Abstract
Equilibrium Climate Sensitivity (ECS) is a key metric when trying to understand the past, present and future behavior of Earth’s climate. Several models used in the latest IPCC report’s Coupled Model Intercomparison Project 6 (CMIP6) have failed to yield an ECS value within the consensus range estimated by several previous climate models (IPCC AR6, Chapter 7). Trying to understand why these state-of-the-art models failed to give an appropriate ECS value is no easy task. Johannes Lenhard and Eric Winsberg (2010, 2011) have argued that complex simulation models such as climate models exhibit a kind of epistemological holism that make it extremely difficult—if not impossible—to tease apart the sources of error in a simulation and attribute them to particular modeling assumptions or components. As a result, they argue that modern, state-of-the-art climate models are “analytically impenetrable” (Lenhard & Winsberg, 2011, p. 115). They identify as a source of this impenetrability what they call “fuzzy modularity,” which arises due to the complex interactions between the modules that make up a climate model. The question remains whether a model’s analytical impenetrability undermines scientists' efforts to identify the cause of the high ECS values and fix these models through a piecemeal approach. Despite these worries about analytical impenetrability and holism, scientists use sensitivity tests which involve replacing individual parameterizations, schemes, or process representations one-by-one in a piecemeal fashion to assess their impact on a model output quantity, such as ECS. Through sensitivity tests, scientists concluded the high ECS values in many climate models were likely due to more realistic parameterizations of cloud feedback (Gettelman et. al., 2019, Zelinka et. al., 2020). This is surprising because the models used in CMIP6 have a better representation of the current climate but the increased realism in cloud parameterization yields an unrealistic result in ECS. How is it that more realistic models can get worse results? Also, if the modules of a model are inextricably linked, how can scientists use sensitivity tests to find what is wrong and fix the model? It might be that fixing cloud parameterization only works because of compensating factors elsewhere. For example, radiative forcing may be compensating for the model’s climate sensitivity (Kiehl, 2007). The recent failure of models to yield an appropriate ECS value presents us with an opportunity to revisit concepts such as holism, realism, and underdetermination (also called equifinality) in current climate models. In this talk, I focus on attempts to diagnose the source of the high ECS in some CMIP6 models, Community Earth System Model 2 in particular, using techniques such as sensitivity testing and feedback analysis. While these techniques can go a long way towards addressing holism, there are limits to their applicability, which I discuss. I conclude by drawing some broader lessons about the more subtle relations between holism, fuzzy modularity, and underdetermination in complex simulation models.
