Atmospheric processes

Gaining knowledge of the past and present variations of the Atlantic Meridional Overturning Circulation (AMOC) is crucial for the development of accurate future climate projections. The short range covered by direct AMOC observations, inconsistent paleoclimate records, and scattered hydrographic data are insufficient to realistically reconstruct the AMOC strength since 1900. An AMOC proxy index based on sea surface temperatures suggests that the AMOC has declined by 15% since the late 19th century but this index received extensive scientific criticism. Here, we use a deep learning algorithm and climate model simulations to accurately reconstruct the AMOC strength between 20°N and 60°N since 1900. In contrast with the existing indices, our reconstructions are well in agreement with AMOC strength variations simulated by climate models and direct observations at 26.5°N. Our novel set of AMOC reconstructions contribute to a larger confidence in 21st century AMOC decline projections from climate models.

Climate change and artificial intelligence (AI) are increasingly linked sciences, with AI already showing capability in identifying early precursors to extreme weather events. There are many AI methods, and a selection of the most appropriate maximizes additional understanding extractable for any dataset. However, most AI algorithms are statistically based, so even with careful splitting between data for training and testing, they arguably remain emulators. Emulators may make unreliable predictions when driven by out-of-sample forcing, of which climate change is an example, requiring understanding responses to atmospheric greenhouse gas (GHG) concentrations potentially much higher than for the present or recent past. The emerging AI technique of “equation discovery” also does not automatically guarantee good performance for new forcing regimes. However, equations rather than statistical emulators guide better system understanding, as more interpretable variables and parameters may yield informed judgements as to whether models are trusted under extrapolation. Furthermore, for many climate system attributes, descriptive equations are not yet fully available or may be unreliable, hindering the important development of Earth system models (ESMs), which remain the main tool for projecting environmental change as GHGs rise. Here, we argue for AI-driven equation discovery in climate research, given that its outputs are more amenable to linking to processes. As the foundation of ESMs is the numerical discretization of equations that describe climate components, equation discovery from datasets provides a format capable of direct inclusion into such models where system component representation is poor. We present three illustrative examples of how AI-led equation discovery may help generate new equations related to atmospheric convection, parameter derivation for existing equations of the terrestrial carbon cycle, and (additional to ESM improvement) the creation of simplified models of large-scale oceanic features to assess tipping point risks.