Simplified global climate change prediction method

Abstract

The invention discloses a simplified climatic change prediction method. The method comprises the following steps: firstly, obtaining annual average net radiation force according to heat absorption and heat release of the earth, and performing regression analysis according to the change of annual average forced radiation and the change of average temperature to obtain a simplified linear model; considering ocean temperature changes, carrying out data fitting on existing ocean temperature data, predicting future global ocean temperature changes through an improved neural network model, and finally carrying out polynomial fitting by combining the ocean temperature changes with earth surface temperature changes to determine global climate changes.

Description

technical field [0001] The invention relates to the application field of climatology, in particular to a simplified climate change prediction method Background technique [0002] The main cause of global warming is that humans have used fossil fuels extensively in the past century and emitted a large amount of greenhouse gases. The existing forecasting methods are mainly chaotic-artificial neural network, which analyzes the change process of temperature, precipitation and runoff in the basin in recent years to predict climate change and analyze the trend. However, the climate is a long-term average state, and there is little change in a short period of time. Global warming is a global problem on the climate scale. The study of global temperature change from the perspective of climate requires long-term accumulation of observations on a global scale, rather than a single While studying the climate change in a certain region within a certain period of time, the neural network...

AI Technical Summary

Problems solved by technology

However, the climate is a long-term average state, and there is little change in a short period of time. Global warming is a global problem on the climate scale. The study of global temperature change from the perspective of climate requires long-term accumulation of observations on a global scale, rather than a single To study climate change in a certain region within a certain period of time, while the neural network model has the problem of predicting the distortion of long-term results, the existing prediction methods have the problem of inaccurate prediction

In addition, the traditional method does not consider factors such as ocean heat absorption, which has a great impact on global climate change.

At the same time, the parameters of traditional methods are complex, which is not conducive to non-professionals to understand and understand the situation of global climate change

Images