Investigation of climate variability accordance with the classification KÖPPEN

The aim of this thesis was to investigate the climatic variability at various regions around the world by means of Köppen climate classification. For this purpose, monthly precipitation and temperature data were collected, which belong to each of the 31 subtypes of 5 basic climate types A, B, C, D and E. For each climate subtype, data were gathered from two stations, the first nearby the sea (distance up to 50 kms) and the other away from it. Data were collected from the Dutch Meteorological Institute website (http://climexp.knmi.nl). Only fully completed years were used for which there were both rainfall and temperature data. Köppen parameters were calculated and criteria which define the climate type in which each station belongs were implemented. Data availability of near and far from the sea stations showed that the coastal stations are being studied for a longer period of time. Basic statistical analysis of all data stations was conducted. Average, standard deviation, seasonality and variance of stations’ meteorological data were calculated. The difference of statistical parameters’ values between the near and far from the sea stations were analyzed. Then, statistical parameters of meteorological variables for all subcategories of each climate type A, B, C and D were correlated. Only those cases in which there was a high correlation were examined. Furthermore, changes in parameter values were commented. The temperature and precipitation seasonality between stations near and far from the sea for each climate category were compared. It was observed that temperature seasonality is more intense in type A coastal areas. In contrast, temperature and rainfall seasonality in B and D types appears greater in mainland areas. Daily rainfall data for the most common subtypes of 5 climate types were collected. These were Aw, Bwh, Cfa, Cfb, Csa and Dfc. Using the Hydrognomon program daily values were aggregated into monthly values. Ten stations from each of the six climate subtypes were examined. In turn, average, standard deviation, seasonality and variance of these monthly values were calculated. Values of the parameters were plotted in diagrams. Studying diagrams, it was concluded that there can be a grouping of some climate subtypes based on their values of the statistical parameters.