Modelling climate change responses in tropical forests: similar productivity estimates across five models, but different mechanisms and responses
- 1School of GeoSciences, University of Edinburgh, Edinburgh, UK
- 2College of Engineering, Mathematics, and Physical Science, University of Exeter, Exeter, UK
- 3Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, Arizona, USA
- 4School of Geography, University of Leeds, Leeds, UK
- 5Grupo de Pesquisas em Interação Atmosfera-Biosfera, Universidade Federal de Viçosa, Minas Gerias, Brazil
- 6Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, Massachusetts, USA
- 7Environmental Change Institute, School of Geography and the Environment, University of Oxford, Oxford, UK
- 8Department of Biological Sciences, University of Southern California, Los Angeles, CA, USA
- 9Research School of Biology, Australian National University, Canberra, ACT 2601, Australia
Abstract. Accurately predicting the response of Amazonia to climate change is important for predicting climate change across the globe. Changes in multiple climatic factors simultaneously result in complex non-linear ecosystem responses, which are difficult to predict using vegetation models. Using leaf- and canopy-scale observations, this study evaluated the capability of five vegetation models (Community Land Model version 3.5 coupled to the Dynamic Global Vegetation model – CLM3.5–DGVM; Ecosystem Demography model version 2 – ED2; the Joint UK Land Environment Simulator version 2.1 – JULES; Simple Biosphere model version 3 – SiB3; and the soil–plant–atmosphere model – SPA) to simulate the responses of leaf- and canopy-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, but all the models were consistent with the prediction that GPP would be higher if tropical forests were 5 °C cooler than current ambient temperatures. There was greater model–data consistency in the response of net ecosystem exchange (NEE) to changes in temperature than in the response to temperature by net photosynthesis (An), stomatal conductance (gs) and leaf area index (LAI). Modelled canopy-scale fluxes are calculated by scaling leaf-scale fluxes using LAI. At the leaf-scale, the models did not agree on the temperature or magnitude of the optimum points of An, Vcmax or gs, and model variation in these parameters was compensated for by variations in the absolute magnitude of simulated LAI and how it altered with temperature.
Across the models, there was, however, consistency in two leaf-scale responses: (1) change in An with temperature was more closely linked to stomatal behaviour than biochemical processes; and (2) intrinsic water use efficiency (IWUE) increased with temperature, especially when combined with drought. These results suggest that even up to fairly extreme temperature increases from ambient levels (+6 °C), simulated photosynthesis becomes increasingly sensitive to gs and remains less sensitive to biochemical changes. To improve the reliability of simulations of the response of Amazonian rainforest to climate change, the mechanistic underpinnings of vegetation models need to be validated at both leaf- and canopy-scales to improve accuracy and consistency in the quantification of processes within and across an ecosystem.