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首页> 外文期刊>Agricultural and Forest Meteorology >Optimization of a biochemical model with eddy covariance measurements in black spruce forests of Alaska for estimating CO2 fertilization effects
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Optimization of a biochemical model with eddy covariance measurements in black spruce forests of Alaska for estimating CO2 fertilization effects

机译:阿拉斯加黑云杉林中涡流协方差测量生化模型的优化,以估算二氧化碳的施肥效果

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Understanding how high -latitude terrestrial productivity and evapotranspiration change in association with rising atmospheric CO2 concentration ([CO2]), also known as 'CO2 fertilization', is important for predicting future climate change. To quantify the magnitude of this fertilization effect, we have developed a big -leaf model that couples photosynthesis and stomatal conductance processes. This model was inverted by inputting eddy covariance CO2 and H2O fluxes from four black spruce forests in Alaska to infer spatially representative ecophysiological parameters using a global optimization technique. Inferred seasonal variations in a maximum carboxylation rate at 25 degrees C per unit leaf area and stomatal conductance suggest greater photosynthetic capacity per unit leaf area during the mid -growing season, compared to spring and autumn. The interannual variability of parameters suggest that warm summers stimulate photosynthetic capacity and dry summers force stomatal regulation. Based on the model with optimized parameters, small but clear increases in gross primary productivity (GPP) and decreases in latent heat flux (LE) were estimated to be associated with rising [CO2] from 2002 to 2014 (p 0.01). With a 23 ppm increase in summertime (June -August) [CO2] from 2002 to 2014, the rates of increase per unit [CO2] were approximately 0.16 +/- 0.04% ppm-1 for GPP and -0.06 +/- 0.03% ppm(-1) for LE from 2002 to 2014. However, considerable uncertainties (greater than 100%) were estimated in the magnitude of the fertilization effect associated with different parameterizations in the biochemical model, indicating the need for ecophysiological studies for boreal plants. A network of eddy covariance instrumentation installed across similar ecosystem types, such as the one used in this study, can be particularly useful for evaluating ecosystem -scale ecophysiological traits and their role under changing environmental conditions. (c) 2016 Elsevier B.V. All rights reserved.
机译:了解高纬度地面生产力和蒸散量与大气中二氧化碳浓度([CO2])升高(也称为“ CO2施肥”)的关系如何,对于预测未来的气候变化非常重要。为了量化这种施肥效果的大小,我们开发了一个大叶模型,该模型将光合作用和气孔导度过程耦合在一起。通过输入来自阿拉斯加的四个黑云杉林的涡动协方差CO2和H2O通量来倒置该模型,以使用全局优化技术推断具有空间代表性的生态生理参数。与春季和秋季相比,推断的最大羧化速率在每单位叶面积25°C时的季节性变化和气孔导度表明在生长中期,每单位叶面积的光合作用能力更高。参数的年际变化表明,温暖的夏天会刺激光合能力,而干燥的夏天会迫使气孔调节。基于具有优化参数的模型,估计从2002年到2014年,总初级生产力(GPP)的微小但明显的增加和潜热通量(LE)的减少与[CO2]的上升有关(p <0.01)。从2002年到2014年,夏季(6月至8月)[CO2]增加了23 ppm,单位[CO2]的增加速率对于GPP约为0.16 +/- 0.04%ppm-1,而-0.06 +/- 0.03% 2002年至2014年LE的ppm(-1)。但是,与生化模型中不同参数化相关的施肥效果的大小估计存在很大的不确定性(大于100%),这表明需要对北方植物进行生理生态研究。在类似的生态系统类型中安装的涡旋协方差仪器网络(例如本研究中使用的一种)对于评估生态系统规模的生态生理特征及其在变化的环境条件下的作用尤其有用。 (c)2016 Elsevier B.V.保留所有权利。

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