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Diagnosing the causes of bias in climate models - why is it so hard?

机译：诊断气候模型偏差的原因-为什么这么难？

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The equations of climate are, in principle, known. Why then is it so hard to formulate a biasfree model of climate? Here, some ideas in nonlinear dynamics are explored to try to answer this question. Specifically it is suggested that the climatic response to physically different forcings shows a tendency to project onto structures corresponding to the systems natural internal modes of variability. This is shown using results from complex climate models and from the relatively simple Lorenz three-component model. It is suggested that this behaviour is consistent with what might be expected from the fluctuation-dissipation theorem. Based on this, it is easy to see how climate models can easily suffer from having errors in the representation of two or more different physical processes, whose responses compensate one another and hence make individual error diagnosis difficult. A proposal is made to try to overcome these problems and advance the science needed to develop a bias-free climate model. The proposal utilises powerful diagnostics from data assimilation. The key point here is that these diagnostics derive from short-range forecast tendencies, estimated long before the model has asymptotically settled down to its (biased) climate attractor. However, it is shown that these diagnostics will not identify all sources of model error, and a so-called "bias of the second kind" is discussed. This latter bias may be alleviated by recently developed stochastic parametrisations.
机译：原则上，气候方程是已知的。为什么要建立一个无偏差的气候模型如此困难？在这里，探索了非线性动力学的一些想法来试图回答这个问题。特别地，建议对物理上不同强迫的气候响应显示出一种趋势，该趋势投射到对应于系统自然内部可变性模式的结构上。使用复杂气候模型和相对简单的洛伦兹三分量模型的结果可以证明这一点。建议该行为与波动耗散定理所预期的一致。基于此，很容易看出气候模型如何容易在两个或多个不同物理过程的表示中出现误差，它们的响应相互补偿，从而使单个误差的诊断变得困难。提出了一项建议，以试图克服这些问题，并提高开发无偏差气候模型所需的科学。该提案利用了来自数据同化的强大诊断功能。这里的关键点是，这些诊断来自于短期预测趋势，该趋势是在模型渐近稳定到其（偏向）气候吸引子之前很长时间估计的。然而，已表明这些诊断不能识别出模型误差的所有来源，并且讨论了所谓的“第二种偏见”。后一种偏差可以通过最近开发的随机参数化来减轻。

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