收稿日期: 2009-02-04
修回日期: 2009-09-08
网络出版日期: 2009-11-10
基金资助
中国科学院知识创新工程重要方向项目“过去2000年东亚季风气候演变及其与人类相互作用研究”（编号：KZCX2-YW-315）；国家自然科学基金项目“中国千年气候变化数值模拟与机理研究”（编号：40890054）；“近千年来东亚季风年代—世纪尺度变化的模拟与重建资料综合研究”（编号：40672210）；“中国东部近千年来土地利用变化对东亚季风气候影响的模拟研究”（编号：40871007）共同资助.
Application of BP Neural Network in Paleoclimatic Reconstruction
Received date: 2009-02-04
Revised date: 2009-09-08
Online published: 2009-11-10
王红丽，顾亚进，刘健，况雪源，提汝媛 . BP神经网络在古气候序列重建中的应用[J]. 地球科学进展, 2009 , 24(11) : 1275 -1279 . DOI: 10.11867/j.issn.1001-8166.2009.11.1275
Researches on the paleoclimatic variabilities in local regions are seriously restricted by the low resolution and uncertainty of the simulated data at present. In order to apply large-scale climate modeling data to paleoclimate research for small region, an effective downscaling method is urgently needed to be built. For this purpose, a triple-layer fitting model of back propagation (BP) neural network was established using relevant meteorological variables as fitting factors. Based on the fitting model, monthly (January and July) and annual mean series of temperature and precipitation were reconstructed in Anhui-Hubei region during the last millennium. Comparison of the fitting series with the observed and simulated records indicates that the fitting series have good precision and reliability. The signals of climate variation in local region on interannual and interdecadal time scales were captured successfully by the BP neural network model. The results show that this downscaling method improves the capacity of research on paleoclimate variability in local regions using large-scale modeling data.
Key words： BP neural network; Paleoclimate; Fitting; Reconstruction
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