Meltwater that pools on the surface of Arctic sea ice enhances solar absorption and accelerates further ice melt. The impact of melt ponds on energy absorption is controlled primarily through their influence on ice albedo, which is, in turn, governed in large part by the ponds' spatial coverage. This work seeks to observe the spatial coverage of melt ponds across the Arctic basin with sufficient accuracy to investigate pond‐albedo feedback and presents an improved technique to achieve this goal. We approach the problem by using the Open Source Sea Ice Processing algorithm to classify surface features in submeter resolution optical satellite imagery over select sites where such imagery is available. These data establish “true” estimates of pond coverage and the ponds' spectral reflectance. This information is then used to inform, improve, and test spectral unmixing and machine learning techniques that seek to determine melt pond coverage from more widely available, but lower resolution, optical satellite imagery (e.g., Moderate Resolution Imaging Spectroradiometer). The new machine learning approach improves accuracy from prior work and can contribute to improved efforts to validate melt pond models or understand trends in pond coverage. Nevertheless, we encounter and carefully document significant challenges to retrieving melt pond fractions from low‐resolution optical imagery. These limit accuracy to levels below that necessary for resolving climatologically important trends. We conclude that greatly expanding the collection of high‐resolution satellite imagery over sea ice is necessary to monitor melt pond coverage with the accuracy needed by the scientific community.
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.
more » « less- Award ID(s):
- 2138786
- PAR ID:
- 10478512
- Publisher / Repository:
- Wiley
- Date Published:
- Journal Name:
- Geophysical Research Letters
- Volume:
- 50
- Issue:
- 5
- ISSN:
- 0094-8276
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract 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.
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Abstract Our ability to predict the future of Arctic sea ice is limited by ice's sensitivity to detailed surface conditions such as the distribution of snow and melt ponds. Snow on top of the ice decreases ice's thermal conductivity, increases its reflectivity (albedo), and provides a source of meltwater for melt ponds during summer that decrease the ice's albedo. In this paper, we develop a simple model of premelt snow topography that accurately describes snow cover of flat, undeformed Arctic sea ice on several study sites for which data were available. The model considers a surface that is a sum of randomly sized and placed “snow dunes” represented as Gaussian mounds. This model generalizes the “void model” of Popović et al. (2018,
https://doi.org/10.1103/PhysRevLett.120.148701 ) and, as such, accurately describes the statistics of melt pond geometry. We test this model against detailed LiDAR measurements of the premelt snow topography. We show that the model snow depth distribution is statistically indistinguishable from the measurements on flat ice, while small disagreement exists if the ice is deformed. We then use this model to determine analytic expressions for the conductive heat flux through the ice and for melt pond coverage evolution during an early stage of pond formation. We also formulate a criterion for ice to remain pond‐free throughout the summer. Results from our model could be directly included in large‐scale models, thereby improving our understanding of energy balance on sea ice and allowing for more reliable predictions of Arctic sea ice in a future climate.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1029/2022GL102102
