A function to calculate the spatial correlation of tree-ring width or isotope data with reanalysis of climate data Please install the required modules such as numpy, pandas, scipy, netCDF4 the function chelsa_correlation(defined below) calculates the spatial correlation and automatically saves an output NetCDF file with correlation and their corresponding significance value. The function requires netCDF_file = location of the stacked netcdf file including its name, for example, "J:/climate/CHELSA/stacked_tmax_data8.nc" chron = the chronology file with year in the first column and RWIs in the second column, month_range = range of the months to define season such that [3,5] represents averaging the temperature from March (3rd month) to May (5th month) or [6,9] representing from June (6th month) to September (9th month) variable = the standard name of the variable: pr, tasmin and tasmax for precipitation, minimum temperature and maximum temperature, respectively
########### To use the function first run the above code block then use/edit the example code below: ########### To read the chronology: all_crn=pd.read_excel('J:/Humboldt_project/pre-chron/powt/all_chronologies_1950-2022.xlsx') all_crn.head() ########### To run the function spatial_correlation(netCDF_file='stacked_tmax_data8.nc', chron=all_crn, month_range=[3,5], variable='tasmax')