Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 273

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Sensitivity of Australian Rainfall to Inter–El Niño Variations

Wang, Guomin, Hendon, Harry H.·2007·Journal of Climate

Abstract

Australia typically experiences drought during El Niño, especially across the eastern two-thirds of the continent during austral spring (September–November). There have, however, been some interesting departures from this paradigm. For instance, the near-record-strength El Niño of 1997 was associated with near-normal rainfall. In contrast, eastern Australia experienced near-record drought during the modest El Niño of 2002. This stark contrast raises the issue of how the magnitude of the drought is related to the character and magnitude of El Niño, for instance as measured by the broadscale sea surface temperature (SST) anomaly in the equatorial eastern Pacific. Internal (unpredictable) atmospheric noise is one plausible explanation for this contrasting behavior during these El Niño events. Here, the authors suggest that Australian rainfall is sensitive to the zonal distribution of SST anomalies during El Niño and, in particular, the greatest sensitivity is to the SST variations on the eastern edge of the Pacific warm pool rather than in the eastern Pacific where El Niño variations are typically largest. Positive SST anomalies maximized near the date line in 2002, but in 1997 maximum anomalies were shifted well into the eastern Pacific, where their influence on Australian rainfall appears to be less. These findings provide a plausible physical basis for the view that forecasting the strength of El Niño is not sufficient to accurately predict rainfall variations across Australia during El Niño.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1175/jcli4228.1

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Cited by

See all 273

Metadata

Authors
Wang, G., & Hendon, H. H.
Venue
Journal of Climate
Published
2007-08-15
Volume
20
Issue
16
Pages
4211-4226
ISSN
  • 0894-8755 (ISSN-L)
  • 1520-0442
OpenAlex
W2107489669
Cited by
273
References
1
FWCI
9.64
Type
journal article
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.