1. Objective

The primary objective of this project was to quantify and visualize the long-term trends in maximum air temperature over the land areas surrounding the Bay of Bengal from 1974 to 2023. The goal was to transform raw satellite climate data into an intuitive time-series chart, enabling the observation of seasonal patterns, inter-annual variability, and potential long-term warming trends. This serves as a foundational analysis for understanding regional climate dynamics.

2. Data Acquisition

The project sourced its data from the TerraClimate dataset, a widely respected global climate reanalysis product available on the Google Earth Engine (GEE) data catalog.

• Dataset:IDAHO_EPSCOR/TERRACLIMATE

• Key Variable: The 'tmmx' band, which represents the monthly average of daily maximum air temperature, measured in Kelvin.

• Spatial Scope: The analysis was bounded by a custom geometry file (fullbayofbengal) to focus specifically on the Bay of Bengal region.

• Temporal Scope: Data was filtered for a 50-year period from January 1, 1974, to January 1, 2024.

3. Data Processing

The raw data required cleaning and preparation before analysis:

• Unit Conversion: The raw 'tmmx' values in TerraClimate are stored as integers to reduce storage. A scaling function was applied to every image in the collection, multiplying each pixel by 0.1 to convert the data to its correct physical unit (degrees Celsius).

• Spatial Filtering: The global TerraClimate dataset was clipped to the specific boundaries of the Bay of Bengal region, ensuring the analysis was geographically relevant.

• Temporal Filtering: The image collection was limited to the exact 50-year timeframe of interest.

• Property Preservation: Crucial metadata, specifically the timestamp (system:time_start), was preserved during the scaling operation to enable accurate time-series plotting.

4. Data Analysis

The core analysis involved aggregating the spatial data into a single, clear temporal trend:

• Zonal Statistics: For each month over the 50-year period, the average maximum temperature across the entire Bay of Bengal region was calculated using the ee.Reducer.mean() function. This reduced the complex spatial data of each image to a single, representative value for the region per time step.

• Time-Series Visualization: These monthly average values were then plotted using the GEE UI charting library. The resulting line chart effectively displays the temperature's progression over time, with the x-axis showing the date and the y-axis showing the temperature in degrees Celsius. The interpolateNulls parameter ensures the line is continuous even if there are minor data gaps.

5. Potential Usage

The outputs and methodology of this project have several practical applications:

• Climate Change Monitoring: The chart provides clear, empirical evidence of temperature changes, useful for environmental agencies and researchers.

• Agricultural Planning: Understanding temperature trends is critical for crop selection, irrigation planning, and predicting growing seasons.

• Disaster Risk Reduction: The data can inform studies on heatwaves, droughts, and their increasing frequency and intensity.

• Policy and Advocacy: The clear visualization can be used to communicate the realities of climate change to policymakers and the public.

• Educational Tool: Serves as an excellent example of using geospatial technology for environmental science.

6. Impact

This project demonstrates a powerful and efficient workflow for environmental monitoring. By leveraging the cloud-computing power of Google Earth Engine, it analyzes five decades of global climate data in seconds, a task that would be prohibitively slow with traditional methods. The final visualization translates terabytes of complex geospatial data into an accessible and impactful narrative about our changing planet, providing valuable insights for sustainable development and climate resilience strategies in the vulnerable Bay of Bengal region.

GitHub Code link: https://github.com/mohammadoney/Bay-of-Bengal-Temperature-Trends-1974-2023-/tree/main