Exploring the climate system response to a range of freshwater representations: Hosing, Regional, and Freshwater Fingerprints
Bibliographic record
Abstract
Abstract. Freshwater, in the form of terrestrial runoff, is hypothesized to have played a critical role in past centennial to millennial scale climate variability by suppressing the production of deep-water in the North Atlantic. It may also play a central role in future climate change as ice sheet and glacier melt accelerates under anthropogenic climate change. In model studies of both past and future climate change, freshwater Hosing (i.e. injection across wide bands in the North Atlantic) is typically used as a means-to-an-end by inducing a strong thermohaline circulation and climate response, with little regard for the other roles that freshwater plays in a complex coupled climate environment. Herein, we evaluate the realism of Hosing relative to two more sophisticated freshwater injection methods, all under glacial boundary conditions: regional injection (allowing the relatively coarse-resolution climate model to transport the freshwater) and a novel freshwater fingerprint method. The latter approach distributes freshwater into an eddy-parametrizing ocean model based upon where it is transported to in a higher resolution eddy-permitting model. Here the COSMOS Earth system model is used as the eddy-parametrizing model, and a configuration of the MITgcm model as the eddy-permitting model. This analysis address three primary questions. Firstly, where is freshwater routed at moderate versus eddy-permitting resolution? Secondly, does the freshwater fingerprint method allow the coarser resolution model to reproduce the net-results of eddy-permitting behaviour? Thirdly, how do climate impacts vary between different forms of freshwater injection? Of the four outlets tested, we find that freshwater released at the Mackenzie River (MAK) outlet results in the most similar freshwater transport patterns at both the eddy-parametrizing and the eddy-permitting resolutions. However at eddy-permitting resolution there is a greater freshening of the contemporary Labrador Sea deep water formation region and mixing of freshwater into the Icelandic Sea. Similarly, Fennoscandian (FEN) freshwater discharge at eddy-permitting resolution results in more freshening of the Greenland-Iceland-Norwegian (GIN) Seas. Freshwater released from within the Gulf of St. Lawrence (GSL) demonstrates large-scale differences between coarse-resolution versus eddy-permitting distributions over the whole North Atlantic. At eddy-permitting resolution, GSL-sourced freshwater freshens the North Atlantic (specifically deep water formation regions important during cold glacial periods) more effectively than at eddy-parametrizing resolution. In COSMOS, freshwater deposited into the GOM results in a larger salinity anomaly at sites of deep-water formation in the northern North Atlantic than freshwater deposited into the GSL, whereas this relationship is reversed in MITgcm. When instead the freshwater fingerprint method is used in COSMOS, the relative strengths of GOM and GSL salinity anomalies in this deep-water formation region align with the eddy-permitting responses. However, for all injection locations considered, the pattern of MITgcm salinity anomalies are best matched by the COSMOS regional injection simulations and not the fingerprint injection simulations. Hosing the North Atlantic in COSMOS results in stronger and earlier sea ice growth and surface cooling versus either regional injection or the fingerprint methods. Comparing regional injections to their respective fingerprint counterparts, injecting freshwater at the outlets results in earlier, but not faster, climate changes in the GIN Seas, mainland Europe, and GRIP ice core regions for the MAK and FEN regions relative to the fingerprint methods. For these injections, regional simulations show both a stronger and faster initial AMOC reduction than the fingerprint simulations. In short, for at least the COSMOS model, both the hosing and fingerprint methodologies are inferior to regional injection when considering salinity distributions alone but when considering AMOC and freshening of deep-water formation regions the fingerprint method corrects the response of the GSL and GOM outlets.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".