Tidal mixing of estuarine and coastal waters in the western English Channel is a control on spatial and temporal variability in seawater CO <sub>2</sub>
Bibliographic record
Abstract
Abstract. Surface ocean carbon dioxide (CO2) measurements are used to compute the oceanic air–sea CO2 flux. The CO2 flux component from rivers and estuaries is uncertain due to the high spatial and seasonal heterogeneity of CO2 in coastal waters. Existing high-quality CO2 instrumentation predominantly utilises showerhead and percolating style equilibrators optimised for open-ocean observations. The intervals between measurements made with such instrumentation make it difficult to resolve the fine-scale spatial variability of surface water CO2 at timescales relevant to the high frequency variability in estuarine and coastal environments. Here we present a novel dataset with unprecedented frequency and spatial resolution transects made at the Western Channel Observatory in the south-west of the UK from June to September 2016, using a fast-response seawater CO2 system. Novel observations were made along the estuarine–coastal continuum at different stages of the tide and reveal distinct spatial patterns in the surface water CO2 fugacity (fCO2) at different stages of the tidal cycle. Changes in salinity and fCO2 were closely correlated at all stages of the tidal cycle and suggest that the mixing of oceanic and riverine endmembers partially determines the variations in fCO2. The correlation between salinity and fCO2 was different in Cawsand Bay, which could be due to enhanced gas exchange or to enhanced biological activity in the region. The observations demonstrate the complex dynamics determining spatial and temporal patterns of salinity and fCO2 in the region. Spatial variations in observed surface salinity were used to validate the output of a regional high-resolution hydrodynamic model. The model enables a novel estimate of the air–sea CO2 flux in the estuarine–coastal zone. Air–sea CO2 flux variability in the estuarine–coastal boundary region is influenced by the state of the tide because of strong CO2 outgassing from the river plume. The observations and model output demonstrate that undersampling the complex tidal and mixing processes characteristic of estuarine and coastal environment biases quantification of air–sea CO2 fluxes in coastal waters. The results provide a mechanism to support critical national and regional policy implementation by reducing uncertainty in carbon budgets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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".