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Learning about parameter and structural uncertainty in carbon cycle models

机译：了解碳循环模型中的参数和结构不确定性

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Uncertainty in the response of the global carbon cycle to anthropogenic emissions plays a key role in assessments of potential future climate change and response strategies. We investigate how fast this uncertainty might change as additional data on the global carbon budget becomes available over the twenty-first century. Using a simple global carbon cycle model and focusing on both parameter and structural uncertainty in the terrestrial sink, we find that additional global data leads to substantial learning (i.e., changes in uncertainty) under some conditions but not others. If the model structure is assumed known and only parameter uncertainty is considered, learning is rather limited if observational errors in the data or the magnitude of unexplained natural variability are not reduced. Learning about parameter values can be substantial, however, when errors in data or unexplained variability are reduced. We also find that, on the one hand, uncertainty in the model structure has a much bigger impact on uncertainty in projections of future atmospheric composition than does parameter uncertainty. But on the other, it is also possible to learn more about the model structure than the parameter values, even from global budget data that does not improve over time in terms of its associated errors. As an example, we illustrate how one standard model structure, if incorrect, could become inconsistent with global budget data within 40 years. The rate of learning in this analysis is affected by the choice of a relatively simple carbon cycle model, the use of observations only of global emissions and atmospheric concentration, and the assumption of perfect autocorrelation in observational errors and variability. Future work could usefully improve the approach in each of these areas.
机译：全球碳循环对人为排放的响应不确定性在评估潜在的未来气候变化和应对策略中起着关键作用。我们调查了随着二十一世纪可获得全球碳预算的其他数据，这种不确定性可能会以多快的速度改变。通过使用简单的全球碳循环模型并集中于地面汇的参数和结构不确定性，我们发现在某些条件下，其他条件下的全球数据可带来大量的学习（即不确定性的变化）。如果假定模型结构已知并且仅考虑参数不确定性，则如果不减少数据中的观察误差或无法解释的自然可变性的幅度，则学习将受到很大限制。但是，当减少数据错误或无法解释的可变性时，了解参数值可能非常重要。我们还发现，一方面，与参数不确定性相比，模型结构的不确定性对未来大气成分预测的不确定性影响更大。但另一方面，甚至可以从模型预算中了解更多有关模型结构的信息，甚至可以从全局预算数据中获得更多的信息，这些数据在相关误差方面不会随着时间的推移而改善。例如，我们说明了一种标准模型结构（如果不正确）如何在40年内变得与全球预算数据不一致。在此分析中，学习率受以下因素的影响：选择相对简单的碳循环模型，仅使用全球排放量和大气浓度的观测值以及观测误差和变异性完全自相关的假设。未来的工作可能会有益地改善这些领域的方法。

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