Pacific subsurface ocean temperature as a long-rangepredictor of South China tropical cyclone landfall
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Published version
Author(s)
Sparks, Nathan
Toumi, Ralf
Type
Journal Article
Abstract
Seasonal forecasts of the tropical cyclones which frequently make landfall along the densely populated South China coast are highly desirable. Here, we analyse observations of landfalling tropical cyclones in South China and of subsurface ocean temperatures in the Pacific warm pool region, and identify the possibility of forecasts of South China tropical cyclone landfall a year ahead. Specifically, we define a subsurface temperature index, subNiño4, and build a predictive model based on subNiño4 anomalies with a robust double cross-validated forecast skill against climatology of 23%, similar in skill to existing forecasts issued much later in the spring. We suggest that subNiño4 ocean temperatures precede the surface El Niño/Southern Oscillation state by about 12 months, and that the zonal shifts in atmospheric heating then change mid-level winds to steer tropical cyclones towards landfall in South China. We note that regional subsurface ocean temperature anomalies may permit atmospheric predictions in other locations at a longer range than is currently thought possible.
Date Issued
2020-10-15
Date Acceptance
2020-09-14
Citation
Communications Earth & Environment, 2020, 1
ISSN
2662-4435
Publisher
Springer Nature Limited
Journal / Book Title
Communications Earth & Environment
Volume
1
Copyright Statement
© The Author(s) 2020. This article is licensed under a Creative Commons
Attribution 4.0 International License, which permits use, sharing,
adaptation, distribution and reproduction in any medium or format, as long as you give
appropriate credit to the original author(s) and the source, provide a link to the Creative
Commons license, and indicate if changes were made. The images or other third party
material in this article are included in the article’s Creative Commons license, unless
indicated otherwise in a credit line to the material. If material is not included in the
article’s Creative Commons license and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from
the copyright holder. To view a copy of this license, visit http://creativecommons.org/
licenses/by/4.0/.
Attribution 4.0 International License, which permits use, sharing,
adaptation, distribution and reproduction in any medium or format, as long as you give
appropriate credit to the original author(s) and the source, provide a link to the Creative
Commons license, and indicate if changes were made. The images or other third party
material in this article are included in the article’s Creative Commons license, unless
indicated otherwise in a credit line to the material. If material is not included in the
article’s Creative Commons license and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from
the copyright holder. To view a copy of this license, visit http://creativecommons.org/
licenses/by/4.0/.
License URL
Sponsor
Met Office
Grant Number
P106409
Publication Status
Published
Article Number
ARTN 30