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Record W4303030587 · doi:10.1175/jcli-d-22-0323.1

How Well Can Current Climate Models Simulate the Connection of the Early Spring Aleutian Low to the Following Winter ENSO?

2022· article· en· W4303030587 on OpenAlexaff
Shangfeng Chen, Wen Chen, Bin Yu, Renguang Wu

Bibliographic record

VenueJournal of Climate · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsEnvironment and Climate Change Canada
FundersJiangsu Collaborative Innovation Center for Climate ChangeNational Natural Science Foundation of China
KeywordsAnticycloneClimatologyAnomaly (physics)SubtropicsGeologySea surface temperatureAtmospheric sciencesEnvironmental science

Abstract

fetched live from OpenAlex

Abstract A recent study revealed an impact of the intensity of early spring Aleutian low (AL) on the succeeding winter ENSO. This study examines the ability of 41 climate models that participated in CMIP6 in simulating the early spring AL–winter ENSO connection. It is shown that there exists a large diversity among the models in simulating this AL–ENSO linkage. A number of models capture well the observed AL–ENSO connection and the associated physical processes. However, the AL–ENSO relation in several models is opposite to the observed. Diversity of the AL–ENSO connection is related to the spread in the spatial structure of AL-related atmospheric anomalies over the North Pacific. In the models that capture the observed AL–ENSO connection, weakened AL induces an anomalous anticyclone over the northern middle and high latitudes and an anomalous cyclone over the subtropical North Pacific. The resultant westerly wind anomalies over the tropical western-central Pacific (TWCP) induce an El Niño sea surface temperature (SST) anomaly pattern in the following winter. By contrast, in the models with the AL–ENSO relation opposite to the observations, the AL-associated anomalous anticyclone over the North Pacific extends too southward. As such, the subtropical North Pacific is dominated by northeasterly wind anomalies and SST cooling. The subtropical North Pacific SST cooling induces easterly wind anomalies over the TWCP via wind–evaporation–SST feedback, and leads to a La Niña anomaly pattern in the following winter. The spread in the spatial structure of the AL-associated atmospheric anomalies over the North Pacific is partly due to the diversity in the amplitude of the climatological mean flow. Significance Statement A recent study suggested that variation of the AL intensity in early spring could exert a significant impact on the following winter ENSO. It indicated that inclusion of the early spring AL signal could improve the prediction of ENSO and to some extent help reduce the spring predictability barrier of ENSO. To employ the AL as a predictor in the ENSO prediction and forecast, the current climate model should have the ability to simulate realistically the early spring AL variation as well as the physical process linking the early spring AL with the subsequent winter ENSO. Hence, this study examines the performance of the current coupled climate models that participated in the phase 6 of the Coupled Model Intercomparison Project (CMIP6) in simulating the linkage between the early spring AL and the following winter ENSO. We show that there exists a large diversity among the CMIP6 models in simulating the early spring AL–winter ENSO connection. A number of models capture well the observed AL–ENSO connection and the associated physical processes. However, the AL–ENSO relation in several models is opposite to the observed. The factors leading to the spread are further examined. Results of this study would have implications in improving our understanding of the impact of extratropical atmospheric forcing on the ENSO and improving the seasonal forecasting of the ENSO.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.121

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.020
GPT teacher head0.245
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2022
Admission routes1
Has abstractyes

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