Modes of variability of Southern Hemisphere atmospheric circulation estimated by AGCMs

Grainger, Simon; Frederiksen, Carsten S.; Zheng, Xunhua; Fereday, David; Folland, Chris K.; Jin, Emilia Kyung; Kinter, James L.; Knight, Jeff; Schubert, Siegfried D.; Syktus, Jozef

doi:10.1007/s00382-009-0720-7

Cited by 11 publications

(15 citation statements)

References 42 publications

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“…Secondary intraseasonal modes of variability have mid-latitude wave-4 (DJF) or wave-3 (JJA) structures reflecting the impact on the seasonal mean of atmospheric blocking, the Madden-Julian oscillation, or the internal instability of the mid-latitude atmospheric flow. Subsequent studies (Zheng et al 2009;Grainger et al 2011b), found very similar modes of variability using ensembles of Atmosphere-only general circulation models (AGCMs). Grainger et al (2013) analysed the modes of variability in CGCMs from the coupled model intercomparison project phase 3 (CMIP3, Meehl et al 2007) dataset.…”

Section: Introductionmentioning

confidence: 82%

Projections of Southern Hemisphere atmospheric circulation interannual variability

2016

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Section: Introductionmentioning

confidence: 82%

Projections of Southern Hemisphere atmospheric circulation interannual variability

2016

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“…Grainger et al [9] showed that for the model given by equation (1), it is possible to estimate covariance matrices for the components of the interannual variability of the seasonal mean. The modes of interannual variability of each component are defined to be the Empirical Orthogonal Functions (eofs) obtained by eigenvalue decomposition [10] of the corresponding covariance matrix, in descending order by variance explained.…”

Section: Methodsmentioning

confidence: 99%

“…For the model 500 hPa geopotential height, covariance matrices for the slow, slow-internal and slow-external components were estimated using the method of Grainger et al [9]. Here, the corresponding modes of variability are referred to as the slow, slow-internal and slow-external eofs, respectively.…”

Section: Examplementioning

confidence: 99%

Estimating components of covariance between two climate variables using model ensembles

Grainger¹,

Frederiksen²,

Zheng³

2011

ANZIAMJ

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The seasonal mean of a climate variable is considered to consist of: (a) slow-external; (b) slow-internal; and (c) intraseasonal components. Using an Analysis of Variance-based method, the interannual variability of the seasonal mean from an ensemble of coupled atmosphere-ocean general circulation model realisations is separable into these components. Here, we propose a method for analysing the covariability of these components between pairs of climate variables. In particular, the method allows for an estimate of the covariability of the projected time series of the modes of variability of one climate variable with the time series of another. To illustrate this, the relationship between time series of the modes of variability of 500 hPa geopotential height and sea surface temperature is examined for an ensemble of coupled general circulation model realisations. The method is applicable to other atmospheric climate variables and datasets.

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“…Although the polar configuration is well represented, the models indicate less intense centres at middle latitudes and reproduce only the observed centres over South Atlantic, South Indian and Southwest Pacific, while ERA5 re‐analysis shows two centres over South Indian Ocean, producing a hemispheric configuration of wave four, and does not have the centre over Southeast Pacific as shown in some model results. However, in intra‐seasonal or seasonal timescales, the first mode of variability in December–January–February at 500 hPa or 200 hPa, from NCEP‐NCAR or ERA Interim re‐analysis, shows centres over South Atlantic, Indian, Southwest Pacific oceans and also over Southeast Pacific (Cavalcanti et al, 2020; Grainger et al, 2011; Osman & Vera, 2020). Another difference is the higher explained variance in the models than in the re‐analysis, which presents around 23% of the total, while the models show variances of around 31% in the second week (NCEP) to 47% in the fourth week (ECMWF).…”

Section: Representation Of Sam and Psa Patterns In The Models Predictionsmentioning

confidence: 99%

Teleconnection patterns in the Southern Hemisphere represented by ECMWF and NCEP S2S project models and influences on South America precipitation

Cavalcanti

Barreto

Alvarez

et al. 2021

Meteorological Applications

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Precipitation predictions in the sub-seasonal timescale are very important for several sectors such as agriculture and energy in regions of South America that are very much affected by precipitation extremes, both excess and lack of rain.The aim of the present study is to investigate the ability of two S2S project models (ECMWF and NCEP) to detect the Southern Hemisphere teleconnections in model hindcasts and the associated anomalous precipitation over South America. The period of analyses is 1999-2010 for the austral summer season (December-January-February). Both models represented adequately the Southern Annular Mode (SAM) pattern in predictions up to 4 weeks ahead and the Pacific South America (PSA) pattern up to 3 weeks. Atmospheric variables of observed extreme cases of SAM were well predicted by the two models, 2 and 3 weeks in advance. The models predicted well atmospheric variables in observed extreme cases of PSA, 2 weeks in advance and with less intensity in the third week. Precipitation anomaly signals associated with these modes were well predicted 2 weeks in advance, although with different intensities. The good ability of the models hindcast in predicting teleconnection patterns and precipitation anomalies over South America provides more confidence to use predictions at sub-seasonal timescale.

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Modes of variability of Southern Hemisphere atmospheric circulation estimated by AGCMs

Cited by 11 publications

References 42 publications

Projections of Southern Hemisphere atmospheric circulation interannual variability

Projections of Southern Hemisphere atmospheric circulation interannual variability

Estimating components of covariance between two climate variables using model ensembles

Teleconnection patterns in the Southern Hemisphere represented by ECMWF and NCEP S2S project models and influences on South America precipitation

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