Abstract. By synthesising remote-sensing measurements made in the central Arctic into a model-gridded Cloudnet cloud product, we evaluate how well the Met Office Unified Model (UM) and European Centre for Medium-Range Weather Forecasting Integrated Forecasting System (IFS) capture Arctic clouds and their associated interactions with the surface energy balance and the thermodynamic structure of the lower troposphere. This evaluation was conducted using a four-week observation period from the Arctic Ocean 2018 expedition, where the transition from sea ice melting to freezing conditions was measured. Three different cloud schemes were tested within a nested limited area model (LAM) configuration of the UM – two regionally-operational single-moment schemes (UM_RA2M and UM_RA2T), and one novel double-moment scheme (UM_CASIM-100) – while one global simulation was conducted with the IFS, utilising its default cloud scheme (ECMWF_IFS). Consistent weaknesses were identified across both models, with both the UM and IFS overestimating cloud occurrence below 3 km. This overestimation was also consistent across the three cloud configurations used within the UM framework, with > 90 % mean cloud occurrence simulated between 0.15 and 1 km in all model simulations. However, the cloud microphysical structure, on average, was modelled reasonably well in each simulation, with the cloud liquid water content (LWC) and ice water content (IWC) comparing well with observations over much of the vertical profile. The key microphysical discrepancy between the models and observations was in the LWC between 1 and 3 km, where most simulations (all except UM_RA2T) overestimated the observed LWC. Despite this reasonable performance in cloud physical structure, both models failed to adequately capture cloud-free episodes: this consistency in cloud cover likely contributes to the ever-present near-surface temperature bias simulated in every simulation. Both models also consistently exhibited temperature and moisture biases below 3 km, with particularly strong cold biases coinciding with the overabundant modelled cloud layers. These biases are likely due to too much cloud top radiative cooling from these persistent modelled cloud layers and were interestingly consistent across the three UM configurations tested, despite differences in their parameterisations of cloud on a sub-grid-scale. Alarmingly, our findings suggest that these biases in the regional model were inherited from the driving model, thus triggering too much cloud formation within the lower troposphere. Using representative cloud condensation nuclei concentrations in our double-moment UM configuration, while improving cloud microphysical structure, does little to alleviate these biases; therefore, no matter how comprehensive we make the cloud physics in the nested LAM configuration used here, its cloud and thermodynamic structure will continue to be overwhelmingly biased by the meteorological conditions of its driving model.

中文翻译：

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评估使用统一模型和集成预测系统建模的北极云

摘要。通过将北极中部的遥感测量结果合成模型网格 Cloudnet 云产品，我们评估了气象局统一模型 (UM) 和欧洲中期天气预报中心综合预报系统 (IFS) 捕获北极云的能力以及它们与表面能量平衡和对流层低层热力学结构的相关相互作用。该评估是在 2018 年北冰洋探险队为期 4 周的观察期内进行的，其中测量了从海冰融化到结冰条件的过渡。在 UM 的嵌套有限区域模型 (LAM) 配置中测试了三种不同的云方案——两种区域性操作的单时刻方案（UM_RA2M 和 UM_RA2T），和一种新颖的双矩方案 (UM_CASIM-100)——同时使用 IFS 进行了一项全局模拟，利用其默认云方案 (ECMWF_IFS)。两种模型都发现了一致的弱点，UM 和 IFS 都高估了低于 3 公里的云发生率。这种高估在 UM 框架内使用的三种云配置中也是一致的，在所有模型模拟中，在 0.15 到 1 公里之间模拟了 > 90 % 的平均云发生率。然而，平均而言，云微物理结构在每次模拟中都得到了相当好的建模，云液态水含量（这种高估在 UM 框架内使用的三种云配置中也是一致的，在所有模型模拟中，在 0.15 到 1 公里之间模拟了 > 90 % 的平均云发生率。然而，平均而言，云微物理结构在每次模拟中都得到了相当好的建模，云液态水含量（这种高估在 UM 框架内使用的三种云配置中也是一致的，在所有模型模拟中，在 0.15 到 1 公里之间模拟了 > 90 % 的平均云发生率。然而，平均而言，云微物理结构在每次模拟中都得到了相当好的建模，云液态水含量（LWC ) 和冰水含量 ( IWC ) 与大部分垂直剖面的观测结果进行了很好的比较。模型和观测之间的关键微物理差异在1 到 3 公里之间的LWC，其中大多数模拟（除 UM_RA2T 外）高估了观测到的LWC. 尽管云物理结构具有这种合理的性能，但两种模型都未能充分捕捉无云事件：云覆盖的这种一致性可能导致每次模拟中模拟的近地表温度偏差始终存在。两个模型也始终表现出低于 3 公里的温度和湿度偏差，特别强烈的冷偏差与过度丰富的模拟云层重合。这些偏差可能是由于来自这些持续建模的云层的云顶辐射冷却过多，并且在测试的三个 UM 配置中有趣地保持一致，尽管它们在子网格尺度上的云参数化存在差异。令人震惊的是，我们的研究结果表明，区域模型中的这些偏差是从驱动模型继承的，从而在对流层低层引发过多的云形成。在我们的双矩 UM 配置中使用具有代表性的云凝结核浓度，同时改善云微物理结构，对减轻这些偏差几乎没有作用；因此，无论我们在此处使用的嵌套 LAM 配置中使云物理多么全面，其云和热力学结构将继续受到其驱动模型的气象条件的压倒性偏差。