Climate Models

Welcome to the Climate Models repository! This collection of climate models was developed as part of a course on Coursera. These models aim to simulate various aspects of Earth's climate system and provide insights into the complexities of climate dynamics.

Table of Contents

1. Introduction
2. Usage
3. Models
4. Contributing

Introduction

Climate models are essential tools for understanding the Earth's climate system and predicting future changes. This repository contains a set of climate models developed using various techniques taught in the Coursera course. The models cover a range of topics including atmospheric circulation, ocean dynamics, greenhouse gas concentrations, and climate feedback mechanisms.

Usage

To use the climate models in this repository, follow these steps:

1. Clone the repository to your local machine (git clone https://github.com/yourusername/climate-models.git)
2. Navigate to the directory containing the desired model.
3. Follow the instructions provided in the model's README file to run the simulation and analyze the results.

Models

1. Pane of Glass model

• Description: This model simulates the global atmospheric temperature assuming that the atmosphere is the same as a pane of glass, emitting in two directions: up and down.
• Features: Produces a graph to demostrate equilibrium time frame as well as outputting the equlibrium temperature.
• Dependencies: Requires Python 3 and Matplotlib library.

2. Ice Temperature Feedback

• Description: This project simulates the Earth's climate response to varying levels of solar radiation by modeling the relationship between albedo and temperature. The simulation utilizes a simple energy balance model to demonstrate how albedo, the reflectivity of the Earth's surface, changes with temperature and how these changes affect the equilibrium temperature of the Earth.
• Features:
  • Energy Balance Model: Calculates equilibrium temperature based on incoming solar radiation and albedo.
  • Dynamic Albedo Adjustment: Albedo is dynamically adjusted based on temperature using linear regression coefficients.
  • Bi-Stability Demonstration: Shows how two stable temperature states can exist for a given solar radiation level by simulating the system with increasing and then decreasing solar radiation.
• Dependencies: Requires Python 3 and Matplotlib library.

3. A Look Into the Future

• Description: This project simulates the impact of human activities on climate change using a simple carbon dioxide (CO2) emission model. It explores the effects of continued CO2 emissions versus a scenario where human emissions cease abruptly. The simulation considers various factors such as radiative forcing, temperature sensitivity, and the masking effect of cooling chemicals on CO2 emissions.

• Features:

  • CO2 Emission Model: Calculates CO2 concentrations over time based on historical trends and projected future emissions.
  • Radiative Forcing Calculation: Estimates the radiative forcing from CO2 emissions and masking effects from cooling chemicals. An assumption is made that all other gases are released with a direct correlation to the amount of ppm change in CO2 per year.
  • Temperature Equilibrium: Computes equilibrium temperature changes based on radiative forcing and sensitivity parameters.
  • Human Intervention Scenario: Analyzes a hypothetical scenario where human emissions suddenly cease.

• Dependencies: Requires Python 3 and Matplotlib library.

• Conclusions: By modifying the cooling chemical coeffcient (which was done by modifying that amount of sulfur dioxide that ships were allowed to release), you can change the speed of climate change.

Contributing

We welcome contributions from the community to enhance the functionality and accuracy of these climate models. If you would like to contribute, please follow these guidelines:

• Fork the repository.
• Create a new branch for your feature or bug fix.
• Make your changes and ensure that the code passes all tests.
• Submit a pull request detailing your changes and the problem they solve.