Classification, Seasonality and Persistence of Low-Frequency Atmospheric Circulation Patterns

1987 ◽  
Vol 115 (6) ◽  
pp. 1083-1126 ◽  
Author(s):  
Anthony G. Barnston ◽  
Robert E. Livezey
Keyword(s):  
Atmospheric Circulation ◽  
Low Frequency ◽  
Atmospheric Circulation Patterns ◽  
Circulation Patterns

scholarly journals Seasonal temperatures over Italy and their relationship with low-frequency atmospheric circulation patterns

2009 ◽  
Vol 99 (1-2) ◽  
pp. 211-227 ◽  
Author(s):  
Andrea Toreti ◽  
Franco Desiato ◽  
Guido Fioravanti ◽  
Walter Perconti
Keyword(s):  
Atmospheric Circulation ◽  
Low Frequency ◽  
Atmospheric Circulation Patterns ◽  
Circulation Patterns

Recent changes in the atmospheric circulation patterns during the dry-to-wet transition season in south tropical South America (1979-2020): Impacts on precipitation and fire season

2021 ◽  
pp. 1-56
Author(s):  
Jhan-Carlo Espinoza ◽  
Paola A. Arias ◽  
Vincent Moron ◽  
Clementine Junquas ◽  
Hans Segura ◽  
...  
Keyword(s):  
South America ◽  
Atmospheric Circulation ◽  
Low Frequency ◽  
Fire Season ◽  
South American ◽  
Low Level ◽  
Atmospheric Circulation Patterns ◽  
Transition Season ◽  
Circulation Patterns ◽  
Tropical South America

AbstractWe analyze the characteristics of atmospheric variations over tropical South America using the pattern recognition framework of weather typing or atmospheric circulation patterns (CPs). During 1979-2020, nine CPs are defined in the region, using a k-means algorithm based on daily unfiltered 850 hPa winds over 0035°N-30°S, 90°W-30°W. CPs are primarily interpreted as stages of the annual cycle of the low-level circulation. We identified three “winter” CPs (CP7, CP8 and CP9), three “summer” CPs (CP3, CP4 and CP5) and three “transitional” CPs (CP1, CP2 and CP6). Significant long-term changes are detected during the dry-to-wet transition season (July-October) over south tropical South America (STSA). One of the wintertime patterns (CP9) increases from 20% in the 1980s to 35% in the last decade while the “transitional” CP2 decreases from 13% to 7%. CP9 is characterized by enhancement of the South American Low-Level Jet and increasing atmospheric subsidence over STSA. CP2 is characterized by southerly cold-air incursions and anomalous convective activity over STSA. The years characterized by high (low) frequency of CP9 (CP2) during the dry-to-wet transition season are associated with a delayed South American Monsoon onset and anomalous dry conditions over STSA. Consistently, a higher frequency of CP9 intensifies the fire season over STSA (1999-2020). Over the Brazilian states of Maranhão, Tocantins, Goiás and São Paulo, the seasonal frequency of CP9 explains around 35%-44% of the interannual variations of fire counts.

On Robust Estimation of Low-Frequency Variability Trends in Discrete Markovian Sequences of Atmospheric Circulation Patterns

2009 ◽  
Vol 66 (7) ◽  
pp. 2059-2072 ◽  
Author(s):  
Illia Horenko
Keyword(s):  
Atmospheric Circulation ◽  
Kernel Smoothing ◽  
Low Frequency ◽  
Algorithm Analysis ◽  
Gaussian Kernel ◽  
Observation Data ◽  
Model Sensitivity ◽  
Atmospheric Circulation Patterns ◽  
Low Frequency Variability ◽  
Circulation Patterns

Abstract Identification and analysis of temporal trends and low-frequency variability in discrete time series is an important practical topic in the understanding and prediction of many atmospheric processes, for example, in analysis of climate change. Widely used numerical techniques of trend identification (like local Gaussian kernel smoothing) impose some strong mathematical assumptions on the analyzed data and are not robust to model sensitivity. The latter issue becomes crucial when analyzing historical observation data with a short record. Two global robust numerical methods for the trend estimation in discrete nonstationary Markovian data based on different sets of implicit mathematical assumptions are introduced and compared here. The methods are first compared on a simple model example; then the importance of mathematical assumptions on the data is explained and numerical problems of local Gaussian kernel smoothing are demonstrated. Presented methods are applied to analysis of the historical sequence of atmospheric circulation patterns over the United Kingdom between 1946 and 2007. It is demonstrated that the influence of the seasonal pattern variability on transition processes is dominated by the long-term effects revealed by the introduced methods. Despite the differences in the mathematical assumptions implied by both presented methods, almost identical symmetrical changes of the cyclonic and anticyclonic pattern probabilities are identified in the analyzed data, with the confidence intervals being smaller than in the case of the local Gaussian kernel smoothing algorithm. Analysis results are investigated with respect to model sensitivity and compared to a standard analysis technique based on a local Gaussian kernel smoothing. Finally, the implications of the discussed strategies on long-range predictability of the data-fitted Markovian models are discussed.

scholarly journals Influence of solar forcing, climate variability and atmospheric circulation patterns on summer floods in Switzerland

2014 ◽  
Vol 11 (12) ◽  
pp. 13843-13890 ◽  
Author(s):  
J. C. Peña ◽  
L. Schulte ◽  
A. Badoux ◽  
M. Barriendos ◽  
A. Barrera-Escoda
Keyword(s):  
Solar Activity ◽  
North Atlantic Oscillation ◽  
Atmospheric Circulation ◽  
North Atlantic ◽  
Low Frequency ◽  
Flood Damage ◽  
Negative Phase ◽  
Atmospheric Circulation Patterns ◽  
Circulation Patterns ◽  
The Alps

Abstract. The higher frequency of severe flood events in Switzerland in recent decades has given fresh impetus to the study of flood patterns and their possible forcing mechanisms, particularly in mountain environments. This paper presents an index of summer flood damage that considers severe and catastrophic summer floods in Switzerland between 1800 and 2009, and explores the influence of solar and climate forcings on flood frequencies. In addition, links between floods and low-frequency atmospheric circulation patterns are examined. The flood damage index provides evidence that the 1817–1851, 1881–1927, 1977–1990 and 2005–present flood clusters occur mostly in phase with palaeoclimate proxies. The cross-spectral analysis documents that the periodicities detected in the coherency and phase spectra of 11 (Schwabe cycle) and 104 years (Gleissberg cycle) are related to a high frequency of flooding and solar activity minima, whereas the 22 year cyclicity detected (Hale cycle) is associated with solar activity maxima and a decrease in flood frequency. The analysis of atmospheric circulation patterns shows that Switzerland lies close to the border of the summer principal mode: the Summer North Atlantic Oscillation. The Swiss river catchments situated on the centre and southern flank of the Alps are affected by atmospherically unstable areas defined by the positive phase of the Summer North Atlantic Oscillation pattern, while those basins located in the northern slope of the Alps are predominantly associated with the negative phase of the pattern. Furthermore, a change in the low-frequency atmospheric circulation pattern related to the major floods occurred over the period from 1800 to 2009: the Summer North Atlantic Oscillation persists in negative phase during the last cool pulses of the Little Ice Age (1817–1851 and 1881–1927 flood clusters), whereas the positive phases of SNAO prevail during warmer climate of the last four decades (flood clusters from 1977 to present).

scholarly journals Automated classification of the atmospheric circulation patterns that drive regional wave climates

2014 ◽  
Vol 14 (8) ◽  
pp. 2145-2155 ◽  
Author(s):  
J. Pringle ◽  
D. D. Stretch ◽  
A. Bárdossy
Keyword(s):  
Atmospheric Circulation ◽  
Low Pressure ◽  
Wave Climate ◽  
Automated Classification ◽  
Coastal Vulnerability ◽  
Extreme Wave ◽  
Atmospheric Circulation Patterns ◽  
Circulation Patterns ◽  
Spatially Distributed ◽  
Wave Heights

Abstract. Wave climates are fundamental drivers of coastal vulnerability; changing trends in wave heights, periods and directions can severely impact a coastline. In a diverse storm environment, the changes in these parameters are difficult to detect and quantify. Since wave climates are linked to atmospheric circulation patterns, an automated and objective classification scheme was developed to explore links between synoptic-scale circulation patterns and wave climate variables, specifically wave heights. The algorithm uses a set of objective functions based on wave heights to guide the classification and find atmospheric classes with strong links to wave behaviour. Spatially distributed fuzzy numbers define the classes and are used to detect locally high- and low-pressure anomalies. Classes are derived through a process of simulated annealing. The optimized classification focuses on extreme wave events. The east coast of South Africa was used as a case study. The results show that three dominant patterns drive extreme wave events. The circulation patterns exhibit some seasonality with one pattern present throughout the year. Some 50–80% of the extreme wave events are explained by these three patterns. It is evident that strong low-pressure anomalies east of the country drive a wind towards the KwaZulu-Natal coastline which results in extreme wave conditions. We conclude that the methodology can be used to link circulation patterns to wave heights within a diverse storm environment. The circulation patterns agree with qualitative observations of wave climate drivers. There are applications to the assessment of coastal vulnerability and the management of coastlines worldwide.

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