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By Vinca Rosmorduc

CLS, France
 

Altimeter significant wave height tracks
Wave height estimates from Jason altimeter
Click for larger image and explanation

El Niño is one of the most famous phenomena of the ocean-atmosphere system. It involves the Pacific Ocean, changing sea surface height and ocean temperature, and also the atmosphere, through a coupling between the winds and the movement of ocean waters.

The lesson will examine the progression of an El Niño across the Pacific Ocean and its turn back to La Niña, using the power of the global overview made possible by satellite observations. It will also look at some of the impacts of El Niño / La Niña, using data from different satellite sensors.

Map of El Nino impacts.

Typical El Niño impacts
Click for larger image and explanation

Introduction

El Niño is a climatic phenomenon occurring in the Pacific Ocean every three to seven years. During an El Niño event, a few months before Christmas anomalous warm waters accumulate off the coast of Peru. (The name "El Niño" refers to Christmas since El Niño is "the child" in Spanish, i.e. Christ child).

Perturbations of the weather patterns around December over South America have been noticed for centuries. The link with fisheries problems (anchovies rarefaction, in particular) has been also known for a long time. However, it is only at the beginning of the 20th century, that a connection was made with a much larger pattern concerning the whole Pacific, then called "El Niño - Southern Oscillation". El Niño is thus referred to as the "warm phase" of ENSO. The "cold phase" in now called "La Niña". Both have widespread impacts on either side of the Pacific, and also on other regions of the Earth.

Since the Pacific Ocean covers about half of the Earth, remote sensing by satellites has a real interest: the measurements are global and continuous. The El Niño event that occurred in 1997 was a good example of when satellites made a major contribution.

The data used in this lesson come from a variety of sensors (altimeters, infrared and microwave radiometers measuring sea surface temperature or water in the atmosphere, scatterometers measuring sea surface winds, optical sensors for ocean colour…). The data used here are also easy-to-use, merged data. This kind of data is further from the actual measurements, but they provide homogenised, gridded files that can span quite a long time.

Lesson Overview

Aim and objectives

This lesson will:

At the end of the lesson you should be able to

Lesson content

The lesson is divided into 4 sections:

  1. ENSO monitoring through sea level variations opening and examining netCDF structure, and loading individual images, using a Hovmöller diagram to aid interpretation
  2. using sea surface temperature (SST) to monitor ENSO variability, and comparing changes in SST with changes in sea surface height (SSH)
  3. Winds: using wind components to understand the sources of El Niño
  4. Impacts of ENSO: rain and chlorophyll

Data and tools for this lesson

Satellite data sets

See background section 2 to 6 for information about the different techniques which data are presented here.

In this lesson, high-level data will be used, i.e. ready-to-use data, highly processed, in homogenised format, projected on grids (regular or not). This is a quite common transformation of raw data, but it must be noticed that it isn not what most satellites are providing.

Altimetry (Sea Level Anomalies)

dt_upd_global_merged_msla_h_y1997_m01.nc.bz2: gridded global merged (upd=merging all available satellites; at that time, ERS-2 and Topex/Poseidon) Sea level anomalies averaged over one month (year 1997, month: January), produced in delayed-time (i.e. best orbit accuracy). Distributed by Aviso (www.aviso.oceanobs.com) - (one file per month from January 1997 up to December 1999)

Sea Surface Temperature Anomalies

ssta_monthlymean_1997_01.nc.bz2: gridded SST anomalies files (seasonal mean removed) computed from the so-called "Reynold" dataset, using AVHRR infrared satellite SST data (NOAA (http://www.ncdc.noaa.gov/oa/climate/research/sst/oi-daily-information.php) - (one file per month from January 1997 up to December 1999).

Ocean colour (Chlorophyll A content)

L3m_19971101-19971130__GLOB_25_GSM-SWF_CHL1_MO_00.nc.bz2: gridded global chlorophyll from SeaWifs, averaged over one month (Nov. 1997). Data available through the GlobColour ESA project, (www.globcolour.info) - (two different months: Nov. 1997, April 1999).

Wind speed

199709010000-199710010000.nc.bz2: ERS-2 monthly gridded mean wind fields (from ERS-2 scatterometer). Produced and distributed by Cersat/Ifremer (projets.ifremer.fr/cersat/) - (two different months: Sep. 1997, April 1998).

Radiometer data

tmr_199711.nc.bz2: monthly averaged radiometer data and derived correction from Topex/Poseidon’s TMR. The file was extracted from Topex/Poseidon GDRs and mapped using the Basic Radar Altimetry Toolbox (for the Topex/Poseidon GDRs, see www.aviso.oceanobs.com) - (two different months: Nov. 1997, April 1999).

Rain

TOPEX_RAIN_monthly1997_11.nc.bz2: rain rate computed from Topex bi-frequency altimeter over one month (Nov. 1997) Produced and distributed by Cersat/Ifremer. (projets.ifremer.fr/cersat/)(two different months: Nov. 1997, April 1999).

Note that the equivalent of most of these datasets (except for rains and radiometer data) for data less than 2 years old can be retrieved from the MyOcean website (www.myocean.eu).

Bilko tools used in the lesson

modjet3.pal: colour scale to use for sea level anomalies (SLA).

sla_1997-1999bz2.set: stack of the 36 monthly SLA files ; useful to plot a longitude-time diagram.

ssta_1997-1999bz2.set: stack of the 36 monthly SST anomaly files ; useful to plot a longitude-time diagram.

CRW_SST_ANOMALY.pal: colour scale to use for SST anomalies.

Chlorophyll.pal: colour palette to use with chlorophyll data.

SST_Pathfinder.pal: colour palette to use with SST data.

Downloading the lesson

The lesson downloads contain everything you need to complete the lesson. This includes the data and tools listed above, and three PDF documents:

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