retrieval method is applied which uses the albedo at 1700 nm wavelength and combines an atmospheric and snow radiative transfer model to account for the direct-to-global fraction of the solar radiation incident on the snow. From this approach, we achieved a significantly improved uncertainty (<25%) and a reduced effect of atmospheric masking compared to the previous method. Ground-based in situ measurements indicated an increase of r opt of 15 µm within a five-day period after a snowfall event which is small compared to previous observations under similar temperature regimes. ICON-ART captured the observed change of r opt during snowfall events, but systematically overestimated the subsequent snow grain growth by about 100%. Adjusting the growth rate factor to 0.012 µm 2 s − 1 minimized the difference between model and observations. Satellite-based and airborne retrieval methods showed higher r opt over sea ice (<300 µm) than over land surfaces (<100 µm) which was reduced by data filtering of surface roughness features. Moderate-Resolution Imaging Spectroradiometer (MODIS) retrievals revealed a large spread within a series of subsequent individual overpasses, indicating their limitations in observing the snow grain size evolution in early spring conditions with low Sun. [ABSTRACT FROM AUTHOR]

Two parameterisation schemes for the turbulent surface fluxes and drag coefficients over the Arctic marginal sea-ice zone (MIZ) are (further) developed, and their results are compared with each other. Although the schemes are based on different principles (flux averaging and parameter averaging), the resulting drag coefficients differ only slightly in the case of neutral and stable stratification. For unstable stratification and sea-ice conditions being typical for the north-eastern Fram Strait, the drag coefficients resulting from the parameter-averaging concept are 5–10% larger than those of the flux-averaging concept. At a sea-ice concentration of 45%, the parameter-averaging method overestimates the heat fluxes by a factor of 1.2. An inclusion in the schemes of form drag caused by floe edges and ridges has a much larger effect on the drag coefficient, and on the momentum fluxes, than the choice between the parameter-averaging or flux-averaging methods. Based on sensitivity studies with the flux-averaging scheme, a simple formula for the effective drag coefficient above the Arctic MIZ is derived. It reduces the computational costs of the more complex parameterisations and could also be used in larger scale models. With this simple formula, the effective drag coefficient can be calculated as a function of the sea-ice concentration and skin drag coefficients for water and ice floes. The results obtained with this parameterisation differ only slightly from those using the more complex schemes. Finally, it is shown that in the MIZ, drag coefficients for sea-ice models may differ significantly from the effective drag coefficients used in atmospheric models. [ABSTRACT FROM AUTHOR]