(This article provides scientific theories regarding last month’s “Higher Tides Than Last January | Here’s What to Do” newsletter story.)
Measurements of surface temperatures in the Pacific Ocean this summer show values much higher than average, extending from the Peruvian coast westward to the north coast of Australia.
This warming is associated with weaker-than-normal easterly trade winds in the Hadley atmospheric circulation cell, which normally power the upwelling of the cool, deeper waters on the South American coast. (See Floating Times, 12/17/25, Global Sea Levels, for a description of upwelling off the coast of California, which is powered by currents, not wind as in this case.)
This warming is an extreme of the El Niño-Southern Oscillation (ENSO) cycle, which typically repeats every 2-7 years.
Warmer Water Increases Evaporation at The Surface
As warm, moist air rises, it cools until the water vapor condenses, falls as rain, and generates more heat, forcing the air higher. This process strengthens all rotating storm systems moving across warm regions. We see this during the Atlantic hurricane season when developing storms move across the hot Gulf of Mexico waters. The warmer the water, the faster the storm grows.
While it is essentially certain that this fall and winter will be wet and windy off of western South America, how far these storms stretch North and South is not so clear. The San Francisco Bay area, at 38 degrees north latitude, is in the Ferrel circulation cell, with westerly winds, and is typically about 500 miles north of this rainy region.
However, note the warm (orange) band of water in the top map stretching from the San Francisco area west to Japan. Prevailing westerly winds should pick up considerable moisture and perhaps dump it on us. Thus, it would be prudent for us to prepare for an unusually wet season, but it may not occur.
The El Niño will likely impact the weather in many other regions, e.g., drier than usual in the Amazon basin and Indonesia.
Why Is Weather So Unpredictable?
It is generally accepted that the weather is uncertain, or unpredictable. That is one of the themes of climate deniers: we can’t predict warming of the planet if carbon dioxide production continues at the present rate because weather is unpredictable. However, this argument ignores the fact that global warming has been documented over many decades, not months or years, and is obvious visually by the melting of the world’s glaciers, which occurred over this extended period.
In fact, short- and medium-term weather prediction has become far more accurate in the last hundred years. A huge advance was availability of frequent pictures from satellites of cloud patterns around the Earth.
However, it is still often difficult to predict the weather more than about 10 days into the future. This is puzzling when you consider our ability to predict the behavior of other systems. Classical physics, i.e., non-quantum mechanics, only needed for very small objects, is well understood.
We have developed powerful mathematical techniques and invented fast computers. We can predict the positions of the Earth, moon, sun, and stars, to great precision. The behavior of machines like the pendulum can be calculated by high school students. Engineers can usually predict the performance of even complex machines, like aircraft, although there are sometimes surprises.
Chaos and Butterflies
An important breakthrough in understanding unpredictability was made by accident, appropriately by a meteorologist. In 1961, Professor Edward Lorenz’s department at MIT acquired the first desk computer, the Royal McBee LGP-30. “Desk computer” isn’t a typo, it was the size and weight of a very heavy desk but could store 8,000 numbers in its rotating magnetic drum. Though modest by today’s standards, it was theirs to play with for 24 hours a day.
Together with Margaret Hamilton, he began computing solutions to a small set of equations that he hoped would be the start of rational weather prediction. Though “simple,” these differential equations could not be solved by standard analytical methods and thus, their behavior was not well known.
He soon obtained puzzling results. When he attempted to predict the progress of weather starting from typical conditions, he got very different predictions when the initial conditions differed by very tiny amounts. If these equations really did predict future weather, it was clear why prediction was difficult; you never know the initial conditions with perfect accuracy, so your predictions were going to be correspondingly poor.
While Lorenz didn’t learn how to accurately predict the weather, he energized a neglected area of applied mathematics, initiated by Poincaré in 1890, and developed methods to predict the degree and nature of the unpredictability. This field is known as Chaos Theory and has captured the imagination of the public as well as mathematicians and scientists.
Chaos, Making a New Science by James Gleick was published in 2011 and became a New York Times bestseller. Lorenz popularized the term Butterfly Effect, imagining that a butterfly flapping its wings could trigger a cascade of events that weeks later culminates in the creation of a tornado.
The Double Pendulum Is Chaotic
It’s easy to believe the global atmosphere, covering 200 million square miles of a rotating sphere with mountain ranges, extreme temperature variations, and hundreds of rotating storms, might be chaotic.
But even seemingly simple machines, like the double pendulum, exhibit chaotic behavior. If you look at the entry for double pendulum in Wikipedia, you will see a simulation showing this chaos.
By Michael Konrad
Image: Global sea surface temperature forecast for June-August 2026 (European Center for Medium-Range Weather Forecasts)