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Bias correction and post-processing under climate change

机译：气候变化下的偏差校正和后处理

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The statistical and dynamical properties of bias correction and linear post-processing are investigated when the system under interest is affected by model errors and is experiencing parameter modifications, mimicking the potential impact of climate change. The analysis is first performed for simple typical scalar systems, an Ornstein-Uhlenbeck process (O-U) and a limit point bifurcation. It reveals system's specific (linear or non-linear) dependences of biases and post-processing corrections as a function of parameter modifications. A more realistic system is then investigated, a low-order model of moist general circulation, incorporating several processes of high relevance in the climate dynamics (radiative effects, cloud feedbacks...), but still sufficiently simple to allow for an extensive exploration of its dynamics. In this context, bias or post-processing corrections also display complicate variations when the system experiences temperature climate changes up to a few degrees. This precludes a straightforward application of these corrections from one system's state to another (as usually adopted for climate projections), and increases further the uncertainty in evaluating the amplitudes of climate changes.
机译：当所关注的系统受到模型误差的影响并且正在经历参数修改（模仿气候变化的潜在影响）时，将研究偏差校正和线性后处理的统计和动力学特性。首先对简单的典型标量系统，Ornstein-Uhlenbeck过程（O-U）和极限点分叉进行分析。它揭示了系统特定的（线性或非线性）偏差和后处理校正与参数修改的关系。然后研究了一个更现实的系统，即潮湿的一般循环的低阶模型，其中包含了与气候动力学高度相关的几个过程（辐射效应，云反馈...），但仍然足够简单，可以广泛地探索它的动力。在这种情况下，当系统经历几度的温度气候变化时，偏差或后处理校正也会显示复杂的变化。这妨碍了将这些校正从一个系统的状态直接应用到另一个系统的状态（通常用于气候预测），并且进一步增加了评估气候变化幅度的不确定性。

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