scholarly journals ENSO’s Impact on Regional U.S. Hurricane Activity

2007 ◽  
Vol 20 (7) ◽  
pp. 1404-1414 ◽  
Author(s):  
Shawn R. Smith ◽  
Justin Brolley ◽  
James J. O’Brien ◽  
Carissa A. Tartaglione
Keyword(s):  
North Atlantic ◽  
East Coast ◽  
Southern Oscillation ◽  
Regional Analysis ◽  
The North ◽  
Hurricane Season ◽  
Hurricane Activity ◽  
National Hurricane Center ◽  
Observational Record ◽  
The U.S

Abstract Regional variations in North Atlantic hurricane landfall frequency along the U.S. coastline are examined in relation to the phase of El Niño–Southern Oscillation (ENSO). ENSO warm (cold) phases are known to reduce (increase) hurricane activity in the North Atlantic basin as a whole. Using best-track data from the U.S. National Hurricane Center, regional analysis reveals that ENSO cold-phase landfall frequencies are only slightly larger than neutral-phase landfall frequencies along the Florida and Gulf coasts. However, for the East Coast, from Georgia to Maine, a significant decrease in landfall frequency occurs during the neutral ENSO phase as compared to the cold phase. Along the East Coast, two or more major (category 3 or above) hurricanes never made landfall in the observational record (1900–2004) during a single hurricane season classified as an ENSO neutral or warm phase.

Cluster Models

2013 ◽  
Author(s):  
James B. Elsner ◽  
Thomas H. Jagger
Keyword(s):  
East Coast ◽  
Gulf Coast ◽  
Southern Oscillation ◽  
Time Correlation ◽  
Additional Time ◽  
Feature Clustering ◽  
The North ◽  
Time Space ◽  
Hurricane Activity ◽  
The North Atlantic Oscillation

A cluster is a group of the same or similar events close together. Clusters arise in hurricane origin locations, tracks, and landfalls. In this chapter, we look at how to analyze and model clusters. We divide the chapter into methods for time, space, and feature clustering. Of the three feature clustering is best known to climatologists. We begin by showing you how to detect and model time clusters. Consecutive hurricanes originating in the same area often take similar paths. This grouping, or clustering, increases the potential for multiple landfalls above what you expect from random events. A statistical model for landfall probability captures clustering through covariates like the North Atlantic Oscillation (NAO), which relates a steering mechanism (position and strength of the subtropical high pressure) to coastal hurricane activity. But there could be additional time correlation not related to the covariates. A model that does not account for this extra variation will underestimate the potential for multiple hits in a season. Following Jagger and Elsner (2006), you consider three coastal regions including the Gulf Coast, Florida, and the East Coast (Fig. 6.2). Regions are large enough to capture enough hurricanes, but not too large as to include many non-coastal strikes. Here you use hourly position and intensity data described in Chapter 6. For each hurricane, you note its wind speed maximum within each region. If the maximum wind exceeds 33 m s−1, then you count it as a hurricane for the region. A tropical cyclone that affects more than one region at hurricane intensity is counted in each region. Because of this, the sum of the regional counts is larger than the total count. Begin by loading annual.RData. These data were assembled in Chapter 6. Subset the data for years starting with 1866. . . . > load("annual.RData") > dat = subset(annual, Year >= 1866) . . . The covariate Southern Oscillation Index (SOI) data begins in 1866 . Next, extract all hurricane counts for the Gulf coast, Florida, and East coast regions. . . . > cts = dat[, c("G.1", "F.1", "E.1")] . . .

scholarly journals Attribution of the Extreme U.S. East Coast Snowstorm Activity of 2010

2012 ◽  
Vol 25 (11) ◽  
pp. 3771-3791 ◽  
Author(s):  
Yehui Chang ◽  
Siegfried Schubert ◽  
Max Suarez
Keyword(s):  
United States ◽  
North Atlantic ◽  
General Circulation ◽  
East Coast ◽  
The United States ◽  
Storm Activity ◽  
The North ◽  
The North Atlantic ◽  
The U.S ◽  
Sst Anomalies

This study examines the cause of the extreme snowstorm activity along the U.S. East Coast during the winter of 2009/10 with a focus on the role of sea surface temperature (SST) anomalies. The study employs the Goddard Earth Observing System, version 5 (GEOS-5) atmospheric general circulation model (AGCM) run at high resolution and forced with specified observed or idealized SST. Comparisons are made with the winter of 1999/2000, a period that is characterized by SST anomalies that are largely of opposite sign. When forced with observed SSTs, the AGCM response consists of a band of enhanced storminess extending from the central subtropical North Pacific, across the southern United States, across the North Atlantic, and across southern Eurasia, with reduced storminess to the north of these regions. Positive precipitation and cold temperature anomalies occur over the eastern United States, reflecting a propensity for enhanced snowstorm activity. Additional idealized SST experiments show that the anomalies over the United States are, to a large extent, driven by the ENSO-related Pacific SST. The North Atlantic SSTs contribute to the cooler temperatures along the East Coast of the United States, while the Indian Ocean SSTs act primarily to warm the central part of the country. It is further shown that the observed upper-tropospheric height anomalies have a large noise (unforced) component over the Northern Hemisphere, represented over the North Atlantic by a North Atlantic Oscillation (NAO)-like structure. The signal-to-noise ratios of the temperature and precipitation fields nevertheless indicate a potential for predicting the unusual storm activity along the U.S. East Coast several months in advance.

Hurricane Clusters in the Vicinity of Florida

2012 ◽  
Vol 51 (5) ◽  
pp. 869-877 ◽  
Author(s):  
Thomas H. Jagger ◽  
James B. Elsner
Keyword(s):  
Poisson Distribution ◽  
North Atlantic ◽  
Cluster Model ◽  
Negative Binomial ◽  
Southern Oscillation ◽  
Inverse Gaussian ◽  
Gaussian Models ◽  
The North ◽  
Hurricane Activity ◽  
The North Atlantic Oscillation

AbstractModels that predict annual U.S. hurricane activity assume a Poisson distribution for the counts. Here the authors show that this assumption applied to Florida hurricanes leads to a forecast that underpredicts both the number of years without hurricanes and the number of years with three or more hurricanes. The underdispersion in forecast counts arises from a tendency for hurricanes to arrive in groups along this part of the coastline. The authors then develop an extension to their earlier statistical model that assumes that the rate of hurricane clusters follows a Poisson distribution with cluster size capped at two hurricanes. Hindcasts from the cluster model better fit the distribution of Florida hurricanes conditional on the climate covariates including the North Atlantic Oscillation and Southern Oscillation index. Results are similar to models that parameterize the extra-Poisson variation in the observed counts, including the negative binomial and the Poisson inverse Gaussian models. The authors argue, however, that the cluster model is physically consistent with the way Florida hurricanes tend to arrive in groups.

scholarly journals C14 content of recent molluscs from Scoresby Sund, central East Greenland

1975 ◽  
Vol 75 ◽  
pp. 95-99
Author(s):  
H Tauber ◽  
S Funder
Keyword(s):  
North Atlantic ◽  
East Coast ◽  
Marine Organisms ◽  
Water Masses ◽  
Lower Activity ◽  
Terrestrial Plants ◽  
East Greenland ◽  
The North ◽  
The North Atlantic ◽  
Turn Over

C14 dating of subfossil marine shelIs presupposes a knowledge of the original C14 activity of the organisms while living. Due to the slow turn over of water masses, the C14 activity of marine bicarbonate and marine organisms is not the same in all parts of the oceans, but may show marked deficiencies in certain oceanic areas, especially at southern latitudes. In large areas of the North Atlantic the C14 activity seems to be fairly uniform and equal to or only slightly lower than that of 'pre-industrial' terrestrial plants (Broecker et al., 1960; Mangerud, 1972; Krog & Tauber, 1974). In certain areas, however, a somewhat lower activity seems to occur; trus has been noted for areas along the east coast of Greenland (Fonselius & Ostlund, 1959; Hjort, 1973).

scholarly journals Impacts of large-scale atmospheric circulation changes due to winter sea-ice retreat on Black Carbon transport and deposition to the Arctic

2016 ◽  
Author(s):  
Luca Pozzoli ◽  
Srdan Dobricic ◽  
Simone Russo ◽  
Elisabetta Vignati
Keyword(s):  
Sea Ice ◽  
Atmospheric Circulation ◽  
North Atlantic ◽  
Large Scale ◽  
Southern Oscillation ◽  
The Arctic ◽  
The North ◽  
Ice Retreat ◽  
The North Atlantic Oscillation ◽  
Sea Ice Retreat

Abstract. Winter warming and sea ice retreat observed in the Arctic in the last decades determine changes of large scale atmospheric circulation pattern that may impact as well the transport of black carbon (BC) to the Arctic and its deposition on the sea ice, with possible feedbacks on the regional and global climate forcing. In this study we developed and applied a new statistical algorithm, based on the Maximum Likelihood Estimate approach, to determine how the changes of three large scale weather patterns (the North Atlantic Oscillation, the Scandinavian Blocking, and the El Nino-Southern Oscillation), associated with winter increasing temperatures and sea ice retreat in the Arctic, impact the transport of BC to the Arctic and its deposition. We found that the three atmospheric patterns together determine a decreasing winter deposition trend of BC between 1980 and 2015 in the Eastern Arctic while they increase BC deposition in the Western Arctic. The increasing trend is mainly due to the more frequent occurrences of stable high pressure systems (atmospheric blocking) near Scandinavia favouring the transport in the lower troposphere of BC from Europe and North Atlantic directly into to the Arctic. The North Atlantic Oscillation has a smaller impact on BC deposition in the Arctic, but determines an increasing BC atmospheric load over the entire Arctic Ocean with increasing BC concentrations in the upper troposphere. The El Nino-Southern Oscillation does not influence significantly the transport and deposition of BC to the Arctic. The results show that changes in atmospheric circulation due to polar atmospheric warming and reduced winter sea ice significantly impacted BC transport and deposition. The anthropogenic emission reductions applied in the last decades were, therefore, crucial to counterbalance the most likely trend of increasing BC pollution in the Arctic.

scholarly journals Connection between Sea Surface Anomalies and Atmospheric Quasi-Stationary Waves

2020 ◽  
Vol 33 (1) ◽  
pp. 201-212
Author(s):  
G. Wolf ◽  
A. Czaja ◽  
D. J. Brayshaw ◽  
N. P. Klingaman
Keyword(s):  
Sea Ice ◽  
North Atlantic ◽  
Southern Oscillation ◽  
Late Summer ◽  
Ice Cover ◽  
Sea Surface ◽  
Late Winter ◽  
Driving Mechanism ◽  
The North ◽  
The North Atlantic

AbstractLarge-scale, quasi-stationary atmospheric waves (QSWs) are known to be strongly connected with extreme events and general weather conditions. Yet, despite their importance, there is still a lack of understanding about what drives variability in QSW. This study is a step toward this goal, and it identifies three statistically significant connections between QSWs and sea surface anomalies (temperature and ice cover) by applying a maximum covariance analysis technique to reanalysis data (1979–2015). The two most dominant connections are linked to El Niño–Southern Oscillation and the North Atlantic Oscillation. They confirm the expected relationship between QSWs and anomalous surface conditions in the tropical Pacific and the North Atlantic, but they cannot be used to infer a driving mechanism or predictability from the sea surface temperature or the sea ice cover to the QSW. The third connection, in contrast, occurs between late winter to early spring Atlantic sea ice concentrations and anomalous QSW patterns in the following late summer to early autumn. This new finding offers a pathway for possible long-term predictability of late summer QSW occurrence.

An Optimization Model for Selecting Sample Days

2021 ◽  
pp. 2050052
Author(s):  
Tao Li
Keyword(s):  
North Atlantic ◽  
Optimization Model ◽  
Federal Aviation Administration ◽  
Selection Method ◽  
North Atlantic Region ◽  
Atlantic Region ◽  
Managerial Decisions ◽  
The North ◽  
The North Atlantic ◽  
The U.S

Sample day selection method plays an important role in managerial decisions which require analyses that are prohibitively expensive to apply to a large number of days. We develop a general sample day selection model that selects sample days based on the cumulative distributions of airspace conditions and characteristics (C&C) by considering factors such as sampling targets, degree of diversity and coverage of the selected days. We introduce indicators that capture the airspace C&C of the North Atlantic region (NAT) and apply the model to select sample days for the NAT. The results show that the model outperforms the methods used by the U.S. Federal Aviation Administration.

scholarly journals The link between the Barents Sea and ENSO events simulated by NEMO model

Ocean Science ◽  
2012 ◽  
Vol 8 (6) ◽  
pp. 971-982 ◽  
Author(s):  
V. N. Stepanov ◽  
H. Zuo ◽  
K. Haines
Keyword(s):  
Sea Ice ◽  
North Atlantic ◽  
Barents Sea ◽  
Southern Oscillation ◽  
Similar Response ◽  
Enso Events ◽  
Ice Volume ◽  
The North ◽  
The Barents Sea ◽  
The North Atlantic

Abstract. An analysis of observational data in the Barents Sea along a meridian at 33°30' E between 70°30' and 72°30' N has reported a negative correlation between El Niño/La Niña Southern Oscillation (ENSO) events and water temperature in the top 200 m: the temperature drops about 0.5 °C during warm ENSO events while during cold ENSO events the top 200 m layer of the Barents Sea is warmer. Results from 1 and 1/4-degree global NEMO models show a similar response for the whole Barents Sea. During the strong warm ENSO event in 1997–1998 an anomalous anticyclonic atmospheric circulation over the Barents Sea enhances heat loses, as well as substantially influencing the Barents Sea inflow from the North Atlantic, via changes in ocean currents. Under normal conditions along the Scandinavian peninsula there is a warm current entering the Barents Sea from the North Atlantic, however after the 1997–1998 event this current is weakened. During 1997–1998 the model annual mean temperature in the Barents Sea is decreased by about 0.8 °C, also resulting in a higher sea ice volume. In contrast during the cold ENSO events in 1999–2000 and 2007–2008, the model shows a lower sea ice volume, and higher annual mean temperatures in the upper layer of the Barents Sea of about 0.7 °C. An analysis of model data shows that the strength of the Atlantic inflow in the Barents Sea is the main cause of heat content variability, and is forced by changing pressure and winds in the North Atlantic. However, surface heat-exchange with the atmosphere provides the means by which the Barents sea heat budget relaxes to normal in the subsequent year after the ENSO events.

scholarly journals The Influence of Surface Forcings on Prediction of the North Atlantic Oscillation Regime of Winter 2010/11

2013 ◽  
Vol 141 (11) ◽  
pp. 3801-3813 ◽  
Author(s):  
Anna Maidens ◽  
Alberto Arribas ◽  
Adam A. Scaife ◽  
Craig MacLachlan ◽  
Drew Peterson ◽  
...  
Keyword(s):  
North Atlantic Oscillation ◽  
Real Time ◽  
North Atlantic ◽  
Southern Oscillation ◽  
Heat Content ◽  
Arctic Sea Ice ◽  
Temperature Fields ◽  
The Real ◽  
The North ◽  
The North Atlantic

Abstract December 2010 was unusual both in the strength of the negative North Atlantic Oscillation (NAO) intense atmospheric blocking and the associated record-breaking low temperatures over much of northern Europe. The negative North Atlantic Oscillation for November–January was predicted in October by 8 out of 11 World Meteorological Organization Global Producing Centres (WMO GPCs) of long-range forecasts. This paper examines whether the unusual strength of the NAO and temperature anomaly signals in early winter 2010 are attributable to slowly varying boundary conditions [El Niño–Southern Oscillation state, North Atlantic sea surface temperature (SST) tripole, Arctic sea ice extent, autumn Eurasian snow cover], and whether these were modeled in the Met Office Global Seasonal Forecasting System version 4 (GloSea4). Results from the real-time forecasts showed that a very robust signal was evident in both the surface pressure fields and temperature fields by the beginning of November. The historical reforecast set (hindcasts), used to calibrate and bias correct the real-time forecast, showed that the seasonal forecast model reproduces at least some of the observed physical mechanisms that drive the NAO. A series of ensembles of atmosphere-only experiments was constructed, using forecast SSTs and ice concentrations from November 2010. Each potential mechanism in turn was systematically isolated and removed, leading to the conclusion that the main mechanism responsible for the successful forecast of December 2010 was anomalous ocean heat content and associated SST anomalies in the North Atlantic.

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