Climate change in Georgia: Statistical and nonlinear dynamics predictions
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Georgia as a whole Caucasus is prone to many negative effects, connected with climate change: the mountain glaciers can melt and partially disappear, the sea level can rise, the vast areas of land can became deserts, water resources can be seriously affected.
Despite some earlier efforts, devoted to assessment of climate change in Georgia, the results are still ambiguous. In particular, the research carried out shows that during last decades the mean temperature in the Eastern Georgia is rising and in Western Georgia it is decreasing. These conclusions are debated and there is a need to re-consider them using new data and new methods of mathematical analysis of meteorological time series. For reliable assessments new modern methods of obtaining and analysis of climate data in the past, present and future is necessary to use.
Another problem is to ascertain whether this warming is exclusively the man-made effect or it is the result of natural cyclicity in the earth climate.
Specific objective is assessment of persistence and memory characteristics of regional air temperature variation in Georgia in the light of global climate change. For this purpose longest available temperature time series of Tbilisi meteorological station (since 1890) are analyzed. Similar time series on shorter time scales of 11 stations in the West and East Georgia will also be used as well as monthly mean temperature time series of 11 stations (1907-2006) in the West and East Georgia. As far as most incorrect conclusions about dynamical properties of complex dynamics are related to “data bleaching” procedures, in order to avoid destruction of original dynamics caused by linear filtering in the present research special noise reduction procedure of time series as well as multi scaling analysis based on CWT are used. Both mono- and multivariate reconstruction procedures of climate change dynamics are implemented. Additionally, temporally and spatially averaged daily and monthly mean air temperature time series are analyzed. Extent of persistence in mentioned time series is evaluated.
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