Warming and irradiance measurements in the Arctic: Determining the link between solar energy absorption and surface warming through long term observations
Bibliographic record
Abstract
The Arctic is considered to be a sentinel system in terms of climate change. The western Arctic in particular has experienced the largest positive anomalies in open water fraction, solar energy input, and sea surface temperature. The attenuation of light in surface waters has been identified as one of the key uncertainties in the modeling of Arctic Ocean physical properties, and has been found to significantly impact the simulation of sea ice thickness and upper ocean heat content. The vertical partitioning of solar radiation absorbed in the mixed layer of the Arctic Ocean, and the type of light absorbing compounds present determine the magnitude of heating. This heat directly impacts the rate of sea ice melt. The objectives of the proposed research are to quantify the connection between seasonal warming of arctic surface waters and the absorption of solar energy, and additionally to identify the presence and seasonal cycling of materials responsible for this absorption. Seasonal changes in the attenuation of solar radiation within the sea ice and upper 30m of the water column will be measured at high temporal resolution (hourly) by a new proof of concept buoy system. Temperature and PAR (photosynthetically active radiation) irradiance measurements will be made using optical sensors paired with thermisters within the water column and sea ice. A fluorometer will be used to quantify chlorophyll, colored dissolved organic material (CDOM) and light backscattering within surface waters. These sensors will be used to calculate diffuse attenuation coefficients (Kd) within the ice and water column, and to determine the seasonal cycling of phytoplankton and CDOM under the ice. The relationship between these optical and temperature observations will enable us to constrain the daily water column absorption component of the Arctic heating budget. This approach will fill in gaps in the current Arctic Observing Network strategy, expanding the observations to include a determination of solar energy absorption, and seasonal cycling of optically active compounds, which are known to have a quantifiable impact on solar heating. The long range goal of this research is to work towards the closure of the Arctic heating budget by constraining an important and poorly understood term. The ultimate goal of this concept would see a network of these buoys throughout the Arctic, in both ice-covered and open waters. Our work plan includes several deployments in multiyear ice. The location of the buoys will be 1) Multi-year ice in the Beaufort gyre in early spring, 2) the Geomission ice base (previously Catlin Arctic Survey) in the Canadian Arctic in early spring. Data at this site will be augmented by additional measurements made whilst at the camp, along with complementary studies by other Geomission projects and 3) in the Beaufort Gyre, deployed by the Beaufort Gyre Exploration Project in late spring within their network of ice-tethered profilers.
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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) | 0.000 | 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 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".