Visualizing Climate Change in India

Visualizing Climate Change in India#

In this vignette, we show how to use varunayan to easily extract annual average temperature data for India and visualize the temperature changes over the period from 1941 to 2024.

Step 1: Download Temperature Data for India#

We use varunayan.era5ify_geojson to download yearly average 2m temperature data for India, using a GeoJSON boundary file from a public URL.

import varunayan

df = varunayan.era5ify_geojson(
    request_id="temp_india_yearly",
    variables=["2m_temperature"],
    start_date="1941-1-1",
    end_date="2024-12-31",
    json_file="https://gist.githubusercontent.com/JaggeryArray/bf296307132e7d6127e28864c7bea5bf/raw/4bce03beea35d61a93007f54e52ba81f575a7feb/india.json",
    frequency="yearly",
)


============================================================
STARTING ERA5 SINGLE LEVEL PROCESSING
============================================================
Request ID: temp_india_yearly
Variables: ['2m_temperature']
Date Range: 1941-01-01 to 2024-12-31
Frequency: yearly
Resolution: 0.25°
GeoJSON File: C:\Users\ATHARV~1\AppData\Local\Temp\temp_india_yearly_temp_geojson.json


--- GeoJSON Mini Map ---

MINI MAP (68.18°W to 97.40°E, 7.97°S to 35.49°N):
┌─────────────────────────────────────────┐
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└─────────────────────────────────────────┘
 ■ = Inside the shape
 · = Outside the shape


Saving files to output directory: temp_india_yearly_output
  Saved final data to: temp_india_yearly_output\temp_india_yearly_yearly_data.csv
  Saved unique coordinates to: temp_india_yearly_output\temp_india_yearly_unique_latlongs.csv
  Saved raw data to: temp_india_yearly_output\temp_india_yearly_raw_data.csv

============================================================
PROCESSING COMPLETE
============================================================

RESULTS SUMMARY:
----------------------------------------
Variables processed: 1
Time period:         1941-01-01 to 2024-12-31
Final output shape:  (84, 2)
Total complete processing time: 447.03 seconds

First 5 rows of aggregated data:
          t2m  year
0  296.386200  1941
1  296.234344  1942
2  295.743408  1943
3  296.361176  1944
4  296.111603  1945

============================================================
ERA5 SINGLE LEVEL PROCESSING COMPLETED SUCCESSFULLY
============================================================

Step 2: Compute Deviation from Long-Term Mean#

We calculate the deviation of each year’s average temperature from the long-term mean. This helps us understand how each year compares to the overall average.

mean_t2m = df["t2m"].mean()

df["dev"] = df["t2m"] - mean_t2m

df.head()
t2m year dev
0 296.386200 1941 -0.202301
1 296.234344 1942 -0.354156
2 295.743408 1943 -0.845093
3 296.361176 1944 -0.227325
4 296.111603 1945 -0.476898

Step 3: Set Up Matplotlib Styling#

We configure Matplotlib to ensure consistent, high-resolution plots with a clean visual style. This setup improves readability, especially in documentation.

import matplotlib.cm as cm
import matplotlib.colors as mcolors
import matplotlib.pyplot as plt


def setup_matplotlib():
    plt.rcParams["figure.dpi"] = 300
    plt.rcParams["savefig.dpi"] = 300
    plt.rcParams["font.family"] = "sans-serif"
    plt.rcParams["font.sans-serif"] = ["Arial"]
    plt.rcParams["axes.labelweight"] = "normal"

    plt.rcParams["mathtext.fontset"] = "custom"
    plt.rcParams["mathtext.rm"] = "Arial"
    plt.rcParams["mathtext.it"] = "Arial:italic"
    plt.rcParams["mathtext.bf"] = "Arial:bold"


setup_matplotlib()

Step 4: Normalize Deviation Values#

We normalize the temperature deviation values so we can map them to a color scale for the bar chart.

norm = mcolors.Normalize(vmin=df["dev"].min(), vmax=df["dev"].max())

Step 5: Set Color Map#

We use the coolwarm color map to represent deviation: cool colors for below-average years and warm colors for above-average years.

cmap = plt.colormaps["coolwarm"]

colors = cmap(norm(df["dev"]))

Step 6: Plot Temperature Deviation Over Time#

We create a color-coded bar plot of yearly temperature deviations. A colorbar is added to indicate the scale of deviation from the mean.

fig, ax = plt.subplots(figsize=(14, 6))
bars = ax.bar(df["year"], df["dev"], color=colors)

sm = cm.ScalarMappable(cmap=cmap, norm=norm)
sm.set_array(df["dev"].values)

cbar = fig.colorbar(sm, ax=ax)
cbar.set_label("Deviation")

ax.set_xlabel("Year")
ax.set_ylabel("Deviation")
ax.set_title("Temperature change in India")

plt.tight_layout()
plt.show()
../_images/db2bafe0586543cd339694a2ae14b51b412a46870969bfc8a89ee5d0dae1bd9d.png

This plot shows the yearly deviation in average temperature across India from the long-term mean (1941–2024).
Bars above zero indicate warmer-than-average years, while bars below zero indicate cooler-than-average years.
The color gradient reinforces this — with warm tones for hotter years and cool tones for colder years.

Conclusion#

This example demonstrates how varunayan can be used to easily access and visualize historical climate data for a specific region — in this case, India’s average temperature from 1941 to 2024. The visualization clearly illustrates how India’s average annual temperature has changed over the last 80+ years.

  • There is a noticeable upward shift in temperature deviations over time, with recent decades showing more frequent and intense warmer-than-average years.

  • The use of color gradients helps visually track how the climate signal has intensified over time.

By combining climate data extracted using varunayan with simple processing and visualization, we gain a compelling and accessible picture of India’s changing climate — one that is both data-driven and intuitive.

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