earth science

Multivariate ENSO index

Multivariate ENSO index is a earth science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Multivariate ENSO index rather than just read about it. In short: The multivariate ENSO index, abbreviated as MEI, is a method used to characterize the intensity of an El Niño Southern Oscillation (ENSO) event. Given that ENSO arises from a complex interaction of a variety of climate systems, MEI is regarded as the most comprehensive index for monitoring ENSO since it combines analysis of multiple meteorological and oceanographic components.

Key takeaways

Multivariate ENSO index belongs to earth science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Multivariate ENSO index to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Multivariate ENSO index from memory before moving on to harder problems.

Reference excerpt

The multivariate ENSO index, abbreviated as MEI, is a method used to characterize the intensity of an El Niño Southern Oscillation (ENSO) event. Given that ENSO arises from a complex interaction of a variety of climate systems, MEI is regarded as the most comprehensive index for monitoring ENSO since it combines analysis of multiple meteorological and oceanographic components.

Overview MEI is determined as the first principal component of six different parameters: sea level pressure, zonal and meridional components of the surface wind, sea surface temperature, surface air temperature and cloudiness using data from the International Comprehensive Ocean-Atmosphere Data Set (ICOADS ). MEI is calculated twelve times per year for each “sliding bi-monthly season”, characterized as January–February, February–March, March–April, and so on. Large positive MEI values indicate the occurrence of El Niño conditions, while large negative MEI values indicate the occurrence of La Niña conditions.

Extended MEI While the National Oceanic and Atmospheric Administration (NOAA) has recorded MEI values from 1950 to the present, various researchers have cited the need for data before 1950 in order to better characterize typical ENSO behavior versus unusual occurrences that may be a result of climate change. According to some sources, it is inadvisable to attempt to calculate MEI before 1950 given that environmental measurements were unreliable during the World Wars and there was a revolution in virtually all meteorological measurement methods on ships during the 1940s. However, a module known as “Extended MEI” or “MEI.ext” has been created that estimates MEI values from as far back as 1871. This was accomplished by using reconstructed data on sea level pressure and sea surface temperature, the two components thought to be most influential to determining MEI. Plots comparing MEI and MEI.ext values have shown that data from both methods are highly correlated, supporting the accuracy and effectiveness of MEI.ext.

Alternate indexes

Southern Oscillation Index The Southern Oscillation Index (SOI) is calculated based on the sea level pressure difference between Tahiti and Darwin, Australia. Despite being used frequently in ENSO studies, it is not considered as reliable as MEI given that it only takes into account one environmental variable.

Niño 3.4 SST

Similar to SOI, Niño 3.4 SST uses one parameter – sea surface temperature – to characterize ENSO. The Niño 3.4 SST region (overlapping regions 3 and 4, see map) consists of temperature measurements from between 5° N – 5° S and 120° – 170° W.

Coupled ENSO Index The Coupled ENSO Index (CEI) uses a combination of both the SOI and Niño 3.4 SST to account for both an atmospheric and oceanic component.

Proxy-based ENSO Index Developed by Braganza, et al., 2009, this index uses coral, tree ring and ice core data to characterize ENSO events from 1525 – 1982. The proxy ENSO index covers a wide area across the Pacific, and includes data from the western and central Pacific, New Zealand, and subtropical North America. Although the proxy ENSO index covers a large time scale of over four centuries, it shows high correlation (> 40%) with the SOI, Niño 3.4 SST and CEI, indicating its accuracy and utility.

References

Worked examples

Example 1 — a first encounter with Multivariate ENSO index

Start with the simplest possible case. Write down what Multivariate ENSO index claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In earth science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Multivariate ENSO index before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Multivariate ENSO index ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Multivariate ENSO index

In research: Multivariate ENSO index appears in earth science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Multivariate ENSO index in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Multivariate ENSO index is common in secondary-school and first-year university syllabi. It links to neighbouring topics Regional climate effects, Tropical meteorology, so understanding it makes those chapters shorter.
In everyday life: Look for Multivariate ENSO index outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

Ask Teacher Smith questions about this articleOpens your AI tutor with a question about “Multivariate ENSO index” →

Affiliate

Preply — study more efficiently by working with a personal tutor. 50% off.

How to study Multivariate ENSO index in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Multivariate ENSO index means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Multivariate ENSO index out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Multivariate ENSO index in simple terms?

Why does Multivariate ENSO index matter?

Because it connects several earth science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Multivariate ENSO index?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Multivariate ENSO index.