Advancing the role and use of remote sensing forunderstanding the impact of sea ice on air-sea gas exchange in polar oceans
Bibliographic record
Abstract
The accuracy of estimates of air-sea exchange of carbon dioxide in the polar oceans is currently limited due to critical gaps in our understanding of the relationship and interactions between the air, sea, and ice. Advances in methods to make direct measurements of air-sea carbon dioxide fluxes using tower and ship mounted eddy covariance systems means that it is increasingly possible to collect high quality air-sea carbon dioxide flux observations within regions of variable sea ice coverage. This thesis focusses on examining the current and future use of remote sensing data for characterising sea ice conditions within air-ice-sea flux studies. Chapter 2 critically reviews the results of previously published polar eddy covariance studies in sea ice environments to determine the current state of the art in terms of measurements and our understanding. This identifies where methodological differences may be influencing these findings, and possible future directions for this area of research; this includes the need for the development of ‘best-practice’ methodologies. Improving the use of spatial data and its associated uncertainties, particularly in mixed ice-water environments, is identified as a research priority. In Chapter 3, an analysis framework using published field data and ice data uncertainties identifies that these uncertainties can significantly impact the relationship between sea ice coverage and gas transfer velocity found in the published literature. This work shows that future effort should focus on improved methods of monitoring sea ice heterogeneity in the flux footprint which include fully characterised ice data uncertainties. In response to this, Chapter 4 presents a drone-based method and solution for collecting fine-scale ocean and sea-ice surface observations which includes characterised uncertainties. This is achieved via an easy to use, open-source automated workflow for georectifying individual aerial images taken over water surfaces without the use of fixed ground control; a key requirement for observations of moving water and ice surfaces. In Chapter 5, this georectification workflow is extensively applied during a specifically designed field experiment to characterise surface ocean and sea ice conditions in the time-and-space varying footprint of an eddy covariance tower, over melting landfast sea ice in the Canadian Arctic Archipelago. Fine-resolution optical data (from drones and satellites in combination) are found to be the only suitable methodology (compared to passive microwave and fixed point-cameras) for characterising ice coverage, melt pond fraction and open water fraction at scales relevant to any flux observations. Hence, fine-spatial (mm – 10 metre) and high-temporal (sub-daily) resolution data, along with the associated uncertainties are needed. Overall, the novel advances detailed in this thesis for providing and exploiting remote sensing observations of sea-ice have questioned previous findings and identified a cause of the conflicting results that have appeared in the literature. This thesis then presents a working methodology and solution for characterising sea ice conditions in the air-sea flux footprint with evidence for its need and value. Overall, the results from this thesis should enable new understanding of air-sea-ice interactions and exchange once incorporated into future polar eddy covariance studies.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".