Chapter 5 Practical 8 CO2 measurement

 

Analysis of Atmospheric CO₂ Trends Using Spreadsheet Software

1) CO2 measurement 

Course: Environmental Science, Climate Science, or Data Analysis Duration: 2-3 Hours (or as a homework assignment)

1. Objective

Upon completion of this practical, you will be able to:

• Source and download real-world atmospheric CO₂ concentration data.
• Clean and prepare raw data for analysis in a spreadsheet.
• Use spreadsheet functions to calculate summary statistics (mean, standard deviation, min, max).
• Create a scatter plot and fit a linear trendline to quantify the long-term CO₂ trend.
• Use a PivotTable to aggregate data and analyze seasonal CO₂ cycles.
• Interpret the results of your manual analysis in a scientific context.

2. Background

Atmospheric carbon dioxide (CO₂) is the primary greenhouse gas driving anthropogenic climate change. The longest continuous record of its atmospheric concentration comes from the Mauna Loa Observatory in Hawaii. This dataset, famously known as the "Keeling Curve," provides irrefutable evidence of two key patterns:

1. A long-term, accelerating increase in CO₂ concentration, primarily due to the burning of fossil fuels.
2. A regular seasonal cycle where CO₂ levels decrease during the Northern Hemisphere's growing season (due to photosynthesis by plants) and increase during its dormant season.

In this practical, you will manually replicate the core functions of a data analysis tool to quantify these patterns yourself.

3. Materials & Software

• A modern web browser.
• A spreadsheet program: Microsoft Excel, Google Sheets, or LibreOffice Calc.
• A text editor (like Notepad or TextEdit).

4. Data Acquisition and Preparation

1. Download the Data: Go to the NOAA website for monthly mean Mauna Loa CO₂ data: https://gml.noaa.gov/webdata/ccgg/trends/co2/co2_mm_mlo.txt Save the page as a text file named mauna_loa_co2.txt.

2. Clean the Data:

   • Open mauna_loa_co2.txt in a text editor. You will see header lines starting with #.
   • Delete all header lines. Your file should now start directly with the first line of data (e.g., 1958 3 1958.21 315.71 315.71 314.62 -1).
   • The columns are: year, month, decimal date, average, interpolated, trend, #days. We only need year, month, and average.

3. Import to Spreadsheet:

   • Open your spreadsheet program (Excel/Sheets).
   • Go to Data > From Text/CSV (or a similar import option).
   • Select your mauna_loa_co2.txt file.
   • The import wizard will likely recognize the space-separated format. Ensure the columns are correctly separated. Click Load.

4. Format the Data:

   • Your data should now be in columns A, B, C, etc.
   • Delete all columns except year, month, and average. Rename the headers to Year, Month, and CO2.
   • Create a Date Column: In the first empty column (e.g., D1), type the header Date. In cell D2, enter the formula to create a proper date: =DATE(A2, B2, 1). Drag this formula down for all your data rows. This creates a date for the first day of each month.

5. Procedure

Part A: Temporal Trend Analysis

1. Create a Scatter Plot:

   • Select your two data columns: the new Date column and the CO2 column.
   • Go to Insert > Chart and choose a Scatter plot (specifically, the one with only markers).

2. Add a Trendline and Equation:

   • Right-click on any of the data points in your scatter plot.
   • Select Add Trendline....
   • In the format pane, choose Linear.
   • Check the boxes for "Display Equation on chart" and "Display R-squared value on chart".
   • You now have a visual representation of the long-term trend. The equation will be in the format y = mx + b, where m is the slope (the annual increase in CO₂) and b is the y-intercept. The R² value tells you how well the line fits the data.

Part B: Seasonal Analysis

1. Create a PivotTable:

   • Select all your data (including headers).
   • Go to Insert > PivotTable. Click "OK" to create it in a new sheet.
   • In the PivotTable Fields pane, drag the Month field into the Rows area.
   • Drag the CO2 field into the Values area. It should say "Sum of CO2". Click on it and change the setting to Average instead of Sum. It should now say "Average of CO2".

2. Create a Seasonal Bar Chart:

   • You now have a table showing the average CO₂ for each month (1 through 12) across all years.
   • Select this PivotTable data.
   • Go to Insert > Chart and choose a Column/Bar chart. This will visually show you the seasonal cycle.

Part C: Statistical Summary

1. In an empty area of your spreadsheet, create the following labels: Mean CO2, Standard Deviation, Minimum CO2, Maximum CO2, Total Records.
2. Use spreadsheet functions to calculate these values for your CO2 column:
   • Mean: =AVERAGE(C:C) (assuming CO2 is in column C).
   • Std Dev: =STDEV.S(C:C)
   • Min: =MIN(C:C)
   • Max: =MAX(C:C)
   • Total Records: =COUNT(C:C)

6. Results & Discussion

Based on your charts and calculated values, answer the following questions:

1. Trend Analysis: From the equation on your scatter plot, what is the slope (m)? This represents the average annual increase in CO₂ in ppm/year. The global average increase is approximately 2.5 ppm/year. How does your calculated trend compare? Propose reasons for any difference.

2. Seasonal Cycle: Describe the pattern in your seasonal bar chart. Which two months have the highest average CO₂? Which two have the lowest? Explain this cycle in terms of global biospheric activity (photosynthesis and respiration).

3. Statistical Significance: What is the R² value from your trendline? An R² value close to 1.0 indicates a strong correlation. What does your value suggest about the relationship between time and CO₂ concentration?

4. Data Variability: What is the standard deviation you calculated? What does this number tell you about the variability of the monthly CO₂ values around the long-term trend?

5. Limitations: The Mauna Loa data is from a single, high-altitude location. What are the limitations of using this dataset to understand global CO₂ dynamics? What important patterns might you miss by only looking at one point?

7. Conclusion

Write a brief conclusion (2-3 paragraphs) summarizing your key findings. State the quantified long-term trend and describe the seasonal pattern you observed. Comment on the significance of your results and the limitations of using a single-point dataset for global analysis. 

  

Bonus: Comparative Analysis

For an additional challenge, repeat the entire procedure using data from the South Pole station: https://gml.noaa.gov/webdata/ccgg/trends/co2/co2_mm_spo.txt . Create a report comparing the trends and seasonal cycles of Mauna Loa and the South Pole. Explain the major differences you observe. 

 B

 

 

CO₂ Dynamics Analysis over Indian Terrestrial Ecosystems

Course: Environmental Science, Climate Science, Remote Sensing, or Geospatial Analysis Duration: 2-3 Hours (or as a homework assignment)

1. Objective

Upon completion of this practical, you will be able to:

• Understand and replicate the core methodology of a published scientific paper.
• Analyze long-term atmospheric CO₂ data to quantify annual and seasonal trends.
• Perform a Standard Deviation Ellipse (SDE) analysis to understand the spatial stability of CO₂ concentrations.
• Investigate the relationship between CO₂ and vegetation health (NDVI).
• Critically evaluate your findings by comparing them directly to the results reported in the paper.

2. Background

The research paper "Quantification of long-term spatio-temporal CO₂ dynamics in Indian terrestrial ecosystems" (Tiwari et al.) analyzes CO₂ concentrations over India from 2002 to 2017 using satellite data. The paper makes several key claims:

1. A steady increase in CO₂ at a mean rate of 2.1 ppm per year.
2. A distinct seasonal pattern, with higher CO₂ in winter and lower concentrations during the monsoon season.
3. The geographical center of CO₂ concentration, measured using a Standard Deviation Ellipse (SDE), remained stationary over central India, implying persistent emission hotspots.
4. A strong correlation between CO₂ and vegetation (NDVI) in specific months (April, May, August).

In this practical, you will use the same analytical techniques on a sample dataset to see if you can reproduce these findings.

3. Materials & Software

• A modern web browser (Chrome, Firefox, Edge, Safari).
• The CO₂ Analysis Tool - Professional Edition (co2_analyzer_pro.html).
• A sample dataset representing a subset of the data used in the paper.

4. Data Acquisition

The paper uses multiple satellite datasets (AIRS CO₂, MODIS NDVI, FIRMS Fire). For this practical, we will use a pre-compiled CSV file that combines these variables for a simplified analysis.

1. Create the Data File: Create a new file named india_co2_sample.csv and copy the data block from the Appendix at the end of this practical into it. Save the file.

   • This data contains monthly average values for CO₂ (ppm) and NDVI (a vegetation index from -1 to 1) for various locations in India from 2002 to 2017.

2. Load the Data:

   • Open the co2_analyzer_pro.html file in your web browser.
   • In the sidebar, click the "Drop file or click to upload" area.
   • Select your india_co2_sample.csv file.
   • Verify that the status message shows that records have been loaded successfully.

5. Procedure

Step 1: Replicate the Long-Term Temporal Trend (Paper Section 3.1)

1. Click the "🚀 Run Full Analysis" button.
2. Navigate to the "📈 Temporal" tab.
3. Observe the Annual CO₂ Trend chart. The trendline equation and R² value are displayed.
4. Record the slope of the trendline. This value represents the annual increase in CO₂ in ppm/year.

Step 2: Replicate the Seasonal Dynamics (Paper Section 3.1)

1. Navigate to the "🌦️ Seasonal" tab.
2. Ensure the view is set to "🌦️ IMD Seasons".
3. Observe the IMD Seasonal CO₂ Distribution bar chart.
4. Record the mean CO₂ value for each of the four seasons: Winter, Pre-Monsoon, Monsoon, and Post-Monsoon.

Step 3: Replicate the Spatial Analysis & SDE (Paper Section 3.2.1)

1. Navigate to the "🗺️ Spatial" tab.
2. Observe the SDE Drift Over Time chart. This chart shows how the geographic center of CO₂ concentrations has moved each year.
3. Look at the SDE Parameters Table below the chart. This table contains the precise coordinates and dimensions of the ellipse for each year.
4. Assess the drift: Does the line on the drift chart show significant movement, or is it relatively stable? Note the coordinates of the center for the first and last years in the table.

Step 4: Investigate the CO₂-NDVI Relationship

1. Navigate to the "📋 Tables" tab.
2. Find the table for Monthly Statistics.
3. Record the Mean CO₂ and Mean NDVI for the months of April, May, and August. The paper highlights these months as having a strong correlation.

6. Results and Discussion

Answer the following questions by comparing your results directly to the claims made in the research paper.

1. Temporal Trend Validation: The paper reports a trend of 2.1 ppm/year. What was your calculated trend? Calculate the percentage difference between your value and the paper's value. Is your result reasonably close? What could account for any small differences?

2. Seasonal Pattern Validation: The paper states that CO₂ is highest in winter/pre-monsoon and lowest in the monsoon. Do your seasonal results support this claim? Explain why CO₂ is lowest during the Indian monsoon season based on principles of photosynthesis and atmospheric mixing.

3. SDE Spatial Stability: The paper's key finding is that the SDE center is "stationary." Does your SDE Drift Chart and SDE Table support this conclusion? Look at the center coordinates from 2002 to 2017. Did the hotspot move significantly, or did it remain in central India? What does a stationary hotspot imply about the sources of CO₂ in India?

4. CO₂-NDVI Correlation: The paper claims a strong correlation between CO₂ and NDVI in April, May, and August. Look at the monthly data you recorded.

   • In April and May (pre-monsoon), as NDVI (vegetation greenness) starts to increase, what happens to CO₂? Is this an inverse relationship, as expected?
   • In August (peak monsoon), NDVI should be at its highest. What is the CO₂ level during this time? Does this support the paper's claim of high vegetation activity leading to carbon uptake?

5. Critical Evaluation: What are the limitations of using a sample dataset for this analysis? If you had access to the full-resolution daily satellite data, what additional analyses could you perform to build upon the paper's findings?

7. Conclusion

Write a brief conclusion summarizing your investigation. State whether your analysis successfully replicated the key findings of the Tiwari et al. paper. Discuss the significance of the results, particularly regarding the stationary CO₂ hotspot over India and its implications for climate mitigation policies.