Enso effects on land surface-biosphere-atmosphere interactions: A global study from satellite remote sensing and NCEP/NCAR reanalysis data
Bibliographic record
Abstract
Two mechanisms are examined to reveal the impact of El Niño-Southern Oscillation (ENSO) on land surface, biosphere, and atmosphere interactions. One mechanism is large-scale dynamics--namely, changes in circulation patterns and the jet stream. Another mechanism is local land cover effects, in particular, vegetation and skin temperature. Non-lag and lag correlation coefficients between Niño 3 indices derived from sea-surface temperature (SST) anomalies and land surface variables from satellite based moderate resolution imaging spectroradiometer (MODIS) data, as well as National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) Reanalysis data are analyzed for 2001-2010. Strong positive correlations between January Niño 3 indices and both air temperature (Tair) skin temperature (Tskin) occur over the northwest United States, western Canada, and southern Alaska, suggesting that an El Niño event is associated with warmer winter temperatures over these regions, consistent with previous studies. In addition, strong negative correlations exist over central and northern Europe in January, meaning colder than normal winters, with positive correlations over central Siberia meaning warmer than normal winters. Despite the different physical meanings between Tair and Tskin, the general response to ENSO is the same. Furthermore, satellite observations of Tskin provide more rich information and higher spatial resolution than NCEP/NCAR Reanalysis data.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".