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.

Methods
The Prairie Heating and CO2 Enrichment experiment is located in a temperate, mixed-grass prairie near Cheyenne, Wyoming (elevation = 1930 m). The PHACE experiment involves an incomplete factorial design with 30 plots randomly assigned to six treatments, with five plots per treatment level (Parton et al., 2007). The circular plots (3.4 m diameter) are separated from surrounding soil by a plastic flange buried to a depth of 60 cm (Bachman et al., 2010). The six treatments – denoted as ct, cT, Ct, CT, ct-d, and ct-s – involve different combinations of atmospheric CO2 [ambient at 380–400 ppm (denoted as ‘c’) vs. elevated at 600 ppm (‘C’)], temperature [ambient/not heated (‘t’) vs. heated by 1.5 (day) or 3.0 (night) ̊C (‘T’)], and watering [none vs. shallow (‘s’) or deep (‘d’) irrigation, which are only applied under ambient CO2 and temperature (‘ct’)]. The goal of the irrigation treatments was to increase soil moisture to approximately match that of the Ct plots by irrigating when soil moisture fell below 85% of Ct at 5–25 cm depth.

The SWC, SoilT, and micrometeorological data had occasional missing time periods or days due to instrument failure (

在CO₂升高（elevated CO₂, eCO₂）和气候变暖的未来情景下，判断陆地生物圈将成为碳（C）源还是碳汇，需要精准量化总初级生产力（Gross Primary Production, GPP）——全球碳循环中规模最大的碳通量。我们评估了来自美国怀俄明州草原的草原升温与CO₂富集（Prairie Heating and CO₂ Enrichment, PHACE）实验2007–2012年共6年的通量衍生GPP数据。利用该GPP数据对光响应模型进行校准，该模型的基础框架已在多种生态系统中得到成功验证与应用。我们通过将最大光合速率（maximum photosynthetic rate, Amax）与光能利用效率（light-use efficiency, Q）建模为土壤水分、气温、水汽压亏缺、植被绿度以及不同当前和前期时间尺度下的氮含量的函数，对该模型进行了扩展。模型对观测GPP的拟合效果优异（决定系数R²=0.79），这一结论通过对比模型不同变体（如考虑与不考虑前期效应的模型）的多组性能验证得到了确认。

升温处理使6年累计GPP提升29%（P=0.02），eCO₂处理则提升26%（P=0.07），其驱动效应分别来源于春季（129%，P=0.001）和秋季（124%，P=0.001）碳吸收的显著增强，且该结果在各观测年份间保持稳定。在多数处理组中，前期气温（air temperature antecedent, Tairant）和前期水汽压亏缺（vapor pressure deficit antecedent, VPDant，分别对应过去3~4天和1~3天）对Amax的影响是GPP时间变异性最显著的预测因子。前期水汽压亏缺的重要性表明，大气干旱对当前及未来气候背景下的GPP预测具有关键意义；我们强调需开展针对性实验研究以阐明此类前期效应背后的生理生态机制。最后，我们将对照组与eCO₂处理组下累计GPP的后验估计值作为基准，与12个陆地生物圈模型（Terrestrial Biosphere Models, TBMs）进行了对比验证。这类数据驱动的GPP估计值具有较窄的不确定性区间，表明其可作为半独立数据流，用于TBMs的校验与优化。

方法
草原升温与CO₂富集实验设于怀俄明州夏延市附近的温带混合草草原，海拔1930米。该PHACE实验采用不完全因子设计，共设置30个圆形样地，直径3.4米，通过埋深60厘米的塑料法兰与周围土壤隔离（Bachman等，2010），样地被随机分配至6种处理，每种处理水平设置5个重复样地（Parton等，2007）。6种处理分别记为ct、cT、Ct、CT、ct-d与ct-s，涵盖了大气CO₂浓度[本底浓度为380~400 ppm（记为‘c’），升高至600 ppm（记为‘C’）]、气温[未加热为‘t’，日间升温1.5℃、夜间升温3.0℃为‘T’]以及灌溉措施[无灌溉为基准，浅灌（‘s’）或深灌（‘d’）仅在本底CO₂与温度的‘ct’处理组中施加]的不同组合。灌溉处理的设置目标为：当5~25cm土层的土壤水分降至Ct处理样地对应水平的85%以下时进行灌溉，使该处理组的土壤水分大致匹配Ct处理组的水平。

土壤含水量（Soil Water Content, SWC）、土壤温度（SoilT）及微气象数据因仪器故障偶尔存在时段或单日数据缺失（