Climate change will impact forest productivity worldwide. Forecasting the magnitude of such impact, with multiple environmental stressors changing simultaneously, is only possible with the help of process-based models. In order to assess their performance, such models require careful evaluation against measurements. However, direct comparison of model outputs against observational data is often not reliable, as models may provide the right answers due to the wrong reasons. This would severely hinder forecasting abilities under unprecedented climate conditions. Here, we present a methodology for model assessment, which supplements the traditional output-to-observation model validation. It evaluates model performance through its ability to reproduce observed seasonal changes of the most limiting environmental driver (MLED) for a given process, here daily gross primary productivity (GPP). We analyzed seasonal changes of the MLED for GPP in two contrasting pine forests, the Mediterranean Pinus halepensis Mill. Yatir (Israel) and the boreal Pinus sylvestris L. Hyytiälä (Finland) from three years of eddy-covariance flux data. Then, we simulated the same period with a state-of-the-art process-based simulation model (LandscapeDNDC). Finally, we assessed if the model was able to reproduce both GPP observations and MLED seasonality. We found that the model reproduced the seasonality of GPP in both stands, but it was slightly overestimated without site-specific fine-tuning. Interestingly, although LandscapeDNDC properly captured the main MLED in Hyytiälä (temperature) and in Yatir (soil water availability), it failed to reproduce high-temperature and high-vapor pressure limitations of GPP in Yatir during spring and summer. We deduced that the most likely reason for this divergence is an incomplete description of stomatal behavior. In summary, this study validates the MLED approach as a model evaluation tool, and opens up new possibilities for model improvement.

中文翻译：

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通过两个不同压力的松林中最具限制性的环境驱动因素来评估模型性能

气候变化将影响全世界的森林生产力。只有借助基于过程的模型，才能在多个环境压力源同时变化的情况下预测此类影响的程度。为了评估它们的性能，此类模型需要根据测量值进行仔细评估。然而，模型输出与观测数据的直接比较通常是不可靠的，因为模型可能会由于错误的原因提供正确的答案。这将严重阻碍在前所未有的气候条件下的预测能力。在这里，我们提出了一种模型评估方法，它补充了传统的输出到观察模型验证。它通过重现给定过程中最受限制的环境驱动因素 (MLED) 观察到的季节性变化的能力来评估模型性能，这里是每日总初级生产力（GPP）。我们分析了两个对比鲜明的地中海松林中 GPP 的 MLED 的季节性变化Pinus halepensis磨坊。Yatir（以色列）和北方樟子松L. Hyytiälä（芬兰）来自三年的涡流协方差通量数据。然后，我们使用最先进的基于流程的模拟模型 (LandscapeDNDC) 模拟了同一时期。最后，我们评估了该模型是否能够重现 GPP 观察结果和 MLED 季节性。我们发现该模型在两个看台上都再现了 GPP 的季节性，但在没有针对特定地点的微调的情况下，它被略微高估了。有趣的是，尽管 LandscapeDNDC 正确捕获了 Hyytiälä（温度）和 Yatir（土壤水分可用性）的主要 MLED，但它未能在春季和夏季重现 Yatir 中 GPP 的高温和高蒸汽压限制。我们推断出这种差异的最可能原因是对气孔行为的不完整描述。总之，