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首页> 外文期刊>Dynamics of Atmospheres and Oceans >Cloud-radiation feedback and atmosphere-ocean coupling in a stochastic multicloud model
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Cloud-radiation feedback and atmosphere-ocean coupling in a stochastic multicloud model

机译：随机多云模型中的云辐射反馈和大气-海洋耦合

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Despite recent advances in supercomputing, current general circulation models (GCMs) have significant problems in representing the variability associated with organized tropical convection. Furthermore, due to high sensitivity of the simulations to the cloud radiation feedback, the tropical convection remains a major source of uncertainty in long-term weather and climate forecasts. In a series of recent studies, it has been shown, in paradigm two-baroclinic-mode systems and in aquaplanet GCMs, that a stochastic multicloud convective parameterization based on three cloud types (congestus, deep and stratiform) can be used to improve the variability and the dynamical structure of tropical convection, including intermittent coherent structures such as synoptic and mesoscale convective systems. Here, the stochastic multicloud model is modified with a parameterized cloud radiation feedback mechanism and atmosphere-ocean coupling. The radiative convective feedback mechanism is shown to increase the mean and variability of the Walker circulation. The corresponding intensification of the circulation is associated with propagating synoptic scale systems originating inside of the enhanced sea surface temperature area. In column simulations, the atmosphere ocean coupling introduces pronounced low frequency convective features on the time scale associated with the depth of the mixed ocean layer. However, in the presence of the gravity wave mixing of spatially extended simulations, these features are not as prominent. This highlights the deficiency of the column model approach at predicting the behavior of multiscale spatially extended systems. Overall, the study develops a systematic framework for incorporating parameterized radiative cloud feedback and ocean coupling which may be used to improve representation of intraseasonal and seasonal variability in GCMs. (C) 2015 Elsevier B.V. All rights reserved.
机译：尽管最近在超级计算方面取得了进展，但是当前的通用循环模型（GCM）在表示与有组织的热带对流有关的变异性方面存在重大问题。此外，由于模拟对云辐射反馈的高度敏感性，热带对流仍然是长期天气预报和气候预测中不确定性的主要来源。在最近的一系列研究中，已经证明在范式双斜度模式系统和滑水行星GCM中，可以使用基于三种云类型（充血，深层和层状）的随机多云对流参数化来提高可变性。热带对流的动力结构，包括间歇性相干结构，如天气和中尺度对流系统。在此，通过参数化云辐射反馈机制和大气-海洋耦合来修改随机多云模型。辐射对流反馈机制显示出可以增加沃克环流的均值和可变性。循环的相应加剧与起源于海面温度升高区域内部的传播天气尺度系统有关。在柱模拟中，大气海洋耦合在与混合海洋层深度相关的时间尺度上引入了明显的低频对流特征。但是，在存在空间扩展模拟的重力波混合的情况下，这些功能并不那么突出。这突出了列模型方法在预测多尺度空间扩展系统行为方面的不足。总体而言，该研究开发了一个系统化的框架，用于结合参数化的辐射云反馈和海洋耦合，可用于改善GCM的季节内和季节变化的代表性。 （C）2015 Elsevier B.V.保留所有权利。

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