The "butterfly effect" refers to an atmosphere so unstable that something as gentle as the flapping of a butterfly's wings can affect weather patterns. At the same time, it means that weather forecasts drop in reliability after 10 days since they were made.

After the 10-day period, temperature fluctuations grow strong, giving rise to increases that are then immediately followed by decreases, and vice-versa. But while the effect is felt as soon as 10 days, the pattern can hold for months and years, even decades.

Shaun Lovejoy explained that temperature's natural tendency to go back to its base state is one of the atmosphere's expressions of its memory, with an expression so strong that the effects of fluctuations that occurred a century ago can still be felt today. He added that man-made atmospheric warming has an effect on temperature trends but these still generally follow long-term memory patterns.

In a study published in the journal Geophysical Research Letters, the physics professor from McGill University showed how the atmosphere's massive memory can be directly harnessed to come up with more accurate temperature forecasts compared to what the usual numerical computer models are capable of. According to him, his devised method that could aid in improving poor forecasts for seasons as well as result in better climate projections for long periods of time.

To benefit from the butterfly effect, the method developed by Lovejoy treats weather as random, using historical data to influence a forecast to show more realistic climate conditions. Taking advantage of the method can overcome current limitations holding the standard approach back, in which inaccurate weather representations push computer models to consistently follow their climate models and not what is actually happening out there. Lovejoy's method is also a representation of improvements over other forecasting techniques based on statistics that utilize only the short-term memory the atmosphere has. In fact, he has concluded that his method is more accurate compared to models used by the International Panel on Climate Change, for example.

Lovejoy also used a simpler version of the method he created to explain that the global warming pause that has been recorded since 1998 can be understood using historical data from the atmosphere. According to results, if greenhouse gases are emitted at the same rate after 2000, the global warming pause has a 97.5 percent of ending before 2020.

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