Climate Change Visual Analysis¶
Context¶
Climate change is a long-term change in the average weather patterns that have come to define Earth's local, regional and global climates. These changes have a broad range of observed effects that are synonymous with the term. This dataset provides an insight into the global temperature changes and the global CO2 emissions over the years.
Content¶
This dataset contains the following columns:
- Year: The year in which the data was recorded
- Mean global temperature: The mean global temperature recorded in that year
- CO2 emissions: The CO2 emissions recorded in that year
Objective¶
To perform an exploratory data analysis on the dataset to understand the trends in the global temperature changes and CO2 emissions over the years.
References¶
The dataset has been taken from Kaggle. The links to the dataset and other references are given below:
Raw Data:
Climate trends: https://ourworldindata.org/grapher/temperature-anomaly
CO2 trends: https://ourworldindata.org/co2-emissions#global-co2-emissions-from-fossil-fuels
x-limits:
Pre-industrial temperatures:
https://earthobservatory.nasa.gov/world-of-change/global-temperatures
https://unfccc.int/process-and-meetings/the-paris-agreement
y-limits:
https://www.europarl.europa.eu/infographic/climate-negotiations-timeline/index_en.html
shaded periods:
https://faculty.wcas.northwestern.edu/jmokyr/castronovo.pdf
https://www.anthroencyclopedia.com/entry/anthropocene#:~:text='The%20Anthropocene'%20is%20a%20term,bio%2Dgeophysical%20composition%20and%20processes.
callouts:
https://www.history.com/news/most-important-gilded-age-inventions
https://www.statista.com/statistics/1246890/vehicles-use-united-states-historical/
https://www.discover.ukri.org/a-brief-history-of-climate-change-discoveries/index.html#group-section-1990s-2000s-bwnXtJHqpd
Let's start by loading the dataset.
In [1]:
import os
import pandas as pd
from matplotlib import pyplot as plt
import numpy as np
from scipy.interpolate import make_interp_spline
from warnings import filterwarnings
filterwarnings("ignore")
In [2]:
#reading in temperature data
temp_trends = pd.read_csv('temperature-anomaly.csv')
#reading in CO2 data
CO2_trends = pd.read_csv('annual-co2-emissions-per-country.csv')
In [3]:
print(temp_trends.head())
print(temp_trends.describe())
Entity Code Year \
0 Global NaN 1850
1 Global NaN 1851
2 Global NaN 1852
3 Global NaN 1853
4 Global NaN 1854
Global average temperature anomaly relative to 1961-1990 \
0 -0.417659
1 -0.233350
2 -0.229399
3 -0.270354
4 -0.291630
Upper bound of the annual temperature anomaly (95% confidence interval) \
0 -0.246115
1 -0.054832
2 -0.049416
3 -0.110700
4 -0.150436
Lower bound of the annual temperature anomaly (95% confidence interval)
0 -0.589203
1 -0.411868
2 -0.409382
3 -0.430009
4 -0.432824
Code Year \
count 0.0 522.000000
mean NaN 1936.500000
std NaN 50.276825
min NaN 1850.000000
25% NaN 1893.000000
50% NaN 1936.500000
75% NaN 1980.000000
max NaN 2023.000000
Global average temperature anomaly relative to 1961-1990 \
count 522.000000
mean -0.072792
std 0.387320
min -0.701569
25% -0.354566
50% -0.193912
75% 0.110119
max 1.275727
Upper bound of the annual temperature anomaly (95% confidence interval) \
count 522.000000
mean 0.038905
std 0.347909
min -0.486698
25% -0.199500
50% -0.056851
75% 0.208315
max 1.333305
Lower bound of the annual temperature anomaly (95% confidence interval)
count 522.000000
mean -0.184489
std 0.432559
min -0.947684
25% -0.521331
50% -0.294819
75% 0.052181
max 1.236863
In [4]:
#cleaning climate change data - restricted to world trends, removing rows with no temp anomoly data
climatechange_world = temp_trends[temp_trends['Entity']=='Global']
#filtering relevant columns
drop_cols = ['Entity','Code']
climatechange_world.drop(drop_cols,axis=1,inplace=True)
#renaming columns
rename_cols = {'Global average temperature anomaly relative to 1961-1990':'relative avg temp',
'Upper bound of the annual temperature anomaly (95% confidence interval)':'upper bound',
'Lower bound of the annual temperature anomaly (95% confidence interval)':'lower bound'}
climatechange_world.rename(columns=rename_cols,inplace=True)
#print df head
print(climatechange_world.head())
Year relative avg temp upper bound lower bound 0 1850 -0.417659 -0.246115 -0.589203 1 1851 -0.233350 -0.054832 -0.411868 2 1852 -0.229399 -0.049416 -0.409382 3 1853 -0.270354 -0.110700 -0.430009 4 1854 -0.291630 -0.150436 -0.432824
In [5]:
print(CO2_trends[CO2_trends['Entity']=='World'].head())
print(CO2_trends[CO2_trends['Entity']=='World'].describe())
Entity Code Year Annual CO₂ emissions
30814 World OWID_WRL 1750 9350528.0
30815 World OWID_WRL 1751 9350528.0
30816 World OWID_WRL 1752 9354192.0
30817 World OWID_WRL 1753 9354192.0
30818 World OWID_WRL 1754 9357856.0
Year Annual CO₂ emissions
count 272.000000 2.720000e+02
mean 1885.500000 6.385772e+09
std 78.663842 1.012492e+10
min 1750.000000 9.350528e+06
25% 1817.750000 4.959041e+07
50% 1885.500000 1.017573e+09
75% 1953.250000 6.683314e+09
max 2021.000000 3.712385e+10
In [6]:
#cleaning CO2 data - restricted to world trends, only taking years greater than 1879
CO2_world = CO2_trends[(CO2_trends['Entity']=='World') & (CO2_trends['Year']>1849)]
#filtering relevant columns
drop_cols = ['Entity','Code']
CO2_world.drop(drop_cols,axis=1,inplace=True)
#print df head
print(CO2_world.head())
Year Annual CO₂ emissions 30914 1850 196896030.0 30915 1851 198804980.0 30916 1852 207550940.0 30917 1853 217209250.0 30918 1854 255138980.0
In [7]:
#merging dataframes
climate_data = pd.merge(climatechange_world,CO2_world,on='Year',how='inner')
#adding actual temperature (1961-1990 mean temperature of 14 degrees)
climate_data['avg temp'] = climate_data['relative avg temp']+14
#adding column to convert CO2 emissions to billions of tonnes
climate_data['CO₂ emissions (billions of tonnes)'] = climate_data['Annual CO₂ emissions']/1e9
#dropping all entries after 2021
climate_data = climate_data[climate_data['Year']<2022]
#dropping unused columns
drop_cols=['relative avg temp','upper bound','lower bound','Annual CO₂ emissions']
climate_data.drop(drop_cols,axis=1,inplace=True)
print(climate_data.tail())
Year avg temp CO₂ emissions (billions of tonnes) 167 2017 14.845174 36.096737 168 2018 14.762654 36.826510 169 2019 14.891073 37.082560 170 2020 14.922794 35.264086 171 2021 14.761856 37.123850
In [8]:
# Creating primary axis (CO2 emissions)
fig, ax1 = plt.subplots(figsize=(15, 9))
ax1.plot(climate_data['Year'], climate_data['CO₂ emissions (billions of tonnes)'],
color='steelblue',linewidth=2, label='CO2 emissions (billions of tonnes)')
ax1.set_xlabel('Year',weight='bold',fontsize=11)
ax1.set_ylabel('CO2 Emissions (Billions of Tonnes)',color='blue',weight='bold',fontsize=11)
# Adjusting gridlines on primary y-axis
ax1.grid(color='blue', linestyle='--', linewidth=0.5, alpha=0.3)
# Creating secondary y-axis (relative avg temp)
ax2 = ax1.twinx()
ax2.plot(climate_data['Year'], climate_data['avg temp'],
color='salmon', linewidth=2, label='Average Temperature (°C)')
ax2.set_ylabel('Average Temperature (°C)',color='red',weight='bold',fontsize=11)
# Setting x-axis limits for ax
ax1.set_xlim(1850, 2022)
# Setting y-axis limits for both ax1 and ax2
ax1.set_ylim(0, 1.15 * max(climate_data['CO₂ emissions (billions of tonnes)']))
ax2.set_ylim(0.999 * min(climate_data['avg temp']), 16.1)
# Combining legends for both axes
lines, labels = ax1.get_legend_handles_labels()
lines2, labels2 = ax2.get_legend_handles_labels()
ax2.legend(lines + lines2, labels + labels2, loc='upper right',fontsize=10)
# Set title
plt.title('Global CO2 Emissions compared to Average Global Temperature, 1850-2021',weight='bold',fontsize=14)
# Show plot
plt.show()
Adding Trendline¶
In [9]:
#Adding Anthropocene period
#https://www.discover.ukri.org/a-brief-history-of-climate-change-discoveries/index.html
#leading narrative - temperature & CO2 trends
# Creating primary axis (CO2 emissions)
fig, ax1 = plt.subplots(figsize=(15, 9))
ax1.plot(climate_data['Year'], climate_data['CO₂ emissions (billions of tonnes)'],
color='steelblue',linewidth=2, label='CO2 emissions (billions of tonnes)')
ax1.set_xlabel('Year',weight='bold',fontsize=11)
ax1.set_ylabel('CO2 Emissions (Billions of Tonnes)',color='blue',weight='bold',fontsize=11)
# Adjusting gridlines on primary y-axis
ax1.grid(color='blue', linestyle='--', linewidth=0.5, alpha=0.3)
# Creating secondary y-axis (relative avg temp)
ax2 = ax1.twinx()
ax2.plot(climate_data['Year'], climate_data['avg temp'],
color='salmon', linewidth=2, label='Average Temperature (°C)')
ax2.set_ylabel('Average Temperature (°C)',color='red',weight='bold',fontsize=11)
# Setting x-axis limits for ax
ax1.set_xlim(1850, 2022)
# Setting y-axis limits for both ax1 and ax2
ax1.set_ylim(0, 1.15 * max(climate_data['CO₂ emissions (billions of tonnes)']))
ax2.set_ylim(0.999 * min(climate_data['avg temp']), 16.1)
# Spline for CO2 data
theta1 = np.polyfit(climate_data['Year'],
climate_data['CO₂ emissions (billions of tonnes)'],3)
CO2_spline = theta1[3]+theta1[2]*pow(climate_data['Year'],
1)+theta1[1]*pow(climate_data['Year'],
2)+theta1[0]*pow(climate_data['Year'],3)
#plotting CO2 spline
ax1.plot(climate_data['Year'], CO2_spline,
color='blue', linestyle ='dashed', linewidth=2, label=None)
# Spline for temp data
theta2 = np.polyfit(climate_data['Year'],
climate_data['avg temp'],3)
temp_spline = theta2[3]+theta2[2]*pow(climate_data['Year'],
1)+theta2[1]*pow(climate_data['Year'],
2)+theta2[0]*pow(climate_data['Year'],3)
#plotting temp spline
ax2.plot(climate_data['Year'], temp_spline,
color='red', linestyle ='dashed', linewidth=2, label=None)
# Combining legends for both axes
lines, labels = ax1.get_legend_handles_labels()
lines2, labels2 = ax2.get_legend_handles_labels()
ax2.legend(lines + lines2, labels + labels2, loc='upper right',fontsize=10)
# Set title
plt.title('Global CO2 Emissions compared to Average Global Temperature, 1850-2021',weight='bold',fontsize=14)
# Show plot
plt.show()
Adding x-limits¶
In [10]:
#Adding Anthropocene period
#https://www.discover.ukri.org/a-brief-history-of-climate-change-discoveries/index.html
#leading narrative - temperature & CO2 trends
# Creating primary axis (CO2 emissions)
fig, ax1 = plt.subplots(figsize=(15, 9))
ax1.plot(climate_data['Year'], climate_data['CO₂ emissions (billions of tonnes)'],
color='steelblue',linewidth=2, label='CO2 emissions (billions of tonnes)')
ax1.set_xlabel('Year',weight='bold',fontsize=11)
ax1.set_ylabel('CO2 Emissions (Billions of Tonnes)',color='blue',weight='bold',fontsize=11)
# Adjusting gridlines on primary y-axis
ax1.grid(color='blue', linestyle='--', linewidth=0.5, alpha=0.3)
# Creating secondary y-axis (relative avg temp)
ax2 = ax1.twinx()
ax2.plot(climate_data['Year'], climate_data['avg temp'],
color='salmon', linewidth=2, label='Average Temperature (°C)')
ax2.set_ylabel('Average Temperature (°C)',color='red',weight='bold',fontsize=11)
# Setting x-axis limits for ax
ax1.set_xlim(1850, 2022)
# Setting y-axis limits for both ax1 and ax2
ax1.set_ylim(0, 1.15 * max(climate_data['CO₂ emissions (billions of tonnes)']))
ax2.set_ylim(0.999 * min(climate_data['avg temp']), 16.1)
# Spline for CO2 data
theta1 = np.polyfit(climate_data['Year'],
climate_data['CO₂ emissions (billions of tonnes)'],3)
CO2_spline = theta1[3]+theta1[2]*pow(climate_data['Year'],
1)+theta1[1]*pow(climate_data['Year'],
2)+theta1[0]*pow(climate_data['Year'],3)
#plotting CO2 spline
ax1.plot(climate_data['Year'], CO2_spline,
color='blue', linestyle ='dashed', linewidth=2, label=None)
# Spline for temp data
theta2 = np.polyfit(climate_data['Year'],
climate_data['avg temp'],3)
temp_spline = theta2[3]+theta2[2]*pow(climate_data['Year'],
1)+theta2[1]*pow(climate_data['Year'],
2)+theta2[0]*pow(climate_data['Year'],3)
#plotting temp spline
ax2.plot(climate_data['Year'], temp_spline,
color='red', linestyle ='dashed', linewidth=2, label=None)
#Constant lines
#setting constant line transparency
cnst_alpha=0.8
# Adding horizontal constant line at 14C (pre-industrial mean temp)
#https://earthobservatory.nasa.gov/world-of-change/global-temperatures
ax2.axhline(y=14, color='black', linestyle='--',alpha=cnst_alpha)
pre_ind_label = 'Pre-industrial Temperatures'
ax2.text(1882,14.03,pre_ind_label,color='black',alpha=cnst_alpha)
#Adding horizontal constant line at 15.5C (+1.5C Mean Temp)
ax2.axhline(y=15, color='darkgoldenrod', linestyle='--',alpha=cnst_alpha)
increase_label1 = '+1°C increase'
ax2.text(1882,15.03,increase_label1,color='darkgoldenrod',alpha=cnst_alpha)
# Adding horizontal constant line at 15C (+1C mean temp)
ax2.axhline(y=15.5, color='red', linestyle='--',alpha=cnst_alpha)
increase_label2 = '+1.5°C increase'
ax2.text(1882,15.53,increase_label2,color='red',alpha=cnst_alpha)
#Adding horizontal constant line at 16C (+2C Mean Temp)
ax2.axhline(y=16, color='darkred', linestyle='--',alpha=cnst_alpha)
increase_label3 = '+2°C increase'
ax2.text(1882,16.03,increase_label3,color='darkred',alpha=cnst_alpha)
# Combining legends for both axes
lines, labels = ax1.get_legend_handles_labels()
lines2, labels2 = ax2.get_legend_handles_labels()
ax2.legend(lines + lines2, labels + labels2, loc='upper right',fontsize=10)
# Set title
plt.title('Global CO2 Emissions compared to Average Global Temperature, 1850-2021',weight='bold',fontsize=14)
# Show plot
plt.show()
Adding y-limits¶
In [11]:
# Creating primary axis (CO2 emissions)
fig, ax1 = plt.subplots(figsize=(15, 9))
ax1.plot(climate_data['Year'], climate_data['CO₂ emissions (billions of tonnes)'],
color='steelblue',linewidth=2, label='CO2 emissions (billions of tonnes)')
ax1.set_xlabel('Year',weight='bold',fontsize=11)
ax1.set_ylabel('CO2 Emissions (Billions of Tonnes)',color='blue',weight='bold',fontsize=11)
# Adjusting gridlines on primary y-axis
ax1.grid(color='blue', linestyle='--', linewidth=0.5, alpha=0.3)
# Creating secondary y-axis (relative avg temp)
ax2 = ax1.twinx()
ax2.plot(climate_data['Year'], climate_data['avg temp'],
color='salmon', linewidth=2, label='Average Temperature (°C)')
ax2.set_ylabel('Average Temperature (°C)',color='red',weight='bold',fontsize=11)
# Setting x-axis limits for ax
ax1.set_xlim(1850, 2022)
# Setting y-axis limits for both ax1 and ax2
ax1.set_ylim(0, 1.15 * max(climate_data['CO₂ emissions (billions of tonnes)']))
ax2.set_ylim(0.999 * min(climate_data['avg temp']), 16.1)
# Spline for CO2 data
theta1 = np.polyfit(climate_data['Year'],
climate_data['CO₂ emissions (billions of tonnes)'],3)
CO2_spline = theta1[3]+theta1[2]*pow(climate_data['Year'],
1)+theta1[1]*pow(climate_data['Year'],
2)+theta1[0]*pow(climate_data['Year'],3)
#plotting CO2 spline
ax1.plot(climate_data['Year'], CO2_spline,
color='blue', linestyle ='dashed', linewidth=2, label=None)
# Spline for temp data
theta2 = np.polyfit(climate_data['Year'],
climate_data['avg temp'],3)
temp_spline = theta2[3]+theta2[2]*pow(climate_data['Year'],
1)+theta2[1]*pow(climate_data['Year'],
2)+theta2[0]*pow(climate_data['Year'],3)
#plotting temp spline
ax2.plot(climate_data['Year'], temp_spline,
color='red', linestyle ='dashed', linewidth=2, label=None)
#Constant lines
#setting constant line transparency
cnst_alpha=0.8
# Adding horizontal constant line at 14C (pre-industrial mean temp)
#https://earthobservatory.nasa.gov/world-of-change/global-temperatures
ax2.axhline(y=14, color='black', linestyle='--',alpha=cnst_alpha)
pre_ind_label = 'Pre-industrial Temperatures'
ax2.text(1882,14.03,pre_ind_label,color='black',alpha=cnst_alpha)
#Adding horizontal constant line at 15.5C (+1.5C Mean Temp)
ax2.axhline(y=15, color='darkgoldenrod', linestyle='--',alpha=cnst_alpha)
increase_label1 = '+1°C increase'
ax2.text(1882,15.03,increase_label1,color='darkgoldenrod',alpha=cnst_alpha)
# Adding horizontal constant line at 15C (+1C mean temp)
ax2.axhline(y=15.5, color='red', linestyle='--',alpha=cnst_alpha)
increase_label2 = '+1.5°C increase'
ax2.text(1882,15.53,increase_label2,color='red',alpha=cnst_alpha)
#Adding horizontal constant line at 16C (+2C Mean Temp)
ax2.axhline(y=16, color='darkred', linestyle='--',alpha=cnst_alpha)
increase_label3 = '+2°C increase'
ax2.text(1882,16.03,increase_label3,color='darkred',alpha=cnst_alpha)
#Adding vertical constant line at 1988 (IPCC)
plt.axvline(x=1988, color='darkgreen',linestyle='--',alpha=cnst_alpha)
IPCC_label='IPCC (1988)'
ax2.text(1986,13.5,IPCC_label,color='darkgreen',weight='bold',fontsize=10,rotation=90,alpha=cnst_alpha)
#Adding vertical constant line at 1995 (Kyoto)
plt.axvline(x=1997, color='darkgreen',linestyle='--',alpha=cnst_alpha)
kyoto_label='Kyoto (1997)'
ax2.text(1995,13.5,kyoto_label,color='darkgreen',weight='bold',fontsize=10,rotation=90,alpha=cnst_alpha)
#Adding vertical constant line at 2015 (Paris)
plt.axvline(x=2015, color='darkgreen',linestyle='--',alpha=cnst_alpha)
paris_label='Paris (2015)'
ax2.text(2013,13.5,paris_label,color='darkgreen',weight='bold', fontsize=10,rotation=90,alpha=cnst_alpha)
# Combining legends for both axes
lines, labels = ax1.get_legend_handles_labels()
lines2, labels2 = ax2.get_legend_handles_labels()
ax2.legend(lines + lines2, labels + labels2, loc='upper right',fontsize=10)
# Set title
plt.title('Global CO2 Emissions compared to Average Global Temperature, 1850-2021',weight='bold',fontsize=14)
# Show plot
plt.show()
Adding shaded periods¶
In [12]:
# Creating primary axis (CO2 emissions)
fig, ax1 = plt.subplots(figsize=(15, 9))
ax1.plot(climate_data['Year'], climate_data['CO₂ emissions (billions of tonnes)'],
color='steelblue',linewidth=2, label='CO2 emissions (billions of tonnes)')
ax1.set_xlabel('Year',weight='bold',fontsize=11)
ax1.set_ylabel('CO2 Emissions (Billions of Tonnes)',color='blue',weight='bold',fontsize=11)
# Adjusting gridlines on primary y-axis
ax1.grid(color='blue', linestyle='--', linewidth=0.5, alpha=0.3)
# Creating secondary y-axis (relative avg temp)
ax2 = ax1.twinx()
ax2.plot(climate_data['Year'], climate_data['avg temp'],
color='salmon', linewidth=2, label='Average Temperature (°C)')
ax2.set_ylabel('Average Temperature (°C)',color='red',weight='bold',fontsize=11)
# Setting x-axis limits for ax
ax1.set_xlim(1850, 2022)
# Setting y-axis limits for both ax1 and ax2
ax1.set_ylim(0, 1.15 * max(climate_data['CO₂ emissions (billions of tonnes)']))
ax2.set_ylim(0.999 * min(climate_data['avg temp']), 16.1)
# Spline for CO2 data
theta1 = np.polyfit(climate_data['Year'],
climate_data['CO₂ emissions (billions of tonnes)'],3)
CO2_spline = theta1[3]+theta1[2]*pow(climate_data['Year'],
1)+theta1[1]*pow(climate_data['Year'],
2)+theta1[0]*pow(climate_data['Year'],3)
#plotting CO2 spline
ax1.plot(climate_data['Year'], CO2_spline,
color='blue', linestyle ='dashed', linewidth=2, label=None)
# Spline for temp data
theta2 = np.polyfit(climate_data['Year'],
climate_data['avg temp'],3)
temp_spline = theta2[3]+theta2[2]*pow(climate_data['Year'],
1)+theta2[1]*pow(climate_data['Year'],
2)+theta2[0]*pow(climate_data['Year'],3)
#plotting temp spline
ax2.plot(climate_data['Year'], temp_spline,
color='red', linestyle ='dashed', linewidth=2, label=None)
#Constant lines
#setting constant line transparency
cnst_alpha=0.8
# Adding horizontal constant line at 14C (pre-industrial mean temp)
#https://earthobservatory.nasa.gov/world-of-change/global-temperatures
ax2.axhline(y=14, color='black', linestyle='--',alpha=cnst_alpha)
pre_ind_label = 'Pre-industrial Temperatures'
ax2.text(1882,14.03,pre_ind_label,color='black',alpha=cnst_alpha)
#Adding horizontal constant line at 15.5C (+1.5C Mean Temp)
ax2.axhline(y=15, color='darkgoldenrod', linestyle='--',alpha=cnst_alpha)
increase_label1 = '+1°C increase'
ax2.text(1882,15.03,increase_label1,color='darkgoldenrod',alpha=cnst_alpha)
# Adding horizontal constant line at 15C (+1C mean temp)
ax2.axhline(y=15.5, color='red', linestyle='--',alpha=cnst_alpha)
increase_label2 = '+1.5°C increase'
ax2.text(1882,15.53,increase_label2,color='red',alpha=cnst_alpha)
#Adding horizontal constant line at 16C (+2C Mean Temp)
ax2.axhline(y=16, color='darkred', linestyle='--',alpha=cnst_alpha)
increase_label3 = '+2°C increase'
ax2.text(1882,16.03,increase_label3,color='darkred',alpha=cnst_alpha)
#Adding vertical constant line at 1988 (IPCC)
plt.axvline(x=1988, color='darkgreen',linestyle='--',alpha=cnst_alpha)
IPCC_label='IPCC (1988)'
ax2.text(1986,13.5,IPCC_label,color='darkgreen',weight='bold',fontsize=10,rotation=90,alpha=cnst_alpha)
#Adding vertical constant line at 1995 (Kyoto)
plt.axvline(x=1997, color='darkgreen',linestyle='--',alpha=cnst_alpha)
kyoto_label='Kyoto (1997)'
ax2.text(1995,13.5,kyoto_label,color='darkgreen',weight='bold',fontsize=10,rotation=90,alpha=cnst_alpha)
#Adding vertical constant line at 2015 (Paris)
plt.axvline(x=2015, color='darkgreen',linestyle='--',alpha=cnst_alpha)
paris_label='Paris (2015)'
ax2.text(2013,13.5,paris_label,color='darkgreen',weight='bold', fontsize=10,rotation=90,alpha=cnst_alpha)
# Specifying regions to shade on the x-axis
plt.axvspan(1870, 1910, alpha=0.1, color='darkorange',
label='Second Industrial Revolution')
plt.axvspan(1945, 2022, alpha=0.1, color='royalblue',
label='The Anthropocene')
# Combining legends for both axes
lines, labels = ax1.get_legend_handles_labels()
lines2, labels2 = ax2.get_legend_handles_labels()
ax2.legend(lines + lines2, labels + labels2, loc='upper right',fontsize=10)
# Set title
plt.title('Global CO2 Emissions compared to Average Global Temperature, 1850-2021',weight='bold',fontsize=14)
# Show plot
plt.show()
Adding callouts¶
In [13]:
# Creating primary axis (CO2 emissions)
fig, ax1 = plt.subplots(figsize=(15, 9))
ax1.plot(climate_data['Year'], climate_data['CO₂ emissions (billions of tonnes)'],
color='steelblue',linewidth=2, label='CO2 emissions (billions of tonnes)')
ax1.set_xlabel('Year',weight='bold',fontsize=11)
ax1.set_ylabel('CO2 Emissions (Billions of Tonnes)',color='blue',weight='bold',fontsize=11)
# Adjusting gridlines on primary y-axis
ax1.grid(color='blue', linestyle='--', linewidth=0.5, alpha=0.3)
# Creating secondary y-axis (relative avg temp)
ax2 = ax1.twinx()
ax2.plot(climate_data['Year'], climate_data['avg temp'],
color='salmon', linewidth=2, label='Average Temperature (°C)')
ax2.set_ylabel('Average Temperature (°C)',color='red',weight='bold',fontsize=11)
# Setting x-axis limits for ax
ax1.set_xlim(1850, 2022)
# Setting y-axis limits for both ax1 and ax2
ax1.set_ylim(0, 1.15 * max(climate_data['CO₂ emissions (billions of tonnes)']))
ax2.set_ylim(0.999 * min(climate_data['avg temp']), 16.1)
# Spline for CO2 data
theta1 = np.polyfit(climate_data['Year'],
climate_data['CO₂ emissions (billions of tonnes)'],3)
CO2_spline = theta1[3]+theta1[2]*pow(climate_data['Year'],
1)+theta1[1]*pow(climate_data['Year'],
2)+theta1[0]*pow(climate_data['Year'],3)
#plotting CO2 spline
ax1.plot(climate_data['Year'], CO2_spline,
color='blue', linestyle ='dashed', linewidth=2, label=None)
# Spline for temp data
theta2 = np.polyfit(climate_data['Year'],
climate_data['avg temp'],3)
temp_spline = theta2[3]+theta2[2]*pow(climate_data['Year'],
1)+theta2[1]*pow(climate_data['Year'],
2)+theta2[0]*pow(climate_data['Year'],3)
#plotting temp spline
ax2.plot(climate_data['Year'], temp_spline,
color='red', linestyle ='dashed', linewidth=2, label=None)
#Constant lines
#setting constant line transparency
cnst_alpha=0.8
# Adding horizontal constant line at 14C (pre-industrial mean temp)
#https://earthobservatory.nasa.gov/world-of-change/global-temperatures
ax2.axhline(y=14, color='black', linestyle='--',alpha=cnst_alpha)
pre_ind_label = 'Pre-industrial Temperatures'
ax2.text(1882,14.03,pre_ind_label,color='black',alpha=cnst_alpha)
#Adding horizontal constant line at 15.5C (+1.5C Mean Temp)
ax2.axhline(y=15, color='darkgoldenrod', linestyle='--',alpha=cnst_alpha)
increase_label1 = '+1°C increase'
ax2.text(1882,15.03,increase_label1,color='darkgoldenrod',alpha=cnst_alpha)
# Adding horizontal constant line at 15C (+1C mean temp)
ax2.axhline(y=15.5, color='red', linestyle='--',alpha=cnst_alpha)
increase_label2 = '+1.5°C increase'
ax2.text(1882,15.53,increase_label2,color='red',alpha=cnst_alpha)
#Adding horizontal constant line at 16C (+2C Mean Temp)
ax2.axhline(y=16, color='darkred', linestyle='--',alpha=cnst_alpha)
increase_label3 = '+2°C increase'
ax2.text(1882,16.03,increase_label3,color='darkred',alpha=cnst_alpha)
#Adding vertical constant line at 1988 (IPCC)
plt.axvline(x=1988, color='darkgreen',linestyle='--',alpha=cnst_alpha)
IPCC_label='IPCC (1988)'
ax2.text(1986,13.5,IPCC_label,color='darkgreen',weight='bold',fontsize=10,rotation=90,alpha=cnst_alpha)
#Adding vertical constant line at 1995 (Kyoto)
plt.axvline(x=1997, color='darkgreen',linestyle='--',alpha=cnst_alpha)
kyoto_label='Kyoto (1997)'
ax2.text(1995,13.5,kyoto_label,color='darkgreen',weight='bold',fontsize=10,rotation=90,alpha=cnst_alpha)
#Adding vertical constant line at 2015 (Paris)
plt.axvline(x=2015, color='darkgreen',linestyle='--',alpha=cnst_alpha)
paris_label='Paris (2015)'
ax2.text(2013,13.5,paris_label,color='darkgreen',weight='bold', fontsize=10,rotation=90,alpha=cnst_alpha)
# Specify the regions to shade on the x-axis
#https://www.nature.com/articles/d41586-019-01641-5
plt.axvspan(1870, 1910, alpha=0.1, color='darkorange', label='Second Industrial Revolution')
plt.axvspan(1945, 2022, alpha=0.1, color='royalblue', label='The Anthropocene')
# Combining legends for both axes
lines, labels = ax1.get_legend_handles_labels()
lines2, labels2 = ax2.get_legend_handles_labels()
ax2.legend(lines + lines2, labels + labels2, loc='upper right',fontsize=10)
#adding callouts for key dates
# Points to annotate (x, y, annotation_text)
callouts = [(1886, climate_data.loc[climate_data['Year'] == 1886, 'avg temp'].values[0], 'Automobile \ninvented'),
(1903, climate_data.loc[climate_data['Year'] == 1903, 'avg temp'].values[0], 'Airplane \ninvented'),
(1921, climate_data.loc[climate_data['Year'] == 1921, 'avg temp'].values[0], 'Automobiles in America\nexceed 10 million'),
(1938, climate_data.loc[climate_data['Year'] == 1938, 'avg temp'].values[0], 'Rise in global\ntemperatures proven;\nOil discovered in\nSaudi Arabia'),
(1968, climate_data.loc[climate_data['Year'] == 1968, 'avg temp'].values[0], 'Melting ice caps \npredicted'),
(1985, climate_data.loc[climate_data['Year'] == 1985, 'avg temp'].values[0], 'Ozone hole \ndiscovered'),
(1994, climate_data.loc[climate_data['Year'] == 1994, 'avg temp'].values[0], '1st climate change \ntreaty signed into law'),
(2003, climate_data.loc[climate_data['Year'] == 2003, 'avg temp'].values[0], 'European heatwave \nkills 70,000+'),
(2021, climate_data.loc[climate_data['Year'] == 2021, 'avg temp'].values[0], 'EU climate law\nratified')
]
# Iterate over specified points and add minimal annotations
for point in callouts:
x_point, y_point, annotation_text = point
ax2.annotate(annotation_text, xy=(x_point, y_point), xytext=(x_point - 35, y_point + 0.7),
arrowprops=dict(arrowstyle='-', color='black'),
fontsize=9, weight='bold') # Set font size for annotation text
# Set title
plt.title('Global CO2 Emissions compared to Average Global Temperature, 1850-2021',weight='bold',fontsize=14)
# Show plot
plt.show()
Summary¶
As can be seen from the trendlines above, the global temperatures are increasing precipitously with a corresponding increase in CO2 emissions. By supplementing these graphics with additional callouts and shaded regions, we can add greater context to the problems faced and why they are occurring, and make more informed recommendations on what needs to be done in order to mitigate this damage.