Estimates of Amazon rainforest gross primary productivity (GPP) differ by a factor of 2 across a suite of three statistical and 18 process models. This wide spread contributes uncertainty to predictions of future climate. We compare the mean and variance of GPP from these models to that of GPP at six eddy covariance (EC) towers. Only one model's mean GPP across all sites falls within a 99% confidence interval for EC GPP, and only one model matches EC variance. The strength of model response to climate drivers is related to model ability to match the seasonal pattern of the EC GPP. Models with stronger seasonal swings in GPP have stronger responses to rain, light, and temperature than does EC GPP. The model to data comparison illustrates a trade-off inherent to deterministic models between accurate simulation of a mean (average) and accurate responsiveness to drivers. The trade-off exists because all deterministic models simplify processes and lack at least some consequential driver or interaction. If a model's sensitivities to included drivers and their interactions are accurate, then deterministically predicted outcomes have less variability than is realistic. If a GPP model has stronger responses to climate drivers than found in data, model predictions may match the observed variance and seasonal pattern but are likely to overpredict GPP response to climate change. High or realistic variability of model estimates relative to reference data indicate that the model is hypersensitive to one or more drivers.

中文翻译：

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准确模拟亚马逊光合作用的敏感性和变异性：要求是否过高？

一套由 3 个统计模型和 18 个过程模型组成的模型对亚马逊雨林总初级生产力 (GPP) 的估计值相差 2 倍。这种广泛的分布给未来气候的预测带来了不确定性。我们将这些模型的 GPP 均值和方差与六个涡流协方差 (EC) 塔的 GPP 均值和方差进行了比较。所有站点上只有一种模型的平均 GPP 落在 EC GPP 的 99% 置信区间内，并且只有一种模型与 EC 方差相匹配。模型对气候驱动因素的响应强度与模型匹配 EC GPP 季节性模式的能力有关。GPP 季节性波动较强的模型对降雨、光照和温度的响应比 EC GPP 更强。模型与数据的比较说明了确定性模型固有的在平均值的准确模拟和对驾驶员的准确响应之间的权衡。存在这种权衡是因为所有确定性模型都简化了流程并且至少缺乏一些相应的驱动因素或交互。如果模型对所包含的驱动因素及其相互作用的敏感性是准确的，那么确定性预测的结果的变异性将小于实际结果的变异性。如果 GPP 模型对气候驱动因素的响应比数据中发现的更强，则模型预测可能与观察到的方差和季节模式相匹配，但可能会高估 GPP 对气候变化的响应。模型估计相对于参考数据的高度或现实变异性表明该模型对一个或多个驱动程序高度敏感。