Pathfinder: a simple yet accurate carbon-climate model to explore climate change scenarios

Simple climate models (SCMs) are most often composed of ad hoc parametric laws that emulate the behaviour of more complex Earth system models (ESMs). The emulation allows investigating experiments or scenarios that would be too costly to compute with ESMs. However, the “SCM” denomination refers to a fairly broad range of models whose complexity can go from a couple of boxes that only emulate one part of the climate system (e.g. a global temperature impulse response function) to hundreds or thousands of boxes representing the different cycles of greenhouse gases and induced climate change (e.g. the compact Earth system model OSCAR). Simpler models are easier and faster to solve, but they may not adequately represent physical processes. Therefore, finding the “simplest but not simpler” model depends on a study’s precise goals.

We developed the Pathfinder model to remedy a deficiency within the spectrum of existing SCMs. Pathfinder is a compilation of existing formulations describing the climate and carbon cycle systems, chosen for their balance between mathematical simplicity and physical accuracy. The resulting model is simple enough that it can be used with Bayesian inference algorithms for calibration, which enables integration of the latest data from CMIP6 Earth system models and the IPCC AR6, as well as a yearly update using observations of global temperature and atmospheric CO2. The model’s simplicity also enables coupling with integrated assessment models (IAMs) and their optimization algorithms, or simply running the model in a backward temperature-driven fashion. In spite of this simplicity, the model accurately reproduces behaviours and results from complex models – including uncertainty ranges – when ran following standardized diagnostic experiments.

Here, we will briefly describe the Pathfinder model, demonstrate its performance, and illustrate its strengths and potential with two example studies. The first one combines a very large-scale ensemble of climate change scenarios generated procedurally, and the physical uncertainty sampling extracted from the Bayesian calibration, to determine which future CO2 emissions pathways remain compatible with the Paris agreement. The second one couples Pathfinder with a stylized IAM and climate impact emulators, to generate cost-effective pathways that limit permafrost carbon thaw, sea level rise speed, and ocean surface acidification.