Geoscientific model development · 2019 · 14 citations · 32 references
Mean ClimateFuture Climatic ChangeEngineeringClimate ModelingEarth System ScienceEarth ScienceGreb ModelClimate PhysicsFirst OrderClimate ProjectionClimate ChangeClimate SciencesMeteorologyGlobal Warming ModellingGeographyClimate SystemClimate DynamicsClimatologyInteractive DatabaseExperiment DatabaseClimate Modelling
Abstract. This study introduces the Monash Simple Climate Model (MSCM) experiment database. The simulations are based on the Globally Resolved Energy Balance (GREB) model to study three different aspects of climate model simulations: (1) understanding processes that control the mean climate, (2) the response of the climate to a doubling of the CO2 concentration, and (3) scenarios of external forcing (CO2 concentration and solar radiation). A series of sensitivity experiments in which elements of the climate system are turned off in various combinations are used to address (1) and (2). This database currently provides more than 1300 experiments and has an online web interface for fast analysis and free access to the data. We briefly outline the design of all experiments, give a discussion of some results, put the findings into the context of previously published results from similar experiments, discuss the quality and limitations of the MSCM experiments, and also give an outlook on possible further developments. The GREB model simulation is quite realistic, but the model without flux corrections has a root mean square error in the mean state of the surface temperature of about 10 ∘C, which is larger than those of general circulation models (2 ∘C). It needs to be noted here that the GREB model does not simulate circulation changes or changes in cloud cover (feedbacks). However, the MSCM experiments show good agreement to previously published studies. Although GREB is a very simple model, it delivers good first-order estimates, is very fast, highly accessible, and can be used to quickly try many different sensitivity experiments or scenarios. It builds a basis on which conceptual ideas can be tested to first order and it provides a null hypothesis for understanding complex climate interactions in the context of response to external forcing or interactions in the climate subsystems.
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