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Cartopy for Climate Scientists & Meteorologists

This hands-on notebook teaches Cartopy for climate & weather mapping using realistic, self-contained examples.

You will learn to: - Understand map projections and GeoAxes - Use the projection= (axes) vs. transform= (data) keywords correctly - Explore common climate projections (PlateCarree, Lambert Conformal, Robinson, Orthographic) - Create regional maps (e.g., Ethiopia) and overlay data - Style maps with Natural Earth features (coastlines, borders) - Add gridlines and tick labels - Read Natural Earth shapefiles at low resolution (110m) directly - Combine Cartopy with Matplotlib (subplots, colorbars)

Environment & Installation

Recommended (conda, works cross-platform):

conda create -n cartopy_env python=3.11 -y
conda activate cartopy_env
conda install -c conda-forge cartopy matplotlib numpy geopandas shapely pyproj rasterio rioxarray xarray -y

pip install cartopy
- Cartopy uses PROJ, GEOS, and Shapely under the hood.

  • The conda-forge channel bundles compatible binaries.

Imports

import numpy as np
import matplotlib.pyplot as plt

import cartopy.crs as ccrs
import cartopy.feature as cfeature
import cartopy.io.shapereader as shpreader

import matplotlib.ticker as mticker
from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter

# Set default figure size and define Ethiopia extent
plt.rcParams['figure.figsize'] = (7.5, 5.5)
ETH_EXTENT = [33, 48, 3, 15]  # Ethiopia approx

# Check Cartopy version
import cartopy
print("Cartopy version:", cartopy.__version__)

Cartopy version: 0.25.0

Core concepts: Projections and GeoAxes

  • Every Cartopy map lives on a GeoAxes with a projection:

ax = plt.axes(projection=ccrs.PlateCarree())
- Your data (lon/lat grids, shapely geometries) have their own coordinate system.

  • When plotting data, specify the data CRS via transform= so Cartopy can project it onto the axes projection.

Your first Cartopy map

# Create a global map using PlateCarree projection
proj = ccrs.PlateCarree()

# Create figure and GeoAxes
fig = plt.figure()

# Create GeoAxes with PlateCarree projection
ax = plt.axes(projection=proj)

# Add features to the map Land, Ocean, Borders, Coastlines
ax.add_feature(cfeature.LAND, facecolor='lightgray')
ax.add_feature(cfeature.OCEAN, facecolor='lightsteelblue')
ax.add_feature(cfeature.BORDERS, linewidth=0.5)
ax.coastlines(resolution='110m', linewidth=0.6) # 
ax.set_global()

# Add gridlines with labels
gl = ax.gridlines(draw_labels=True,
                   linewidth=0.3, 
                   color='gray', 
                   linestyle='--',
                  x_inline=False, 
                  y_inline=False)

# Disable top and right labels
gl.top_labels = False

# Disable right labels
gl.right_labels = False

# Set custom formatters for longitude and latitude
gl.xformatter = LongitudeFormatter(
    zero_direction_label=True,)   

gl.yformatter = LatitudeFormatter()

ax.set_title("Global map — PlateCarree")
plt.show()

<Figure size 750x550 with 1 Axes>

proj = ccrs.Mercator() # ccrs.Mercator(), ccrs.Robinson(), ccrs.Mollweide(), ccrs.EqualEarth(), ccrs.LambertCylindrical(), etc.
fig = plt.figure()
ax = plt.axes(projection=proj)

ax.add_feature(cfeature.LAND, facecolor='lightgray')
ax.add_feature(cfeature.OCEAN, facecolor='lightsteelblue')
ax.add_feature(cfeature.BORDERS, linewidth=0.5)
ax.coastlines(resolution='110m', linewidth=0.6) # 
ax.set_global()

gl = ax.gridlines(draw_labels=True,
                   linewidth=0.3, 
                   color='gray', 
                   linestyle='--',
                  x_inline=False, 
                  y_inline=False)

gl.top_labels = False
gl.right_labels = False
gl.xformatter = LongitudeFormatter()
gl.yformatter = LatitudeFormatter()

ax.set_title("Global map — PlateCarree")
plt.show()

<Figure size 750x550 with 1 Axes>

Exploring several projections

# Create 4 subplots with different projections
fig = plt.figure(figsize=(12, 9))

# Subplot 1: PlateCarree projection
ax1 = plt.subplot(2, 2, 1, projection=ccrs.PlateCarree())
ax1.coastlines('110m'); ax1.add_feature(cfeature.BORDERS, linewidth=0.4)
ax1.set_global(); ax1.set_title("PlateCarree")

# Subplot 2: Robinson projection
ax2 = plt.subplot(2, 2, 2, projection=ccrs.Robinson())
ax2.coastlines('110m'); ax2.add_feature(cfeature.BORDERS, linewidth=0.4)
ax2.set_global(); ax2.set_title("Robinson")

# Subplot 3: Orthographic projection centered on Africa
ax3 = plt.subplot(2, 2, 3, projection=ccrs.Orthographic(central_longitude=20, central_latitude=10))
ax3.coastlines('110m'); ax3.add_feature(cfeature.BORDERS, linewidth=0.4)
ax3.set_global(); ax3.set_title("Orthographic (Africa)")


# Subplot 4: Lambert Conformal projection focused on Ethiopia
ax4 = plt.subplot(2, 2, 4, projection=ccrs.LambertConformal(central_longitude=40, central_latitude=9, standard_parallels=(5, 15)))
ax4.coastlines('10m'); ax4.add_feature(cfeature.BORDERS, linewidth=0.4)
ax4.set_extent(ETH_EXTENT, crs=ccrs.PlateCarree()); ax4.set_title("Lambert Conformal (regional)")

plt.tight_layout()
plt.show()

<Figure size 1200x900 with 4 Axes>

Creating regional maps (Ethiopia extent)

fig = plt.figure()
ax = plt.axes(projection=ccrs.PlateCarree())
ax.set_extent(ETH_EXTENT, crs=ccrs.PlateCarree())
ax.add_feature(cfeature.LAND, facecolor='0.9')
ax.add_feature(cfeature.OCEAN, facecolor='lightsteelblue')
ax.add_feature(cfeature.LAKES, edgecolor='0.4', facecolor='aliceblue')
ax.add_feature(cfeature.BORDERS, linewidth=0.6)
ax.coastlines('10m', linewidth=0.7)

gl = ax.gridlines(draw_labels=True, linewidth=0.3, color='gray', linestyle='--',
                  x_inline=False, y_inline=False)
gl.top_labels = False; gl.right_labels = False
gl.xformatter = LongitudeFormatter(); gl.yformatter = LatitudeFormatter()
gl.xlocator = mticker.FixedLocator(np.arange(33, 49, 3))
gl.ylocator = mticker.FixedLocator(np.arange(3, 16, 3))

ax.set_title("Regional map — Ethiopia (PlateCarree)")
plt.show()

<Figure size 750x550 with 1 Axes>

Cartopy + Matplotlib: overlay a synthetic climate field

# Lon/lat grid and synthetic field
lons = np.linspace(33, 48, 121)
lats = np.linspace(3, 15, 97)
lon2d, lat2d = np.meshgrid(lons, lats)
field = (20*np.exp(-((lon2d-39)**2 + (lat2d-8)**2)/6.0)
         + 10*np.exp(-((lon2d-42)**2 + (lat2d-12)**2)/4.0)
         + 0.8*(lat2d-3))

fig = plt.figure()
ax = plt.axes(projection=ccrs.PlateCarree())
ax.set_extent(ETH_EXTENT, crs=ccrs.PlateCarree())

im = ax.pcolormesh(lons, lats, field, cmap='YlGnBu', shading='auto',
                   transform=ccrs.PlateCarree())

ax.coastlines('10m', linewidth=0.7)
ax.add_feature(cfeature.BORDERS, linewidth=0.6)
ax.add_feature(cfeature.LAKES, edgecolor='0.4', facecolor='aliceblue')

gl = ax.gridlines(draw_labels=True, linewidth=0.3, color='gray', linestyle='--',
                  x_inline=False, y_inline=False)
gl.top_labels=False; gl.right_labels=False
gl.xformatter=LongitudeFormatter(); gl.yformatter=LatitudeFormatter()
gl.xlocator = mticker.FixedLocator(np.arange(33, 49, 3))
gl.ylocator = mticker.FixedLocator(np.arange(3, 16, 3))

cb = plt.colorbar(im, ax=ax, pad=0.02, shrink=0.9, aspect=25)
cb.set_label("Synthetic precip (mm)")

ax.set_title("pcolormesh with transform=PlateCarree")
plt.show()

<Figure size 750x550 with 2 Axes>

The Cartopy Feature interface

fig = plt.figure()
ax = plt.axes(projection=ccrs.PlateCarree())
ax.set_extent(ETH_EXTENT, crs=ccrs.PlateCarree())

ax.add_feature(cfeature.LAND, facecolor='0.92')
ax.add_feature(cfeature.RIVERS, edgecolor='steelblue', linewidth=0.6)
ax.add_feature(cfeature.BORDERS, linewidth=0.6)
ax.coastlines('10m', linewidth=0.7)

ax.set_title("Features: LAND, RIVERS, BORDERS, COASTLINE")
plt.show()

<Figure size 750x550 with 1 Axes>

Gridlines & tick labels — fine control

fig = plt.figure()
ax = plt.axes(projection=ccrs.PlateCarree())
ax.set_extent(ETH_EXTENT, crs=ccrs.PlateCarree())
ax.coastlines('10m'); ax.add_feature(cfeature.BORDERS, linewidth=0.6)

gl = ax.gridlines(draw_labels=True, linewidth=0.4, color='gray', linestyle='--',
                  x_inline=False, y_inline=False)
gl.top_labels = False; gl.right_labels = False
gl.xformatter = LongitudeFormatter()
gl.yformatter = LatitudeFormatter()
gl.xlocator = mticker.FixedLocator(np.arange(33, 49, 2))
gl.ylocator = mticker.FixedLocator(np.arange(3, 16, 2))

ax.set_title("Custom locators & formatters")
plt.show()

<Figure size 750x550 with 1 Axes>

Use lower-resolution Natural Earth shapefiles (110m)

shp_countries = shpreader.natural_earth(resolution='110m', category='cultural', name='admin_0_countries') # reslution 110m, 
reader = shpreader.Reader(shp_countries)
geoms  = list(reader.geometries())

fig = plt.figure()
ax = plt.axes(projection=ccrs.PlateCarree())
ax.set_extent(ETH_EXTENT, crs=ccrs.PlateCarree())
ax.add_geometries(geoms, crs=ccrs.PlateCarree(), facecolor='none', edgecolor='k', linewidth=0.4)
ax.set_title("Natural Earth 110m — admin_0_countries via shapereader")
plt.show()

c:\Users\yonas\Documents\ICPAC\python-ml-gha-venv\Lib\site-packages\cartopy\io\__init__.py:242: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/110m_cultural/ne_110m_admin_0_countries.zip
  warnings.warn(f'Downloading: {url}', DownloadWarning)

📝 Test Your Knowledge

Ready to test your understanding of Cartopy? Take the interactive quiz to assess your knowledge of map projections, coordinate systems, and climate data visualization.

Take the Cartopy Quiz →

📚 Summary

In this tutorial, you've learned:

✅ Map Projections - Understanding different projections and their use cases
✅ GeoAxes Fundamentals - Creating maps with projection parameter
✅ Transform vs Projection - Correctly specifying data and display coordinate systems
✅ Common Projections - PlateCarree, LambertConformal, Robinson, Orthographic
✅ Regional Mapping - Creating focused maps for specific regions (e.g., Ethiopia)
✅ Map Features - Adding coastlines, borders, land, ocean with Natural Earth
✅ Gridlines - Adding and customizing gridlines and tick labels
✅ Shapefiles - Reading and plotting Natural Earth shapefiles
✅ Data Overlay - Combining climate data with cartographic features
✅ Advanced Layouts - Creating multi-panel maps with subplots

🔗 Additional Resources

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