Step 1 Run pollen_modern.R code first, making sure pollen_modern_pivot.RDS is saved RDS files come from data folder
Step 2 Run pollen_time.R, making sure pollen_time-full.RDS is saved
Step 3 Run monthly_ABOVE.R for fine albedo, which saves datafram with pollen LCT in a df bluesky_process.R is only necessary if resampling albedo data
Step 4 snow_probability.R is the code for snow prob from MODIS data, saves df with x,y,LCT and snow prob from jan-dec. modern data merges fine albedo and snow probabilty in one dataframe
Step 5
elevation.R adds elevation data for coordinates where we have pollen data, final df all-data.RDS includes x,y,LCT,elevation,finealb,snowprob
figures_main.R and albedo_figure.R use ggplot to demonstrate different relationships between albedo, snow, etc... difference_maps.R calculates the difference in albedo every 2000 years for prediction albedo data
step 6 thornthwaite model script predicts snowpack......
step ... using all-data can be plugged into model calibration_model.R file
step... model_predict.R uses the model created to predict albedo values back through time
#all scripts here good to be pushed except github is being dumbbb