Spatial Weighting and Iterative Projection Methods for EOFs

Baldwin, Mark P.; Stephenson, David B.; Jolliffe, Ian T.

doi:10.1175/2008jcli2147.1

Cited by 69 publications

(45 citation statements)

References 35 publications

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“…The standard deviations of DIC (mmol/m The NAO is defined by an empirical orthogonal function (EOF) analysis of sea level pressure (SLP) in the North Atlantic area (308 to 808N and 1008W to 408E). Prior to the EOF analysis, the SLP field is area-weighted to account for the convergence of meridians on a latitudeÁlongitude grid (Baldwin et al, 2009). The leading EOF patterns of the six models ( Fig.…”

Section: Model Evaluationmentioning

confidence: 99%

Variability of the ocean carbon cycle in response to the North Atlantic Oscillation

Keller

Joos

Raible

et al. 2012

Tellus B: Chemical and Physical Meteorology

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A B S T R A C T Climate modes such as the North Atlantic Oscillation (NAO), representing internal variability of the climate system, influence the ocean carbon cycle and may mask trends in the sink of anthropogenic carbon. Here, utilising control runs of six fully coupled Earth System Models, the response of the ocean carbon cycle to the NAO is quantified. The dominating response, a seesaw pattern between the subtropical gyre and the subpolar Northern Atlantic, is instantaneous ( B3 months) and dynamically consistent over all models and with observations for a range of physical and biogeochemical variables. All models show asymmetric responses to NAO ' and NAO ( forcing, implying non-linearity in the connection between NAO and the ocean carbon cycle. However, model differences in regional expression and magnitude and conflicting results with regard to airÁsea flux and CO 2 partial pressure remain. Typical NAO-driven variations are 910 mmol/m 3 in the surface concentration of dissolved inorganic carbon and alkalinity and 98 ppm in the airÁsea partial pressure difference. The effect on the basin-wide airÁsea CO 2 flux is small due to compensating fluxes on the sub-basin scale. Two models show a reduced carbon sink in the north-eastern North Atlantic during negative NAO phases, qualitatively in accordance with the observed decline during a phase of predominantly negative NAO.The results indicate that wind-driven dynamics are the main driver of the response to the NAO, which Á via vertical mixing, upwelling and the associated entrainment of dissolved inorganic carbon and nutrients Á leave an imprint on surface pCO 2 and the airÁsea CO 2 flux as well as on biological export production, pH and the calcium carbonate saturation state. The biogeochemical response to the NAO is predominantly governed by vertical exchange between the surface and the thermocline; large-scale horizontal transport mechanisms are of minor importance.

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Section: Model Evaluationmentioning

confidence: 99%

Variability of the ocean carbon cycle in response to the North Atlantic Oscillation

Keller

Joos

Raible

et al. 2012

Tellus B: Chemical and Physical Meteorology

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“…The leading EOF in the Eurasian sector (208-1308E) is also considered. In calculating the eigenvectors and the associated principal component (PC) time series we have area weighted the data to account for the uneven resolution of the spherical coordinate grid (North et al 1982;Baldwin et al 2009). Hemispheric patterns are produced by regressing the hemispheric zonal wind field onto the sectoral PCs.…”

Section: Data and Analysis Proceduresmentioning

confidence: 99%

Patterns of Wintertime Jet Stream Variability and Their Relation to the Storm Tracks*

Athanasiadis

Wallace

Wettstein

2010

Journal of the Atmospheric Sciences

139

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A new approach is put forward for defining extratropical teleconnection patterns. The zonal wind field at 250 hPa is analyzed separately in the North Atlantic and North Pacific Ocean sectors during the winter season (December-March). Teleconnectivity of this field is found to be particularly strong. EOF analysis of the zonal wind field yields patterns that (i) are robust with respect to the range of frequencies included in the data, (ii) relate clearly to the position of the climatological-mean jets, and (iii) are broadly consistent with their traditionally defined counterparts in terms of climatic impacts. The patterns are characterized by a northsouth shifting or an extension/retraction of the eddy-driven jet in its exit region and similar changes at the entrance region of the subtropical jet. The patterns also reflect the degree of separation between the subtropical and eddy-driven jets. Atlantic EOFs 1 and 2 are counterparts of the North Atlantic Oscillation (NAO) and eastern Atlantic pattern, respectively, while Pacific EOF 1 is the counterpart of the Pacific-North America (PNA) pattern. Pacific EOF 2, a pattern that has not been previously noted, has a pronounced impact on the jet configuration and precipitation over the western coast of North America. This pattern may be of particular interest for precipitation forecasting applications. Atlantic EOF 1 exhibits a long decorrelation time and strong negative skewness. The relation between these jet variability patterns and the stormtrack variability is examined, including the dynamical interaction between baroclinic waves and the jets. In each sector, the eddy forcing is found to maintain the respective jet anomalies.

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“…Although for simplicity we neglected spatial and temporal weighting in constructing the theory in Section 3, it is straightforward to include them, as Kuroda (1998) and Baldwin et al (2009) have done. Inclusion of spatial weighting, in particular, is important to compensate for inhomogeneity in a dataset.…”

Section: Examplementioning

confidence: 99%

“…The conversion from a spatial to a temporal array is possible because the basic equations relating pattern vectors and time coefficients have complete symmetry, or a dual relationship (Kuroda 1998). Baldwin et al (2009) have more recently proposed performing EOF analyses by this iteration technique. As EOF is a special case of SVD analysis, it is sufficient for us to start from the theory of SVD analysis and construct a new method to extract common variability from multiple datasets, called Multivariable Maximum Covariance Analysis (MMCA).…”

Section: Introductionmentioning

confidence: 99%

A Simple Multivariable Maximum Covariance Analysis Method

Kuroda¹,

Kodera

2017

Journal of the Meteorological Society of Japan

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This paper proposes a new simple method of multivariable maximum covariance analysis (MMCA) for extracting common variability from multiple (more than two) datasets that expands the singular value decomposition analysis method. The method is based on iteration of a recurrence equation derived by a dual relationship between pattern vectors and time coefficients. Two approaches of the method are proposed, one using the extreme of a summation of covariances (sum MMCA) and the other using the product of covariances (product MMCA). Both approaches are demonstrated by successfully extracting the variability related to the Arctic Oscillation from three monthly-mean meteorological datasets. The method is useful because it is easily programmed and is computationally inexpensive. The method can be applied to an arbitrary number of datasets, although a complete set of the product MMCA method cannot be applied to an even number of datasets.

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Spatial Weighting and Iterative Projection Methods for EOFs

Cited by 69 publications

References 35 publications

Variability of the ocean carbon cycle in response to the North Atlantic Oscillation

Variability of the ocean carbon cycle in response to the North Atlantic Oscillation

Patterns of Wintertime Jet Stream Variability and Their Relation to the Storm Tracks*

A Simple Multivariable Maximum Covariance Analysis Method

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