<p>Accurate simulations of gross primary production (GPP) are vital for Earth System Models that must inform public policy decisions. &#160;The instantaneous controls of leaf-level photosynthesis, which can be measured in manipulative experiments, are well established. &#160;At the canopy scale, however, there is no consensus on how GPP depends on (a) light or (b) other aspects of the physical environment such as temperature and CO<sub>2</sub>. &#160;Models of GPP make a variety of different assumptions when &#8216;scaling-up&#8217; the standard model of photosynthesis. &#160;As a troublesome consequence, they make a variety of different predictions about how GPP responds to contemporary environmental change.</p><p>This problem can be tackled by theoretically based modelling, or by empirical analysis of GPP as reconstructed from eddy-covariance flux measurements. &#160;Theoretical modelling has provided an explanation for why &#8216;light-use efficiency&#8217; (LUE) models work well at time scales of a week or longer. &#160;The same logic provides a justification for the use of LUE as a key metric in an empirical analysis. &#160;By focusing on LUE, we can isolate the controls of GPP that are distinct from its over-riding control by absorbed light. &#160;We have used open-access eddy covariance data from over 100 sites, collated over 20 years (the number of sites has grown with time). &#160;These sites, located in a wide range of biomes and climate zones, form part of the FLUXNET network. &#160;We have combined the flux data with a satellite product (FPAR from MODIS) that provides spatial estimates of the fraction of incident light absorbed by green vegetation. &#160;Soil moisture at flux sites was estimated using the SPLASH model, with appropriate meteorological inputs, and soil water-holding capacity derived using SoilGrids. &#160;LUE was then calculated as the amount of carbon fixed per unit of absorbed light. &#160;We then considered additive models (incorporating multiple explanatory factors) that support non-linear responses, including a peaked response to temperature. &#160;Recognising that our longitudinal data are not fully independent, we controlled for the hierarchical nature of the dataset through a variance structure that nests measurement year within site location.</p><p>In arriving at a final parsimonious model, we show that daytime air temperature and vapour pressure deficit, and soil moisture content, are all salient predictors of LUE. &#160;The same explanatory terms are retained in iterations of this analysis run at timescales from weeks to months. &#160;Model performance was not significantly improved by inclusion of additional variables such as rainfall, site elevation or vegetation category (e.g. Plant Functional Type, PFT). &#160;This empirical analysis supports the notion that GPP is predictable using a single model structure that is common to different PFTs.</p>
Dispersion of the Retardation of a Photoelastic Modulator
