Influence of Nonseasonal River Discharge on Sea Surface Salinity and Height
Bibliographic record
Abstract
Abstract River discharge influences ocean dynamics and biogeochemistry. Due to the lack of a systematic, up‐to‐date global measurement network for river discharge, global ocean models typically use seasonal discharge climatology as forcing. This compromises the simulated nonseasonal variation (the deviation from seasonal climatology) of the ocean near river plumes and undermines their usefulness for interdisciplinary research. Recently, a reanalysis‐based daily varying global discharge data set was developed, providing the first opportunity to quantify nonseasonal discharge effects on global ocean models. Here we use this data set to force a global ocean model for the 1992–2017 period. We contrast this experiment with another experiment (with identical atmospheric forcings) forced by seasonal climatology from the same discharge data set to isolate nonseasonal discharge effects, focusing on sea surface salinity (SSS) and sea surface height (SSH). Near major river mouths, nonseasonal discharge causes standard deviations in SSS (SSH) of 1.3–3 practical salinity unit (1–2.7 cm). The inclusion of nonseasonal discharge results in notable improvement of model SSS against satellite SSS near most of the tropical‐to‐midlatitude river mouths and minor improvement of model SSH against satellite or in‐situ SSH near some of the river mouths. SSH changes associated with nonseasonal discharge can be explained by salinity effects on halosteric height and estimated accurately through the associated SSS changes. A recent theory predicting river discharge impact on SSH is found to perform reasonably well overall but underestimates the impact on SSH around the global ocean and has limited skill when applied to rivers near the equator and in the Arctic Ocean.
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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 | 0.000 | 0.000 |
| 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".