Deep Learning Based Reconstruction of Total Solar Irradiance

TL;DR

This paper introduces TSInet, a deep learning model that reconstructs total solar irradiance over periods exceeding 9,000 years, surpassing the limitations of traditional physics-based models and aligning well with existing reconstructions.

Contribution

The paper presents the first deep learning approach to reconstruct total solar irradiance for periods longer than 9,000 years, extending beyond previous physics-based methods.

Findings

TSInet agrees with state-of-the-art models on available data.

Deep learning enables reconstruction beyond 9,000 years.

First application of deep learning for long-term TSI reconstruction.

Abstract

The Earth's primary source of energy is the radiant energy generated by the Sun, which is referred to as solar irradiance, or total solar irradiance (TSI) when all of the radiation is measured. A minor change in the solar irradiance can have a significant impact on the Earth's climate and atmosphere. As a result, studying and measuring solar irradiance is crucial in understanding climate changes and solar variability. Several methods have been developed to reconstruct total solar irradiance for long and short periods of time; however, they are physics-based and rely on the availability of data, which does not go beyond 9,000 years. In this paper we propose a new method, called TSInet, to reconstruct total solar irradiance by deep learning for short and long periods of time that span beyond the physical models' data availability. On the data that are available, our method agrees well with the state-of-the-art physics-based reconstruction models. To our knowledge, this is the first time that deep learning has been used to reconstruct total solar irradiance for more than 9,000 years.