Data Analysis (Monthly)

Data Analysis (Monthly)#

This chapter introduces common techniques for analyzing monthly climate datasets using Python. These techniques include:

  • Computing monthly climatology, anomalies, and standard deviation

  • Calculating seasonal, annual, and water year averages

  • Calculating long-term trends and removing them (detrending)

  • Performing correlation and regression analysis

  • Conducting Empirical Orthogonal Function (EOF) analysis

📦 Required Python Packages#

The following packages are used throughout this monthly data analysis workflow. Make sure they are installed in your Python environment.

— Packages —#

General Packages#

import pandas as pd
import xarray as xr
import numpy as np
import os
import ipynbname

GeoCAT Tools#

import geocat.comp as gccomp
import geocat.viz as gv
import geocat.viz.util as gvutil

Visualization#

import cmaps  
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import shapely.geometry as sgeom

Matplotlib Essentials#

import matplotlib.pyplot as plt
import matplotlib.ticker as mticker
import matplotlib.patches as mpatches
import matplotlib.dates as mdates

from matplotlib.colors import ListedColormap, BoundaryNorm
from matplotlib.ticker import MultipleLocator

Specialized Layouts#

import matplotlib.gridspec as gridspec

📂 Reading the Dataset#

Before we begin our analysis, we load the NetCDF dataset containing monthly sea surface temperature.

# --- Parameter setting ---
data_dir = "../data"
fname = "sst.cobe2.185001-202504.nc"
ystr, yend = 1991, 2020
fvar = "sst"

# --- Reading NetCDF Dataset ---
# Construct full path and open dataset
path_data = os.path.join(data_dir, fname)
ds = xr.open_dataset(path_data)

# Extract the variable
var = ds[fvar]

# Ensure dimensions are (time, lat, lon)
var = var.transpose("time", "lat", "lon", missing_dims="ignore")

# Ensure latitude is ascending
if var.lat.values[0] > var.lat.values[-1]:
    var = var.sortby("lat")

# Ensure time is in datetime64 format
if not np.issubdtype(var.time.dtype, np.datetime64):
    try:
        var["time"] = xr.decode_cf(ds).time
    except Exception as e:
        raise ValueError("Time conversion to datetime64 failed: " + str(e))

# === Select time range ===
dat = var.sel(time=slice(f"{ystr}-01-01", f"{yend}-12-31"))
print(dat)

🧭 Topics Covered in This Module#

This module will walk through the following analysis techniques in sequence:

  • Monthly climatology
    Calculate the average conditions for each calendar month over a baseline period, typically 30 years or more. This forms the foundation for identifying departures from typical conditions.

  • Monthly anomalies
    Determine the difference between observed monthly values and the corresponding climatological mean. Anomalies highlight deviations from normal conditions.

  • Monthly standard deviation
    Quantify the typical spread or variability of monthly anomalies, helping to assess regions or times with greater fluctuation.

  • Seasonal, annual, and water year averages
    Aggregate data across defined periods:

    • Seasonal (e.g., DJF, JJA) averages capture variability tied to meteorological seasons.

    • Annual averages summarize calendar year totals.

    • Water year averages (October–September) are commonly used in hydrology and water resource planning.

  • Long-term trends
    Assess changes over time using linear regression or other methods. Useful for detecting climate change signals.

  • Detrending
    Remove long-term trends from a dataset to focus on interannual or decadal variability. Often a preprocessing step for EOF or correlation analysis.

  • Correlation and regression analysis
    Examine the statistical relationships between variables. Correlation maps can reveal spatial coherence with a reference time series, while regression helps quantify the magnitude of response.

  • Empirical Orthogonal Function (EOF) analysis
    Decompose spatial-temporal fields into orthogonal modes of variability. Useful for identifying dominant patterns like ENSO, the North Atlantic Oscillation, or Pacific Decadal Oscillation.

📁 Note: Instructions for accessing and preparing datasets are provided in the File I/O chapter.

🔜 Next: Climatology

Contents