This repository contains a Jupyter Notebook that walks through the creation of the Keeling Curve using Python. The project uses observational data from the NOAA Mauna Loa Observatory to visualize trends in atmospheric CO₂ concentrations over time.
The Keeling Curve is one of the most iconic representations in climate science—a time series plot that shows the continuous increase in atmospheric CO₂ concentrations as well as its seasonal variations. In this project, I:
- Acquired the data: Downloaded the latest CO₂ concentration dataset from the NOAA Mauna Loa Observatory.
- Preprocessed the data: Cleaned and formatted the raw data to extract the necessary date and concentration information.
- Visualized the data: Used Python libraries to plot the Keeling Curve, capturing both the long-term trend and seasonal fluctuations.
- Explored insights: Provided basic trend analysis and visualization techniques to make sense of the atmospheric data.
All data used in this analysis is obtained from the NOAA Mauna Loa Observatory. Check out the data at:
https://gml.noaa.gov/ccgg/trends/data.html
NOAA/GML calculation of global means
Understanding CO2 and its relation with parts per million (ppm)
Ensure you have Python 3.7 or later and the following Python libraries installed:
- numpy
- pandas
- matplotlib
- jupyter (for running the Notebook)
You can install these requirements using pip:
pip install numpy pandas matplotlib jupyter-
Clone the repository:
git clone https://github.com/ian0671/CO2-Trends-Python.git cd keeling-curve-analysis -
Launch the Jupyter Notebook:
jupyter notebook
-
Open the Notebook:
Locate and open
CO2 Trends.ipynbto explore the project interactively.
The Jupyter Notebook is organized into the following sections:
-
Introduction:
Explains the significance of the Keeling Curve and provides background information on the NOAA Mauna Loa Observatory data. -
Data Acquisition and Preprocessing:
- Instructions on how the dataset was obtained from the NOAA website.
- Cleaning and structuring the raw data using
pandas(including handling comments, unwanted rows, or missing values). - Conversion of date fields and ensuring the data is in a time series format.
-
Data Visualization:
- Use of
matplotlibto create the Keeling Curve plot. - Plot customization to highlight the seasonal cycles and long-term trend in CO₂ concentrations.
- Additional plots (if any) to visualize other aspects of the data analysis.
- Use of
-
Analysis and Interpretation:
- Discussion of the observed trends, including the overall rise in CO₂ and seasonal fluctuations.
- Comments on potential implications related to climate change on a global scale.
-
Data Handling:
The Notebook usespandasto read the dataset, filtering out commentary rows and converting the raw data into a structured DataFrame. -
Plotting the Keeling Curve:
Withmatplotlib, the Notebook produces a clear, well-labeled graph that illustrates the CO₂ concentrations over time. The code adjusts plot parameters (e.g., axis labels, title, grid lines) to enhance readability. -
Statistical Insights:
While the primary output is the visual curve, additional analysis is performed to show trends and seasonal effects, providing a deeper understanding of the data dynamics.
Running the Notebook will produce:
- A time-series plot of atmospheric CO₂ concentration, visually representing the Keeling Curve.
- Visual evidence of both the long-term increase of CO₂ in the atmosphere and the cyclical seasonal variations.
Potential extensions for this project include:
- Incorporating more advanced statistical analyses or forecasting models.
- Comparing the Mauna Loa data with other atmospheric measurements from different observatories.
- Adding interactive visualization tools (like Plotly) to enhance the exploration of trends.
This project is licensed under the MIT License. See the LICENSE file for more details.