Climate Model Benchmarking: Building Trust and Advancing Science
A critical look at how well climate models work, how to successfully use them, and the anticipated challenges for their next-generation development.
The origins of climate modeling trace back over a century, to Lewis Fry Richardson's 1922 book that proposed using numerical processes to predict weather. It wasn't until 1950 that the first computerized weather forecast was run, leading to significant advancements in the 1960s with the development of General Circulation Models (GCMs) which laid the foundation for Earth System Models (ESMs).
GCMs focus on physical processes and exchanges of energy and matter between various components of the climate system, while ESMs expand upon GCMs by incorporating additional processes such as atmospheric chemistry, biogeochemical cycles, and ecosystems. This expansion helps provide a more realistic representation of the climate system, but it also comes with increased computational demands.
Evaluating and benchmarking climate models is crucial for understanding their performance, identifying biases, and prioritizing future scientific development. By comparing model simulations against observational or reanalyzes datasets, scientists can assess the "goodness" of the simulation or model based on predetermined standards.
This process can involve a variety of diagnostics, ranging from basic comparisons of climatological means and inter-annual variability to more complex analyses of water, energy, and carbon budgets, as well as cross-domain interactions. Regular evaluations of climate models help uncover biases, such as those in cloud and water vapor process representations, which have been progressively reduced due to routine assessments.
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