Surface Temperature Patterns Associated with the Southern Oscillation

Halpert, Michael S.; Ropelewski, Chester F.

doi:10.1175/1520-0442(1992)005<0577:stpawt>2.0.co;2

Cited by 540 publications

(360 citation statements)

References 20 publications

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“…Halpert, 1986, 1987;Halpert and Ropelewski, 1992) showed, for the winter season, US precipitation and temperature responses to El Niño in a way consistent with PNA pattern, with drier and warmer conditions over the northwest USA and colder and wetter conditions over the southeast USA (Leathers et al, 1991).…”

Section: Stationary Wavenumbermentioning

confidence: 84%

“…The ENSO composites are determined by averaging over the individual ENSO years by season, obtained from the NOAA Climate Prediction Centre's (CPC) web site 1958, 19661965, 19721969, 19731987, 19871992, 1995La Niña 1950, 19511955, 19731955, 19561975, 19881971, 19741976, 19891999 (http://www.cpc.ncep.noaa.gov/products/analysis monitoring/ensostuff/ensoyears.shtml). The ENSO episodes, on the CPC web site, are based on a threshold of ±0.5 • C for the Oceanic Niño Index (3-month running mean of SST anomalies in the Niño 3.4 region (5 • N-5 • S, 120-170 • W) using Version 3b of the extended reconstructed SST dataset), based on the 1971-2000 base period.…”

Section: Methodsmentioning

confidence: 99%

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Multidecadal and interannual changes of stationary Rossby waves

Freitas

Rao

2011

Quart J Royal Meteoro Soc

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Changes in stationary Rossby waves (SRW) are connected to regional climate change and the propagation of these waves has a large influence on the evolution and development of the general circulation. Since the late 1970s and early 1980s many studies have noted a major shift in the structure of large-scale circulation occurring in both hemispheres. This paper explores the changes in the SRW pattern and their propagation associated with these climate shifts. First, multidecadal changes in SRW activity are examined, using NCEP-NCAR reanalysis data, for two periods:

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Section: Stationary Wavenumbermentioning

confidence: 84%

Section: Methodsmentioning

confidence: 99%

Multidecadal and interannual changes of stationary Rossby waves

Freitas

Rao

2011

Quart J Royal Meteoro Soc

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“…In JFM, the anomalously positive (negative) frequency of GA and WBL (EA and ZO) during EN events could be explained by the following mechanism, involving a dynamical link between the intensity of the Aleutian and Icelandic lows (Honda et al, 2001): the Aleutian low is anomalously intensified during EN winters (Hoerling et al 1997;Robertson and Ghil, 1999), leading to warm temperature anomalies across North America, mainly western and eastern Canada where above-normal temperatures occur during the December-May period synchronous with an EN event (Halpert and Ropelewski, 1992). Hoerling et al (1997) found a 2°C increase (in December-February) between the Great Lakes and Hudson Bay for nine EN events between 1958 and 1995.…”

Section: Discussionmentioning

confidence: 99%

“…The extratropical cyclones are also steered on a southern route between North America and Europe (Fraedrich, 1990(Fraedrich, , 1993May and Bengtsson, 1998;May, 1999) and then as far south as the Mediterranean area (Laïta and Grimalt, 1997). During LN winters, the intensity of the Aleutian low is reduced (Hoerling et al, 1997;Robertson and Ghil, 1999), leading to colder conditions across North America, especially the northwestern quarter part of it (Halpert and Ropelewski, 1992), and strengthening of the north-south thermal gradient on each side of 45-50°N across North America (Hoerling et al, 1997). This anomalous negative temperature could enhance the sea-continent thermal contrast, then strengthen the North Atlantic storm track over the northwest Atlantic, consistent with the anomalously positive (negative) frequency of EA (GA and WBL) observed here.…”

Section: Discussionmentioning

confidence: 99%

The impact of El Niño–southern oscillation upon weather regimes over Europe and the North Atlantic during boreal winter

Moron

Plaut

2003

Intl Journal of Climatology

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The influence of the warm and cold sea-surface temperatures in the eastern and central equatorial Pacific associated with El Niño-southern oscillation (ENSO) on the probability of occurrence of weather regimes (WRs) over the North Atlantic sector is investigated for the period November-March. Five WRs are identified from daily sea-level pressure anomalies (SLPAs) during 119 winters over this sector by applying cluster analysis: the positive North Atlantic oscillation (NAO; called ZO for zonal) and negative NAO (called WBL for west blocking) patterns; GA (for Greenland anticyclone), with a positive SLPA shifted north of 60°N; EA (for European anticyclone) with a positive SLPA over Europe but enhanced north-south SLPA gradient over the western and central North Atlantic; and AR (for Atlantic Ridge) with a positive (negative) SLPA over the central North Atlantic (northern and central Europe).El Niño winters are associated with a significant increase (decrease) in the prevalence of ZO (WBL) in November-December and a significant increase (decrease) in the prevalence of GA and WBL (EA and ZO)

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“…It is well known that there is a strong relationship between the Southern Oscillation Index (SOI) and Australian surface temperature (e.g. Coughlan 1979, Halpert and Ropelewski 1992, Jones 1999, Jones and Trewin 2000a, and sea surface temperature (SST) has been successfully used as a predictor in statistical forecasts of Australian seasonal mean temperature (Fawcett et al 2005). The skill of seasonal forecasts can be verified (e.g.…”

Section: Introductionmentioning

confidence: 99%

The estimated predictability of seasonal mean Australian surface temperature

Grainger¹,

Frederiksen²,

Zheng³

2013

AMOJ

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The seasonal mean of a climate variable is affected by processes with timescales from less than seasonal to interannual or longer. In this paper, the seasonal mean is conceptualised as consisting of intraseasonal and slowly varying (longer than a season) components. The slow component of the seasonal mean is related to slowly varying internal and external processes which are potentially predictable, and is therefore itself regarded as potentially predictable. An analysis of variance method, which separates the interannual variance of the seasonal mean of these components, is applied to Australian surface maximum and minimum temperature. The potential predictability is defined as the percentage of the interannual variance of the seasonal mean that is due to the slow component. Using data from the Australian Water Availability Project dataset for the period 1958-2007, it is found that there is high estimated potential predictability (over 50 per cent) for surface maximum and minimum temperature for northern Australia (north of 25°S) in most of the 12 three-month seasons. In contrast, there are regions of southern Australia (south of 25°S) with low estimated potential predictability (under 30 per cent) in many seasons. The results given here provide guidance for where and when longrange forecasts of seasonal mean temperature variables are most likely to be skilful. IntroductionForecasts of seasonal mean Australian surface temperature have been produced using both statistical (e.g. Jones 1998) and dynamical forecast schemes (e.g. Hudson et al. 2011). Changes in surface temperature are also often used to assess the impact of climate variability and climate change on Australian society and the environment (CSIRO 2007). It is well known that there is a strong relationship between the Southern Oscillation Index (SOI) and Australian surface temperature (e.g. Coughlan 1979, Halpert and Ropelewski 1992, Jones 1999, Jones and Trewin 2000a, and sea surface temperature (SST) has been successfully used as a predictor in statistical forecasts of Australian seasonal mean temperature (Fawcett et al. 2005). The skill of seasonal forecasts can be verified (e.g. Fawcett et al. 2005, Fawcett 2008, Fawcett and Stone 2010. However, an alternative question can be proposed: to what extent is the seasonal mean temperature potentially predictable for any given season and region? Leith (1973) introduced the concept that the seasonal mean of a climate variable can be considered as a statistical random variable with 'signal' and 'noise' components. Within a threemonth season, the 'noise' component changes on timescales from daily to intraseasonal. Thus, it is more likely to be related to meteorological phenomena that vary significantly within the season. The 'noise' component has been referred to as the intraseasonal component (e.g. Frederiksen and Zheng 2007). The 'signal' is regarded as being constant within the season. Therefore it is more likely to arise from slowly varying (season or longer) boundary or external forcings on the climate s...

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Surface Temperature Patterns Associated with the Southern Oscillation

Cited by 540 publications

References 20 publications

Multidecadal and interannual changes of stationary Rossby waves

Multidecadal and interannual changes of stationary Rossby waves

The impact of El Niño–southern oscillation upon weather regimes over Europe and the North Atlantic during boreal winter

The estimated predictability of seasonal mean Australian surface temperature

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