Evidence map›Paper›PMID 35254900›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2022

Early warning of the Indian Ocean Dipole using climate network analysis.

Zhenghui Lu, Wenjie Dong, Bo Lu, Naiming Yuan, Zhuguo Ma, Mikhail I Bogachev, Juergen Kurths

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Zhenghui LuCAS Key Laboratory of Regional Climate Environment for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China.ORCID 0000-0003-4510-384X
Wenjie DongSchool of Atmospheric Sciences, Sun Yat-sen University, Zhuhai 519082, China.
Bo LuKey Laboratory for Climate Studies, National Climate Center, China Meteorological Administration, Beijing 100081, China.ORCID 0000-0002-4063-3035
Naiming YuanSchool of Atmospheric Sciences, Sun Yat-sen University, Zhuhai 519082, China.ORCID 0000-0003-0580-609X
Zhuguo MaCAS Key Laboratory of Regional Climate Environment for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China.
Mikhail I BogachevDepartment of Radio Engineering Systems, Saint Petersburg Electrotechnical University, Saint Petersburg 197022, Russia.ORCID 0000-0002-0356-5651
Juergen KurthsDepartment of Physics, Humboldt University, Berlin 12489, Germany.ORCID 0000-0002-5926-4276

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

SignificanceThe Indian Ocean Dipole (IOD), an air-sea coupled phenomenon over the tropical Indian Ocean, has substantial impacts on the climate, ecosystems, and society. Due to the winter predictability barrier, however, a reliable prediction of the IOD has been limited to 3 or 4 mo in advance. Our work approaches this problem from a new data-driven perspective: the climate network analysis. Using this network-based method, an efficient early warning signal for the IOD event was revealed in boreal winter. Our approach can correctly predict the IOD events one calendar year in advance (from December of the previous year) with a hit rate of higher than 70%, which strongly outperforms current dynamic models.

Indexed as

ClimateModels, TheoreticalNatureIndian Oceanclimate networkIndian Ocean Dipoleprediction

Identifiers

PMID35254900
PMCPMC8931208

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.