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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 1-year drift campaign Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) in the Transpolar Drift 2019/2020. Pond depth is a dominating factor in describing the surface meltwater volume; it is necessary to estimate budgets and used in model parameterization to simulate pond coverage evolution. However, observational data on pond depth are spatially and temporally strongly limited to a few in situ measurements. Pond bathymetry, which is pond depth spatially fully resolved, remains 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 the importance of such high-resolution data for new satellite retrievals, 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 uncrewed aerial vehicles (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 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]

Global climate change poses significant societal and political challenges. The rapid increase in the near-surface air temperatures and the drastic retreat of the Arctic sea ice during summer are not well represented by climate models. The data sets introduced here intend to help improving the current understanding of the ongoing Arctic climate changes. In particular, this study considers observations from 24 helicopter flights (June–September 2020) and 5 flights with the helicopter-towed probe HELiPOD (May–July 2020) during MOSAiC. Distributions of various surface types (white ice/snow, bright melt ponds, dark melt ponds, open water, and bare ice) were determined using fisheye camera images. They were related to collocated broadband irradiance measurements to analyse the temporal and spatial changes of the surface albedo. Multiple linear regression was applied to assign the measured areal albedo to the corresponding surface-types. The resulting surface-type fractions, the albedo data and respective upward and downward broadband solar irradiances of several flights throughout the melting and refreezing season are provided. [ABSTRACT FROM AUTHOR]

Airborne laser scanners (ALS) are used to map the sea-ice surface at sub-meter resolution. We conducted 64 flights over the Arctic sea ice between September 2019 and September 2020 during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition to measure sea-ice surface elevation. The flights ranged from repeated, local-scale 5 × 5 km2 floe grid surveys to regional-scale transects more than 100 km long. We provide data at different processing levels: geolocated elevation point clouds and gridded segments of elevation and freeboard with a spatial resolution of 0.5 m. The latter product is corrected for atmospheric backscatter, sea-ice drift, and offset in elevation due to degraded INS/GNSS solutions > 85° N. For floe grid surveys, all data are combined to merged two-dimensional elevation maps. Other provided parameters include laser reflectance and echo width. The presented data offer a unique possibility to study the temporal evolution, spatial distribution, and variability of the snow and sea-ice surface and their properties in addition to validating satellite products. [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 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]

Hyperspectral remote-sensing instruments on unmanned aerial vehicles (UAVs), aircraft and satellites offer new opportunities for sea ice observations. We present the first study using airborne hyperspectral imagery of Arctic sea ice and evaluate two atmospheric correction approaches (ATCOR-4 (Atmospheric and Topographic Correction version 4; v7.0.0) and empirical line calibration). We apply an existing, field data-based model to derive the depth of melt ponds, to airborne hyperspectral AisaEAGLE imagery and validate results with in situ measurements. ATCOR-4 results roughly match the shape of field spectra but overestimate reflectance resulting in high root-mean-square error ( R M S E ) (between 0.08 and 0.16). Noisy reflectance spectra may be attributed to the low flight altitude of 200 ft and Arctic atmospheric conditions. Empirical line calibration resulted in smooth, accurate spectra ( R M S E < 0.05) that enabled the assessment of melt pond bathymetry. Measured and modeled pond bathymetry are highly correlated (r = 0.86) and accurate ( R M S E = 4.04 cm), and the model explains a large portion of the variability ( R 2 = 0.74). We conclude that an accurate assessment of melt pond bathymetry using airborne hyperspectral data is possible subject to accurate atmospheric correction. Furthermore, we see the necessity to improve existing approaches with Arctic-specific atmospheric profiles and aerosol models and/or by using multiple reference targets on the ground. [ABSTRACT FROM AUTHOR]