Modelling climate change responses in tropical forests: similar productivity estimates across five models, but different mechanisms and responses

L Rowland,S R Saleska,B O Christoffersen,H M A Imbuzeiro,P Meir,N M Levine,T L Powell,D R Galbraith,C Doughty,Y Malhi,P R Moorcroft,M Williams,A Harper

doi:10.5194/gmd-8-1097-2015

Abstract

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.

Highlights

Continuing increases in atmospheric CO2 are likely to cause increases in temperature and changes in precipitation across Amazonia (Good et al, 2013; Jupp et al, 2010; Malhi et al, 2009; Marengo et al, 2012)
As a 6 ◦C rise in temperature and a 50 % reduction in rainfall are changes which may occur in Amazonia during the 21st century (Christensen et al, 2007; Collins et al, 2013), we suggest that there is currently no consensus among vegetation models as to whether there will be a stronger drought or temperature response to future climate change within tropical forests
Given that the models underestimate the. This is the first study in which canopy and leaf temperature responses from multiple vegetation models are analysed and compared to existing data on leaf and canopy temperature responses from a tropical forest site

Summary

Introduction

Continuing increases in atmospheric CO2 are likely to cause increases in temperature and changes in precipitation across Amazonia (Good et al, 2013; Jupp et al, 2010; Malhi et al, 2009; Marengo et al, 2012). Significant uncertainty remains regarding the response of tropical forests to warming (Corlett, 2011; Reed et al, 2012; Wood et al, 2012), altered precipitation (Meir et al, 2008; Meir and Woodward, 2010) and short-term abrupt changes in both precipitation and temperature (Cox et al, 2008; Marengo et al, 2011; Reichstein et al, 2013) Such uncertainties are propagated into models, resulting in substantial variability in modelled responses to changes in temperature and drought (Friedlingstein et al, 2006; Galbraith et al, 2010; Powell et al, 2013; Sitch et al, 2008). There are particular challenges when considering short-to-medium-term responses (Luo et al, 2008) linked to climatic extremes, such as severe drought (Cox et al, 2008; Marengo et al, 2011)

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Discussion

Conclusion