Project Description

• project-name: SAI_Seasonal_Prediction
• authors: Kirsten J. Mayer, Elizabeth A. Barnes, James Hurrell
• date: June 13, 2022

The climate is projected to continue warming in the coming decades. In addition to extensive mitigation, one possible method to reduce the impacts of climate change is stratospheric aerosol injection (SAI). By injecting aerosols into the stratopshere, we can enhance Earth's reflectivitly and thus, cool the planet. However, little is known about how SAI may impact the Earth System, including its dynamics and predictability. Previous research suggests that El Nino Southern Oscillation teleconnections may change in a warmer world. These teleconnections provide a large source of seasonal predictability to North America in our current climate. Therefore, this work examines how SAI may impact seasonal predictability over the West Coast of North America compared to a warmer world with no solar radiation management.

Run the code

Create empty directories

• You will also need to make additional directories:
  • functions/
  • saved_models/
  • figures/

Data Access

• ARISE data can be accessed at this url:
• Data should be stored in the directory called data/

Order of code execution

• Seasonal Variability Analysis:
  • Step 1: t2m_variance.py
  • Step 2: Figure2.py
• ENSO Teleconnection Analysis:
  • Step 1: Nino34_t2mteleconnections.py
  • Step 2: Figure3.py
• Neural Network Analysis:
  • Step 1: trainNN.py
  • Step 2: evaluateNN.py
  • Step 3: Figure4.py

Extra Information

License

This project is licensed under an MIT license.

MIT © Kirsten J. Mayer