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Multiple sources of predictive uncertainty in modeled estimates of net ecosystem CO2 exchange

机译：净生态系统CO2交换模型估计中预测不确定性的多种来源

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Net ecosystem CO2 exchange (NEE) is typically measured directly by eddy covariance towers or is estimated by ecosystem process models, yet comparisons between the data obtained by these two methods can show poor correspondence. There are three potential explanations for this discrepancy. First, estimates of NEE as measured by the eddy-covariance technique are laden with uncertainty and can potentially provide a poor baseline for models to be tested against. Second, there could be fundamental problems in model structure that prevent an accurate simulation of NEE. Third, ecosystem process models are dependent on ecophysiological parameter sets derived from field measurements in which a single parameter for a given species can vary considerably. The latter problem suggests that with such broad variation among multiple inputs, any ecosystem modeling scheme must account for the possibility that many combinations of apparently feasible parameter values might not allow the model to emulate the observed NEE dynamics of a terrestrial ecosystem, as well as the possibility that there may be many parameter sets within a particular model structure that can successfully reproduce the observed data. We examined the extent to which these three issues influence estimates of NEE in a widely used ecosystem process model, Biome-BGC, by adapting the generalized likelihood uncertainty estimation (GLUE) methodology. This procedure involved 400,000 model runs, each with randomly generated parameter values from a uniform distribution based on published parameter ranges, resulting in estimates of NEE that were compared to daily NEE data from young and mature Ponderosa pine stands at Metolius, Oregon. Of the 400,000 simulations run with different parameter sets for each age class (800,000 total), over 99% of the simulations underestimated the magnitude of net ecosystem CO2 exchange, with only 4.07% and 0.045% of all simulations providing satisfactory simulations of the field data for the young and mature stands, even when uncertainties in eddy-covariance measurements are accounted for. Results indicate fundamental shortcomings in the ability of this model to produce realistic carbon flux data over the course of forest development, and we suspect that much of the mismatch derives from an inability to realistically model ecosystem respiration. However, difficulties in estimating historic climate data are also a cause for model-data mismatch, particularly in a highly ecotonal region such as central Oregon. This latter difficulty may be less prevalent in other ecosystems, but it nonetheless highlights a challenge in trying to develop a dynamic representation of the terrestrial biosphere.
机译：净生态系统CO2交换量（NEE）通常直接由涡度协方差塔测量或由生态系统过程模型估算，但是通过这两种方法获得的数据之间的比较显示出较差的对应性。对于这种差异，有三种可能的解释。首先，通过涡度协方差技术测得的NEE估计值充满不确定性，并可能为要测试的模型提供较差的基准。其次，模型结构中可能存在一些基本问题，阻碍了对NEE的精确仿真。第三，生态系统过程模型取决于从田间测量中得出的生态生理参数集，其中给定物种的单个参数可能会有很大差异。后一个问题表明，由于多个输入之间存在如此广泛的差异，任何生态系统建模方案都必须考虑以下可能性：表面上可行的参数值的许多组合可能不允许该模型模拟观察到的陆地生态系统的NEE动态，以及在特定模型结构中可能存在许多可以成功重现观察到的数据的参数集的可能性。我们通过采用广义似然不确定性估计（GLUE）方法，研究了这三个问题在广泛使用的生态系统过程模型Biome-BGC中影响NEE估计的程度。该过程涉及400,000个模型运行，每个模型运行均基于已发布的参数范围从均匀分布中随机生成参数值，从而得出NEE估计值，并将其与来自俄勒冈州Metolius的年轻和成熟美国黄松的每日NEE数据进行比较。在针对每个年龄段使用不同参数集的40万次模拟中（总共80万次），超过99％的模拟低估了生态系统净二氧化碳交换量，只有4.07％和0.045％的模拟提供了令人满意的现场数据模拟对于年轻的和成熟的看台，即使考虑了涡旋协方差测量的不确定性。结果表明，该模型在森林开发过程中无法生成实际碳通量数据的能力存在根本缺陷，我们怀疑许多失配是由于无法对生态系统呼吸进行真实建模所致。但是，估算历史气候数据的困难也是造成模型数据不匹配的原因，特别是在经济高度发达的地区（如俄勒冈州中部）。后一困难在其他生态系统中可能不那么普遍，但它仍然突出了试图发展陆地生物圈动态表示的挑战。

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