Bayesian neural network modeling of tree-ring temperature variability record from the Western Himalayas

Nonlinear Processes in Geophysics

Volumn 18, Issue 4, 2011, Pages 515-528

  Tiwari, R K ^a   Maiti, S ^b  

^a NATIONAL GEOPHYSICAL RESEARCH INSTITUTE   (India)

^b INDIAN INSTITUTE OF GEOMAGNETISM   (India)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTIFICIAL NEURAL NETWORK; BAYESIAN ANALYSIS; COMPUTER SIMULATION; DATA SET; MARKOV CHAIN; MONTE CARLO ANALYSIS; NUMERICAL MODEL; PALEOTEMPERATURE; PROBABILITY; TEMPERATURE ANOMALY; THEORETICAL STUDY; TREE RING;

HIMALAYAS;

EID: 80051695913     PISSN: 10235809     EISSN: 16077946     Source Type: Journal    
DOI: 10.5194/npg-18-515-2011     Document Type: Article

References (43)

1. Andrieu, C., de Freitas, N., Doucet, A., and Jordan, M.: An introduction to MCMC for machine learning, Mach. Learn., 50, 5-43, 2003.
2. Bishop, C. M.: Neural networks for pattern recognition, Oxford University Press, 1995.
3. Blanco, J. A., Seely, B.,Welham, C., Kimmins, J. P., and Seebacher, T. M.: Testing the performance of FORECAST, a forest ecosystem model, against 29 years of field data in a Pseudotsuga menziesii plantation, Can. J. Forest Res., 37, 1808-1820, 2007.
4. Boreux, J.-J., Naveau, P., Guin, O., Perreault, L., and Bernier, J.: Extracting a common high frequency signal from Northern Quebec black spruce tree-rings with a Bayesian hierarchical model, Clim. Past, 5, 607-613, doi:10.5194/cp-5-607-2009, 2009.
5. Bracuning, A.: Climate history of Tibetan Plateau during the last 1000 years derived from a network of juniper chronologies, Dendrochronologia, 19, 217-137, 2001.
6. Briffa, K. R., Osborn, T. J., Schweingruber, F. H., Harris, I. C., Jones, P. D., Shiyatov, S. G., and Vaganov, E. A.: Low frequency temperature variations from northern tree ring density network, J. Geophys. Res., 106, 2929-2941, 2001.
7. Cook, E. R.: A time series approach to tree-ring standardization, PhD Dissertation, Univ. of Arizona, Tucson, 1985.
8. Cook, E. R., Krusic, P. J., and Jones, P. D.: Dendroclimatic signals in long tree-ring chronologies from the Himalayas of Nepal, Int. J. Climatol., 23, 707-732, 2003. (Pubitemid 36803916)
9. Crowley, T. J.: Causes of climate change over the past 1000 years, Science 289(5477), 270-277, 2000. (Pubitemid 30469900)
10. D'Odorico, P., Revelli, R., and Ridolfi, L.: On the use of neural networks for dendroclimatic reconstructions, Geophys. Res. Lett., 27, 791-794, 2000.
11. Duane, S., Kennedy, A. D., Pendleton, B., and Roweth, D.: Hybrid Monte Carlo, Phys. Lett. B, 195, 216-222, 1987.
12. Esper, J., Schweingruber, F. H., and Winiger, M.: 1300 years of climatic history for western Central Asia inferred from tree rings, Holocene, 12, 267-277, 2002. (Pubitemid 34409278)
13. Esper, J., Shiyatov, S. G., Mazepa, V. S., Wilson, R. J. S., Graybill, D. A., and Funkhouser, G.: Temperature sensitive Tien Shan tree ring chronologies show multi-centennial growth trends, Clim. Dynam., 21, 699-706, 2003. (Pubitemid 38098395)
14. Fritts, H. C.: Bristtecone pine in the White Mountains of California; growth and ring-width characteristics. Papers of the laboratory of tree-ring research 4, University of Arizone Press, Tucson, 44 pp., 1969.
15. Fritts, H. C.: Reconstructing large-scale climatic patterns from treering data, University of Arizona Press, Tucson, 567 pp., 1991.
16. Fuller, W. A.: Introduction to the statistical time series, John Wiley and Sons, New York, 1976.
17. Gramlich, L. G. and Brubaker L. B.: Reconstruction of annual temperature (1590-1979) for Longmire, Washington, derived tree rings, Quaternary Res., 25, 223-234, 1986.
18. Hastings, W. K.: Monte Carlo sampling methods using Markov Chain and their applications, Biometrika, 57, 97-109, 1970.
19. Helama, S., Makarenko, N. G., Karimova, L. M., Kruglun, O. A., Timonen, M., Holopainen, J., Meriläinen, J., and Eronen, M.: Dendroclimatic transfer functions revisited: Little Ice Age and Medieval Warm Period summer temperatures reconstructed using artificial neural networks and linear algorithms, Ann. Geophys., 27, 1097-1111, doi:10.5194/angeo-27-1097-2009, 2009.
20. Holmes, R. L.: A computer-assisted quality control program, Tree- Ring Bulletin, 43, 69-78, 1983.
21. Jones, P. D. and Mann, M. E.: Climate over past millennia, Rev. Geophys., 42(2), RG2002, doi:10.1029/2003RG000143, 2004. (Pubitemid 39174704)
22. Khan, M. S. and Coulibaly, P.: Bayesian neural network for rainfall-runoff modeling, Water Resour. Res., 42, W07409, doi:10.1029/ 2005WR003971, 2006. (Pubitemid 44300476)
23. Lindholm, M., Lehtonen, H., Kolstrom,T., Merilainen, J., Eronen, M., and Timonen, M. Climatic signals extracted from ring-width chronologies of Scots pine from the Northern, Middle and Southern parts of the boreal forest belt in Finland, Silva Fenn., 34, 317-329, 2000.
24. May, R.: Simple mathematical models with very complicated dynamics, Nature, 261, 459-467, 1976.
25. Metropolis, N., Rosenbluth, A.W., Rosenbluth, M. N., Teller, A. H., and Teller, E.: Equations of state calculations by fast computing machines, J. Chem. Phys., 21, 1087-1091, 1953.
26. Maiti, S. and Tiwari, R. K.: A hybrid Monte Carlo method based artificial neural networks approach for rock boundaries identification: A case study from the KTB bore hole, Pure Appl. Geophys., 166, 2059-2090, doi:10.1007/s00024-009- 0533-y, 2009.
27. Maiti, S. and Tiwari, R. K. Automatic discriminations among geophysical signals via the Bayesian neural networks approach, Geophysics, 75(1) E67-E78, doi:10.1190/1.3298501, 2010a.
28. Maiti, S. and Tiwari, R. K.: Neural network modeling and an uncertainty analysis in Bayesian framework: A case study from the KTB borehole site, J. Geophys. Res., 15, B10208, doi:10.1029/2010JB000864, 2010b.
29. Maiti, S., Tiwari, R. K., and Kuempel, H.-J.: Neural network modeling and classification of litho-facies using well log data: A case study from KTB borehole site, Geophys. J. Int., 169, 733-746, doi:10.1111/j.1365-246X.2007. 03342.x, 2007. (Pubitemid 46605007)
30. Mihalakakou, G., Santamouris, M., and Asimakopoulos, D.: Modeling ambient air temperature time series using neural networks, J. Geophys. Res., 103(D16), 19509-19517, 1998. (Pubitemid 128700048)
31. Monge Sanz, B. M. and Medrano Marqués, N. J.: Total ozone time series analysis: A neural network model approach, Nonlin. Processes Geophys., 11, 683-689, doi:10.5194/npg-11-683-2004, 2004. (Pubitemid 40162127)
32. Nabney, I. T.: Netlab Algorithms for pattern recognition, Springer, New York, 2004.
33. Neal, R. M.: Bayesian learning for neural networks, Springer- Verlag, Inc., New York, 1993.
34. Poulton, M.: Computational neural networks for geophysical data processing, Pergamon, 2001.
35. Rumelhart, D. E., Hinton, G. E., and Williams, R. J.: Learning representations by back-propagating errors, Nature, 323, 533-536, 1986. (Pubitemid 16025374)
36. Tarantola, A.: Inverse Problem Theory, Elsevier, New York, 1987.
37. Tiwari, R. K. and Srilakshmi, S.: Periodicities and Non-stationary modes in tree rings temperature variability record of the western Himalayas by multi-taper and wavelet spectral analyses, Curr. Sci. India, 97(5), 705-709, 2009.
38. Van der Bann, M. and Jutten, C.: Neural networks in geophysical applications, Geophysics, 65, 1032-1047, 2000.
39. Yadav, R. R., Park, W. K., Singh, J., and Dubey, B.: Do the western Himalayas defy global warming?, Geophys. Res. Lett., 31, 1-5, 2004.
40. Zhang, J. and Crowley, T. J.: Historical climate records in China and reconstruction of past climates (1470-1970), J. Climate, 2, 833-849, 1989.
41. Zhang, G., Patuwo, B. E., and Hu, M. Y.: Forecasting with artificial neural networks: The state of the art, Int. J. Forecast., 14, 35-62, 1998. (Pubitemid 128340470)
42. Willmott, C. J.: On the validation of models, Phys. Geogr., 2, 184-194, 1981.
43. Woodhouse, C. A.: Artificial neural networks and dendroclimatic reconstructions: An example from the Front Range, Colorado, USA, Holocene 9, 521-529, 1999.