Climate Modeling Project

This project contains Python scripts for analyzing climate phenomena, particularly ENSO (El Niño-Southern Oscillation) dynamics and energy balance modeling.

Files Overview

Core Scripts

• energy_balance_model.py - Simple energy balance model for global temperature
• enso_jin_model.py - Implementation of Jin's ENSO model with feedback mechanisms
• enso_spectrum_analysis.py - Spectral analysis of ENSO34 observational data
• enso_parameter_optimization.py - Parameter optimization for ENSO model using MSE minimization

Data Files

• ENSO34.txt - ENSO 3.4 index observational data (1950-2023)
• ENSO34_anomalies.txt - ENSO 3.4 anomaly data
• Jin1998.pdf - Jin's 1998 paper on ENSO dynamics
• Project_2024_2025.pdf - Project description and requirements

Environment Setup

• shell.nix - Nix environment configuration with required Python packages
• .envrc - Direnv configuration for automatic environment activation

Dependencies

The project uses Python with the following packages:

• numpy
• matplotlib
• scipy
• scikit-learn
• pygame
• keyboard

Quick Start

Activate the environment:

nix-shell
# or if using direnv
direnv allow

Run the energy balance model:

python energy_balance_model.py

Analyze ENSO spectrum:

python enso_spectrum_analysis.py

Optimize ENSO model parameters:

python enso_parameter_optimization.py

Model Descriptions

Energy Balance Model

Computes global temperature evolution using heat capacity and radiative balance:

• Heat capacity: 8.36×10⁸ J/(m²·K)
• Solar constant: 1360 W/m²
• Albedo: 0.3

Jin ENSO Model

Recharge oscillator model for ENSO with:

• Bjerknes feedback (μ parameter)
• Zonal advection feedback (ad parameter)
• Hadley cell feedback (μH parameter)
• Stochastic forcing (σ parameter)

Parameter Optimization

Finds optimal μ and σ values by minimizing mean squared error between simulated and observed ENSO power spectra.