DataSheet1_Application of feedback control to stomatal optimisation in a global land surface model.pdf

Accurate representations of stomatal conductance are required to predict the effects of climate change on terrestrial ecosystems. Stomatal optimisation theory, the idea that plants have evolved to maximise carbon gain under certain constraints, such as minimising water loss or preventing hydraulic damage, is a powerful approach to representing stomatal behaviour that bypasses the need to represent complex physiological processes. However, while their ability to replicate observed stomatal responses is promising, optimisation models often present practical problems for those trying to simulate the land surface. In particular, when realistic models of photosynthesis and more complex cost functions are used, closed-form solutions for the optimal stomatal conductance are often very difficult to find. As a result, implementing stomatal optimisation in land surface models currently relies either on simplifying approximations, that allow closed-form solutions to be found, or on numerical iteration which can be computationally expensive. Here we propose an alternative approach, using a method motivated by control theory that is computationally efficient and does not require simplifying approximations to be made to the underlying optimisation. Stomatal conductance is treated as the control variable in a simple closed-loop system and we use the Newton-Raphson method to track the time-varying maximum of the objective function. We compare the method to both numerical iteration and a semi-analytical approach by applying the methods to the SOX stomatal optimisation model at multiple sites across the Amazon rainforest. The feedback approach is able to more accurately replicate the results found by numerical iteration than the semi-analytical approach while maintaining improved computational efficiency.</p

South American mountain ecosystems and global change – a case study for integrating theory and field observations for land surface modelling and ecosystem management

Plot-based monitoring has yielded much information on the taxonomic diversity and carbon (C) storage in tropical lowland forests of the Amazon basin. This has resulted in an improved understanding of the relationship between lowland forest biomass dynamics and global change drivers, such as climate change and atmospheric CO2 concentration. Much less attention has been paid to the mountain ecosystems of South America that comprise montane forests and alpine vegetation (páramo, puna, high Andean grasslands, wetlands, and alpine heath). This vegetation complex provides a variety of ecosystem services and forms a natural laboratory along various physiographic, geological and evolutionary history/biogeography, and land use history gradients. Here we review existing empirical understanding and model-based approaches to quantify the contribution of mountain ecosystems to ecosystem service provision in the rapidly changing socioecological setting of the South American mountains. The objective of this paper is to outline a broad road map for the implementation of mountain vegetation into dynamic global vegetation models (DGVM) for use in Earth System Models (ESM), based on our current understanding of their structure and function and of their responsiveness to global change drivers. We also identify treeline processes, critical in mountain ecosystems, as key missing elements in DGVMs/ESMs, and thus explore in addition a treeline model. A stocktaking of availability of empirical data was undertaken from eight research sites along the Andes and in south-eastern Brazil. Out of eight sites, two (one each in Venezuela and Brazil) had some climate, ecological and ecophysiological data potentially suitable to parametrise a DGVM. Tree biomass data were available for six sites. A preliminary assessment of the Joint UK Land Environment Simulator (JULES) DGVM was made to identify gaps in available data and their impacts on model parametrisation and calibration. Additionally, the potential climate-determined elevation of the treeline was modelled to check the DGVM for its ability to identify the transition between the montane forest and alpine vegetation. Outcomes of the evaluation of the JULES land surface model identified the following key processes in montane forests: temperature-related decrease in net primary production, respiration, and allocation to above-ground biomass and increase in soil C stocks with elevation. There was a variable agreement between simulated biomass and those derived from field measurements via allometric equations. We identified major gaps between data availability and the needs of process-based modelling of South American mountain vegetation and its dynamics in DGVMs. To bridge this gap, we propose a transdisciplinary network, composed of members of the theoretical/modelling and empirical scientific communities to study the natural dynamics of mountain ecosystems and their responses to global change drivers locally, regionally and at the continent scale, within a social-ecological system framework. The work presented here forms the basis for the design of data collection from field measurements and instrumental monitoring stations to parametrise and verify DGVMs. The network is designed to collaborate with and complement existing long-term research initiatives in the region and will adopt existing standard field protocols. Complementary protocols will ensure compatibility between field data collection and data needs for process-based and empirical models.</p