Expert judgment can be seen throughout climate science and even more prominently when discussing climate tipping points. To provide an accurate characterization of expert judgment we begin by evaluating the existing literature on expertise as it relates to climate science as a whole, before then focusing the literature review on the role of expert judgment in the unique context of climate tipping points. From this we turn our attention to the structured expert elicitation protocols specifically developed for producing expert judgments about tipping points. We highlight that expert elicitation is not only used for the quantification of uncertainty in this context, but also for the very identification and characterization of tipping points and their interactions, making expert judgment in itself a genuine scientific output. The central role of expert judgment in this domain raises several epistemic issues that require careful attention. Among other topics, we discuss the relationship between expert judgment and modeling, as well as the nonepistemic values that are involved in the production of expert judgments, highlighting how the elicitation protocols can be used to manage these values. In the perspective of climate change, clarifying the epistemic foundations of expert judgment in this context can help to navigate the epistemic situation between self-defeating alarmism and blind dismissal, thus contributing to a better understanding of the challenges related to climate (and Earth system) tipping points.

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Parameterization and parameter tuning are central aspects of climate modeling, and there is widespread consensus that these procedures involve certain subjective elements. Even if the use of these subjective elements is not necessarily epistemically problematic, there is an intuitive appeal for replacing them with more objective (automated) methods, such as machine learning. Relying on several case studies, we argue that, while machine learning techniques may help to improve climate model parameterization in several ways, they still require expert judgment that involves subjective elements not so different from the ones arising in standard parameterization and tuning. The use of machine learning in parameterizations is an art as well as a science and requires careful supervision.