Ocean Surface Albedo and Its Impact on Radiation Balance in Climate Models
Bibliographic record
Abstract
Abstract An analysis of several ocean surface albedo (OSA) schemes is undertaken through offline comparisons and through application in the Canadian Centre for Climate Modelling and Analysis (CCCma) fourth-generation atmospheric general circulation model (AGCM4). In general, each scheme requires different input quantities to determine the OSA. Common to all schemes is a dependence on the solar zenith angle (SZA). A direct comparison of the SZA dependence of the schemes reveals significant differences in the predicted albedos. Other input quantities include wind speed and aerosol/cloud optical depth, which are also analyzed. An offline one-dimensional radiative transfer model is used to quantitatively study the impact of ocean surface albedo on the radiative transfer process. It is found that, as a function of SZA and wind speed, the difference in reflected solar flux at the top of the atmosphere is in general agreement between OSA schemes that depend on these quantities, with a difference <10 W m−2. However, for simpler schemes that depend only on SZA the difference in this flux can approach 10–20 W m−2. The impact of the different OSA schemes is assessed through multiyear simulations of present-day climate in AGCM4. Five-year means of the reflected clear-sky flux at the top of the atmosphere reveal local differences of up to several watts per meters squared between any of the schemes. Globally, all schemes display a similar negative bias relative to the Earth Radiation Budget Experiment (ERBE) observations. This negative bias is largely reduced by comparison with the recently released Clouds and the Earth’s Radiant Energy System (CERES) data. It is shown that the local upward clear-sky flux at the surface is more sensitive to the OSA formulation than the clear-sky upward flux at the top of atmosphere. It is found that the global energy balance of the model at the top of the atmosphere and at the surface is surprisingly insensitive to which OSA scheme is employed.
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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 | 0.000 |
| 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 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".