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

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 (Amax ) and light-use efficiency (Q) 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 (R2  = 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%, P = 0.02) and eCO2 (26%, P = 0.07) was primarily driven by enhanced C uptake during spring (129%, P = 0.001) and fall (124%, P = 0.001), respectively, which was consistent across years. Antecedent air temperature (Tairant ) and vapor pressure deficit (VPDant ) effects on Amax (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 VPDant 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.

Challenging terrestrial biosphere models with data from the long-term multifactor Prairie Heating and CO2 Enrichment experiment.

Multifactor experiments are often advocated as important for advancing terrestrial biosphere models (TBMs), yet to date, such models have only been tested against single-factor experiments. We applied 10 TBMs to the multifactor Prairie Heating and CO2 Enrichment (PHACE) experiment in Wyoming, USA. Our goals were to investigate how multifactor experiments can be used to constrain models and to identify a road map for model improvement. We found models performed poorly in ambient conditions; there was a wide spread in simulated above-ground net primary productivity (range: 31-390 g C m-2  yr-1 ). Comparison with data highlighted model failures particularly with respect to carbon allocation, phenology, and the impact of water stress on phenology. Performance against the observations from single-factors treatments was also relatively poor. In addition, similar responses were predicted for different reasons across models: there were large differences among models in sensitivity to water stress and, among the N cycle models, N availability during the experiment. Models were also unable to capture observed treatment effects on phenology: they overestimated the effect of warming on leaf onset and did not allow CO2 -induced water savings to extend the growing season length. Observed interactive (CO2  × warming) treatment effects were subtle and contingent on water stress, phenology, and species composition. As the models did not correctly represent these processes under ambient and single-factor conditions, little extra information was gained by comparing model predictions against interactive responses. We outline a series of key areas in which this and future experiments could be used to improve model predictions of grassland responses to global change.

Antecedent moisture and temperature conditions modulate the response of ecosystem respiration to elevated CO2 and warming.

Terrestrial plant and soil respiration, or ecosystem respiration (Reco ), represents a major CO2 flux in the global carbon cycle. However, there is disagreement in how Reco will respond to future global changes, such as elevated atmosphere CO2 and warming. To address this, we synthesized six years (2007-2012) of Reco data from the Prairie Heating And CO2 Enrichment (PHACE) experiment. We applied a semi-mechanistic temperature-response model to simultaneously evaluate the response of Reco to three treatment factors (elevated CO2 , warming, and soil water manipulation) and their interactions with antecedent soil conditions [e.g., past soil water content (SWC) and temperature (SoilT)] and aboveground factors (e.g., vapor pressure deficit, photosynthetically active radiation, vegetation greenness). The model fits the observed Reco well (R2  = 0.77). We applied the model to estimate annual (March-October) Reco , which was stimulated under elevated CO2 in most years, likely due to the indirect effect of elevated CO2 on SWC. When aggregated from 2007 to 2012, total six-year Reco was stimulated by elevated CO2 singly (24%) or in combination with warming (28%). Warming had little effect on annual Reco under ambient CO2 , but stimulated it under elevated CO2 (32% across all years) when precipitation was high (e.g., 44% in 2009, a 'wet' year). Treatment-level differences in Reco can be partly attributed to the effects of antecedent SoilT and vegetation greenness on the apparent temperature sensitivity of Reco and to the effects of antecedent and current SWC and vegetation activity (greenness modulated by VPD) on Reco base rates. Thus, this study indicates that the incorporation of both antecedent environmental conditions and aboveground vegetation activity are critical to predicting Reco at multiple timescales (subdaily to annual) and under a future climate of elevated CO2 and warming.