Indian Journal of Science and Technology

Abstract

Background: The directed climate change is one of the most important global challenges of the XXIst century, which is beyond the scope of scientific research and makes a complex interdisciplinary problem. Methods: The object of study is the long-term changes of climatic conditions in the southern subzone of the East European Plain steppe. The subject of research is the temporal regularities of climatic parameters development (air temperature, total amount of precipitation). Results: The results of processing and forecasting changes for long-term climatic indexes parameters (ambient temperature and precipitation) that reflect the age-long rhythm of the of steppe ecosystem development of the East European Plain southern part. Using the multivariate statistics the regularities of long-term climate dynamics are obtained. The main rhythms are specified, the abnormal manifestations, the main time period changes and the functions of climate indicator provision are determined. The probabilities of annual inertia and periodic climatic changes using Markov networks are calculated. Conclusion: The highly accurate prediction of climate change is implemented on the basis of nonlinear multilayer artificial neural networks.

Keywords: Air Temperature, Climate Change, Markov Chains, Precipitation, Steppe Ecosystems, Time Series Analysis