Atmospheric rivers (ARs) are narrow regions of strong horizontal water vapor transport that play important roles in the global water cycle, weather, and hydrology. Motivated by challenges in simulating ARs with state‐of‐the‐art global models, this paper diagnoses model errors with a focus on relative contributions of moisture convergence, evaporation, and precipitation to AR column–integrated water vapor (IWV) budget. Using 20‐year simulations by 24 global weather/climate models, budget terms are calculated for four AR sectors: postfrontal, frontal, prefrontal, and pre‐AR, with biases assessed against two reanalysis products. The results indicate that each sector is unique in terms of the dominant water vapor balance, and that the terms exhibiting the largest intermodel spread are the same terms dominating the water vapor balance in each sector. Overall, simulated bulk AR characteristics (e.g., geometry, frequency, and intensity) are more sensitive to biases in IVT convergence and IWV tendency than to biases in evaporation and precipitation, although evaporation/precipitation biases do affect key AR bulk characteristics in selected sectors. The large intermodel spread (particularly for precipitation) and, in certain cases, discrepancies between the reanalysis references themselves (particularly for precipitation types) highlight the need for observational efforts that target better constraining AR processes in weather/climate models and reanalyses.

中文翻译：

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大气河流水汽收支的多模型评估

大气河流 (AR) 是具有强烈水平水汽输送的狭窄区域，在全球水循环、天气和水文中发挥着重要作用。受用最先进的全球模型模拟 AR 的挑战的启发，本文诊断模型错误，重点是水分收敛、蒸发和降水对 AR 柱综合水汽 (IWV) 预算的相对贡献。使用 24 个全球天气/气候模型的 20 年模拟，计算四个 AR 部门的预算条款：后额叶、额叶、前额叶和前 AR，并根据两种再分析产品评估偏差。结果表明，每个部门在主要的水汽平衡方面都是独一无二的，并且表现出最大模型间传播的项是在每个部门中主导水汽平衡的相同项。总体而言，尽管蒸发/降水偏差确实会影响选定部门的关键 AR 体特性，但模拟的整体 AR 特征（例如几何形状、频率和强度）对 IVT 收敛和 IWV 趋势的偏差比蒸发和降水的偏差更敏感。大的模式间传播（特别是降水）以及在某些情况下，再分析参考本身之间的差异（特别是降水类型）突出了观测工作的需要，以更好地约束天气/气候模型和再分析中的 AR 过程。尽管蒸发/沉淀偏差确实会影响选定部门的关键 AR 体积特性。大的模式间传播（特别是降水）以及在某些情况下，再分析参考本身之间的差异（特别是降水类型）突出了观测工作的需要，以更好地约束天气/气候模型和再分析中的 AR 过程。尽管蒸发/沉淀偏差确实会影响选定部门的关键 AR 体积特性。大的模式间传播（特别是降水）以及在某些情况下，再分析参考本身之间的差异（特别是降水类型）突出了观测工作的需要，以更好地约束天气/气候模型和再分析中的 AR 过程。