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Fuzzy Models: Easier to Understand and an Easier Way to Handle Uncertainties in Climate Change Research

机译:模糊模型:更容易理解和处理气候变化研究中不确定性的更简单的方法

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Greenhouse gas emission scenarios (through 2100) developed by the Intergovernmental Panel on Climate Change when converted to concentrations and atmospheric temperatures through the use of climate models result in a wide range of concentrations and temperatures with a rather simple interpretation: the higher the emissions the higher the concentrations and temperatures. Therefore the uncertainty in the projected temperature due to the uncertainty in the emissions is large. Linguistic rules are obtained through the use of linear emission scenarios and the Magicc model. These rules describe the relations between the concentrations (input) and the temperature increase for the year 2100 (output) and are used to build a fuzzy model. Another model is presented that includes, as a second source of uncertainty in input, the climate sensitivity to explore its effects on the temperature. Models are attractive because their simplicity and capability to integrate the uncertainties to the input and the output.
机译:通过使用气候模型转换为浓度和大气温度的温室气候变化开发的温室气体发射场景(通过2100),导致各种浓度和温度,具有相当简单的解释:排放量越高越高浓度和温度。因此,由于排放中的不确定性导致的预计温度的不确定性很大。通过使用线性发射场景和MagicC模型获得语言规则。这些规则描述了浓度(输入)与年度2100(输出)的温度增加之间的关系,并用于构建模糊模型。提出了另一种模型,其包括输入的第二个不确定性,气候敏感性探索其对温度的影响。模型很有吸引力,因为它们的简单性和能力将不确定性集成到输入和输出。

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