Winds, Waves and Sea Ice during the 1999-2007 Open Water Seasons of the Beaufort and Chukchi Seas
Bibliographic record
Abstract
Abstract A team of sea ice specialists, marine meteorologists, and Geographic Information System (GIS) experts at the Arctic Ice and Meteorology Service (AIMS) conducted a study of publicly available sea-ice and wind data in the Beaufort and Chukchi Seas in order to gain a better understanding of the varying temporal scales of sea ice and weather in the region. Data sources used in the study included wind and wave data from the NOAA WAVEWATCH III regional model and ice analyses charts and data from the National Ice Center (NIC) for 1999-2007. The study focused on wind, wave, and sea ice conditions at several representative areas in the Beaufort and Chukchi Seas during the open water seasons. Frequencies and extremes of wind speed and direction as well as significant wave height, period, and direction were evaluated. Dates of sea ice free conditions were identified in addition to the general factors affecting sea ice development. Years with abnormally long or short open water seasons were also scrutinized for similarities. Results showed a high variability from year to year in the duration of the open water seasons and storm events. East-northeast winds dominate the area, though interesting variations were identified. Open water seasons that began with similar ice coverage patterns did not generally continue to evolve in the same way. An understanding of the variability in weather conditions and sea ice coverage can be an aid for operational ice and weather experts who create forecasts for these regions. This study served to highlight the need for collecting data at a larger scale in order to more acurately evaluate the conditions to be encountered in years to come. It is possible that the high yearly variability in these environmental conditions may be effectively mitigated through improved understanding of local weather patterns and sea ice formation scenarios. Introduction Due to the prospect of major oil reserves lying beneath the waters off the North Slope of Alaska, primarily in the Beaufort and Chukchi Seas, marine vessel operations in these areas have been steadily increasing in recent years and are expected to continue to rise as exploration activities progress and begin to transition to drilling and extraction phases. Past, current, and planned operations by oil and gas industry companies in the region have exposed a dearth of vital information necessary for successfully realizing the full potential of these reserves. Much has been learned about the subsurface environment, but increased knowledge of the sea ice and weather regimes will be required in the years to come to navigate the difficult physical, political, and regulatory environment of the area. Accurate and timely sea ice and weather forecasting is crucial for successful, efficient, and safe operations in the Alaskan offshore environment. This information is used to plan and execute tasks involving vessels operating in seasonal open water. Reliable forecasting can warn of coming storms or dangerous conditions, allowing time for the crew to prepare. An ideal solution to managing risks associated with sea ice and weather is to have an experienced forecasting service supporting operations. While much of the recent climatological studies of the Arctic have focused on potential long-term scenarios, it is critical that oil and gas operators have a clear understanding of active forces, patterns, and inputs for real-time forecasting. The authors of this paper have attempted to increase their institutional knowledge and improve their analytical and forecasting skills related to these phenomenon through this desktop study. To that end, this paper describes general observations on regional sea ice and weather for the years 1999-2007 doscovered using publicly available data sources. Regional Weather Observations There are three primary storm tracks that affect the coastal waters of northern Alaska in summer and early fall. The most prevalent track has lows that move from the Bering Sea into the Chukchi Sea from the south. These lows are usually strongest when they first enter the Chukchi Sea. The associated pressure gradient will tighten across the entire North Slope and winds will increase out of the east and east-northeast, increasing most in the northern Chukchi Sea and in the western Beaufort Sea. Lows on this track typically begin to fill and weaken as they reach the northern Chukchi Sea area and then fade to the northwest.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".