Sea Surface Salinity Variability from Satellite and In Situ Observations around Greenland
Bibliographic record
Abstract
Abstract This study investigates sea surface salinity (SSS) south of Greenland using satellite radiometric data products at pixel resolution and quality-assured in situ measurements. The analysis focuses on the weekly Climate Change Initiative (CCI), version 4.4, dataset from the European Space Agency (ESA), covering 2010–22, and the high-temporal-resolution dataset from the Downstream Data Processing Center for Soil Moisture Ocean Salinity (SMOS)—High Resolution (CATDS-HR), available for 2015–22. Both satellite data products reveal significant positive biases on the shelves, with the CCI, version 4.4, product removing more data in this region and underestimating variability by at least a factor of 2 compared to CATDS-HR. In regions with low SSS variability, such as the central North Atlantic subpolar gyre, satellite products overestimate variability by 40% [0.17 instead of 0.12 on the Practical Salinity Scale (pss)] due to retrieval uncertainties (∼0.14 pss). However, CATDS-HR is 10% closer to in situ measurements than CCI, version 4. Beyond 100 km from the Greenland coast, in areas of high SSS variability, both products capture significant seasonal and interannual patterns, resolving scales down to 50 km. Satellite and in situ data for 2010–22 highlight an extreme freshwater event in the eastern Labrador Sea in September–October 2021, characterized by a > 2 pss salinity decrease near the Greenland continental slope that diminishes to > 1 pss up to 300 km offshore. These results demonstrate the capability of satellite SSS products to resolve freshwater exchange from Greenland shelves to the ocean interior, while emphasizing the need for adjustments to address systematic biases closer to the coast. Significance Statement Low salinity water originating from the Arctic and Greenland is present over the shelves off Greenland, which at times is exported to the Labrador Sea, where it can influence its surface salinity and density. We explore surface salinity variability from an unprecedented in situ surface dataset as well as surface salinity products from L-band radiometry retaining the satellite data spatial resolution. These products often present large average biases over the Greenland shelves, which originate from the adjustment of their long-time average to a biased climatology, which can be regionally improved. The variability in the satellite products satisfactorily reproduces the variability observed in in situ data, with underestimated variance on the shelves and overestimated variance associated with the uncertainties in the less variable central North Atlantic subpolar gyre. We identify a major export of freshwater from the southwest Greenland shelf into the eastern Labrador Sea in September–October 2021, with unprecedented spatial resolution.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".