Analysis for a chapter on ENSO in an Encyclopedia

Getting started

Install

This method uses pip
Download the repository: git clone <repo>
Enter the directory: cd ensoclopedia
If you haven't already installed virtualenv: pip install virtualenv
Create your new environment (called 'ensoclopedia'): virtualenv ensoclopedia
Activate your new environment: source ensoclopedia/bin/activate
Install the requirements in the current environment: pip install -r requirements.txt

Download data

To download data: python download_data.py
To download the sea surface height data you need an account. Using the download tab, you need at least 3 batches (alternatively you can use the API):

1. download 1980-1999
   • Product type: Consolidated
   • Vertical resolution: Single level
   • Variable: Sea surface height
   • Year: 1980-1999
   • Month: Select all
     Then press Submit form
2. download 2000-2014
   • Product type: Consolidated
   • Vertical resolution: Single level
   • Variable: Sea surface height
   • Year: 2000-2014
   • Month: Select all
     Then press Submit form
3. download 2015-2024
   • Product type: Operational
   • Vertical resolution: Single level
   • Variable: Sea surface height
   • Year: 2015-2024
   • Month: Select all
     Then press Submit form

Then move all files to the 'data_input' directory: mv sossheig_control_monthly_highres_2D_*_v0.1.nc ensoclopedia/data_input

El Niño Southern Oscillation (ENSO): leading mode of earth’s year-to-year climate fluctuations

Abstract

To come

Links

URL or DOI: to come

Figure 1

a) First principal pattern from an empirical orthogonal function (EOF) analysis of SST anomalies computed over 1980-2024 (linearly detrended); insert indicates the percentage of explained variance of the first five patterns.
b) Time series of GSAT relative to 1961-1990 (12-month moving average); grey shading indicate the 95% confidence interval, red and blue vertical shading indicate respectively El Niño and La Niña events according to the Climate Prediction Center.
c) GSAT anomalies (linearly detrended) regressed on NDJ averaged Niño3.4 rSST normalized anomalies computed over 1980-2024.
d) JJA averaged PRA (linearly detrended) relative to JJA mean climatology regressed on NDJ averaged Niño3.4 rSST normalized anomalies computed over 1980-2024.
e) Same as d) but using NDJ precipitations.

Figure 7

a) ENSO oceanic precursors regressed on NDJ averaged Niño3.4 rSST normalized anomalies computed over 1980-2024; solid black, dashed red and dash-dotted blue lines represent respectively Niño3.4 rSSH anomalies, equatorial Pacific rSSH anomalies and western equatorial Pacific rSSH anomalies.

Definitions

• Anomalies vs. normalized anomalies:
  • anomalies: monthly mean seasonal cycle removed
  • normalized anomalies: monthly mean seasonal cycle removed then divided by monthly standard deviation seasonal cycle
• Detrending:
  • linear: polynomial of degree 1 (computed using the least square fit) removed from time series
  • relative: tropic mean removed at each time step ('r' added to variable name, e.g., rSST for relative sea surface temperature)
• Regions:
  • equatorial Pacific (EP): [5S-5N ; 120E-80W]
  • Niño3.4 (N3.4): [5S-5N ; 120-170W]
  • tropic: [20S-20N ; 0-360E]
  • western equatorial Pacific (WEP): [5S-5N ; 120E-155W]
• Seasons:
  • JJA: June-July-August average
  • NDJ: November-December-January average
• Variables:
  • global surface air temperature (GSAT) from HadCRUT5
  • precipitation (PR) from GPCPv2.3
  • sea surface height (SSH) from ORAS5
  • sea surface temperature (SST) from HadISST
  • 'r' prefix: tropic mean removed at each time step ('r' for relative, e.g., rSST for relative sea surface temperature)