Global Temperature Anomaly by Volterra-Laguerre Model from CO2 Emission, Solar Irradiance, Population, and the Oceans Heat Content

C. Medina-Ramos, D. Carbonel-Olazabal, J. Betetta-Gomez, Irene Tafur-Anzualdo
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Abstract

This paper presents a model based on the first-order Volterra series with Laguerre polynomials to identify the dynamics of global temperature anomalies in the last century. The independent variables to use in the model must be capable of predicting future anomalies, which should be chosen after analyzing the parameters used in significant models to predict global warming. Consequently, based on such studies and the criteria of this research, the selected set of parameters related to the proposed model is the following: the anthropogenic CO2, the total solar irradiance, global population, and ocean heat content. Focused on obtaining that model, the Volterra series, six Laguerre polynomials, unitary normalization, and the correlation factors between variables were applied to identify such anomalies. Results show a mathematical model multivariate techniques consistently can outperform other models like statistical models.Further, the performance of the Volterra-Laguerre model provides evidence that the variables in this proposal can forecast temperature anomalies with an error of less than 5% in the last thirty years of the period study. The proposed model has identified the dynamics of the global temperature anomaly, and the variables reveal that industrial activities and human actions must be part of the reflection to implement international policies that cushion climatic anomalies. Safely, only effective rules will prevent the increasing global warming effect and consequently deterioration of the marine habitat, the alteration of the water cycle, and the reproductive change of vegetables.
基于CO2排放、太阳辐照度、人口和海洋热含量的voltera - laguerre模式的全球温度异常
本文提出了一个基于一阶Volterra级数和Laguerre多项式的模型来识别上个世纪全球温度异常的动态。模型中使用的自变量必须能够预测未来的异常,这些异常应该在分析用于预测全球变暖的重要模型中使用的参数后选择。因此,基于这些研究和本研究的标准,与所建议模式相关的一组选定参数如下:人为CO2、太阳总辐照度、全球人口和海洋热含量。为了获得该模型,我们应用了Volterra级数、六个拉盖尔多项式、酉归一化和变量之间的相关因子来识别这种异常。结果表明,数学模型多变量技术始终优于其他模型,如统计模型。此外,voltera - laguerre模型的性能提供了证据,表明该建议中的变量可以在周期研究的最后三十年中以小于5%的误差预测温度异常。所提出的模型已经确定了全球温度异常的动态,变量表明,工业活动和人类活动必须是反映的一部分,以实施缓解气候异常的国际政策。安全的是,只有有效的规则才能防止全球变暖效应的加剧以及由此导致的海洋栖息地的恶化、水循环的改变和蔬菜繁殖的变化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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