scholarly journals Modulation of global sea surface temperature on tropical cyclone rapid intensification frequency

2021 ◽  
Author(s):  
Jiacheng Hong ◽  
Qiaoyan Wu
Keyword(s):  
Tropical Cyclone ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Rapid Intensification ◽  
Global Sea Surface Temperature

Dominant Influence of ENSO-Like and Global Sea Surface Temperature Patterns on Changes in Prevailing Boreal Summer Tropical Cyclone Tracks over the Western North Pacific

2020 ◽  
Vol 33 (22) ◽  
pp. 9551-9565
Author(s):  
Haikun Zhao ◽  
Philp J. Klotzbach ◽  
Shaohua Chen
Keyword(s):  
Tropical Cyclone ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Time Scales ◽  
North Pacific ◽  
Western North Pacific ◽  
Sea Surface ◽  
Track Pattern ◽  
Global Sea Surface Temperature

AbstractA conventional empirical orthogonal function (EOF) analysis is performed on summertime (May–October) western North Pacific (WNP) tropical cyclone (TC) track density anomalies during 1970–2012. The first leading EOF mode is characterized by a consistent spatial distribution across the WNP basin, which is closely related to an El Niño–Southern Oscillation (ENSO)-like pattern that prevails on both interannual and interdecadal time scales. The second EOF mode is represented by a tripole pattern with consistent changes in westward and recurving tracks but with an opposite change for west-northwestward TC tracks. This second EOF pattern is dominated by consistent global sea surface temperature anomaly (SSTA) patterns on interannual and interdecadal time scales, along with a long-term increasing global temperature trend. Observed WNP TC tracks have three distinct interdecadal epochs (1970–86, 1987–97, and 1998–2012) based on EOF analyses. The interdecadal change is largely determined by the changing impact of ENSO-like and consistent global SSTA patterns. When global SSTAs are cool (warm) during 1970–86 (1998–2012), these SSTAs exert a dominant impact and generate a tripole track pattern that is similar to the positive (negative) second EOF mode. In contrast, a predominately El Niño–like SSTA pattern during 1987–97 contributed to increasing TC occurrences across most of the WNP during this 11-yr period. These findings are consistent with long-term trends in TC tracks, with a tripole track pattern observed as global SSTs increase. This study reveals the potential large-scale physical mechanisms driving the changes of WNP TC tracks in association with climate change.

scholarly journals The impact of climate model sea surface temperature biases on tropical cyclone simulations

2018 ◽  
Vol 53 (1-2) ◽  
pp. 173-192 ◽  
Author(s):  
Wei-Ching Hsu ◽  
Christina M. Patricola ◽  
Ping Chang
Keyword(s):  
Tropical Cyclone ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Climate Model ◽  
Sea Surface ◽  
The Impact

scholarly journals Wavelet Analysis of Seasonal Rainfall Variability of the Upper Blue Nile Basin, Its Teleconnection to Global Sea Surface Temperature, and Its Forecasting by an Artificial Neural Network

2014 ◽  
Vol 142 (5) ◽  
pp. 1771-1791 ◽  
Author(s):  
Mohamed Helmy Elsanabary ◽  
Thian Yew Gan
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
River Basin ◽  
Principal Component ◽  
Sea Surface ◽  
Seasonal Rainfall ◽  
Blue Nile ◽  
Artificial Neural ◽  
Mean Square Errors ◽  
Global Sea Surface Temperature

Abstract Rainfall is the primary driver of basin hydrologic processes. This article examines a recently developed rainfall predictive tool that combines wavelet principal component analysis (WPCA), an artificial neural networks-genetic algorithm (ANN-GA), and statistical disaggregation into an integrated framework useful for the management of water resources around the upper Blue Nile River basin (UBNB) in Ethiopia. From the correlation field between scale-average wavelet powers (SAWPs) of the February–May (FMAM) global sea surface temperature (SST) and the first wavelet principal component (WPC1) of June–September (JJAS) seasonal rainfall over the UBNB, sectors of the Indian, Atlantic, and Pacific Oceans where SSTs show a strong teleconnection with JJAS rainfall in the UBNB (r ≥ 0.4) were identified. An ANN-GA model was developed to forecast the UBNB seasonal rainfall using the selected SST sectors. Results show that ANN-GA forecasted seasonal rainfall amounts that agree well with the observed data for the UBNB [root-mean-square errors (RMSEs) between 0.72 and 0.82, correlation between 0.68 and 0.77, and Hanssen–Kuipers (HK) scores between 0.5 and 0.77], but the results in the foothills region of the Great Rift Valley (GRV) were poor, which is expected since the variability of WPC1 mainly comes from the highlands of Ethiopia. The Valencia and Schaake model was used to disaggregate the forecasted seasonal rainfall to weekly rainfall, which was found to reasonably capture the characteristics of the observed weekly rainfall over the UBNB. The ability to forecast the UBNB rainfall at a season-long lead time will be useful for an optimal allocation of water usage among various competing users in the river basin.

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