Updated Arctic melt pond fraction dataset and trends 2002–2023 using ENVISAT and Sentinel-3 remote sensing data
Bibliographic record
Abstract
Melt ponds on Arctic sea ice affect the radiative balance of the region as they introduce darkening of the sea ice during the Arctic summer. The temporal extent and spatial extent of the ponding, as well as its amplitude, reflect the state of Arctic sea ice and are important for our understanding of Arctic sea ice change. Remote sensing retrievals of melt pond fraction (MPF) provide information on both the present state of the melt pond development and its change throughout the years, which is valuable information in the context of climate change and Arctic amplification. In this work, we transfer the earlier published Melt Pond Detector (MPD) remote sensing retrieval to the Ocean and Land Colour Instrument (OLCI) data on board the Sentinel-3 satellite and so complement the existing Medium Resolution Imaging Spectrometer (MERIS) MPF dataset (2002–2011) from Environmental Satellite (ENVISAT) with recent data (2017–present). To evaluate the bias of the MPF product, comparisons to Sentinel-2 MultiSpectral Instrument (MSI) high-resolution satellite imagery are presented, in addition to earlier published validation studies. Both MERIS and OLCI MPD tend to overestimate the small MPFs (ranging from 0 to 0.2), which can be attributed to the presence of water-saturated snow and sea ice before onset of ponding. Good agreement for the middle-range MPF (0.2–0.8) is observed, and the areas of exceptionally high MPF = 100 % are recognized as well. The earlier published MERIS MPFs (2002–2011) were reprocessed using an improved cloud clearing routine and together with recent Sentinel-3 data provide an internally consistent dataset, which allows the MPF development in the past 20 years to be analyzed. Although the total summer hemispheric MPF trend is moderate, at +0.75 % per decade, the regional weekly MPF trends display a pronounced dynamic and range from −10 % to as high as +20 % per decade, depending on the region. We conclude the following effects: The global Arctic melt onset shifted towards spring by at least 2 weeks, with the melt onset happening in late May in recent years as compared to early June to mid-June in the beginning of the dataset. There has been a change in the pond onset regime in recent years, with the East Siberian and Laptev Sea dominating the melt onset and not the Beaufort Gyre region as before. The central Arctic, north Greenland and the Canadian Arctic Archipelago (CAA) have shown signs of increasing first-year ice (FYI) fraction in recent years. The daily gridded MPF averages are available on the web page of the Institute of Environmental Physics, University of Bremen, as a historic dataset for the ENVISAT data and as ongoing operational processing for the Sentinel-3 data.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".