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Characterizing the performance of ecosystem models across time scales: A spectral analysis of the North American Carbon Program site-level synthesis

机译：表征跨时间尺度的生态系统模型的性能：对北美碳计划站点级综合的频谱分析

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摘要

Ecosystem models are important tools for diagnosing the carbon cycle and projecting its behavior across space and time. Despite the fact that ecosystems respond to drivers at multiple time scales, most assessments of model performance do not discriminate different time scales. Spectral methods, such as wavelet analyses, present an alternative approach that enables the identification of the dominant time scales contributing to model performance in the frequency domain. In this study we used wavelet analyses to synthesize the performance of 21 ecosystem models at 9 eddy covariance towers as part of the North American Carbon Program’s site-level intercomparison. This study expands upon previous single-site and single-model analyses to determine what patterns of model error are consistent across a diverse range of models and sites. To assess the significance of model error at different time scales, a novel Monte Carlo approach was developed to incorporate flux observation error. Failing to account for observation error leads to a misidentification of the time scales that dominate model error. These analyses show that model error (1) is largest at the annual and 20–120 day scales, (2) has a clear peak at the diurnal scale, and (3) shows large variability among models in the 2–20 day scales. Errors at the annual scale were consistent across time, diurnal errors were predominantly during the growing season, and intermediate-scale errors were largely event driven. Breaking spectra into discrete temporal bands revealed a significant model-by-band effect but also a nonsignificant model-by-site effect, which together suggest that individual models show consistency in their error patterns. Differences among models were related to model time step, soil hydrology, and the representation of photosynthesis and phenology but not the soil carbon or nitrogen cycles. These factors had the greatest impact on diurnal errors, were less important at annual scales, and had the least impact at intermediate time scales.
机译：生态系统模型是诊断碳循环并预测其跨时空行为的重要工具。尽管生态系统在多个时间尺度上对驱动程序做出响应，但大多数模型性能评估并未区分不同的时间尺度。频谱方法（例如小波分析）提出了另一种方法，该方法可以识别对频域中的模型性能有贡献的主要时间尺度。在这项研究中，我们使用小波分析在9个涡流协方差塔上综合了21种生态系统模型的性能，这是北美碳计划在现场进行比对的一部分。这项研究扩展了以前的单站点和单模型分析，以确定在各种模型和站点范围内哪些模型错误模式是一致的。为了评估模型误差在不同时间尺度上的重要性，开发了一种新颖的蒙特卡洛方法来合并通量观测误差。不考虑观察误差会导致错误地识别主导模型误差的时间尺度。这些分析表明，模型误差（1）在年度尺度和20–120天尺度上最大，（2）在昼夜尺度上具有明显的峰值，（3）在2–20天尺度上显示模型之间的较大差异。年尺度的误差在整个时间范围内是一致的，昼夜误差主要发生在生长季节，而中等尺度的误差在很大程度上是事件驱动的。将光谱分解为离散的时间带显示出显着的逐带效应，但也没有显着的逐点效应，这共同表明各个模型的误差模式具有一致性。模型之间的差异与模型时间步长，土壤水文学以及光合作用和物候学的表示有关，但与土壤碳或氮循环无关。这些因素对日间误差的影响最大，在年度尺度上的重要性较小，而在中间时间尺度上的影响最小。

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