Remote Control of Sea Surface Temperature on the Variability of Tropical Cyclone Activity Affecting Vietnam’s Coastline

2021 ◽  
Author(s):  
Duong Hoang Trinh ◽  
Hoang Duc Cuong ◽  
Duong Van Kham ◽  
Chanh Kieu
Keyword(s):  
Tropical Cyclone ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Large Scale ◽  
Coastal Region ◽  
Principal Component ◽  
Strong Relationship ◽  
Sea Surface ◽  
Philippine Sea ◽  
Principal Component Analyses

AbstractThis study examines the teleconnection between sea surface temperature (SST) in different ocean regions and tropical cyclone (TC) activity affecting Vietnam’s coastal region. Using spatial correlation and principal component analyses, it is found that the variability of TCs affecting Vietnam during 1982-2018 is remotely connected with SST in the Indian Ocean, the southwestern Pacific Ocean, and the northern Philippine Sea. Among the three regions, SST in the northern Philippine Sea displays the most significant inverse relationship with TC activity in the South China Sea (SCS), with lower June-November TC accumulated energy (ACE) for warmer northern Philippine Sea SST. Further analyses of large-scale atmospheric circulations show that this teleconnection between the northern Philippine Sea SST and TC activity in the SCS is linked to the East Asian subtropical jet (EASJ). Principal component analyses of the 200-hPa zonal wind associated with EASJ capture indeed a strong relationship between the second principal component, which characterizes the EASJ intensity, and ACE. Specifically, higher EASJ intensity corresponding to colder northern Philippine Sea SST would enhance large-scale ascending motion and low-level cyclonic anomalies in the SCS, which are favorable for TC formation and result in an overall increased ACE. Examination of correlation between this second principal component and the northern Philippine Sea SST confirms that this correlation is statistically significant at a 95% confidence level. In this regard, these results support the Pacific-Japan teleconnection between the northern Philippine Sea SST and TC activity in the SCS.

scholarly journals The Influence of Large-Scale Environment on the Extremely Active Tropical Cyclone Activity in November 2019 over the Western North Pacific

Atmosphere ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 501
Author(s):  
Mengying Shi ◽  
Sulei Wang ◽  
Xiaoxu Qi ◽  
Haikun Zhao ◽  
Yu Shu
Keyword(s):  
Tropical Cyclone ◽  
Environmental Factors ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
North Pacific ◽  
Western North Pacific ◽  
Large Scale ◽  
Vertical Wind Shear ◽  
Sea Surface ◽  
Formation Mechanisms

In November 2019, tropical cyclone (TC) frequency over the western North Pacific reached its record high. In this study, the possible causes and formation mechanisms of that record high TC frequency are investigated by analyzing the effect of large-scale environmental factors. A comparison between the extremely active TC years and extremely inactive TC years is performed to show the importance of the large-scale environment. The contributions of several dynamic and thermodynamic environmental factors are examined on the basis of two genesis potential indexes and the box difference index that can measure the relative contributions of large-scale environmental factors to the change in TC genesis frequency. Results indicate that dynamical factors played a more important role in TC genesis in November 2019 than thermodynamic factors. The main contributions were from enhanced low-level vorticity and strong upward motion accompanied by positive anomalies in local sea surface temperature, while the minor contribution was from changes in vertical wind shear. Changes in these large-scale environmental factors are possibly related to sea surface temperature anomalies over the Pacific (e.g., strong Pacific meridional mode).

Dynamical seasonal predictions of tropical cyclone activity: Roles of sea surface temperature errors and atmosphere/land initialization

2020 ◽  
pp. 1-61
Author(s):  
Gan Zhang ◽  
Hiroyuki Murakami ◽  
Xiaosong Yang ◽  
Kirsten L. Findell ◽  
Andrew T. Wittenberg ◽  
...  
Keyword(s):  
Tropical Cyclone ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Large Scale ◽  
Climate Models ◽  
Evaluation Framework ◽  
Sea Surface ◽  
Geophysical Fluid Dynamics Laboratory ◽  
Weather Extremes ◽  
Error Sources

AbstractClimate models often show errors in simulating and predicting tropical cyclone (TC) activity, but the sources of these errors are not well understood. This study proposes an evaluation framework and analyzes three sets of experiments conducted using a seasonal prediction model developed at the Geophysical Fluid Dynamics Laboratory (GFDL). These experiments apply the nudging technique to the model integration and/or initialization to estimate possible improvements from nearly perfect model conditions. The results suggest that reducing sea surface temperature (SST) errors remains important for better predicting TC activity at long forecast leads—even in a flux-adjusted model with reduced climatological biases. Other error sources also contribute to biases in simulated TC activity, with notable manifestations on regional scales. A novel finding is that the coupling and initialization of the land and atmosphere components can affect seasonal TC prediction skill. Simulated year-to-year variations in June land conditions over North America show a significant lead correlation with the North Atlantic large-scale environment and TC activity. Improved land-atmosphere initialization appears to improve the Atlantic TC predictions initialized in some summer months. For short-lead predictions initialized in June, the potential skill improvements attributable to land-atmosphere initialization might be comparable to those achievable with perfect SST predictions. Overall, this study delineates the SST and non-oceanic error sources in predicting TC activity and highlights avenues for improving predictions. The nudging-based evaluation framework can be applied to other models and help improve predictions of other weather extremes.

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 Estudo da Temperatura da Superfície do Mar para os Oceanos Atlântico e Pacífico Utilizando a Técnica de Análises de Componente Principal e de Agrupamento (Study of Sea Surface Temperature for the Atlantic and Pacific Oceans Using the Technique of...)

2011 ◽  
Vol 4 (2) ◽  
pp. 264
Author(s):  
Madson Tavares Silva ◽  
Stephany C. F. Do Egito Costa ◽  
Manoel Francisco Gomes Filho ◽  
Daisy B. Lucena
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Research Laboratory ◽  
Principal Component ◽  
Component Analysis ◽  
Sea Surface ◽  
Earth System ◽  
System Research

Apresenta-se neste estudo a avaliação da metodologia de Análises Multivariadas: Análises em Componente Principal (ACP) e de Agrupamento (AA), aos dados de Temperatura da Superfície do Mar (TSM) para os Oceanos Atlântico (Norte (NATL), Tropical (TROP) e Sul (SATL)) e Pacifico (NIÑO1+2, NIÑO3.4, NIÑO3 e NIÑO4). Foram utilizados dados mensais de janeiro de 1950 a dezembro de 2010 de TSM obtidos na NOAA (National Oceanic and Atmospheric Administration/Earth System Research Laboratory). As regiões TROP e NIÑO4 apresentam as maiores TSM para os meses entre dezembro-julho. A região NATL apresenta no período de agosto-outubro seu maiores valores de TSM. A região NIÑo1+2 apresentou os menores valores de TSM. Os resultados da Análise em Componente Principal (ACP) identificaram maiores pesos na variação total explicada pelas duas primeiras componentes, que representam cerca de 100% da variância total dos dados de TSM. A Análise de Agrupamento (AA), pelo método Ward, permitiu o agrupamento das estações em três grupos homogêneos. Palavras - chave: Análises Multivariadas, Mudanças climáticas, Aquecimento Global.   Study of Sea Surface Temperature for the Atlantic and Pacific Oceans Using the Technique of Principal Component Analysis and Cluster   ABSTRACT Presented in this study was to evaluate the methodology of Multivariate Analysis: Principal Component Analysis (PCA) and cluster analysis (CA), the data of sea surface temperature (SST) for the Atlantic (North (NATL), Tropical (TROP) and South (Satler)) and Pacific (+2 NIÑO1, NIÑO3.4, and NIÑO3 NIÑO4). We used monthly data from January 1950 to December 2010 SST obtained from NOAA (National Oceanic and Atmospheric Administration / Earth System Research Laboratory). TROP and NIÑO4 regions have the highest SST for the months from December to July. NATL The region has in the period August-October SST your highest values +2 NIÑo1 The region had the lowest values of TSM. Results on Principal Component Analysis (PCA) identified higher weights in the total variation explained by the first two components, which represent about 100% of the total variance of SST. The Cluster Analysis (AA), the Ward method, allowed the grouping of stations into three homogeneous groups. Keywords: Multivariate Analysis, Climate Change, Global Warming.

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.

scholarly journals Statistical Downscaling Forecasts for Winter Monsoon Precipitation in Malaysia Using Multimodel Output Variables

2010 ◽  
Vol 23 (1) ◽  
pp. 17-27 ◽  
Author(s):  
Liew Juneng ◽  
Fredolin T. Tangang ◽  
Hongwen Kang ◽  
Woo-Jin Lee ◽  
Yap Kok Seng
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Statistical Downscaling ◽  
Large Scale ◽  
Winter Season ◽  
Peninsular Malaysia ◽  
Sea Surface ◽  
Asia Pacific ◽  
Forecast Data ◽  
Multimodel Ensembles

Abstract This paper compares the skills of four different forecasting approaches in predicting the 1-month lead time of the Malaysian winter season precipitation. Two of the approaches are based on statistical downscaling techniques of multimodel ensembles (MME). The third one is the ensemble of raw GCM forecast without any downscaling, whereas the fourth approach, which provides a baseline comparison, is a purely statistical forecast based solely on the preceding sea surface temperature anomaly. The first multimodel statistical downscaling method was developed by the Asia-Pacific Economic Cooperation (APEC) Climate Center (APCC) team, whereas the second is based on the canonical correlation analysis (CCA) technique using the same predictor variables. For the multimodel downscaling ensemble, eight variables from seven operational GCMs are used as predictors with the hindcast forecast data spanning a period of 21 yr from 1983/84 to 2003/04. The raw GCM forecast ensemble tends to have higher skills than the baseline skills of the purely statistical forecast that relates the dominant modes of observed sea surface temperature variability to precipitation. However, the downscaled MME forecasts have higher skills than the raw GCM products. In particular, the model developed by APCC showed significant improvement over the peninsular Malaysia region. This is attributed to the model’s ability to capture regional and large-scale predictor signatures from which the additional skills originated. Overall, the results showed that the appropriate downscaling technique and ensemble of various GCM forecasts could result in some skill enhancement, particularly over peninsular Malaysia, where other models tend to have lower or no skills.

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