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Assessment of zonally symmetric and asymmetric components of the Southern Annular Mode using a novel approach

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Abstract

The Southern Annular Mode (SAM) is the main mode of variability in the Southern Hemisphere extra-tropical circulation and it is so called because of its zonally symmetric ring-like shape. However, the SAM pattern actually contains noticeable deviations from zonal symmetry. Thus, the purpose of this study is to describe the zonally asymmetric and symmetric components of the SAM variability and their impacts. We regress monthly geopotential height fields at each level onto the asymmetric and symmetric component of the SAM to create two new indices: Asymmetric SAM (A-SAM) and Symmetric SAM (S-SAM). In the troposphere, the A-SAM is associated with a zonal wave 3 which is rotated a quarter wavelength with respect to the climatological zonal wave 3, is much stronger in the Pacific ocean, where it extends vertically to the stratosphere with an equivalent barotropic structure. On the other hand, the S-SAM is associated with negative geopotential height anomalies over Antarctica surrounded by a zonally symmetric ring of positive geopotential height anomalies. The observed relationship between the El Niño Southern Oscillation and the SAM is fully explained by the A-SAM index. The positive trend of the SAM is present only in its symmetric component. Despite this, the SAM is becoming more zonally asymmetric. The regional impacts of the SAM in temperature and precipitation are strongly affected by its asymmetric component. We show that the asymmetric component of the SAM has its own unique variability, trends and impacts, some of these signals are only evident when the two SAM components are separated.

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Availability of data and materials

The SAM, A-SAM and S-SAM indices are available at http://www.cima.fcen.uba.ar/~elio.campitelli/asymsam/. All data used in this paper is freely available from their respective sources: ERA5 data can be obtained via the Copernicus Climate Data Store (https://cds.climate.copernicus.eu/cdsapp/#!/dataset/reanalysis-era5-pressure-levels-monthly-means/). CMAP Precipitation data provided by the NOAA/OAR/ESRL PSL, Boulder, Colorado, USA, from their Web site at https://psl.noaa.gov/data/gridded/data.cmap.html. The Oceanic Niño Index is available via NOAA’s Climate Prediction Center: https://www.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/detrend.nino34.ascii.txt.

Code availability

A version-controlled repository of the code used to create this analysis, including the code used to download the data can be found at https://github.com/eliocamp/asymsam.

References

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Acknowledgements

NOAA Global Surface Temperature (NOAAGlobalTemp) data provided by the NOAA/OAR/ESRL PSL, Boulder, Colorado, USA, from their Web site at https://psl.noaa.gov/. We are grateful to the reviewers for their constructive comments that improved the manuscript.

Funding

The research was supported by UBACyT20020170100428BA and the CLIMAX Project funded by Belmont Forum/ANR-15-JCL/-0002-01. Elio Campitelli was supported by a PhD Grant from CONICET, Argentina.

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Authors and Affiliations

  1. Departamento de Ciencias de la Atmósfera y los Océanos, Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Buenos Aires, Argentina

    Elio Campitelli, Leandro B. Díaz & Carolina Vera

  2. Centro de Investigaciones del Mar y la Atmósfera (CIMA), CONICET, Universidad de Buenos Aires, Buenos Aires, Argentina

    Elio Campitelli, Leandro B. Díaz & Carolina Vera

  3. CNRS, IRD, CONICET, UBA, Instituto Franco-Argentino para el Estudio del Clima y sus Impactos (IRL 3351 IFAECI), Buenos Aires, Argentina

    Elio Campitelli, Leandro B. Díaz & Carolina Vera

Authors
  1. Elio Campitelli
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  2. Leandro B. Díaz
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  3. Carolina Vera
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Correspondence to Elio Campitelli.

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382_2021_5896_MOESM1_ESM.png

Supplementary figure 1: 50 hPa geopotential height zonal anomalies (meters) of composites of positive and negative SAM months selected using \(\pm 1\) standard deviation as threshhold for the 1979–2018 period. Numbers in the column headers are the spatial correlation between SAM+ and SAM- composites and number of monthly fields used to construct each composite (PNG 357 kb)

Supplementary figure 2: Same as Supplementary figure 1 but for 700 hPa geopotential height (PNG 388 kb)

382_2021_5896_MOESM3_ESM.png

Supplementary figure 3: Regression coefficients of 50 hPa and 700 hPa geopotential height zonal anomalies (meters) onto the standardised timeseries of the leading EOF computed for each season independently for the 1979–2018 period (PNG 215 kb)

382_2021_5896_MOESM4_ESM.png

Supplementary figure 4: Regression of 50 hPa and 700 hPa geopotential height zonal anomalies (meters) onto the standardised timeseries of the leading EOF computed for the periods 1979–1998 and 1999–2018. Spatial correlation between both fields is 0.86 for the 50 hPa fields and 0.76 for the 700 hPa fields (PNG 337 kb)

382_2021_5896_MOESM5_ESM.png

Supplementary figure 5: Lag-correlation between the A-SAM and the S-SAM index at each level. Negative lags imply A-SAM leading S-SAM and vice versa. For the 1979–2018 period (PNG 387 kb)

382_2021_5896_MOESM6_ESM.png

Supplementary figure 6: Fourier spectrum of each timeseries computed as Fourier transform smoothed with modified Daniell smoothers with widths 3 and 5. The shading indicates de 95% confidence area derived by computing the spectrum for 5000 simulated samples from a fitted autorregressive model and (95% of the simulated sampels had an amplitude equal or lower). The light line indicates the theoretical expected amplitude from the autorregressive model. For the 1979–2018 period (PNG 208 kb)

382_2021_5896_MOESM7_ESM.png

Supplementary figure 7: Regression of seasonal mean surface land air and sea temperature anomalies (Kelvin) with SAM, A-SAM and S-SAM for the 1979–2018 period. Black contours indicate areas with p-value smaller than 0.05 controlling for False Detection Rate. Gray areas in Antarctica have more than 15% of missing data (PNG 929 kb)

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Campitelli, E., Díaz, L.B. & Vera, C. Assessment of zonally symmetric and asymmetric components of the Southern Annular Mode using a novel approach. Clim Dyn 58, 161–178 (2022). https://doi.org/10.1007/s00382-021-05896-5

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  • DOI: https://doi.org/10.1007/s00382-021-05896-5

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