Predictions of Gross Primary Production from a suite of Terrestrial Biome Models display divergent relationships with key environmental variables
Authors: Bloomfield Keith , Stocker Benjamin , Keenan Trevor , Prentice Colin 
Published: March 2021

&lt;p&gt;Accurate simulations of gross primary production (GPP) are vital for Earth System Models that must inform public policy decisions. &amp;#160;The instantaneous controls of leaf-level photosynthesis, which can be measured in manipulative experiments, are well established.&amp;#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&lt;sub&gt;2&lt;/sub&gt;. &amp;#160;Models of GPP make a variety of different assumptions when &amp;#8216;scaling-up&amp;#8217; the standard model of photosynthesis. &amp;#160;As a troublesome consequence, they make a variety of different predictions about how GPP responds to projected environmental change.&lt;/p&gt;&lt;p&gt;This problem can be tackled by theoretical modelling and by empirical analysis of GPP as reconstructed from eddy-covariance flux measurements. &amp;#160;Theoretical modelling has provided an explanation for why &amp;#8216;light-use efficiency&amp;#8217; (LUE) models work well at time scales of a week or longer. &amp;#160;The same logic provides a justification for the use of LUE as a key metric in an empirical analysis. &amp;#160;By focusing on LUE, we can isolate the drivers of GPP independent of its over-riding control by absorbed light. &amp;#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).&amp;#160; These sites, located in a wide range of biomes and climate zones, form part of the FLUXNET network.&amp;#160; We have combined the flux data with a satellite product (EVI from MODIS) that allows spatial estimates of the fraction of incident light absorbed by green vegetation.&amp;#160; Matching soil moisture data were estimated using the SPLASH model, with appropriate meteorological inputs, and soil water-holding capacity derived using SoilGrids. &amp;#160;LUE was then calculated as the amount of carbon fixed per unit of absorbed light.&amp;#160; We then explored additive models (incorporating multiple explanatory factors) that support non-linear responses.&amp;#160; Recognising that our longitudinal data lack independence, we controlled for the hierarchical nature of the dataset through a variance structure that nests measurement year within site location.&lt;/p&gt;&lt;p&gt;In arriving at a preferred parsimonious model, we show that daytime air temperature and vapour pressure deficit, and soil moisture content are all salient predictors of LUE.&amp;#160; The same explanatory terms are retained in iterations of this analysis run at timescales from weeks to months. &amp;#160;As a model-comparison exercise, we used that portion of our dataset which overlaps the North American Carbon Program to apply our empirical model structure to site-based estimates of GPP generated by 19 discrete Terrestrial Biosphere Models (TBMs).&amp;#160; The comparative analysis reveals wide variation between the TBMs in the shape, strength and even sign of the environmental effects on modelled GPP.&lt;/p&gt;&lt;p&gt;This empirical analysis suggests it is feasible to predict GPP using a single model structure, common across vegetation categories.&amp;#160; And the appeal of such universal approaches is highlighted by inconsistent relationships with key environmental drivers within extant terrestrial models.&lt;/p&gt;