In the first part we will work with Near-Surface Air Temperature data (tas) from climate models. We will load global, gridded temperature fields and compute the global mean temperature. We look at climate simulations from 1850 to 2100.

In this recipe, we will plot the global mean temperature spatially. <CF Field: long_name=near-surface temperature(long_name=time(1452), long_name=latitude(360), long_name=longitude(720)) degrees Celsius>] : long_name=latitude(360) = [-89.75, ..., 89.75] degrees_north. : long_name=longitude(720) = [-179.75, ..., 179.75] degrees_east.

Mar 1, 2024 · Calculate and plot the area weighted mean surface temperature for each time: global_avg = temp . collapse ( "area: mean" , weights = True ) cfp . lineplot ( global_avg , color = "red" , title = "Global mean surface temperature" )

Sep 9, 2014 · I know that in order to calculate the mean temperature at a depth, I could use: import numpy as np. import datetime as dt. import matplotlib.pyplot as plt. #read in data here. temper2out = [] temperature2m = np.mean(temperature[depth==2]) temper2out.extend(list(temperature2m) T2out = np.array(temper2out) t2 = np.mean(T2out)

To produce a map plot, the temperature values need to be averaged across the entire time period of the dataset. This will give 1 average value per grid point. The line below averages the temperature variable by the time axis. Now the gridded average has been calculated, the following code shows how a map can be plotted with this data.

Global temperature changes over the past century have been a subject of extensive study and debate, given their profound implications on climate, ecosystems, and human life. Understanding these changes through data analysis helps to visualize trends, detect anomalies, and formulate predictive models.

Plotting the Warming Stripes¶ In this recipe, we will plot the Warming Stripes (Climate Stripes) created by Professor Ed Hawkins at NCAS, University of Reading. Here we will use the ensemble mean of the HadCRUT.5.0.1.0 analysis gridded data for the same. Import cf-python and matplotlib.pyplot:

Global Average Annual Temperature Plot# Produces a time-series plot of North American temperature forecasts for 2 different emission scenarios. Constraining data to a limited spatial area also features in this example.

This project, "Global Earth Temperatures Analysis in Python," focuses on leveraging Python's powerful data processing and visualization libraries to analyze historical temperature data and visualize trends that can offer insights into these alarming changes.

We now have a plot of historical and projected global mean temperature change from 1850-2100 for 20 different climate models. What can we learn from this figure? Note your findings in a new cell.