The Pacific Decadal Precession and its Relationship to Tropical Pacific Decadal Variability in CMIP6 Models
Bibliographic record
Abstract
Abstract Persistent, multi-year shifts in atmospheric circulations and their associated influence on regional climates have profound impacts on physical, biological, and socioeconomic systems. The Pacific Decadal Precession (PDP), an atmospheric mode of variability consisting of a lower tropospheric height dipole which rotates counterclockwise over several years in the North Pacific, describes a series of such shifts in atmospheric circulations. One phase of the PDP, the north-south (N-S) phase, is hypothesized to be partially driven by central tropical Pacific (CP) sea surface temperature (SST) variability, but robust assessment of this dynamical connection in climate models remains to be done. In this study, we investigate this hypothesis with analyses in both reanalysis and selected models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) archive. We show that the emergence of the N-S phase is both related to and influenced by tropical Pacific decadal SST variability, specifically variability associated with CP El Niño-Southern Oscillation (ENSO) events. When examining the CMIP6 model output, we find that most models cannot recover the characteristic cyclonic precession of the dipoles of the PDP, instead featuring only amplitude and sign changes of the N-S phase, Moreover, the models do not replicate the dynamical connections between the tropical Pacific and this North Pacific mode. Our results suggest that primary reasons for this inconsistency are that models inaccurately simulate both the SST pattern associated with the PDP, shared low-frequency power associated with CP ENSO events, and incorrect Rossby wavetrains emanating from the tropical Pacific into the North Pacific on quasi-decadal timescales. Taken together, our analyses offer another benchmark by which to test the fidelity of the climate model simulations in capturing Pacific decadal climate variability in order to improve decadal-to-centennial climate projections.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".