Abstract ID: 464

Amazon dieback predictions in dynamic global vegetation models: rainfall vs. thermal drivers

Global climate models predict increases in temperature over the Amazon of up to 8°C until the end of the present century. In addition, some climate models predict that there will be decreases in rainfall, in some instances of up to 50%. Although climactic uncertainty has been the focus of several recent studies, less attention has been paid to the uncertainty in land surface models in response of Amazonian vegetation to increasing temperatures and decreasing rainfall. This study compares the sensitivity of three dynamic global vegetation models (LPJ, MOSES-TRIFFID, HYLAND) to decreases in rainfall and increases in temperature and attempts to disentangle the relative influences of each in driving loss of vegetation carbon (dieback) and to identify the most sensitive processes in current predictions of Amazon dieback. All three models used in the study were found to be largely insensitive to precipitation reduction, with some models such as HYLAND and MOSES-TRIFFID only simulating dieback at rainfall reductions of approximately 60%. Sensitivity to temperature was much higher. Temperature was found to be very important in driving dieback in all three models, while only the LPJ model suggested that decreasing rainfall was a significant cause of dieback. We investigated the temperature sensitivity further to determine whether this was the result of a direct effect on plant physiology, or of an indirect effect, via increasing vapour pressure deficit and plant water balance. We also discuss the model results in the light of field data, particularly from rainfall exclusion experiments.

Session:  Feedbacks to Climate - Land cover, surface hydrology, and atmospheric feedbacks. (B)

Presentation Type:  Oral