Abstract
Climate model emulators have a crucial role in assessing warming levels of many emission scenarios from probabilistic climate projections, based on new insights into Earth system response to CO2 and other forcing factors. This article describes one such tool, MCE, from model formulation to application examples associated with a recent model intercomparison study. The MCE is based on impulse response functions and parameterized physics of effective radiative forcing and carbon uptake over ocean and land. Perturbed model parameters for probabilistic projections are generated from statistical models and constrained with a Metropolis-Hastings independence sampler. A part of the model parameters associated with CO2-induced warming have a covariance structure, as diagnosed from complex climate models of the Coupled Model Intercomparison Project (CMIP). Although perturbed ensembles can cover the diversity of CMIP models effectively, they need to be constrained toward substantially lower climate sensitivity for the resulting historical warming to agree with the observed trends over recent decades. The model's simplicity and resulting successful calibration imply that a method with less complicated structures and fewer control parameters offers advantages when building reasonable perturbed ensembles in a transparent way. Experimental results for future scenarios show distinct differences between CMIP- and observation-consistent ensembles, suggesting that perturbed ensembles for scenario assessment need to be properly constrained with new insights into forced response over historical periods.
Highlights
Climate model emulators, or simple climate models, are numerical tools for representing the complex Earth system in reduced dimensions using aggregated variables, such as global mean surface temperature (GMST) and global CO2 uptake over ocean and land
The Minimal CMIP Emulator (MCE) is based on impulse response functions and parameterized physics of effective radiative 10 forcing and carbon uptake over ocean and land
This is similar to the scenario assessment of the 2018 Intergovernmental Panel on Climate Change (IPCC) Special Report on global warming of 1.5 °C (SR15) 40 (Rogelj et al, 2018), where the same method as in AR5 was used for scenario classification but noticeable differences in radiative forcing and temperature response were identified between the results of MAGICC and of a different emulator, FaIR version 1.3 (Smith et al, 2018)
Summary
Simple climate models, are numerical tools for representing the complex Earth system in reduced dimensions using aggregated variables, such as global mean surface temperature (GMST) and global CO2 uptake over ocean and land. Complex formulations generally are more capable of emulation, they are not necessarily advantageous for emulating individual CMIP models and representing their uncertainty ranges For thermal response, this has been confirmed by the author’s previous studies (Tsutsui, 2017; Tsutsui, 2020), which have demonstrated that a simple IRM can accurately emulate a variety of CMIP models in terms of temperature response to CO2 forcing and provide a basis of parameter 75 sampling that covers model diversity. This has been confirmed by the author’s previous studies (Tsutsui, 2017; Tsutsui, 2020), which have demonstrated that a simple IRM can accurately emulate a variety of CMIP models in terms of temperature response to CO2 forcing and provide a basis of parameter 75 sampling that covers model diversity These findings imply that less complex emulators are suitable for knowledge transfer in a transparent way.
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