Machine learning for weather and climate are worlds apart.

Area of Science:

• Climate Science
• Machine Learning
• Computational Modeling

Background:

• Weather and climate models share heritage but differ in application and problem type.
• Weather modeling focuses on initial conditions, while climate modeling addresses boundary conditions.
• Climate model emulation has a long history, unlike newer machine learning applications in weather forecasting.

Purpose of the Study:

• To review the current state of climate model emulation.
• To demonstrate how machine learning advances can create effective statistical climate models.
• To highlight machine learning challenges and opportunities in climate science.

Main Methods:

• Review of existing literature on climate model emulation.
• Discussion of machine learning techniques applied to climate modeling.
• Analysis of challenges such as large datasets and non-linear relationships.

Main Results:

• Machine learning is increasingly applicable to climate model emulation.
• Emulating steady-state climate responses is feasible and offers speed advantages.
• Recent machine learning advances present new avenues for statistical climate modeling.

Conclusions:

• Machine learning provides valuable tools for advancing climate model emulation.
• Despite challenges, AI can enhance the creation of useful statistical climate models.
• The distinct nature of climate modeling necessitates tailored machine learning approaches.