ClimateBC can generate climate variables for any year between 1901 and 2000 given appropriately-scaled latitude, longitude and elevation data points. This resolution proved to be cumbersomely high for efficient data processing and unnecessary for the scope of the project, therefore the first step was to resample the DEM to 5000 meters. The resampled DEM was converted to a feature file and saved as a vector shapefile in order to capture elevation values as points. Coordinates were added to the NAD83 shape file by creating new ‘x’ and ‘y’ fields in the attribute table and plugging in the appropriate processing code from the ‘help’ menu. There was not enough time to create animations for all 16 variables, so three variables were selected for analysis in this project: mean annual temperature (MAT), number of frost-free days (NFFD) and summer heat:moisture index (SH:M). The summer heat:moisture index is a scaled, decadally-averaged ratio between mean warmest month temperature and mean summer precipitation. The data was reformatted into five columns with the headers ‘id1’, ‘id2’, ‘lat’, ‘lon’, ‘el’ as per ClimateBC input standards. Within ClimateBC, it is possible to calculate climate variables for a one or ten-year time period anytime between 1901 and 2000, it is also possible to generate climate coverages for 2020, 2050 and 2080 using a variety of climate model projections. The csv outputs from ClimateBC are not particularly exciting unto themselves, but when transformed into colorful maps they provide the viewer with enormous quantities of climatic information in an easily interpretable format. For this project, mean annual temperature (MAT), summer heat:moisture index (SH:M) and number of frost free days (NFFD) were chosen for mapping and analysis because of their importance as correlates of vegetative growth. Before inputting the ClimateBC csv decadal outputs into ArcGIS, it was necessary to delete all symbols from the climate variable headings (for example, delete the ‘<,’ from DD<,18) and to convert all null values (symbolized as ‘.’) into numbers that would not be confused for real values (something like ‘-9999’). Kriging is a statistically-enhanced interpolation process that takes into account the relationship between the measured points. It was necessary to multiply the output maps by a mask of BC using the raster calculator in order to exclude the unwanted areas. In order to prevent errors and save time, the data import, kriging and masking tasks were automated using ‘modelbuilder’. The csv for each decade needed to be specified at the beginning of the model, and an output name for each map needed to be specified at the end, but otherwise the model ran itself. Figure 3: The ArcGIS model created to automate the xy data input, kriging and masking processes for the output of 30 climate variable maps. Regular scaling was achieved by manually creating intervals that encompassed the full data range for all ten maps per climate variable. However, the histogram showed very few values below -6 on any of the maps, so -8 chosen as the lower boundary and 10.5 as the upper in the final symbology. However, most of the values were lower than 140 in all of the histograms, so 140 was used as the upper boundary and 3 as the lower in the final symbology. While the decadally-averaged climate variable maps can be scrolled through using ArcGIS, it is easier to see climate trends when the images are animated into an automated slide show. Source.