Asymmetric Response of Land Storage to ENSO Phase and Duration
Bibliographic record
Abstract
Emergence of global mean sea level (GMSL) from a ‘hiatus’ following a persistent La Niña highlights the need to understand the causes of interannual variability in GMSL. Several studies link interannual variability in GMSL to anomalous transport of water mass between land and ocean—and subsequent changes in water storage in these reservoirs—primarily driven by El Niño/Southern Oscillation (ENSO). Despite this, asymmetries in teleconnections between ENSO mode and land water storage have received less attention. We use historical simulations of natural climate variability to characterize asymmetries in the hydrological response to ENSO based on phase and duration. Findings indicate pronounced phase-specific and duration-specific asymmetries covering up to 93 and 50 million km2 land area, respectively. The asymmetries are seasonally dependent, and based on the mean regional climate are capable of influencing inherently bounded storage by pushing the storage-precipitation relationship towards nonlinearity. The nonlinearities are more pronounced in dry regions in the dry season, wet regions in the wet season, and during Year 2 of persistent ENSO events, limiting the magnitude of associated anomalies under persistent ENSO influence. The findings have implications for a range of stakeholders, including sea level researchers and water managers.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".