El Nino Southern Oscillation and Atlantic Multidecadal Oscillation Impact on Hurricanes North Atlantic Basin
Tropical cyclones (TCs), including hurricanes and typhoons, cause significant property damage and result in fatalities, making it crucial to understand the factors driving extreme TCs. The El Nino Southern Oscillation (ENSO) influences TC formation through tropospheric vorticity, wind shear, and atmospheric circulations. Apart from atmospheric changes, oceans influence activity through sea surface temperatures (SSTs) and deep ocean heat content. These Atlantic SSTs determine the Atlantic Multidecadal Oscillation (AMO), which indicates SST variability in the Atlantic. This research focuses on ENSO, AMO, and SSTs impact on the strength and frequency of TCs in the North Atlantic Basin. AMO and SST anomalies are increasing at an alarming rate, but it remains unclear how their dynamics will influence future TC behavior. I used observational cyclone track data from 1950 to 2023, the Oceanic Nino Index (ONI), and NOAAs Extended Reconstructed SST V5 (ERSST). I found that Increasing SSTs over the past decade indicate stronger TCs, while warm phase AMO periods correspond with higher TC frequency. Meanwhile, a greater frequency of landfalling TCs can be attributed to La Nina or ENSO-neutral, with El Nino decreasing the frequency of landfalling TCs. Such relationships suggest that as the seasonal predictability of ENSO and SSTs improve, seasonal TC forecasts may improve.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Wavelet Coherence Of Total Solar Irradiance and Atlantic Climate
The oscillations of climatic parameters of North Atlantic Ocean play important role in various events in North America and Europe. Several climatic indices are associated with these oscillations. The long term Atlantic t…
Time SeriesClimate Prompting: Generating the Madden-Julian Oscillation using Video Diffusion and Low-Dimensional Conditioning
Generative Deep Learning is a powerful tool for modeling of the Madden-Julian oscillation (MJO) in the tropics, yet its relationship to traditional theoretical frameworks remains poorly understood. Here we propose a vide…
Machine learning models for predicting catastrophe bond coupons using climate data
In recent years, the growing frequency and severity of natural disasters have increased the need for effective tools to manage catastrophe risk. Catastrophe (CAT) bonds allow the transfer of part of this risk to investor…
A New Spectral Clustering Algorithm
We present a new clustering algorithm that is based on searching for natural gaps in the components of the lowest energy eigenvectors of the Laplacian of a graph. In comparing the performance of the proposed method with …
ClusteringPredicting Atlantic Multidecadal Variability
Atlantic Multidecadal Variability (AMV) describes variations of North Atlantic sea surface temperature with a typical cycle of between 60 and 70 years. AMV strongly impacts local climate over North America and Europe, th…