Changed Ocean Optical Properties: How Shallower Sunlight Absorption impacts Earth’s Surface Energy Budget
Bibliographic record
Abstract
This study investigates the variations in the last decades of underwater sunlight attenuation coefficient (Kd) and its forcing (W/m2¬) on the Earth’s surface heat budget. It first compares the trends of essential climate variables Kd and sea surface temperature (SST) to gauge their connection. To estimate an equivalent global forcing, it also finds the variation of radiant heating rate (RHR) profiles under the ocean surface. The trends over a different period (14 to 30 years) for Kd, SST and diurnal SST (DSST) were extracted using the statistical Seasonal Mann-Kendal test, from remote sensing datasets available respectively on ESA OC-CCI, NOAA and REMSS public FTP servers. The results were geographically compared on 360°x180° gridded maps. They were used to estimate global linear regressions of their correlation coefficients. A positive correlation of 0.38 between Kd and SST was found. This result highlights the relevance to investigate the weight of the fate of irradiance decay on the surface heat budget. Using results of Kd trends over the 18-year period, changed incoming solar radiation (I0) decay in the upper 50 meters, and associated changed RHR profiles, it was possible to map the critical depth (Zcrit) at which the (1998) and (2017) RHR curves crossed path. A slightly better negative correlation of -0.42 was found between SST and Zcrit. The latter was used to calculate and map the theorical forcing on the air-sea interface attributed to ocean color changes. Averaged over the Earth’s surface, the forcing is 0.33 W/m2. This order of magnitude compares to the 3.1 W/m2 attributed to greenhouse gases (GHG) forcing. It suggests that the optically responsive aquatic components accumulated into the oceans may significantly drive the surface energy transfer by retaining the solar irradiance closer to the surface. It is recommended to further the study by including Kd in an OAGCM to account for the feedback of the four main fluxes (shortwave, longwave, latent, and sensible heat) composing the budget. The weighting of the forcing attributed to Kd in the budget will improve the significance of the result. Further investigation toward the management of Shortwave (SW), the black carbon (BC) and the ocean color are also recommended. Solutions against anthropogenic warming are proposed, such as the mixing enhancement of stratified waters.
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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 | 0.001 |
| 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 | 0.000 |
| 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".