Forecasting CO2 concentration in Excel involves using time series analysis techniques. Here's a simplified step-by-step guide:

Data Preparation:

Organize your historical data with dates and corresponding CO2 concentrations.

Ensure a consistent time interval between data points.

Plot the Data:

Create a line chart to visualize the historical CO2 concentrations.

Trend Analysis:

Use Excel's built-in features or regression analysis to identify any trends in your historical data.

Time Series Forecasting:

Utilize Excel's forecast functions (like FORECAST.ETS or LINEST) to predict future CO2 concentrations based on the identified trend.

Scenario Analysis:

Create different scenarios by adjusting variables affecting CO2 concentration (e.g., emission and sink rates).

Modify input assumptions and observe the impact on the forecast.

Sensitivity Analysis:

Assess how changes in different assumptions affect the forecast by varying one factor at a time.

SPSS Integration:

Import your data into SPSS for more advanced statistical analysis if needed.

Explore time series forecasting models available in SPSS.

Validation:

Validate your model's accuracy by comparing forecasted values with actual historical data.

Remember to consider the uncertainties in your assumptions and the model itself. Collaborate with experts in the field to refine your assumptions and ensure the model's reliability.