LAUSR

Artificial intelligence (AI) technology has been rapidly reshaping all aspects of our lives since the 1990s. The recent release of ChatGPT in November 2022 represents one of the biggest advancements in AI since AlphaGo won the firstever game against a human professional Go player in 2015. Machine learning (ML)— one of the AI tools— is able to solve complex relationships in a system, handle big data, improve its own efficiency and predictability with more data, learn new knowledge (i.e., unknowns) through deep learning (DL), and evolve openaccess algorithms and data from diverse disciplines (LeCun et al., 2015). These advantageous capabilities, coupled with rapid advancements in computing power, have been recognized in all fields of science and engineering, as is evidenced by the cascading escalations in their applications. One major reason for the widespread and speedy adoption of ML technology is because the human brain has a limited capacity for comprehending large, complex systems. Scholars and practitioners in natural science have adopted various MLs to address basic and applied challenges, such as modeling complex causes, processes, and consequences in the Earth system at multiple temporal and spatial scales (Reichstein et al., 2019). The Earth system is traditionally examined through the construction of simulation models, also known as Earth system models (ESMs; sometimes called terrestrial biosphere models, Sun et al., 2023), built to estimate past, current, and future conditions. ESMs evolved from a suite of simple algorithms (Chen, 2021) to approximate the complex interactions among components of the Earth system (Fisher & Koven, 2020; Gettelman et al., 2022). ESMs are capable of integrating critical processes from atmospheric science, biogeochemistry, biological systems, human influences, and ecosystem processes to meet the diverse needs of multiple disciplines. Yet, modern ESMs pose unprecedented challenges, such as the number and types of ESMs (30+ in CMIP6 of IPCC; Smith et al., 2020), the high number of potential parameters (hundreds to thousands), complex architecture and structure, accurate values of the parameters (i.e., parameterizations), large discrepancies among the models and high uncertainty for their predictions, and applications at high spatial and temporal resolutions (Schaefer et al., 2012). An even greater challenge arises from the computing time, which hinders their practical use, especially at landscaperegional scales. Sun and colleagues recently looked into whether ML tools could be good alternatives of conventional ESMs for predicting the functions of terrestrial ecosystems (Sun et al., 2023). In their pioneering work, they applied the same input variables (27) to three versions of ORCHIDEE (i.e., a major ESM) and Baggin decision trees (i.e., a ML tool) for predicting terrestrial carbon, nitrogen, and phosphorous at global scale. They demonstrated that ML reduced the computing demand by 78– 80% while maintaining similar or even more accurate predictions than ORCHIDEEs. Such reductions are substantial, though computing power soon may no longer be a bottleneck hindering ESM runs due to rapid advancements in computing technology. An equally important finding in the Sun et al. study relates to the contributions of input variables for predicting carbon, nitrogen, and phosphorus production. It was especially interesting to see that ML only required a small number of input variables (20– 25) compared to a similar number of input variables to reach better accuracy in predictions using ORCHIDEE for different components of carbon, nitrogen, and phosphorus (figures 2– 4, respectively, in Sun et al., 2023). Over the past century, quantitative models in climatology, forestry, agronomy, ecology, and other fields have evolved from a few algorithms with a few input variables for predicting system functions