Accurately predicting the response of Amazonia to climate change is important for predicting changes across the globe. However, changes in multiple climatic factors simultaneously may result in complex non-linear responses, which are difficult to predict using vegetation models. Using leaf and canopy scale observations, this study evaluated the capability of five vegetation models (CLM3.5, ED2, JULES, SiB3, and SPA) to simulate the responses of canopy and leaf scale productivity to changes in temperature and drought in an Amazonian forest. The models did not agree as to whether gross primary productivity (GPP) was more sensitive to changes in temperature or precipitation. There was greater model–data consistency in the response of net ecosystem exchange to changes in temperature, than in the response to temperature of leaf area index (LAI), net photosynthesis (An) and stomatal conductance (gs). Modelled canopy scale fluxes are calculated by scaling leaf scale fluxes to LAI, and therefore in this study similarities in modelled ecosystem scale responses to drought and temperature were the result of inconsistent leaf scale and LAI responses among models.
Across the models, the response of An to temperature was more closely linked to stomatal behaviour than biochemical processes. Consequently all the models predicted that GPP would be higher if tropical forests were 5 °C colder, closer to the model optima for gs. There was however no model consistency in the response of the An–gs relationship when temperature changes and drought were introduced simultaneously. The inconsistencies in the An–gs relationships amongst models were caused by to non-linear model responses induced by simultaneous drought and temperature change. To improve the reliability of simulations of the response of Amazonian rainforest to climate change the mechanistic underpinnings of vegetation models need more complete validation to improve accuracy and consistency in the scaling of processes from leaf to canopy.