Leveraging machine learning to enhance climate models: a review

TL;DR

This review discusses recent advances in applying machine learning techniques over the past five years to improve climate models, addressing challenges like computational costs, uncertainties, and data analysis.

Contribution

It provides a comprehensive overview of how machine learning has been utilized to enhance climate modeling accuracy and efficiency in recent years.

Findings

ML helps analyze vast climate data effectively.

ML techniques reduce uncertainties in climate predictions.

ML integration improves model computational efficiency.

Abstract

Recent achievements in machine learning (Ml) have had a significant impact on various fields, including climate science. Climate modeling is very important and plays a crucial role in shaping the decisions of governments and individuals in mitigating the impact of climate change. Climate change poses a serious threat to humanity, however, current climate models are limited by computational costs, uncertainties, and biases, affecting their prediction accuracy. The vast amount of climate data generated by satellites, radars, and earth system models (ESMS) poses a significant challenge. ML techniques can be effectively employed to analyze this data and extract valuable insights that aid in our understanding of the earth climate. This review paper focuses on how ml has been utilized in the last 5 years to boost the current state-of-the-art climate models. We invite the ml community to join in the global effort to accurately model the earth climate by collaborating with other fields to leverage ml as a powerful tool in this endeavor.