(untitled) (International Journal of Advanced Research in Computer Science, 2017)
Climate Signal identifying and attribution of observed record plays vital role in incorporate information of the climate system. Traditional techniques for detecting and attributing changes due to statistical forcing require for large number of general circulation model statistically results of various primary conditions and forcing scenarios, and these have been completed with a average number of GCMs. In this paper, we proposed a novel approach based on Curvelet Transform to identify the climate change in time series statistics. The parameters like auto-correlation and maximum likelihood ratio through global mean temperature are estimated for statistical climate detection of climate change. The object of this paper is discussed about natural climate variability; It is various claims of anthropogenic signal detection. Numerical results illustrate that our proposed method is self-consistent, accurate and quantitative results than established methods. The detection and attribution analysis can be further extended to recognize the effects of non climatic localized effects like land use and land cover changes, urbanization. Keywords: Curvelet Transform, Time Series, Attribution, Detection, Climate signal, Statistical analysis.