A step-response approach for predicting and understanding non-linear precipitation changes

Abstract

Listen

Translate article

Future changes in precipitation represent one of the most important and uncertain possible effects of future climate change. We demonstrate a new approach based on idealised CO2 step-change general circulation model (GCM) experiments, and test it using the HadCM3 GCM. The approach has two purposes: to help understand GCM projections, and to build and test a fast simple model for precipitation projections under a wide range of forcing scenarios. Overall, we find that the CO2 step experiments contain much information that is relevant to transient projections, but that is more easily extracted due to the idealised experimental design. We find that the temporary acceleration of global-mean precipitation in this GCM following CO2 ramp-down cannot be fully explained simply using linear responses to CO2 and temperature. A more complete explanation can be achieved with an additional term representing interaction between CO2 and temperature effects. Energy budget analysis of this term is dominated by clear-sky outgoing long-wave radiation (CSOLR) and sensible heating, but cloud and short-wave terms also contribute. The dominant CSOLR interaction is attributable to increased CO2 raising the mean emission level to colder altitudes, which reduces the rate of increase of OLR with warming. This behaviour can be reproduced by our simple model. On regional scales, we compare our approach with linear ‘pattern-scaling’ (scaling regional responses by global-mean temperature change). In regions where our model predicts linear change, pattern-scaling works equally well. In some regions, however, substantial deviations from linear scaling with global-mean temperature are found, and our simple model provides more accurate projections. The idealised experiments reveal a complex pattern of non-linear behaviour. There are likely to be a range of controlling physical mechanisms, different from those dominating the global-mean response, requiring focussed investigation for individual regions, and in other GCMs.