Impact of the Pacific Decadal Oscillation on relationships between temperature and the Arctic Oscillation in the USA in winter

Budikova, Dagmar

doi:10.3354/cr029199

Cited by 22 publications

(15 citation statements)

References 20 publications

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“…Rogers and van Loon, 1979;Ramage, 1983Ramage, , 1986Esbensen, 1984;Barnston et al, 1991;Hurrell, 1995Hurrell, , 1996Ropelewski and Halpert, 1996;Jones et al, 1997;Rogers, 1997;Maheras et al, 1999;Stephenson et al, 2000;Wanner et al, 2001;Trigo et al, 2002;Kryjov, 2002;Kutiel and Benaroch, 2002;Fernandez et al, 2003;Xoplaki et al, 2004;Luterbacher et al, 2004;Budikova, 2005;Gallego et al, 2005;Erlat, 2005, 2006;Kim et al, 2006).…”

Section: Introductionunclassified

Synchronization between the North Sea–Caspian pattern (NCP) and surface air temperatures in NCEP

Tatlı

2007

Intl Journal of Climatology

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Abstract:In this study, synchronous linkages of the North Sea-Caspian pattern (NCP), associated with large-scale surface air temperature series selected from National Centers for Environmental Prediction-National Center of Atmospheric Research reanalysis (NCEP-NCAR) for the region over 50°W-120°E and 0-80°N are investigated via teleconnection patterns based on phase synchronization. Phase synchronization is the process by which two or more cyclic systems (or subsystems) tend to oscillate with a repeating sequence of relative phase angles. Phase synchronization is usually applied to two waveforms of the same frequency with identical phase angles in each cycle. However, the notion of phase synchronization has been introduced as an extension of classical Huygens' synchronization in the case of interacting chaotic systems if there is an integer relationship of frequency, such that the cyclic systems share a repeating sequence of phase angles over consecutive cycles. The results of the phase synchronization method are compared with those of the classical cross-correlation method in cases in which cross-correlation method shows linear spatial cross-dependency, but the phase synchronization yields nonlinear spatial phase dependency. In the analyses, teleconnection patterns derived from phase synchronization are first calculated for raw and de-seasoned data (by removing periodic components in the surface air temperature series) in yearmonth scale and then for the data in seasonal-scale (namely, winter, spring, summer and autumn). Teleconnection patterns calculated by phase synchronization improve the experiential intra-phase relationships between the NCP Index (NCPI) and large-scale surface air temperature series, and ensures that the major modes of mid and high Northern Hemisphere variability in climate are characterized. Statistically significant phase synchronization patterns related to geopotential dipole heights of the NCPI extending over Greenland, the Balkans, Caspian Sea basin, eastern Mediterranean, Turkey and the Middle East, and further phase synchronization patterns over the circumpolar mode, which are utilized by phase interaction of Arctic oscillation (AO) with NCP, are obtained. In addition, significant phase synchronization patterns are also obtained over Tibetan plateau-Mongolia (Gobi Desert) interacting with the Siberian high, Asiatic monsoon and the intertropical convergence zone.

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Section: Introductionunclassified

Synchronization between the North Sea–Caspian pattern (NCP) and surface air temperatures in NCEP

Tatlı

2007

Intl Journal of Climatology

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“…Climate divisions have a wide variety of applications beyond simply identifying regions with similar climate types and variability. In the CONUS, studies have shown the influence of climatic teleconnection indices in each division (Wolter et al 1999;Budikova 2005), which is highly valuable for seasonal climatic forecasting. The CONUS climate divisions are currently used as the zones for the seasonal climatic predictions made by the Climate Prediction Center (CPC).…”

Section: July 2012 B I E N I E K E T a Lmentioning

confidence: 99%

Climate Divisions for Alaska Based on Objective Methods

Bieniek

Bhatt

Thoman

et al. 2012

Journal of Applied Meteorology and Climatology

126

143

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Alaska encompasses several climate types because of its vast size, high-latitude location, proximity to oceans, and complex topography. There is a great need to understand how climate varies regionally for climatic research and forecasting applications. Although climate-type zones have been established for Alaska on the basis of seasonal climatological mean behavior, there has been little attempt to construct climate divisions that identify regions with consistently homogeneous climatic variability. In this study, cluster analysis was applied to monthly-average temperature data from 1977 to 2010 at a robust set of weather stations to develop climate divisions for the state. Mean-adjusted Advanced Very High Resolution Radiometer surface temperature estimates were employed to fill in missing temperature data when possible. Thirteen climate divisions were identified on the basis of the cluster analysis and were subsequently refined using local expert knowledge. Divisional boundary lines were drawn that encompass the grouped stations by following major surrounding topographic boundaries. Correlation analysis between station and gridded downscaled temperature and precipitation data supported the division placement and boundaries. The new divisions north of the Alaska Range were the North Slope, West Coast, Central Interior, Northeast Interior, and Northwest Interior. Divisions south of the Alaska Range were Cook Inlet, Bristol Bay, Aleutians, Northeast Gulf, Northwest Gulf, North Panhandle, Central Panhandle, and South Panhandle. Correlations with various Pacific Ocean and Arctic climatic teleconnection indices showed numerous significant relationships between seasonal division average temperature and the Arctic Oscillation, Pacific-North American pattern, North Pacific index, and Pacific decadal oscillation.

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“…For example, there is evidence for significant teleconnections between the Pacific Decadal Oscillation (PDO), the Atlantic Multidecadal Oscillation (AMO), the Southern Oscillation, Pacific/North American Oscillation (PNA), and the North Atlantic Oscillation (NAO) and precipitation in the northeast United States [e.g., Richman et al, 1991;McCabe et al, 2004;Notaro et al, 2006a]. The surface air temperatures in this region have also been reported to be closely correlated with PNA, the North Atlantic Oscillation (NAO), the Arctic Oscillation (AO), the Quasi-Biennial Oscillation (QBO), and the East PacificNorth Pacific (EP-NP) pattern [e.g., Leathers et al, 1991;Bradbury et al, 2002;Thompson et al, 2002;Budikova, 2005;Notaro et al, 2006a]. In addition, the El Niño-Southern Oscillation has been reported to affect drought and snowfall over the northeast United States [Ropelewski and Halpert, 1986;Griffiths and Bradley, 2007;Kunkel et al, 2009].…”

Section: Introductionmentioning

confidence: 99%

Relationships between large-scale circulation patterns and carbon dioxide exchange by a deciduous forest

Zhang

Huang

et al. 2011

J. Geophys. Res.

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[1] In this study, we focus on a deciduous forest in central Massachusetts and investigate the relationships between global climate indices and CO 2 exchange using eddy-covariance flux measurements from 1992 to 2007. Results suggest that large-scale circulation patterns influence the annual CO 2 exchange in the forest through their effects on the local surface climate. Annual gross ecosystem exchange (GEE) in the forest is closely associated with spring El Niño-Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO), previous fall Atlantic Multidecadal Oscillation (AMO), and previous winter East Pacific-North Pacific (EP-NP) pattern. Annual net ecosystem exchange (NEE) responds to previous fall AMO and PDO, while annual respiration (R) is impacted by previous fall ENSO and Pacific/North American Oscillation (PNA). Regressions based on these relationships are developed to simulate the annual GEE, NEE, and R. To avoid problems of multicollinearity, we compute a "Composite Index for GEE (CI GEE )" based on a linear combination of spring ENSO and PDO, fall AMO, and winter EP-NP and a "Composite Index for R (CI R )" based on a linear combination of fall ENSO and PNA. CI GEE , CI R , and fall AMO and PDO can explain 41, 27, and 40% of the variance of the annual GEE, R, and NEE, respectively. We further apply the methodology to two other northern midlatitude forests and find that interannual variabilities in NEE of the two forests are largely controlled by large-scale circulation patterns. This study suggests that global climate indices provide the potential for predicting CO 2 exchange variability in the northern midlatitude forests.Citation: Zhang, J., L. Wu, G. Huang, and M. Notaro (2011), Relationships between large-scale circulation patterns and carbon dioxide exchange by a deciduous forest,

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Impact of the Pacific Decadal Oscillation on relationships between temperature and the Arctic Oscillation in the USA in winter

Cited by 22 publications

References 20 publications

Synchronization between the North Sea–Caspian pattern (NCP) and surface air temperatures in NCEP

Synchronization between the North Sea–Caspian pattern (NCP) and surface air temperatures in NCEP

Climate Divisions for Alaska Based on Objective Methods

Relationships between large-scale circulation patterns and carbon dioxide exchange by a deciduous forest

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