Understanding polar atmosphere-ocean-sea ice momentum transfer using remote sensing and modeling techniques
Bibliographic record
Abstract
Over the last half a century, the Arctic sea ice extent and volume have been decreasing as a result of the amplified warming taking place in the Arctic. Similarly, the Antarctic summertime sea ice extent maximum has been the lowest in the satellite record for the last three years. As sea ice at both poles is changing in a warming climate, it is necessary to better understand the fundamental processes that determine sea ice properties such as extent, thickness, volume and drift. These processes, namely dynamic and thermodynamic ones, are triggered by the surrounding atmosphere and ocean. The overarching goal of this dissertation is to study dynamic processes while also considering thermodynamic aspects. Chapter 3 delves into the abovementioned dynamic and thermodynamic processes at mesoscale in the study of polynya events and thin sea ice anomalies above Maud Rise in the Antarctic. Chapter 4 looks at parameters that quantify dynamics, specifically at drag coefficients (Cd) that determine the momentum transfer between the atmosphere and sea ice, on a pan-Arctic scale. Finally, Chapter 5 implements the derived estimates of drag from observations into a coupled regional atmosphere-ocean-sea ice model in order to investigate the impact of variable drag on sea ice properties Arctic-wide. The Weddell Sea Polynya (occurring in 1974-1976 and 2016-2017) is an excellent case study in the impact of mesoscale as well as synoptic scale processes on sea ice. My analysis of the events corroborates past studies that identify the Weddell Sea polynya as one that is driven by dynamic as well as thermodynamic processes. In addition, using satellite-borne microwave imaging radiometers, large thin sea ice anomalies have been identified in polynya-free years (2010-2020). Given the reported links between the polynya and different dynamic and thermodynamic ocean and atmosphere processes, our results suggest that when an insufficient amount of these processes are active, a thin sea ice anomaly may emerge instead. The neutral sea ice-atmosphere Cd data-set is the first-ever assessment of drag on both pan- Arctic spatial and sub-yearly temporal scales. Leveraging the high resolution of Ice, Cloud and land Elevation Satellite 2 (IS2), as well as near-coincident Operation IceBridge (OIB) airborne surveys of sea ice topography, it was possible to observe the spatiotemporal evolution of drag from November 2018 to May 2022. My results showed the ice area directly north of the Canadian Archipelago and Greenland to have a Cd consistently above 2.0 × 10−3, while for most of the multiyear ice portion of the Arctic it is typically around ∼1.5 × 10−3. The first-year and young ice portion of the Arctic has a comparatively lower Cd (∼9 × 10−4) with an increase along the marginal ice zone that exceeds 1.5 × 10−3. This dataset was then used to derive a parameterization linking Cd to coincident IS2 sea ice thickness measurements, which was implemented into the regional atmosphere-ocean-sea ice model HIRHAM-NAOSIM. By running the model with and without the implementation, my results showed reasonable albeit small differences between the sea ice properties modelled by the two runs. Using sensitivity studies that varied the coefficients and integration of the Cd parameterization, I was then able to explain the differences observed. The main findings from the model study are that atmospheric and oceanic drag have the opposite effect on both sea ice drift and thickness on a pan-Arctic scale, and that over a period of three years, regardless of the range in drag variability, the impact of drag on sea ice in a coupled model is typically small in magnitude (<5% differences in both sea ice drift and thickness).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".