Pathfinder is designed to fill a perceived gap within the existing simple climate models by fulfilling three key requirements: (1) the capacity to be calibrated using Bayesian inference, (2) the capacity to be coupled with integrated assessment models (IAMs), and (3) the capacity to explore a very large number of climate scenarios to narrow down those compatible with limiting climate impacts.

DOI GitHub release (latest by date)

Overview. The Pathfinder model 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 to be used with Bayesian inference algorithms for calibration, which enables assimilation of the latest data from complex Earth system models that contributed to the Coupled Model Intercomparison Project phase 6 (CMIP6), on additional data from the sixth assessment report of the IPCC (AR6), and on observations of global Earth properties up to the year 2021. The model's simplicity also enables coupling with integrated assessment models and their optimization algorithms or 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 several uncertainty ranges – when run following standardized diagnostic experiments.

Compared to other SCMs (Nicholls et al., 2020), Pathfinder is much simpler than models like MAGICC, OSCAR, or even HECTOR. It is comparable in complexity to FaIR or BernSCM, although it is closer to the latter as it trades off an explicit representation of non-CO2 species for one of the carbon cycle's main components. This choice was made to help calibration, keep the model invertible, and make the model compatible with IAMs such as DICE. While most SCMs are calibrated using procedures that resemble Bayesian inference (Nicholls et al., 2021), Pathfinder relies on an established algorithm whose implementation is fully tractable and that allows for an annual update as observations of atmospheric CO2 and global temperature become available.

Structure. The model is composed of a climate module, of three separate modules for the carbon cycle (ocean, land without land use and land permafrost), and of two additional modules describing global impacts: sea level rise (SLR) and surface ocean acidification. The cycles of other non-CO2 gases are not emulated. Mathematically, the model is driven by prescribing time series of any combination of two of four variables: global mean surface temperature (GMST) anomaly (T), global atmospheric CO2 concentration (C), global non-CO2 effective radiative forcing (Rx), and global anthropogenic emissions of CO2 (ECO2). The model can therefore be run in the traditional emission-driven and concentration-driven modes but also in a temperature-driven mode (in terms of code, implemented as separate versions of the model). This is notably important for the calibration, during which it is driven by observations of GMST and atmospheric CO2.

pathfinder_model_blocks © Thomas Bossy | IIASA
Pathfinder in a nutshell. Green blocks represent the carbon cycle, and red blocks represent the climate response. Blue blocks with dotted arrows are impacts that can be derived with the model. Grey blocks are variables that are directly related to anthropogenic activity. Possible inputs of the model are distinguishable through the bold contours of the blocks. In this scheme, arrows correspond to a forward mode where inputs would be ECO2 and Rx.
 
Publications

Bossy, T., Gasser, T., & Ciais, P. (2022). Pathfinder v1.0.1: a Bayesian-inferred simple carbon–climate model to explore climate change scenarios. Geoscientific Model Development, 15: 8831–8868. https://doi.org/10.5194/gmd-15-8831-2022