Assimilation of CryoSat-2 radar Freeboard data in a global ocean-sea ice modelling system.
Bibliographic record
Abstract
Sea ice is a key element in our climate system, and it is very sensitive to the current observed climate change. Sea ice volume is a sensitive indicator of the health of Arctic although very challenging to estimate precisely since it is a combination of sea ice area and sea ice thickness. Arctic sea ice volume has decreased by as much as 75% at the end of the summer season if compared with the conditions 40 years ago. The ongoing decline of Arctic sea ice exposes the ocean to anomalous surface heat and freshwater fluxes that can have potential implication for the Arctic region and beyond, for the general oceanic circulation itself. For more than a decade, Mercator Ocean International develops and produces Global Ocean Reanalysis with a 1/4° resolution system. Based on the NEMO modelling platform, observations are assimilated by a reduced-order Kalman filter. In-situ CORA database, altimetric data, sea surface temperature, and sea ice concentration are jointly assimilated to constrain the ocean and sea ice model. In previous reanalysis, long-term sea ice volume drift has been observed in the Arctic. To obtain a better constraint on the sea ice thickness, Cryosat-2 radar Freeboard data are assimilated jointly with the sea ice concentration in a multidata/multivariate sea ice analysis. The coupled ocean and ice assimilation system runs on a 7-day cycle, using IAU (Incremental Analysis Update) and a 4D increment. The “white ocean” is modelled with the multi-categories LIM3.6 sea ice numerical model. The aim of this study is to initiate the development of the future operational multi-variate and multi-data sea ice analysis system with freeboard radar assimilation. After describing this global sea ice reanalysis system, we present results on the abilities of this configuration to reproduce sea ice extent and volume interannual variability in both hemispheres. Comparisons between experiments with and without assimilation show that the joint assimilation of CryoSat-2 radar freeboard and sea ice concentration reduces most of model biases of sea ice thickness, e.g., in the north of the Canadian Arctic Archipelago and in the Beaufort Sea in the Arctic. Moreover, radar freeboard assimilation does not hinder the good results in simulating sea ice extent previously obtained with the assimilation of only sea ice concentration. Validation with non-assimilated satellite data and in-situ data supports these findings. Lastly, snow depth significantly influences the Freeboard measurement: this study also reveals the importance of including snow information on freeboard retrieval and on the ice volume assimilation methodology. These experiments take place in a context of increasing interest in polar regions and prepare the launch of Copernicus Sentinel expansion satellite missions.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".