I provided some explanation about the mechanics of El Niño in an earlier blog:
We measure the strength of an El Niño event by the difference in the surface temperature of the ocean from its normal or average condition at the same time of the year. The figure below shows this difference over the recent week: Warm colors indicate regions that are warmer than their average condition.
The most important region for identifying El Niño is along the equator from 180° longitude east to the west coast of South America. The present temperatures are higher than ever observed this time of year across some of the western part of this region, while near the west coast of South America, waters are warm, but not as warm as in 1997.
Scientists have devised several ways to track and categorize El Niño events. When we try to simplify complicated phenomena to better understand or explain them, we sometimes go too far, and our oversimplifications sometimes lead to misleading conclusions about how natural systems actually behave. Scientists craft questions in light of evidence and their own assumptions, and like anyone else, they are susceptible to folly. Luckily, over time, the scientific process tends to correct our false conclusions.
El Niño might seem easy to define, but every event has its warm water distributed differently. Scientists at JAMSTEC, the Japan Agency for Marine-Earth Science and Technology, along with other climate specialists around the world, describe El Niño events as if they all fit nicely into two types: the canonical, or east Pacific form, and the central Pacific form, often called “Modoki”, which means, “of the other sort”.
Images courtesy of JAMSTEC.
The figure on the left shows an average east Pacific event and the figure on the right shows an average central Pacific event. Events that are more like the east Pacific form tend to be associated with above average rainfall in California and abnormal warmth in eastern Canada and the northeastern United States. Events more like the central Pacific type often have brutally cold periods in winter in the Northeast and below normal precipitation in California. Given these different relationships with weather extremes, it is clearly useful to consider the behaviors of events that behave like the two categories. However, a scientist could incorrectly assume that all events fit cleanly into one type or the other, and then attempt to sort them all that way.
People like the convenience of being able to sort things into simple boxes. Describing El Niño events as one type or the other has become something of a fad in the community of scientists who specialize in climate variability. Yet, a more careful look at the scientific literature suggests that those scientists who like to sort them this way often disagree on the categories into which some past events should be placed. To further complicate matters, my own research has shown that individual events can evolve from one type into the other and even back again over the course of their lifetimes.
One way we track the progress of El Niño events is through instruments placed on buoys anchored in the tropical Pacific Ocean. Measurements from these buoys show the recent state:
The 1997-1998 event looked like the image below at the same time of year:
Note the much higher temperatures observed in 1997 on the east side of the map, but also note the higher temperatures in 2015 in the middle of the map.
December 2009, for comparison, is frequently labeled as a Modoki event:
Conditions in the central Pacific region during 2009 were similar to those presently observed there, but the east Pacific is much warmer this year than in 2009. After comparison with 1997, I have heard a small subset of scientists label the present event as a Modoki El Niño. I find that argument preposterous. The present east Pacific sea surface temperatures seem to fit nicely in between the events of 1997 and 2009. These events illustrate clearly that El Niño occurs across a wide range of possible states. Trying to hammer each event into oversimplified categories is clearly misleading. El Niño is not simply an either/or phenomenon.
I think the example of scientists arguing about how to describe El Niño events provides a good model for teaching science. Textbooks and science classes all too frequently yield the impression that science constitutes a set of clearly defined theories that cleanly address a specific set questions. Real world science is not usually so easy. Developing a theory tends to be a long, arduous process. To begin with, the best questions to ask are not always obvious. Textbooks typically challenge students with pre written questions and with data, but they tend to spend too little time helping them to learn to ask questions themselves. Textbooks also seldom help students learn how to struggle through the process of seeking answers to their own questions in light of evidence. This process is difficult for scientists as well. Many questions scientists raise also turn out to be poorly posed when new evidence is considered, and these questions should be discarded or reposed. Identifying the type of El Niño represented in a given event is one such question. When the whole range of possible configurations of warm water is considered, a much better question is, how does the pattern of warm water in one event compare with the patterns of all past events? This more general question better allows the reality of El Niño diversity to shine through.
The quest for better science education cries out that each student of science be considered as an individual who can both pose questions and propose and test solutions. Experiencing the process of science requires allowing students, when presented with information, to learn to sift through it, crafting questions and testing their answers to those questions against the evidence. The best place for a wise teacher or mentor is to provide the environment, then to review the student’s methods and progress, providing feedback to the student. In other words, I think that the best way for students to learn science is to actually do science, like real scientists do, but at their own academic levels. That does not necessarily imply that students should constantly be performing expensive experiments. Mountains of information are already available, and students need to learn to engage that information to ask questions, to design pathways to answers, and to test those answers.