Search

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. 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. 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 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 vicinity High spatial and temporal variability of melt pond fraction on local and regional scales

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. 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. 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 Winter warm surface temperature anomalies are co‐located with melt pond locations in the following summer Warm anomalies appear in refrozen leads, in refrozen melt ponds, and in troughs between ridges, due to thinner snow and ice We show the potential for prediction of summer melt pond fraction from winter surface temperatures

A parameterization of subgridscale surface fluxes over the marginal sea ice zone which has been used earlier in several studies is modified and applied to a nonhydrostatic mesoscale model. The new scheme accounts for the form drag of ice floes and is combined with a so‐called flux averaging method for the determination of surface fluxes over inhomogeneous terrain. Individual fluxes over ice and water are calculated as a function of the blending height. It is shown by comparison with observations that the drag coefficients calculated with the new parameterization agree well with data. The original scheme strongly overestimates the form drag effect. An improvement is mainly obtained by an inclusion of stratification and by use of a more adequate coefficient of resistance for individual ice floes. The mesoscale model is applied to off‐ice flows over the polar marginal sea ice zone. The model results show that under certain meteorological conditions the form drag can have a strong influence on the near‐surface wind velocity and on the turbulent fluxes of momentum. Four case studies are carried out. The maximum influence of form drag occurs in the case with strong unstable stratification and with wind oblique to the ice edge. Under these conditions the wind stress on sea ice is modified by at least 100% for ice concentrations less than 50% if form drag is taken into account.

A parameterization of subgridscale surface fluxes over the marginal sea ice zone which has beenused earlier in several studies is modified and applied to a nonhydrostatic mesoscale model.The new scheme accounts for the form drag of ice floes and is combined with a so-called fluxaveraging method for the determination of surface fluxes over inhomogeneous terrain. Individualfluxes over ice and water are calculated as a function of the blending height. It is shown bycomparison with observations that the drag coefficients calculated with the new parameterizationagree well with data. The original scheme strongly overestimates the form drag effect.An improvement is mainly obtained by an inclusion of stratification and by use of a moreadequate coefficient of resistance for individual ice floes. The mesoscale model is applied to officeflows over the polar marginal sea ice zone. The model results show that under certainmeteorological conditions the form drag can have a strong influence on the near-surface windvelocity and on the turbulent fluxes of momentum. Four case studies are carried out. Themaximum influence of form drag occurs in the case with strong unstable stratification and withwind oblique to the ice edge. Under these conditions the wind stress on sea ice is modified byat least 100% for ice concentrations less than 50% if form drag is taken into account.