Surface meteorology and fluxes over the Iceland and Greenland Seas in ERA5 reanalysis: The impact of sea ice distribution
Bibliographic record
Abstract
<!--!introduction!--><b></b> The Iceland and Greenland Seas are a crucial region for the climate system, being the headwaters of the lower limb of the Atlantic Meridional OverturningCirculation. Investigating the atmosphere–ocean–ice processes in this region often necessitates the use of meteorological reanalyses—a representation of the atmospheric state based on the assimilation of observations into a numerical weather prediction system. Knowing the quality of reanalysis products is vital for their proper use. Here we evaluate the surface-layer meteorology and surface turbulent fluxes in winter and spring for the latest reanalysis from the European Centre for Medium-Range Weather Forecasts, i.e., ERA5. In situ observations from a meteorological buoy, a research vessel, and a research aircraft during the Iceland–Greenland Seas Project provide unparalleled coverage of this climatically important region. The observations are independent of ERA5. They allow a comprehensive evaluation of the surface meteorology and fluxes of these subpolar seas and, for the first time, a specific focus on the marginal ice zone. Over the ice-free ocean, ERA5 generally compares well to the observations of surface-layer meteorology and turbulent fluxes. However, over the marginal ice zone, the correspondence is noticeably less accurate: for example, the root-mean-square errors are significantly higher for surface temperature, wind speed, and surface sensible heat flux. The primary reason for the difference in reanalysis quality is an overly smooth sea-ice distribution in the surface boundary conditions used in ERA5. Particularly over the marginal ice zone, unrepresented variability and uncertainties in how to parameterize surface exchange compromise the quality of the reanalyses. A parallel evaluation of higher-resolution forecast fields from the Met Office’s Unified Model corroborates these findings.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".