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• The ENSO state is characterised here using NINO3.4 - an index defined as the area-average sea surface temperature (SST) anomalies in the region (170W-120W, 5S-5N). The period used as reference for the analysis is 1970 - 2022; the selection of ENSO years is based on average SST anomalies in December-February (DJF).

• Data used in this analysis is from ERA5 (ERA5, Hersbach et al., 2020).

• Here, the thresholds for selection of El Niño and la Niña years are, respectively, the upper or lower quartile of the sample of years in the reference period. These correspond to, approximately, for winter (DJF, shown for ND & JA): El Niño (n34 > 0.6), La Niña (n34 < - 0.7)

• The selected ENSO years for ND and JF (labelled by the year in which January falls) are:

  • El Niño  :   1973, 1983, 1987, 1988, 1992, 1995, 1998, 2003, 2007, 2010, 2015, 2016, 2019

  • La Niña :   1971, 1974, 1976, 1981, 1985, 1989, 1996, 1999, 2000, 2008, 2011, 2012, 2021

• The composites above are a weighted average of the MSLP fields in the years above, where the weights are the strength of the NINO3.4 SST anomalies.

• The regression and composite calculation to obtain the MSLP composites is performed as follows:

  • If Z’ (x, t) is the anomaly of a meteorological field defined at spatial coordinates x and time t, and s3.4 (t) is the SST anomaly in the NINO3.4 region, the regression pattern of Z’ against s3.4 is defined by:
    

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    \begin{equation*} R(\underline{x}) = \frac{\sum_{t} s3.4(t)\cdot Z'(\underline{x},t)}{\sum_{t} s3.4(t)^2} \end{equation*}

    
    

  • The regression pattern is computed separately for two sub-samples including periods when the s3.4 seasonal-mean value is either in upper third (El Niño years) or lower third (La Niña years) of the distribution. The following two-month periods are used for the regression analysis: November-December (ND) and January-February (JF); for the selection of ENSO years, average SST anomalies in DJF are used, as described above.

  • For both El Niño and La Niña years, composite anomalies are defined by: 
    

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    \begin{equation*} C(\underline{x}) = \mu3.4\cdot R(\underline{x}) \end{equation*}

    where μ3.4 is the average value of the Nino3.4 anomaly s3.4 over the selected years.
    

This method is further discussed in Molteni & Brookshaw 2023.

These MSLP anomalies are in units of hPa.

The NINO3.4 anomalies printed above each map are DJF means, which were used for the selection. 

El Niño years, ND anomaly, MSLP	La Niña years, ND anomaly, MSLP Image Modified
El Niño years, JF anomaly, MSLP	La Niña years, JF anomaly, MSLP

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