The futures of climate modeling

We reflect on the current state of climate modeling and the future divergent paths that have been proposed for a step change that leverages different tools. We review the history of successful Earth system predictions across timescales, highlighting how multiple tools and steps were involved. We argue that the past is prologue for climate modeling and that embracing a variety of tools and methodologies is key to achieving convergent paths for meaningful progress.

Where we are today

In the mid-20th century, with the combined advances in computing and numerical modeling, the first general circulation models of the atmosphere came into being. These models quickly advanced following two strands: one devoted to global weather prediction on short timescales and one devoted to simulation and prediction of the longer-term climate. The latter played an influential role in the 1960’s in verifying that anthropogenic greenhouse gas emissions warm the climate - a development that would lead to Syukuro Manabe winning the Nobel Prize in physics in 2021. These climate models have since solidified our theoretical understanding of global warming and have allowed us to understand the changes in the climate system as they have unfolded.

Climate models began with just the underlying physics of the atmosphere and ocean but have since expanded massively in complexity to the Earth System Models (ESMs) of today (Fig. 1) that not only simulate the physical aspects of the climate system but also the biogeophysical and chemical aspects of the fully coupled atmosphere-ocean-land-sea ice-land ice system1. Projects such as the Coupled Model Intercomparison Project (CMIP) have allowed the global community to work together to address the question of what we should expect in terms of future climate change and to understand how our models behave. Furthermore, the use of simulations starting from different initial conditions but produced with a single climate model and identical external forcing has highlighted the importance of natural variability as a source of uncertainty in regional climate predictions across a range of timescales. Global ESMs such as those used in CMIP or initial-condition large ensembles are an important source of understanding of the impacts of climate change and are one of the primary sources of information for stakeholders as to what the future might hold, either directly or by serving as the large-scale boundary conditions for various downscaling methodologies. The increasingly pressing need for climate information on a regional scale means that the demand for accurate information from climate models is now greater than ever before.