Impact of refined oceanic model resolution on the simulation of Atlantic meridional overturning circulation in LICOM3
Bibliographic record
Abstract
The present study compares the Atlantic Meridional Overturning Circulation (AMOC) in the North Atlantic from two simulations by an oceanic general circulation model with 1° × 1° and 0.1° × 0.1° resolution, respectively, which explores the sensitivity of AMOC to the resolution. The ocean model is the latest version of the LASG/IAP Climate System Model (LICOM3), and it is forced with atmospheric data from phase 2 of the Ocean Model Intercomparison Project (OMIP2). Comprehensive comparison between the two simulations indicates that the simulated AMOC is highly sensitive to the spatial resolution. The high-resolution model (LICOM-H) simulates a deeper mixed-layer depth (MLD), due to increased surface salinity, than that in the low-resolution model (LICOM-L) in the Labrador Sea, and it also has stronger AMOC strength (the maximum climatic mean AMOC from 1958 to 2018 is 23.4 Sv for LICOM-H and 21.2 Sv for LICOM-L) and larger variability of AMOC index (2.3 Sv for LICOM-H and 1.7 Sv for LICOM-L). These differences can mainly be attributed to two main dynamic processes that benefit from high resolution. Firstly, LICOM-H can simulate much stronger boundary currents (∼0.6 m s −1 ) than LICOM-L (0.3 m s −1 ) in the upper ocean, which leads to saltier and warmer seawater being transported to the Labrador Sea, where it enhances deep convection. Secondly, in LICOM-H, the stronger variability of AMOC is related to the higher sensitivity of the MLD and transformation to the prescribed atmospheric forcing in the Labrador Sea. 摘要 本研究关注大西洋经圈翻转环流 (AMOC) 对分辨率的敏感性, 比较了大气物理研究所自主发展的海洋环流模式LICOM (LASG/IAP Climate system Ocean Model) 不同分辨率版本对AMOC的模拟结果, 两个版本分辨率分别为1° × 1°和0.1° × 0.1°.LICOM高分辨版本模拟的AMOC强度和变率较低分辨率版本明显增加, 而且在拉布拉多海也模拟出了更深的混合层深度.在上层海洋, 高分辨率版本可以模拟比低分辨率版本强得多的边界流, 这导致更多高盐海水被输送到拉布拉多海, 增强了该区域的深对流.在高分辨率版本中, AMOC较强的变率与拉布拉多海混合层深度较高的变率和模式对大气强迫更敏感的响应有关.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".