Advances in Earth Science ›› 2009, Vol. 24 ›› Issue (11): 1275-1279. doi: 10.11867/j.issn.1001-8166.2009.11.1275

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Application of BP Neural Network in Paleoclimatic Reconstruction

WANG Hongli 1,2, GU Yajin 3, LIU Jian 1, KUANG Xueyuan 4, TI Ruyuan 1,2   

  1. 1.State Key Laboratory of Lake Science and Enviroment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China; 2. Graduate University of Chinese Academy of Sciences, Beijing100049, China; 3. School of Atmospheric Sciences, Nanjing University of Information Science and Technology, Nanjing210044, China; 4.School of Atmopheric Sciences, Nanjing University,Nanjing210093, China
  • Received:2009-02-04 Revised:2009-09-08 Online:2009-11-10 Published:2009-11-10

WANG Hongli, GU Yajin, LIU Jian, KUANG Xueyuan, TI Ruyuan. Application of BP Neural Network in Paleoclimatic Reconstruction[J]. Advances in Earth Science, 2009, 24(11): 1275-1279.

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.

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