兰州大学机构库 >大气科学学院
区域气候模式情景预估的统计释用方法研究
Alternative TitleStatistical bias correction methods of Regional Climate Model Projection
周林
Thesis Advisor张镭
2013-05-30
Degree Grantor兰州大学
Place of Conferral兰州
Degree Name硕士
Keyword统计释用 区域气候模式 SRES A1B情景 平均态 概率分布
Abstract对气候变量进行可信的预估是气候变化影响评估的基础和前提。模式情景预估的统计释用方法研究旨在减小气候模式的各类偏差,是提高模式预估水平的一个重要方面。本文利用区域气候模式系统PRECIS对中国区域在SRES A1B情景下的降水与温度预估进行两种统计释用方法订正的研究分析。两种方法分别基于平均态调整与基于概率分布调整。比较分析两种方法的释用效果,发现:DBS方法对气候变量均值的释用效果与DC方法相近,且对多数地区变量年内循环、概率分布以及极端指标的释用效果相对显著得多,同时存在DC方法释用效果更显著的情况。由于DBS方法对变量进行统计订正时不仅仅考虑均值的调整,更考虑了方差、极值等其他统计特征的调整,DBS方法总体上比DC方法优越。 因此,在影响评估研究中,应根据地区、气候变量以及研究侧重点(例如,平均态或极值)的不同,有必要选择最合适的统计释用方法订正的气候情景预估,作为评估模式的气候输入。
Other AbstractCredible estimates of climate variables are the basis and premise of the assessment of the climate change impacts. The elimination of various types from climate models is the important aspect of improving climate projection. This paper aims to make a research on the different statistical correction methods based on Delta Change and Distribution-based Scailing used to reduce the bias from PRECIS projections of precipitation and temperature, in China under SRES A1B scenario. Compare the results and find that DBS can correct the average biases as well as DC, but it can correct annual cycle, probability distribution and extreme indices over most regions better than DC. There are some circumstances that DC acts better than DBS. Therefore, DBS is better than DC, since it takes not only mean value but also the standard deviation and extreme values into account when correcting the variables. As a result, in the impact assessment researches, it is necessary to choose the better statistical correction method to correct the climate projections as the input of assessment models, based on the different regions, climate variables and research goals.
URL查看原文
Language中文
Document Type学位论文
Identifierhttp://ir.lzu.edu.cn/handle/262010/199652
Collection大气科学学院
Recommended Citation
GB/T 7714
周林. 区域气候模式情景预估的统计释用方法研究[D]. 兰州. 兰州大学,2013.
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