Selection of Optimal Gridded Dataset for Application in Polish Sudetes Mountains

Abstract

High quality measured weather data (MWD) are limited or not available for many areas. Also their time coverage can be relatively short. That is why the use of gridded climatic data (GCD) in environmental studies is very popular. However, GCD are valuable source of information, their accuracy can be sometimes insufficient for particular study. That is why GCD applicability should be checked before the study run. The objective of this study was to check the applicability of gridded data for dendroclimatological studies of larch from Sudetes Mountains. To do so we compared GCD with available MWD from several weather stations located in the mentioned area. Because many gridded time-series datasets are available, we also wanted to check which dataset is the best to use for the mentioned area. In the analysis high-resolution gridded data on monthly mean temperature and total precipitation, which cover the common period 1901-2013, in 0.5° x 0.5° network, created by:- Center for Climatic Research Department of Geography University of Delaware Newark (UD; model V4.01 for precipitation and temperature data),- Global Precipitation Climatology Centre Deutscher Wetterdienst (GPCC; model V7 for precipitation data)- Climate Research Unit, University of East Anglia (CRU; model CRU TS v.4.01 for precipitation and temperature data)were used. The available MWD data from several weather stations started in 1951, 1956 and 1957. The common period for the analysis covered years 1951(‘56 or ‘57) - 2013.For a given precipitation and temperature data the agreement and biases between GCDs and MWDs were assessed with the absolute mean error (ME) and root mean square error (RMSPE), L1- norm and Pearson correlation coefficient. Finally, linear regression analysis was performed to detect biases in the relationship between GCD and MWD, and the coefficient of determination (R2) was also calculated. GCD for precipitation show high similarity to MWD. Mean Pearson correlation coefficient values equal to 0.87 for GPCC, 0.82 for CRU and 0.8 for UD GCDs. For temperature the received values of Pearson correlation were relatively high and very similar, e.g. 0.97 for CRU and 0.96 (UD). L1-norm, ME, RMSPE and regression model confirmed small differences between analysed GCDs, but with better fitting of CRU GCD.