Deriving scientific insights from artificial intelligence methods requires adhering to best practices and moving beyond off-the-shelf approaches.
Artificial intelligence (AI) methods have emerged as useful tools in many Earth science domains (e.g., climate models, weather prediction, hydrology, space weather, and solid Earth). AI methods are being used for tasks of prediction, anomaly detection, event classification, and onboard decision-making on satellites, and they could potentially provide high-speed alternatives for representing subgrid processes in climate models [Rasp et al., 2018; Brenowitz and Bretherton, 2019].
Although the use of AI methods has spiked dramatically in recent years, we caution that their use in Earth science should be approached with vigilance and accompanied by the development of best practices for their use. Without best practices, inappropriate use of these methods might lead to “bad science,” which could create a general backlash in the Earth science community against the use of AI methods. Such a backlash would be unfortunate because AI has much to offer Earth scientists, helping them sift through and gain new knowledge from ever-increasing amounts of data. Thus, it is time for the Earth science community to develop thoughtful approaches for the use of AI.
Earth scientists have a long tradition of using methods based on physics (e.g., dynamical models) and sophisticated statistics (e.g., empirical orthogonal function analysis and spectral analysis). They have thus accepted statistical methods, which are a type of data-driven method, as useful tools. However, the sudden rise of AI methods—another type of data-driven method—in Earth science, coupled with a terminology and culture unfamiliar to Earth scientists, may make AI methods seem more foreign than they actually are. AI simply provides an extended set of new data-driven methods, many of which are derived from statistical principles. For example, one basic type of artificial neural network (deep learning) is essentially a linked series of linear regression models interspersed with scalar nonlinear transformations.
We address here the question of how best to leverage both physics-based and data-driven methods simultaneously by outlining several proposed steps for researchers. For brevity we use only the term “AI methods” below, although most of our discussion applies equally to all data-driven methods.
We suggest that Earth scientists ask themselves the following questions before choosing a specific AI approach:
We encourage researchers to thoroughly reflect on these questions to select the best AI method for their application. Furthermore, to promote substantial advances in scientific research, editors of Earth science journals may need to create guidelines for the review of AI-focused manuscripts to ensure that findings are explained clearly and placed in the context of existing Earth science. Likewise, editors might encourage comparison to standard or simpler approaches to discern the scientific advances that AI offers.
Integrating scientific knowledge into AI approaches greatly improves transparency because the more scientific knowledge is used, the easier it is to follow the reasoning of the algorithm. Generalization, robustness, and performance are also improved because the scientific knowledge can fill many gaps left by small sample sizes, as explained below.
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