The butterfly effect is one of the chain effects, which means that a seemingly unrelated and very small thing may bring about huge changes.

This effect shows that the results of the development of things are extremely sensitive to initial conditions. Changes in initial conditions will cause great differences in the results. In the winter of 1961, American meteorologist Edward Lorenz was using a computer program to calculate a mathematical model he had designed to simulate the flow of air in the atmosphere. When performing the second calculation, I wanted to save trouble by starting execution directly from the middle of the program and input the data printed from the previous simulation result. However, the calculated result was completely different from the first time.

After inspection, it was found that the reason was that the printed data was 0.506, with an accuracy of only 3 decimal places, but the correct value of the data was 0.506127, with 6 decimal places. In 1963, Lorenz published the paper "Deterministic Aperiodic Flow", analyzing this effect. He also wrote in another journal article that a meteorologist mentioned that if the theory was proven correct, the flapping of a seagull's wings could change the weather forever.

Explanation

In later speeches and papers, he used a more poetic butterfly. The most common explanation of this effect is "A butterfly flaps its wings in Brazil, Can cause a tornado in Texas a month later The butterfly effect is best known in weather, and it can be easily demonstrated in standard weather prediction models, climate scientists James Annan and William Connolly explain. say that chaos is important in the development of weather forecasting methods; models are sensitive to initial conditions

Of course, the presence of an unknown butterfly flapping its wings is not directly relevant to weather forecasting, they added. It takes a long time for a small disturbance to grow to a large size, and we have more immediate uncertainties to worry about, so the direct effects of this phenomenon on weather forecasts tend to be somewhat wrong.