节点文献
北半球及中国古气候重建的建模和数值模拟
Modeling and Numerical Simulation of Paleoclimate Reconstruction in the Northern Hemisphere and China
【作者】 王聪；
【导师】 黄宁；
【作者基本信息】 兰州大学 ， 工程力学， 2013， 博士
【摘要】 目前气候重建的研究中,代用数据点稀少及其空间分布的不均匀性对重建过去全球或某一区域的平均温度的变化规律造成很大困难；而且现有重建方法都是基于近千年温度变化空间格局与器测数据时期完全相同的假设。本论文采用适用于计算数据点稀少且空间分布不均匀区域的平均值的区域优化平均法,并利用权威的器测数据资料或代用数据资料,对北半球和中国区域进行了气候重建,分析讨论了数据点个数、空间分布状况和重建方法中各个参数对重建结果的影响。在此基础上,利用区域优化平均法和球谐分析法重建了过去千年北半球平均温度序列,此方法克服了以往研究中假设近千年温度变化空间格局与已有器测数据时期完全相同的缺点。主要工作如下：1.针对目前区域优化平均法中协方差仅适合于求小区域平均值的问题,首先将研究区域进行网格划分得到各小区域的协方差,再通过小区域的协方差得到适合大区域的协方差,利用区域优化平均法和器测数据重建了北半球的平均温度,验证了区域优化平均法的可靠性,并且分析讨论了数据点个数及其空间分布状况对计算结果的影响,结果表明,北半球范围内分布较为合理的10个站点即可较好地重建平均温度,远少于其他方法所需的数据点；2.利用区域优化平均法和代用数据资料Maximum Latewood Density Dataset重建了北半球过去500年的温度序列,结果表明,采用少于前人研究中所用的数据点数目,重建结果与前人研究结果吻合很好；3.利用区域优化平均法和中国气候区域划分新方案,以及《中国地面气候资料日值数据集》中记录的器测数据重建了中国区域平均温度序列；4.针对以往求温度平均值方法中假设各数据点权值仅随空间而不随时间变化和求解协方差时存在的问题,引入球谐分析法得到整个球面温度场,由此通过各数据点温度直接计算得到其协方差,进而利用区域优化平均法得到随时间空间变化的各数据点权值和少于其他方法所需要的数据点重建了过去千年北半球平均温度序列,结果表明,所得结果与前人已有结果吻合很好。
【Abstract】 The rareness and inhomogeneity of the proxy data points bring many difficulties for the climate reconstruction. And the existing methods are based on an assumption that the major spatial patterns of the temperature field obtained in the period having measured data were also the main spatial patterns in the other periods. The optimal regional averaging method that is suitable for obtaining the average temperature of the region where the proxy data is rare and inhomogeneous is applied to reconstruct the average temperature of the northern hemisphere and China in this paper. The influence of the number and spatial distribution of stations and the various paramenters of this method on the reconstruction results are discussed. On the basis, we combine the optimal regional averaging method and the spherical harmonic analysis method to reconstruct the past thousand year average temperature in the northern hemisphere, which overcomes the defect of the past research, which assumped that the major spatial patterns of the temperature field obtained in the period having measured data were also the main spatial patterns in the other periods. And the main works are as follows:1. Targeting at the question of the covariance pattern in the optimal regional averaging method only suitable for getting the average in small region, the covariance pattern for large region is calculated by meshing summation of the covariance pattern for small region. The average temperature of the northern hemisphere is got by the optimal regional averaging method, and the results are verified through comparing with the published data. And the influence of the number and spatial distribution of data points on the results obtained from the optimal averaging method are discussed. And the result shows that10data points with reasonable distribution can get a available average temperature in the reconstruction of the northern hemisphere, which is far less than the data points needed by other method.2. The optimal regional averaging method and the proxy data-Maximum Latewood Density Temperature Sequence Dataset are applied to reconstruct the past500year average temperature in the northern hemisphere, and the results is very well with the previous studies even if the number of data points is less than other studies;3. The optimal regional averaging method, a new scheme for climate regionalization in China regional average and the measured temperature data from Dataset of Surface Climatic Information in China are used to get the average temperature in China4. Aim at the questions that the weights change only with space and do not change with time in past research and the question existed in the solving of covariance pattern, the spherical harmonic analysis method is introduced to calculate the global temperature field. The covariance pattern is caculated directly through the temperatures of all the sites by the spherical harmonic analysis method. And then weights changing with space and time are got by the optimal regional averaging method. The past thounsand year average temperature are reconstructed with the data points less than that of other methods. The results are in good consistent with the results of predecessors.