A complete hydro-climate model chain to investigate the influence of sea surface temperature on recent hydroclimatic variability in subtropical South America (Laguna Mar Chiquita, Argentina)
Bibliographic record
Abstract
During the 1970s, Laguna Mar Chiquita (Argentina) experienced a dramatic hydroclimatic anomaly, with a substantial rise in its level. Precipitations are the dominant driving factor in lake level fluctuations. The present study investigates the potential role of remote forcing through global sea surface temperature (SST) fields in modulating recent hydroclimatic variability in Southeastern South America and especially over the Laguna Mar Chiquita region. Daily precipitation and temperature are extracted from a multi-member LMDz atmospheric general circulation model (AGCM) ensemble of simulations forced by HadISST1 observed time-varying global SST and sea-ice boundary conditions from 1950 to 2005. The various members of the ensemble are only different in their atmospheric initial conditions. Statistical downscaling (SD) is used to adjust precipitation and temperature from LMDz ensemble mean at the station scale over the basin. A coupled basin-lake hydrological model ( cp HM) is then using the LMDz-downscaled (LMDz-SD) climate variables as input to simulate the lake behavior. The results indicate that the long-term lake level trend is fairly well depicted by the LMDz-SD- cp HM simulations. The 1970s level rise and high-level conditions are generally well captured in timing and in magnitude when SST-forced AGCM-SD variables are used to drive the cp HM. As the LMDz simulations are forced solely with the observed sea surface conditions, the global SST seems to have an influence on the lake level variations of Laguna Mar Chiquita. As well, this study shows that the AGCM-SD- cp HM model chain is a useful approach for evaluating long-term lake level fluctuations in response to the projected climate changes.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".