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ATMS 523

Module 4 Project

Submit this code as a pull request back to GitHub Classroom by the date and time listed in Canvas.

For this assignment, use the ERA-5 monthly mean data at NCAR (DOI: 10.5065/P8GT-0R61) as we did in Module 2. Feel free to reduce the data to 1 degree resolution if you need to save data download times.

  1. Create a dataset that contains the monthly means of Sea Surface Temperature anomalies and total column water vapor from Jan 1979-Dec 2023 over the Pacific Basin (65°N to 65°S, 120°E to 60°W) masked out over land - save this to your computer. The land sea mask is available here: https://rda.ucar.edu/thredds/dodsC/files/g/ds633.0/e5.oper.invariant/197901/e5.oper.invariant.128_172_lsm.ll025sc.1979010100_1979010100.nc

Skill check in: due Friday, Mar 1, end of day to slack: Plot maps of the mean SST and mean total column water vapor for the entire period of record.

  1. From the dataset shown in 1, compute anomalies by deseasonalizing the data (remove the mean monthly anomaly from the annual mean from each point), detrend, and standardize the SST anomalies. Standardization means subtract the long term mean and divide by the standard deviation, which can be accomplished using the methods described here: 6.3. Preprocessing data — scikit-learn 1.1.2 documentation

Hints: Monthly data analysis — PyCLIM_101 0.2 documentation

  1. Perform an EOF analysis (with cosine latitude weighting) on the SST anomalies and plot a map of the first 5 EOFs.

  2. Plot the percent of variance explained by the first 10 EOFs.

  3. Reconstruct the SST field using the first 5 EOFs and plot a map of the Pearson's correlation coefficient (xarray.corr) of the reconstructed monthly time series and the "observed" SST time series.

  4. Compute a map of the Pearson's correlation coefficient between SST EOF1 and monthly mean detrended, deseasonalized, and standardized monthly mean precipitation anomalies (don't mask these over land for the plot). See anything interesting?