Abstract
Abstract. This paper provides a comprehensive description of OSCAR v2.2, a simple Earth system model. The general philosophy of development is first explained, followed by a complete description of the model's drivers and various modules. All components of the Earth system necessary to simulate future climate change are represented in the model: the oceanic and terrestrial carbon cycles – including a book-keeping module to endogenously estimate land-use change emissions – so as to simulate the change in atmospheric carbon dioxide; the tropospheric chemistry and the natural wetlands, to simulate that of methane; the stratospheric chemistry, for nitrous oxide; 37 halogenated compounds; changing tropospheric and stratospheric ozone; the direct and indirect effects of aerosols; changes in surface albedo caused by black carbon deposition on snow and land-cover change; and the global and regional response of climate – in terms of temperature and precipitation – to all these climate forcers. Following the probabilistic framework of the model, an ensemble of simulations is made over the historical period (1750–2010). We show that the model performs well in reproducing observed past changes in the Earth system such as increased atmospheric concentration of greenhouse gases or increased global mean surface temperature.
Highlights
Simple biogeochemistry–climate models, qualified as compact or reduced-form models, are widely used in the climate change research community
The climate variables, are reconstructed on the basis of the prescribed radiative forcing (RF), so that we can discuss the performance of the climate module alone, i.e., when it is not coupled to any other module
We have provided a complete description of the compact Earth system model OSCAR v2.2, and we have presented the model’s results in the case of an historical simulation
Summary
Simple biogeochemistry–climate models, qualified as compact or reduced-form models, are widely used in the climate change research community. They are not spatially resolved and as such they can be referred to as box models, the number of boxes – and of state variables – may vary greatly: from a couple to several hundred. The time step of analysis and of numerical solving is about 1 year, which implies they usually cannot include representations of seasonal processes One consequence of these two features is their very high computing efficiency: one simulation typically takes about 1 min on a laptop. Compact models can be used in a variety of setups, such as the following for instance: to translate a large number of pathways of greenhouse gases emissions into projected climate change (e.g., Clarke et al, 2014), to complement a study by a process-based model (e.g., Schneider von Deimling et al, 2012) or an economic model (e.g., Rogelj et al, 2013), to extend a given experiment or assess its uncertainty with a Monte Carlo analysis (e.g., Gasser et al, 2015), to attribute changes in a variable of the climate system to physical processes (e.g., Raupach et al, 2014) or to emitting countries (e.g., Höhne et al, 2011), or to discuss theoretical frameworks (e.g., Raupach, 2013) or policy-relevant indica-
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