Calibrating Simple Climate Models To Individual Earth System Models: Lessons Learned From Calibrating Hector

Simple climate models (SCMs) are computationally efficient and capable of emulating global mean output of more complex Earth system models (ESMs). In doing so, SCMs can play a critical role in climate research as stand-ins for the computationally more expensive models, especially in studies involving low, spatial, and/or temporal resolution, providing more computationally efficient sources of climate data. Here we use Hector v2.5.0 to emulate the multiforcing historical and RCP scenario output for 31 concentration and seven emission-driven ESMs. When calibrating Hector, sufficient calibration data must be used to constrain the model; otherwise, climate and/or carbon parameters affecting physical processes may be able to trade off with one another, allowing forPlain Language Summary We calibrated a Simplified Climate Model (SCM), Hector, to efficiently reproduce (emulate) the output of more complex climate models. Using SCMs as emulators is a common practice in the climate science and climate-economic research communities where the aim is to simulate long-term changes in climate for other (nonclimate) sectors, meaning that fine-scale climate simulation requires too much computational effort. In order to use Hector as an emulator that satisfactorily reproduces an ESM's modeled climate, we found that Hector must be constrained with multiple output variables. Otherwise, parameters can trade off with arbitrarily large values; for example, an unrealistically high climate sensitivity balanced by unrealistically large aerosol forcing can produce temperature change comparable to those produced by an ESM. We also document a new method that can use the multimodel range as calibration data if need be. solutions to use physically unreasonable fitted parameter values as well as limiting the application of the SCM as an emulator. We also present a novel methodology that uses the ESM range as a calibration data, which can be adopted when faced with missing variable output from a specific model.