Enhanced India‐Africa Carbon Uptake and Asia‐Pacific Carbon Release Associated With the 2019 Extreme Positive Indian Ocean Dipole
Bibliographic record
Abstract
Abstract The 2019 extreme positive Indian Ocean dipole drove climate extremes over Indian Ocean rim countries with unclear carbon‐cycle responses. We investigated its impact on net biome productivity (NBP) and its constituent fluxes, using the Global Carbon Assimilation System (GCASv2) product, process‐based model simulations from TRENDYv9, and satellite‐based gross primary productivity (GPP). By distinguishing two separate regions, the India‐Africa and Asia‐Pacific, GCASv2 indicated enhanced terrestrial carbon uptake of 0.23 ± 0.20 PgC and release of 0.38 ± 0.15 PgC, respectively, during September–December (SOND) 2019. These NBP anomalies had comparable magnitudes to those following the 2015 extreme El Niño which, however, caused the consistent carbon release in both regions. The TRENDYv9 model ensemble confirmed these NBP responses, albeit with smaller magnitudes. These regional NBP anomalies were related to soil moisture variations with a dominant role of GPP. Understanding the impact of IOD provides new insights into mechanisms driving interannual variations in regional carbon cycling.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".