Gross primary production responses to warming, elevated CO2 , and irrigation: quantifying the drivers of ecosystem physiology in a semiarid grassland

<p>Determining whether the terrestrial biosphere will be a source or sink of carbon (C) under a future climate of elevated CO₂ (eCO₂) and warming requires accurate quantification of gross primary production (GPP), the largest flux of C in the global C cycle. We evaluated 6 years (2007–2012) of flux‐derived GPP data from the Prairie Heating and CO₂ Enrichment (PHACE) experiment, situated in a grassland in Wyoming, USA. The GPP data were used to calibrate a light response model whose basic formulation has been successfully used in a variety of ecosystems. The model was extended by modeling maximum photosynthetic rate (<i>A</i>_max) and light‐use efficiency (<i>Q</i>) as functions of soil water, air temperature, vapor pressure deficit, vegetation greenness, and nitrogen at current and antecedent (past) timescales. The model fits the observed GPP well (<i>R</i>² = 0.79), which was confirmed by other model performance checks that compared different variants of the model (e.g. with and without antecedent effects). Stimulation of cumulative 6‐year GPP by warming (29%, <i>P</i> = 0.02) and eCO₂ (26%, <i>P</i> = 0.07) was primarily driven by enhanced C uptake during spring (129%, <i>P</i> = 0.001) and fall (124%, <i>P</i> = 0.001), respectively, which was consistent across years. Antecedent air temperature (Tair_ant) and vapor pressure deficit (VPD_ant) effects on <i>A</i>_max (over the past 3–4 days and 1–3 days, respectively) were the most significant predictors of temporal variability in GPP among most treatments. The importance of VPD_ant suggests that atmospheric drought is important for predicting GPP under current and future climate; we highlight the need for experimental studies to identify the mechanisms underlying such antecedent effects. Finally, posterior estimates of cumulative GPP under control and eCO₂ treatments were tested as a benchmark against 12 terrestrial biosphere models (TBMs). The narrow uncertainties of these data‐driven GPP estimates suggest that they could be useful semi‐independent data streams for validating TBMs.</p>