SGER: Statistical Study of Global Climate Change and Sea Level

这项统计研究是基于一种分析窗户音爆破损的响应概率函数技术进行的。在响应概率密度函数技术中，将响应表示为随机变量的乘积。然后检查每个因素的概率密度函数，看看是否可以通过使用某些类型的概率密度函数的特定特征来进行任何简化，例如高斯或对数正态分布。响应概率密度函数技术代表了一种分析气候变化引起的海平面上升问题的新方法。在这一分析中，海平面上升是要分析的反应。它被表示为三个随机变量的乘积(1)温室气体排放吨数(2)每百万吨气温上升和(3)每度气温上升海平面上升。在这项研究中，获得了关于这三个随机变量的每个变量的数据，并检验了概率密度函数。如果这些概率密度函数易于简化，问题就会变得容易些。如果不是，可以使用卷积技术来找出概率。如果响应概率密度函数技术适用，将用于对全球变暖情况下海平面不同幅度上升的概率进行初步估计。广泛影响：海平面不同幅度上升的概率是气候变化问题中的关键问题。在规划适当的行动时，必须回答这些问题。如果要在成本效益的基础上处理气候变化问题，则在估计温室气体限制或沿海适应的期望值时必须考虑这些概率。改进对海平面上升概率的估计可能会指出需要对关键参数进行更多的观测。

This statistical study is based on a response probability function technique developed to analyze sonic boom breakage of windows. In the response probability density function technique, the response is framed as the product of random variables. The probability density function of each factor is then examined to see if any simplifications can be made by using the specific characteristics of some types of probability density functions, such as Gaussian or lognormal. The response probability density function technique represents a new approach to the problem of analyzing sea level rise from climate change. In this analysis, the sea level rise is the response to be analyzed. It is expressed as the product of three random variables (1) the tons of greenhouse gas emissions (2) the temperature rise per million tons and (3) the sea level rise per degree of temperature rise. In this study, data are obtained on each of the three random variables and the probability density functions are examined. If these probability density functions lend themselves to simplifications, it will make the problem easier. If not, convolution techniques can be used to find the probabilities. If the response probability density function technique is applicable it will be used to make preliminary estimates of the probabilities of various amounts of sea level rise under global warming scenarios.Broader Impacts: The probabilities of various amounts of sea level rise are key questions in the climate change problem. These questions must be answered in planning appropriate actions. If climate change is to be approached on a cost-benefit basis, then the probabilities must be considered in estimating the expected value of greenhouse gas limitations or coastal adaptations. Improving the estimation of sea level rise probabilities may point out the need for additional observations of key parameters.