Global Surface Temperature Anomalies

Global Surface Temperature Anomalies#

Map of global surface temperature anomalies

The NOAA Global Surface Temperature Dataset provides monthly global land-ocean surface temperature data, derived from:

The Extended Reconstructed Sea Surface Temperature (ERSST) analysis.
The Global Historical Climatology Network - Monthly (GHCN-M) land surface air temperature analysis. It supports global climate monitoring by combining sea surface temperatures with land surface air temperatures.

Data characteristics#

Spatial resolution: 5° latitude × 5° longitude.
Temporal coverage: Monthly values from 1850 to the present.
Reference period: Anomalies calculated relative to the 1971–2000 climatology.

Note: To adjust anomalies for other climatology periods, calculate the average anomaly for the desired period and subtract it from the original anomalies.

Useful Links#

Dataset Information Page
https://www.ncei.noaa.gov/products/land-based-station/noaa-global-temp
Dataset Landing Page
https://www.ncei.noaa.gov/metadata/geoportal/rest/metadata/item/gov.noaa.ncdc:C01704/html
OPeNDAP URL
https://www.ncei.noaa.gov/thredds/dodsC/noaa-global-temp-v6/NOAAGlobalTemp_v6.0.0_gridded_s185001_e202411_c20241208T145954.nc

You can use this URL in your scripts like a local file path!
WMS URL https://www.ncei.noaa.gov//thredds/wms/noaa-global-temp-v6/NOAAGlobalTemp_v6.0.0_gridded_s185001_e202411_c20241208T145954.nc
THREDDS Catalogue
- Human interface: https://www.ncei.noaa.gov/thredds/catalog/noaa-global-temp-v6/catalog.html
- Machine interface: https://www.ncei.noaa.gov/thredds/catalog/noaa-global-temp-v6/catalog.xml

Crediting the Data Providers#

When using this dataset in publications or presentations, please provide the following citation:

Huang, Boyin; Yin, Xungang; Menne, Matthew J.; Vose, Russell S.; and Zhang, Huai-Min. 2024.
NOAA Global Surface Temperature Dataset (NOAAGlobalTemp), Version 6.0. [indicate subset used].
NOAA National Centers for Environmental Information. https://doi.org/10.25921/rzxg-p717.
Accessed [date].

Writing the data to a CSV file#

You can write all or a subset of the data to a pandas dataframe that you can export as a CSV file.

df = xrds['anom'].to_dataframe()
df.head()

				anom
time	z	lat	lon
1850-01-01	0.0	-87.5	2.5	-0.890190
			7.5	-1.231461
			12.5	0.498334
			17.5	-0.632899
			22.5	1.512975

df.to_csv('global_surface_temperature_anomalies.csv')

Plotting the data for one month#

Firstly, we need to isolate a single month of data for a desired date. The time coordinate has values for the 1st day of each month.

xrds['time'].values

array(['1850-01-01T00:00:00.000000000', '1850-02-01T00:00:00.000000000',
       '1850-03-01T00:00:00.000000000', ...,
       '2024-11-01T00:00:00.000000000', '2024-12-01T00:00:00.000000000',
       '2025-01-01T00:00:00.000000000'], dtype='datetime64[ns]')

So to select data from one month:

desired_date = '2022-11-01'
data_for_desired_date = xrds.sel(time=desired_date)

If you accidentally select a date that isn’t present in the time series, you can use method = 'nearest' to select the data from the nearest timestamp to the date you provide. Other methods can also be used, you can read about this here:

https://docs.xarray.dev/en/latest/generated/xarray.Dataset.sel.html#xarray-dataset-sel

data_for_desired_date = xrds.sel(time=desired_date, method='nearest')

We can very quickly plot the data.

data_for_desired_date['anom'].plot()

<matplotlib.collections.QuadMesh at 0x7f2b2e66b790>

_images/67a2f64976cc11188f782eb65944a7f52678388e1290fffe409adaea21d16ca7.png

Without the coastlines these data are difficult to interpret. Let’s now explore how to improve this plot and perhaps use some different projections.

Full working example#

Below is a full working example you can copy and play with.

import xarray as xr
import matplotlib.pyplot as plt
import cartopy.crs as ccrs

desired_date = '2024-11-01'  # Date of interest
projection = ccrs.Mollweide()  # Map projection for visualisation https://scitools.org.uk/cartopy/docs/v0.15/crs/projections.html
transform = ccrs.PlateCarree()  # Data coordinate reference system 
cmap = 'seismic'  # Select from diverging colour maps at https://matplotlib.org/stable/users/explain/colors/colormaps.html#diverging
missing_values_colour = 'black'  # Colour to fill areas outside the data extent

# Set up the figure and axes for plotting
fig = plt.figure(figsize=(16, 8)) 
ax = plt.axes(projection=projection)

# Load the data from the remote THREDDS server
catalog_url = 'https://www.ncei.noaa.gov/thredds/catalog/noaa-global-temp-v6/latest.xml'
catalog = TDSCatalog(catalog_url)
dataset = list(catalog.datasets.values())[0]
opendap_url = dataset.access_urls['OPENDAP']
xrds = xr.open_dataset(opendap_url)

# Select the data for the desired date (nearest available date if exact match isn't found)
data_for_desired_date = xrds.sel(time=desired_date, method='nearest')

# Compute the colour map range (vmin, vmax) by ignoring NaN values
vmin = xrds['anom'].min(skipna=True).compute()
vmax = xrds['anom'].max(skipna=True).compute()
abs_max = max(abs(vmin), abs(vmax))  # Use the maximum absolute value for the colour map range

# Plot the temperature anomaly data for the selected date
data_for_desired_date['anom'].plot(
    ax=ax,
    transform=transform,  # Reproject the data from Plate Carree to the map projection
    vmin=-abs_max,
    vmax=abs_max, 
    cmap=cmap
)

# Add background to areas outside the data extent (areas without data)
xmin, xmax, ymin, ymax = ax.get_extent(transform)
ax.add_patch(plt.Rectangle(
    (xmin, ymin), xmax - xmin, ymax - ymin,  # Create a rectangle to cover the areas outside the data
    facecolor=missing_values_colour,  # Fill the rectangle with the chosen colour
    transform=transform,  # Apply the correct projection to the rectangle
    zorder=-1  # Ensure the rectangle is drawn behind the data
))

ax.coastlines()  # Draw coastlines on the map

plt.savefig('Global_surface_temperature_anomalies.png')
plt.show()