Polarization surrounding bias correction (BC) in creating climate projections arises from its lack of physicality. Here, we perform and analyze 18 dynamical downscaling simulations (with and without BC) to better understand the physical impacts of BC, applied before downscaling, on regional climate output across the western United States. Without BC, downscaled precipitation is systematically and unrealistically wet biased compared to a hierarchy of observationally based datasets over the 1980–2014 period due to cascading mean-state Global Climate Model (GCM) biases: (a) overly strong lower-tropospheric lapse rates (5 K/km), (b) overly cold (2 K) tropospheric temperatures, and (c) anomalous mid-tropospheric cyclonic vorticity advection. With BC, downscaled precipitation (snow) biases are virtually eliminated (halved). Identified GCM biases are common to the broader Coupled Model Intercomparison Project ensemble. Physical effects of BC on the quality of the regionalized projections, pending an evaluation of BC's distortion of the downscaled climate response, may motivate its broader application by dynamical downscalers.

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了解级联：消除 GCM 偏差可改善动态缩小的气候预测

创建气候预测时围绕偏差校正（BC）的两极分化源于其缺乏物理性。在这里，我们执行并分析了 18 个动态降尺度模拟（有和没有 BC），以更好地了解在降尺度之前应用的 BC 对美国西部区域气候输出的物理影响。如果没有 BC，由于级联平均状态全球气候模型 (GCM) 偏差，与 1980-2014 年期间基于观测的数据集层次相比，降水量缩小是系统性的、不切实际的湿偏差：(a) 对流层低层递减率过强（ 5 K/km)，(b) 过冷 (2 K) 对流层温度，以及 (c) 异常的对流层中部气旋涡度平流。对于 BC，降水（雪）偏差实际上已消除（减半）。已确定的 GCM 偏差对于更广泛的耦合模型比对项目整体来说是常见的。在对 BC 对缩小气候响应的扭曲进行评估之前，BC 对区域化预测质量的物理影响可能会促使动态缩小器更广泛地应用它。