Global temperature anomalies

environment
python
Revealing climate change through deviations in Earth’s surface temperature from the historical average.
Published

Nov 3, 2025

Keywords

temperature

Summary

Global temperature anomalies indicate how Earth’s surface temperature deviates from a historical average, providing crucial insights into climate change. Persistent positive anomalies signal a warming trend driven by greenhouse gas emissions, while negative anomalies are less frequent in recent decades. By tracking these variations, scientists can assess long-term climate patterns, identify extreme weather risks, and evaluate the impact of human activities on global temperatures.

Code 
# Libraries
# =========================================
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.ticker as ticker

# Data Extraction (temperature)
# =========================================
# URL NASA GISS global temperature
url = "https://data.giss.nasa.gov/gistemp/tabledata_v4/GLB.Ts+dSST.csv"
dft = pd.read_csv(url, skiprows=1)

# Data Extraction (co2)
# =========================================
# URL del archivo CSV
url = "https://zenodo.org/records/13981696/files/GCB2024v17_MtCO2_flat.csv?download=1"
dfc = pd.read_csv(url)

# Data Manipulation (temperature)
# =========================================
# Select columns
dft = dft[["Year", "Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec", "J-D"]]

# Rename columns
dft.columns = dft.columns.str.lower()
dft = dft.rename(columns=lambda x: x.lower())

# Unpivot columns
dft = dft.melt(id_vars=["year"], var_name="month", value_name="value")
dft = dft[dft["month"] == "j-d"]

# Data Manipulation (co2)
# =========================================
# Transform Data
dfc = dfc[dfc['ISO 3166-1 alpha-3'] == 'WLD']
dfc.rename(columns={'Year': 'year', 'ISO 3166-1 alpha-3': 'iso', 'Total': 'co2'}, inplace=True)
dfc = dfc[['year', 'iso', 'co2']]

# Merge dataframes
# =========================================
df = dft.merge(dfc, on='year', how='left')

print(df)

# Data Visualization
# =========================================
# Font and style
plt.rcParams.update({'font.family': 'sans-serif', 'font.sans-serif': ['Open Sans'], 'font.size': 9})
sns.set(style="white", palette="muted")

# Define a color map
cmap = plt.get_cmap('coolwarm')
norm = plt.Normalize(-0.5, 1)

# Create figure and plot
fig, ax1 = plt.subplots(figsize=(10, 5))
ax2 = ax1.twinx()
bars =ax1.bar(df['year'], df['value'], color=cmap(norm(df['value'])), width=1, edgecolor='none')
line = ax2.plot(df['year'], df['co2'], label='CO2', color='#262626', linestyle=':', linewidth=1)

# Add title and labels
ax1.text(0, 1.12, f'Global Temperature Anomaly', fontsize=16, fontweight='bold', ha='left', transform=ax1.transAxes)
ax1.text(0, 1.07, 'Compared with mid -20th century (°C)', fontsize=11, color='#262626', ha='left', transform=ax1.transAxes)
ax1.text(0, 1.02, r'(In contrast with CO$_2$ emissions)', fontsize=9, fontweight='light', color='#262626', ha='left', transform=ax1.transAxes)
ax1.set_xlim(1877, 2027)
ax1.axhline(y=0, color='black', linestyle='-', linewidth=0.75)
yticks = [-0.5, -0.25, 0, 0.25, 0.5, 0.75, 1, 1.25]
ytick_labels = [f'{y:+.2f}' if y != 0 else '0' for y in yticks]
ax1.set_yticks(yticks, ytick_labels)
ax1.set_yticklabels(ytick_labels, fontsize=9)
ax1.set_xticks([1880, 1890, 1900, 1910, 1920, 1930, 1940, 1950, 1960, 1970, 1980, 1990, 2000, 2010, 2020])
ax1.tick_params(axis='x', labelsize=9)
ax1.yaxis.set_ticks_position('none')
ax1.grid(axis='y', linestyle='-', color='#262626', linewidth=0.1, alpha=0.6)
ax2.set_ylim(0, 40000)
ymin, ymax = ax2.get_ylim()
yticks = np.linspace(ymin, ymax, 8)
ax2.set_yticks(yticks)
yticks_rounded = np.round(yticks / 5000) * 5000
yticks_k = [f"{int(tick / 1000)}Mt" for tick in yticks_rounded]
ax2.set_yticklabels(yticks_k)
ax2.tick_params(axis='y', labelsize=9)
ax2.yaxis.set_ticks_position('none')

# Add column labels
for i, year in enumerate(df['year']):
    if year in [1973, 1990, 1998, 2010, 2016, 2024]:
        # Check positive and negative values
        symbol = "+" if df['value'].iloc[i] >= 0 else "-"
        value_text = f"{symbol}{abs(df['value'].iloc[i]):,.2f}"
        
        # Recent value bold
        fontweight = 'bold' if year == df['year'].max() else 'normal'

        # Set the offset based on the year
        offset = 0.11 if year in [1973, 1990] else 0.05

        # Add label
        ax1.text(year, df['value'].iloc[i] + offset, value_text,
                 ha='center', va='bottom', fontsize=7, color='#363636', fontweight=fontweight)

# Remove spines
for ax in [ax1, ax2]:
    for spine in ["top", "bottom"]:
        ax.spines[spine].set_visible(False)

# Add Data Source
spaces = ' ' * 23
ax1.text(0, -0.16, f'{spaces}NASA Goddard Institute for Space Studies\nThe Global Carbon Project\'s fossil CO₂ emissions dataset', 
         transform=ax1.transAxes, 
         fontsize=8, 
         color='gray', 
         ha='left', 
         family='sans-serif')

# Add Data Source bold
ax1.text(0, -0.16, 'Data Source:\n ', 
         transform=ax1.transAxes, 
         fontsize=8, 
         color='gray', 
         ha='left', 
         family='sans-serif', 
         fontweight='bold')

 # Add Year label
formatted_date = 2024 
ax1.text(1, 1.12, f'{formatted_date}',
    transform=ax1.transAxes, 
    fontsize=18, ha='right', va='top',
    fontweight='bold', color='#D3D3D3')

# Add Celsius
ax1.text(-0.04, -0.08, '°C', 
    transform=ax1.transAxes, 
    fontsize=10, 
    fontweight='bold',
    color='black')

# Add CO2
ax1.text(1.01, -0.08, r'CO$_2$', 
    transform=ax1.transAxes, 
    fontsize=10, 
    fontweight='bold',
    color='black')

# Adjust layout
plt.tight_layout()

# Save it..
plt.savefig('C:/Users/guill/Downloads/FIG_NASA_Temperature_Anomalies.png', format='png', bbox_inches='tight')

# Show the plot!
plt.show()

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