A seasonal prediction for the wet–cold spells leading to winter crop damage in northwestern Taiwan with a combined empirical–dynamical approach
ABSTRACT Winter crop losses from extreme weather in Taiwan have increased in the recent decade, with those losses associated with pronounced wet‐and‐cold events (temperature < 10 °C and precipitation >5 mm day −1 ). The regional and global patterns of atmospheric circulation and the sea surfac...
Published in: | International Journal of Climatology |
---|---|
Main Authors: | , , , , |
Other Authors: | |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
Wiley
2017
|
Subjects: | |
Online Access: | http://dx.doi.org/10.1002/joc.5194 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fjoc.5194 https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/joc.5194 |
Summary: | ABSTRACT Winter crop losses from extreme weather in Taiwan have increased in the recent decade, with those losses associated with pronounced wet‐and‐cold events (temperature < 10 °C and precipitation >5 mm day −1 ). The regional and global patterns of atmospheric circulation and the sea surface temperature (SST) related to the extreme cold that damages fruits, vegetables, and paddy rice in northwest Taiwan were investigated. Cool SST anomalies in the western North Pacific (WNP) and warm SST in the central‐eastern Pacific associated with the Pacific meridional mode (PMM) shared a significant role in the occurrence of wet‐and‐cold events in northwest Taiwan. The interactions of the WNP/PMM with the North Pacific Oscillation (NPO) and the Central Pacific type of El Niño led to a pronounced lead–lag relationship with the occurrence of wet‐and‐cold events. An empirical model was subsequently developed to predict the wet‐and‐cold event frequency using observed values of WNP, Niño‐3.4, and Arctic Oscillation from year‐1 and predicted indices of WNP and PMM derived from the Climate Forecast System Version 2 (CFSv2) outputs. The predictive skill of this hybrid empirical–dynamical model was statistically significant throughout the 6 months leading up to the occurrence of wet‐and‐cold events. |
---|