Arctic Ocean bottom pressure variations from 2002 to 2020: merging GRACE with GRACE-FO
Bibliographic record
Abstract
Ocean bottom pressure (OBP) from the Gravity Recovery and Climate Experiment (GRACE) revealed Arctic Ocean circulation patterns and variability that were previously unknown (Morison et al., 2007; Morison et al., 2012; Peralta-Ferriz et al., 2014). OBP measurements from the GRACE Follow-On mission (GRACE-FO) are therefore increasingly important for monitoring Arctic Ocean variability, and critical for understanding and predicting the fate of the rapidly changing Arctic environment. In this work we use GRACE data from 2002 to 2017 jointly with a 10-year record of in situ OBP at the North Pole (2005-2015) complemented with in situ OBP in the Canada Basin (2015-2018), and wind reanalysis products, to create a proxy representation of the OBP anomalies that explains the largest possible fraction of the observed OBP variability in the Arctic Ocean and the Nordic Seas. We do this by performing a linear regression analysis, combined with maximum covariance analysis (MCA) – a technique that was tested prior to the decommission of GRACE and the launch of GRACE-FO (Peralta-Ferriz et al., 2016). Here, the first predictor time series is the in situ OBP record at the North Pole and Canada Basin; the second predictor time series is the expansion coefficients time series of the leading mode of MCA between the GRACE OBP coupled with the winds. We use this proxy OBP to merge GRACE with the first 2 years of available GRACE-FO OBP. We compare our merged OBP field with OBP output from the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS). Preliminary results suggest a good agreement between the proxy and predicted OBP fields and both GRACE and GRACE-FO data, especially in the central Arctic, but also in the Nordic Seas. The OBP variations from the merged GRACE and GRACE-FO and from PIOMAS will be also explored. References: Morison, J. H., J. Wahr, R. Kwok and C. Peralta-Ferriz (2007), Recent trends in Arctic Ocean mass distribution revealed by GRACE, Res. Lett.,34, L07602, doi:10.1029/2006GL029016. Morison, J., R. Kwok, C. Peralta-Ferriz, M. Alkire, I. Rigor, R. Andersen and M. Steele (2012), Changing Arctic Ocean freshwater pathways. Nature, 481, 66-7 Peralta-Ferriz, C., J. H. Morison, J. M. Wallace, J. Bonin and J. Zhang (2014), Arctic Ocean circulation patterns revealed by GRACE, of Climate, 27:1445–1468 doi:10.1175/JCLI-D-13-00013.1. Peralta-Ferriz, C., J. H. Morison and J. M. Wallace(2016), Proxy representation of Arctic ocean bottom pressure variability: Bridging gaps in GRACE observations, Res. Lett., 43, 9183–9191, doi:10.1002/2016GL070137
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".