Articles | Volume 16, issue 24
https://doi.org/10.5194/acp-16-15789-2016
https://doi.org/10.5194/acp-16-15789-2016
Research article
 | 
22 Dec 2016
Research article |  | 22 Dec 2016

Dynamic climate emulators for solar geoengineering

Douglas G. MacMartin and Ben Kravitz

Abstract. Climate emulators trained on existing simulations can be used to project project the climate effects that result from different possible future pathways of anthropogenic forcing, without further relying on general circulation model (GCM) simulations. We extend this idea to include different amounts of solar geoengineering in addition to different pathways of greenhouse gas concentrations, by training emulators from a multi-model ensemble of simulations from the Geoengineering Model Intercomparison Project (GeoMIP). The emulator is trained on the abrupt 4 × CO2 and a compensating solar reduction simulation (G1), and evaluated by comparing predictions against a simulated 1 % per year CO2 increase and a similarly smaller solar reduction (G2). We find reasonable agreement in most models for predicting changes in temperature and precipitation (including regional effects), and annual-mean Northern Hemisphere sea ice extent, with the difference between simulation and prediction typically being smaller than natural variability. This verifies that the linearity assumption used in constructing the emulator is sufficient for these variables over the range of forcing considered. Annual-minimum Northern Hemisphere sea ice extent is less well predicted, indicating a limit to the linearity assumption.

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Short summary
Solar geoengineering has been proposed as a possible additional approach for managing risks of climate change, by reflecting some sunlight back to space. To project climate effects resulting from future choices regarding both greenhouse gas emissions and solar geoengineering, it is useful to have a computationally efficient "emulator" that approximates the behavior of more complex climate models. We present such an emulator here, and validate the underlying assumption of linearity.
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