scholarly journals Observed Changes in Cyclone Activity in Canada and Their Relationships to Major Circulation Regimes

2006 ◽  
Vol 19 (6) ◽  
pp. 896-915 ◽  
Author(s):  
Xiaolan L. Wang ◽  
H. Wan ◽  
Val R. Swail
Keyword(s):  
Regression Analysis ◽  
Great Lakes ◽  
East Coast ◽  
Southern Oscillation ◽  
Linear Regression Analysis ◽  
Cyclone Activity ◽  
The North ◽  
The Pacific ◽  
Central Canada ◽  
The North Atlantic Oscillation

Abstract This study assessed the climate and trend of cyclone activity in Canada using mainly the occurrence frequency of cyclone deepening events and deepening rates, which were derived from hourly mean sea level pressure data observed at 83 Canadian stations for up to 50 years (1953–2002). Trends in the frequency of cyclone activity were estimated by logistic regression analysis, and trends of seasonal extreme cyclone intensity, by linear regression analysis. The results of trend analysis show that, among the four seasons, winter cyclone activity has shown the most significant trends. It has become significantly more frequent, more durable, and stronger in the lower Canadian Arctic, but less frequent and weaker in the south, especially along the southeast and southwest coasts. Winter cyclone deepening rates have increased in the zone around 60°N but decreased in the Great Lakes area and southern Prairies–British Columbia. However, extreme winter cyclone activity seems to have experienced a weaker increase in northwest-central Canada but a stronger decline in the Great Lakes area and in southern Prairies. The results also show more frequent summer cyclone activity with slower deepening rates on the east coast, as well as less frequent cyclone activity with faster deepening rates in the Great Lakes area in autumn. Cyclone activity in Canada was found to be closely related to the North Atlantic Oscillation (NAO), the Pacific Decadal Oscillation (PDO), and El Niño–Southern Oscillation (ENSO). Overall, cyclone activity in Canada is most closely related to the NAO. The simultaneous NAO index explains about 44% (41%) of the winter (autumn) cyclone activity variance in the east coast, 31% of winter cyclone activity variance in the 60°–70°N zone, and 17% of autumn cyclone activity variance in the Great Lakes area. Also, in several regions (e.g., the east coast, the southwest, and the 60°–70°N zone) up to 15% of the seasonal cyclone activity variance can be explained by the NAO/PDO/ENSO index one–three seasons earlier, which is useful for seasonal forecasting.

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 Intraseasonal and Interannual Variability in North American Storm Tracks and Its Relationship to Equatorial Pacific Variability

2013 ◽  
Vol 141 (10) ◽  
pp. 3610-3625 ◽  
Author(s):  
Kevin M. Grise ◽  
Seok-Woo Son ◽  
John R. Gyakum
Keyword(s):  
North America ◽  
Interannual Variability ◽  
North American ◽  
Southern Oscillation ◽  
Extratropical Cyclones ◽  
The North ◽  
The Pacific ◽  
Cyclone Tracks ◽  
Lagrangian Tracking ◽  
The North Atlantic Oscillation

Abstract Extratropical cyclones play a principal role in wintertime precipitation and severe weather over North America. On average, the greatest number of cyclones track 1) from the lee of the Rocky Mountains eastward across the Great Lakes and 2) over the Gulf Stream along the eastern coastline of North America. However, the cyclone tracks are highly variable within individual winters and between winter seasons. In this study, the authors apply a Lagrangian tracking algorithm to examine variability in extratropical cyclone tracks over North America during winter. A series of methodological criteria is used to isolate cyclone development and decay regions and to account for the elevated topography over western North America. The results confirm the signatures of four climate phenomena in the intraseasonal and interannual variability in North American cyclone tracks: the North Atlantic Oscillation (NAO), the El Niño–Southern Oscillation (ENSO), the Pacific–North American pattern (PNA), and the Madden–Julian oscillation (MJO). Similar signatures are found using Eulerian bandpass-filtered eddy variances. Variability in the number of extratropical cyclones at most locations in North America is linked to fluctuations in Rossby wave trains extending from the central tropical Pacific Ocean. Only over the far northeastern United States and northeastern Canada is cyclone variability strongly linked to the NAO. The results suggest that Pacific sector variability (ENSO, PNA, and MJO) is a key contributor to intraseasonal and interannual variability in the frequency of extratropical cyclones at most locations across North America.

scholarly journals Influence of Modes of Climate Variability on Global Temperature Extremes

2008 ◽  
Vol 21 (15) ◽  
pp. 3872-3889 ◽  
Author(s):  
Jesse Kenyon ◽  
Gabriele C. Hegerl
Keyword(s):  
North America ◽  
Climate Variability ◽  
Large Scale ◽  
Southern Oscillation ◽  
Temperature Extremes ◽  
Modes Of Variability ◽  
The North ◽  
The Pacific ◽  
The North Atlantic Oscillation ◽  
Modes Of Climate Variability

Abstract The influence of large-scale modes of climate variability on worldwide summer and winter temperature extremes has been analyzed, namely, that of the El Niño–Southern Oscillation, the North Atlantic Oscillation, and Pacific interdecadal climate variability. Monthly indexes for temperature extremes from worldwide land areas are used describe moderate extremes, such as the number of exceedences of the 90th and 10th climatological percentiles, and more extreme events such as the annual, most extreme temperature. This study examines which extremes show a statistically significant (5%) difference between the positive and negative phases of a circulation regime. Results show that temperature extremes are substantially affected by large-scale circulation patterns, and they show distinct regional patterns of response to modes of climate variability. The effects of the El Niño–Southern Oscillation are seen throughout the world but most clearly around the Pacific Rim and throughout all of North America. Likewise, the influence of Pacific interdecadal variability is strongest in the Northern Hemisphere, especially around the Pacific region and North America, but it extends to the Southern Hemisphere. The North Atlantic Oscillation has a strong continent-wide effect for Eurasia, with a clear but weaker effect over North America. Modes of variability influence the shape of the daily temperature distribution beyond a simple shift, often affecting cold and warm extremes and sometimes daytime and nighttime temperatures differently. Therefore, for reliable attribution of changes in extremes as well as prediction of future changes, changes in modes of variability need to be accounted for.

scholarly journals Influence of Modes of Climate Variability on Global Precipitation Extremes

2010 ◽  
Vol 23 (23) ◽  
pp. 6248-6262 ◽  
Author(s):  
Jesse Kenyon ◽  
Gabriele C. Hegerl
Keyword(s):  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
Precipitation Extremes ◽  
El Niño Southern Oscillation ◽  
El Nino Southern Oscillation ◽  
Northern Annular Mode ◽  
The North ◽  
The Pacific ◽  
The North Atlantic Oscillation

Abstract The probability of climate extremes is strongly affected by atmospheric circulation. This study quantifies the worldwide influence of three major modes of circulation on station-based indices of intense precipitation: the El Niño–Southern Oscillation, the Pacific interdecadal variability as characterized by the North Pacific index (NPI), and the North Atlantic Oscillation–Northern Annular Mode. The study examines which stations show a statistically significant (5%) difference between the positive and negative phases of a circulation regime. Results show distinct regional patterns of response to all these modes of climate variability; however, precipitation extremes are most substantially affected by the El Niño–Southern Oscillation. The effects of the El Niño–Southern Oscillation are seen throughout the world, including in India, Africa, South America, the Pacific Rim, North America, and, weakly, Europe. The North Atlantic Oscillation has a strong, continent-wide effect on Eurasia and affects a small, but not negligible, percentage of stations across the Northern Hemispheric midlatitudes. This percentage increases slightly if the Northern Annular Mode index is used rather than the NAO index. In that case, a region of increase in intense precipitation can also be found in Southeast Asia. The NPI influence on precipitation extremes is similar to the response to El Niño, and strongest in landmasses adjacent to the Pacific. Consistently, indices of more rare precipitation events show a weaker response to circulation than indices of moderate extremes; the results are quite similar, but of opposite sign, for negative anomalies of the circulation indices.

scholarly journals Evaluation of Leading Modes of Climate Variability in the CMIP Archives

2020 ◽  
Vol 33 (13) ◽  
pp. 5527-5545 ◽  
Author(s):  
John T. Fasullo ◽  
A. S. Phillips ◽  
C. Deser
Keyword(s):  
Climate Variability ◽  
Southern Oscillation ◽  
Internal Variability ◽  
Intrinsic Variability ◽  
Annular Modes ◽  
Internal Climate Variability ◽  
The North ◽  
The Pacific ◽  
The North Atlantic Oscillation ◽  
Modes Of Climate Variability

AbstractThe adequate simulation of internal climate variability is key for our understanding of climate as it underpins efforts to attribute historical events, predict on seasonal and decadal time scales, and isolate the effects of climate change. Here the skill of models in reproducing observed modes of climate variability is assessed, both across and within the CMIP3, CMIP5, and CMIP6 archives, in order to document model capabilities, progress across ensembles, and persisting biases. A focus is given to the well-observed tropical and extratropical modes that exhibit small intrinsic variability relative to model structural uncertainty. These include El Niño–Southern Oscillation (ENSO), the Pacific decadal oscillation (PDO), the North Atlantic Oscillation (NAO), and the northern and southern annular modes (NAM and SAM). Significant improvements are identified in models’ representation of many modes. Canonical biases, which involve both amplitudes and patterns, are generally reduced across model generations. For example, biases in ENSO-related equatorial Pacific sea surface temperature, which extend too far westward, and associated atmospheric teleconnections, which are too weak, are reduced. Stronger tropical expression of the PDO in successive CMIP generations has characterized their improvement, with some CMIP6 models generating patterns that lie within the range of observed estimates. For the NAO, NAM, and SAM, pattern correlations with observations are generally higher than for other modes and slight improvements are identified across successive model generations. For ENSO and PDO spectra and extratropical modes, changes are small compared to internal variability, precluding definitive statements regarding improvement.

scholarly journals Influence of the North Atlantic Oscillation on the Winter Season in Cuba

2021 ◽  
Vol 8 (1) ◽  
pp. 45
Author(s):  
Graciela González ◽  
Amílcar Calzada ◽  
Alejandro Rodríguez
Keyword(s):  
Regression Analysis ◽  
North Atlantic Oscillation ◽  
North Atlantic ◽  
Southern Oscillation ◽  
Winter Season ◽  
Tropical Climate ◽  
Negative Phase ◽  
The North ◽  
The North Atlantic ◽  
The North Atlantic Oscillation

There have been several advances in understanding the North Atlantic Oscillation (NAO), but there are still uncertainties regarding its level of influence on the tropical climate. That is why this work determines the influence of the NAO on the main hydrometeorological events that affected Cuba in the 1999–2016 period. To comply with this, a regression analysis is carried out in the CurveExpert software where the combined influence of the NAO and El Niño-Southern Oscillation on hydrometeorological events is also examined. It was found that the NAO exerts a greater influence on Cuba when it is in its negative phase during the winter season.

scholarly journals Atmospheric and Ocean Dynamics May Explain Cycles in Oceanic Oscillations

Climate ◽  
2019 ◽  
Vol 7 (6) ◽  
pp. 77
Author(s):  
Knut L. Seip ◽  
Øyvind Grøn
Keyword(s):  
North Atlantic ◽  
Southern Oscillation ◽  
Distinct Pattern ◽  
Ocean Dynamics ◽  
Cyclic Pattern ◽  
The North ◽  
The Pacific ◽  
Cycle Lengths ◽  
The North Atlantic ◽  
The North Atlantic Oscillation

What causes cycles in oceanic oscillations, and is there a change in the characteristics of oscillations in around 1950? Characteristics of oceanic cycles and their sources are important for climate predictability. We here compare cycles generated in a simple model with observed oceanic cycles in the great oceans: The North Atlantic Oscillation (NAO), El Niño, the Southern Oscillation Index (SOI), and the Pacific Decadal Oscillation (PDO). In the model, we let a stochastic movement in one oceanic oscillation cause a similar but lagging movement in another oceanic oscillation. The two interacting oscillations show distinct cycle lengths depending upon how strongly one oscillation creates lagging cycles in the other. The model and observations both show cycles around two to six, 13 to 16, 22 to 23, and 31 to 32 years. The ultimate cause for the distinct cycles is atmospheric and oceanic “bridges” that connect the ocean basins, but the distinct pattern in cycle lengths is determined by properties of statistical distributions. We found no differences in the leading or lagging strength between well separated basins (the North Atlantic and the Pacific) and overlapping ocean basins (both in the Pacific). The cyclic pattern before 1950 appears to be different from the cyclic pattern after 1950.

Non-linearity in the pathway of El Niño-Southern Oscillation to the tropical North Atlantic

2021 ◽  
Author(s):  
Jake W. Casselman ◽  
Andréa S. Taschetto ◽  
Daniela I.V. Domeisen
Keyword(s):  
North Atlantic ◽  
Southern Oscillation ◽  
Static Stability ◽  
Reanalysis Data ◽  
Trade Winds ◽  
The North ◽  
The Pacific ◽  
Tropical North Atlantic ◽  
Sst Anomaly ◽  
The North Atlantic Oscillation

<p>El Niño-Southern Oscillation can influence the Tropical North Atlantic (TNA), leading to anomalous sea surface temperatures (SST) at a lag of several months. Several mechanisms have been proposed to explain this teleconnection. These mechanisms include both tropical and extratropical pathways, contributing to anomalous trade winds and static stability over the TNA region. The TNA SST response to ENSO has been suggested to be nonlinear. Yet the overall linearity of the ENSO-TNA teleconnection via the two pathways remains unclear. Here we use reanalysis data to confirm that the SST anomaly (SSTA) in the TNA is nonlinear with respect to the strength of the SST forcing in the tropical Pacific, as further increases in El Niño magnitudes cease to create further increases of the TNA SSTA. We further show that the tropical pathway is more linear than the extratropical pathway by sub-dividing the inter-basin connection into extratropical and tropical pathways. The extratropical pathway is modulated by the North Atlantic Oscillation (NAO) and the location of the SSTA in the Pacific, but this modulation insufficiently explains the nonlinearity in TNA SSTA. As neither extratropical nor tropical pathways can explain the nonlinearity, this suggests that external factors are at play. Further analysis shows that the TNA SSTA is highly influenced by the preconditioning of the tropical Atlantic SST. This preconditioning is found to be associated with the NAO through SST-tripole patterns.</p>

scholarly journals Discovering Associations between Climatic and Oceanic Parameters to Monitor Drought in Nebraska Using Data-Mining Techniques

2005 ◽  
Vol 18 (10) ◽  
pp. 1541-1550 ◽  
Author(s):  
Tsegaye Tadesse ◽  
Donald A. Wilhite ◽  
Michael J. Hayes ◽  
Sherri K. Harms ◽  
Steve Goddard
Keyword(s):  
Data Mining ◽  
Southern Oscillation ◽  
Drought Indices ◽  
Data Mining Algorithm ◽  
Time Lags ◽  
Data Mining Algorithms ◽  
The North ◽  
The Pacific ◽  
Time Lagged ◽  
The North Atlantic Oscillation

Abstract Drought is a complex natural hazard that is best characterized by multiple climatological and hydrological parameters. Improving our understanding of the relationships between these parameters is necessary to reduce the impacts of drought. Data mining is a recently developed technique that can be used to interact with large databases and assist in the discovery of associations between drought and oceanic data by extracting information from massive and multiple data archives. In this study, a new data-mining algorithm [i.e., Minimal Occurrences With Constraints and Time Lags (MOWCATL)] has been used to identify the relationships between oceanic parameters and drought indices. Rather than using traditional global statistical associations, the algorithm identifies drought episodes separate from normal and wet conditions and then uses drought episodes to find time-lagged relationships with oceanic parameters. As with all association-based data-mining algorithms, MOWCATL is used to find existing relationships in the data, and is not by itself a prediction tool. Using the MOWCATL algorithm, the analyses of the rules generated for selected stations and state-averaged data for Nebraska from 1950 to 1999 indicate that most occurrences of drought are preceded by positive values of the Southern Oscillation index (SOI), negative values of the multivariate ENSO index (MEI), negative values of the Pacific–North American (PNA) index, negative values of the Pacific decadal oscillation (PDO), and negative values of the North Atlantic Oscillation (NAO). The frequency and confidence of the time-lagged relationships between oceanic indices and droughts at the selected stations in Nebraska indicate that oceanic parameters can be used as indicators of drought in Nebraska.

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