Deep Learning Based Reconstruction of Total Solar Irradiance
This work addresses climate science and solar variability research by enabling TSI reconstruction for longer time spans, though it is incremental as it applies deep learning to an existing problem with limited data.
The paper tackles the problem of reconstructing total solar irradiance (TSI) for periods beyond 9,000 years, where physics-based models are limited by data availability, by proposing TSInet, a deep learning method that matches state-of-the-art physics-based models on available data.
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.