At present, climate change is a “hot topic”, not only in scientific analyses and papers by researchers, but also in wider discussions among economists and policy-makers.

In whatever area you are, the role of modeling appears crucial in order to understand the behavior of the climate system and to grasp its complexity. Furthermore, once validated on the past, a model represents the only chance to make projections about the future behavior of the climate system.

Keywords

Dioxide Convection Assimilation Turkey Vorticity 

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References

  1. Antonić, O., Križan, J., Marki, A., & Bukovec, D. (2001). patio-temporal interpolation of climatic variables over egion of complex terrain using neural networks. Ecological Modelling, 138, 255–263CrossRefGoogle Scholar
  2. Boulanger, J.-P., Martinez, F., & Segura, E. C. (2006). Projection of future climate change conditions using IPCC simulations, neural networks and Bayesian statistics. Part I: Temperature mean state and seasonal cycle in South America. Climate Dynamics, 27, 233–259CrossRefGoogle Scholar
  3. Boulanger, J.-P., Martinez, F., & Segura, E. C. (2007). Projection of future climate change conditions using IPCC simulations, neural networks and Bayesian statistics. Part II: Precipitation mean state and seasonal cycle in South America. Climate Dynamics, 28, 255–271CrossRefGoogle Scholar
  4. Breiman, L. (1996). Bagging predictors. Machine Learning, 24, 123–140Google Scholar
  5. Cannon, A. J. (2006a). Nonlinear principal predictor analysis: Application to the Lorenz system. Journal of Climate, 19, 579–589CrossRefGoogle Scholar
  6. Cannon, A. J. (2006b). A hybrid neural network/analog model for climate downscaling. Proceedings of the 2006 IEEE World Conference on Computational Intelligence. IEEE: Vancouver, CanadaGoogle Scholar
  7. Cannon, A. J., & Whitfield, P. H. (2002). Downscaling recent streamflow conditions in British Columbia, Canada using ensemble neural network models. Journal of Hydrology, 259, 136–151CrossRefGoogle Scholar
  8. Casaioli, M., Mantovani, R., Proietti Scorzoni, F., Puca, S., Speranza, A., & Tirozzi, B. (2003). Linear and nonlinear postprocessing of numerical forecasted surface temperature. Nonlinear Processes in Geophysics, 10, 373–383Google Scholar
  9. Cavazos, T. (2000). Using self-organizing maps to investigate extreme climate events: An application to wintertime precipitation in the Balkans. Journal of Climate, 13, 1718–1732CrossRefGoogle Scholar
  10. Cavazos, T., & Hewitson, B. C. (2005). Performance of NCEP- NCAR reanalysis variables in statistical downscaling of daily precipitation. Climate Research, 28, 95–107CrossRefGoogle Scholar
  11. Cavazos, T., Comrie, A. C., & Liverman, D. M. (2002). Intrasea- sonal variability associated with Wet Monsoons in Southeast Arizona. Journal of Climate, 15, 2477–2490CrossRefGoogle Scholar
  12. Corti, S., Molteni, F., & Palmer, T. N. (1999). Signature of recent climate change in frequencies of natural atmospheric circulation regimes. Nature, 398, 799–802CrossRefGoogle Scholar
  13. Dibike, Y. B., & Coulibaly, P. (2006). Temporal neural networks for downscaling climate variability and extremes. Neural Networks, 19, 135–144CrossRefGoogle Scholar
  14. Evans, E., Bhatti, N., Kinney, J., Pann, L., Peñla, M., Yang, S.- C., Kalnay, E., & Hansen, J. (2004). RISE undergraduates find that regime changes in Lorenz's model are predictable. Bulletin of the American Meteorological Society, 85, 520– 524CrossRefGoogle Scholar
  15. Gutierrez, J. M., Cano R., Cofiño, A. S., & Sordo, C. (2005). Analysis and downscaling multi-model seasonal forecast in Peru using self-organizing maps. Tellus, 57A, 435–447Google Scholar
  16. Haylock, M. R., Cawley, G. C., Harpham, C., Wilby, R. L., & Goodess, C. M. (2006). Downscaling heavy precipitation over the United Kingdom: A comparison of dynamical and statistical methods and their future scenarios. International Journal of Climatology, 26, 1397–1415CrossRefGoogle Scholar
  17. Hewitson, B. C., & Crane, R. G. (2006). Consensus between GCM climate change projections with empirical downscaling: Precipitation downscaling over South Africa. International Journal of Climatology, 26, 1315–1337CrossRefGoogle Scholar
  18. Houghton, J. T., Ding, Y., Griggs, D. J., Noguer, M., van der Linden, P. J., Dai, X., Maskell, K., & Johnson, C. A. (Eds.). (2001). Climate change 2001: The scientific basis (pp. 881). Cambridge: Cambridge University PressGoogle Scholar
  19. Kalnay, E., Corazza, M., & Cai, M. (2002). Are bred vectors the same as Lyapunov vectors?. Proceedings of the Symposium on Observations, Data Assimilations, and Probabilistic Prediction, 82nd Annual Meeting of the American Meteorological Society. AMS, CD ROM: Orlando, FLGoogle Scholar
  20. Khan, M. S., Coulibaly, P., & Dibike, Y. (2006). Uncertainty analysis of statistical downscaling methods. Journal of Hydrology, 319, 357–382CrossRefGoogle Scholar
  21. Knutti, R., Stocker, T. F., Joos, F., & Plattner, G.-K. (2003). Probabilistic climate change projections using neural networks. Climate Dynamics, 21, 257–272CrossRefGoogle Scholar
  22. Knutti, R., Joos, F., Müller, S. A., Plattner, G.-K., & Stocker, T. F. (2005). Probabilistic climate change projections for CO2 stabilization profiles, Geophysical Research Letters, 32(L20707). DOI: 10.1029.2005GL023294Google Scholar
  23. Leloup, J. A., Lachkar, Z., Boulanger, J.-P., & Thiria, S. (2007). Detecting decadal changes in ENSO using neural networks. Climate Dynamics, 28, 147–162CrossRefGoogle Scholar
  24. Lorenz, E. N. (1963). Deterministic non-periodic flow. Journal of Atmospheric Sciences, 20, 130–141CrossRefGoogle Scholar
  25. Marzban, C. (2003). A neural network for post-processing model output: ARPS. Monthly Weather Review, 131, 1103–1111CrossRefGoogle Scholar
  26. Marzban, C., Sandgathe, S., & Kalnay, E. (2005). MOS, Perfect Prog, and Reanalysis Data. Monthly Weather Review, 134, 657–663CrossRefGoogle Scholar
  27. Mehrotra, R., & Sharma, A. (2005). A nonparametric nonhomo- geneous hidden Markov model for downscaling of multisite daily rainfall occurrences. Journal of Geophysical Research, 110(D16108). DOI: 10.1029/2004JD005677Google Scholar
  28. Miksovsky, J., & Raidl, A. (2005). Testing the performance of three nonlinear methods of time series analysis for prediction and downscaling of European daily temperatures. Nonlinear Processes in Geophysics, 12, 979–991Google Scholar
  29. Moriondo, M., & Bindi, M. (2006). Comparisons of temperatures simulated by GCMs, RCMs and statistical downscal- ing: Potential application in studies of future crop development. Climate Research, 30, 149–160CrossRefGoogle Scholar
  30. Olsson, J., Uvo, C. B., & Jinno, K. (2001). Statistical atmospheric downscaling of short-term extreme rainfall by neural networks. Physics and Chemistry of the Earth (B), 26, 695–700CrossRefGoogle Scholar
  31. Palmer, T. N. (1999). A nonlinear dynamical perspective on climate prediction. Journal of Climate, 12, 575–591CrossRefGoogle Scholar
  32. Pasini, A. (2005). From observations to simulations. A conceptual introduction to weather and climate modelling (201 pp.). Singapore: World ScientificGoogle Scholar
  33. Pasini, A. (2007). Predictability in past and future climate conditions: a preliminary analysis by neural networks using unforced and forced Lorenz systems as toy models. Proceedings of the 5th Conference on Artificial Intelligence and its Applications to Environmental Sciences, 87th Annual Meeting of the American Meteorological Society. AMS, CD ROM: San Antonio, TXGoogle Scholar
  34. Pasini, A., & Pelino, V. (2005). Can we estimate atmospheric predictability by performance of neural network forecasting? The toy case studies of unforced and forced Lorenz Models. Proceedings of the IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (pp. 69–74). IEEE: Giardini Naxos, ItalyGoogle Scholar
  35. Pasini, A., & Potestà, S. (1995). Short-range visibility forecast by means of neural-network modelling: A case study. Nuovo Cimento, C24, 505–516Google Scholar
  36. Pasini, A., Pelino, V., & Potestà, S. (2001). A neural network model for visibility nowcasting from surface observations: Results and sensitivity to physical input variables. Journal of Geophysical Research, 106(D14), 14951–14959CrossRefGoogle Scholar
  37. Pasini, A., Ameli, F., & Lorè, M. (2003). Short range forecast of atmospheric radon concentration and stable layer depth by neural network modelling. Proceedings of the IEEE International Symposium on Computational Intelligence for Measurement Systems and Applications (pp. 85–90). IEEE: Lugano, SwitzerlandGoogle Scholar
  38. Pasini, A., Lorè, M., & Ameli, F. (2006). Neural network modelling for the analysis of forcings/temperatures relationships at different scales in the climate system. Ecological Modelling, 191, 58–67CrossRefGoogle Scholar
  39. Sailor, D. J., Hu, T., Li, X., & Rosen, J. N. (2000). A neural network approach to local downscaling of GCM output for assessing wind power implications of climate change. Renewable Energy, 19, 359–378CrossRefGoogle Scholar
  40. Schoof, J. T., & Pryor, S. C. (2001). Downscaling temperature and precipitation: A comparison of regression-based methods and artificial neural networks. International Journal of Climatology, 21, 773–790CrossRefGoogle Scholar
  41. Snell, S. E., Gopal, S., & Kaufmann, R. K. (2000). Spatial interpolation of surface air temperatures using artificial neural networks: Evaluating their use for downscaling GCMs. Journal of Climate, 13, 886–895CrossRefGoogle Scholar
  42. Tatli, H., Dalfes, H. N., & Mentes, S. S. (2004). A statistical downscaling method for monthly total precipitation over Turkey. International Journal of Climatology, 24, 161–180CrossRefGoogle Scholar
  43. Trigo, R. M., & Palutikof, J. P. (1999). Simulation of daily temperatures for climate change scenarios over Portugal: a neural network model approach. Climate Research, 13, 45– 59CrossRefGoogle Scholar
  44. Trigo, R. M., & Palutikof, J. P. (2001). Precipitation scenarios over Iberia: A comparison between direct GCM output and different downscaling techniques. Journal of Climate, 14,4422–4446CrossRefGoogle Scholar
  45. Wang, Y, Leung, L. R., McGregor, J. L., Lee, D.-K., Wang, W.-C., Ding, Y., & Kimura, F. (2004). Regional climate modelling: Progress, challenges, and prospects. Journal of the Meteorological Society of Japan, 82, 1599–1628CrossRefGoogle Scholar
  46. Weichert, A., & Bürger, G. (1998). Linear versus nonlinear techniques in downscaling. Climate Research, 10, 83–93CrossRefGoogle Scholar
  47. Wilby, R. L., Charles, S. P., Zorita, E., Timbal, B., Whetton, P., & Mearns, L. O. (2004). Guidelines for use of climate scenarios developed from statistical downscaling methods (Report of the IPCC Task Group TGICA), from http://ipcc-ddc.cru.uea.ac.uk/guidelines/StatDown_Guide.pdf
  48. Wu, A., & Hsieh, W. W. (2002). Nonlinear canonical correlation analysis of the tropical Pacific wind stress and sea surface temperature. Climate Dynamics, 19, 713–722CrossRefGoogle Scholar
  49. Wu, A., Hsieh, W. W., & Tang, B. (2006). Neural network forecasts of the tropical Pacific sea surface temperatures. Neural Networks, 19, 145–154CrossRefGoogle Scholar
  50. Yuval & Hsieh, W. W. (2003). An adaptive nonlinear MOS scheme for precipitation forecasts using neural networks. Weather and Forecasting, 18, 303–310CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V 2009

Authors and Affiliations

  1. 1.CNR – Institute of Atmospheric PollutionRomeItaly

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