Lakes have important influence on weather and climate from local to global scales. However, their prediction using numerical models is notoriously difficult because lakes are highly heterogeneous across the globe, but observations are sparse. Here, we assessed the performance of a 1‐D lake model in simulating the thermal structures of 58 lakes with diverse morphometric and geographic characteristics by following the phase 2a local lake protocol of the Intersectoral Impact Model Intercomparison Project (ISIMIP2a). After calibration, the root‐mean‐square errors (RMSE) were below 2°C for 70% and 75% of the lakes for epilimnion and full‐profile temperature simulations, with an average of 1.71°C and 1.43°C, respectively. The model performance mainly depended on lake shape rather than location, supporting the possibility of grouping model parameters by lake shape for global applications. Furthermore, through machine‐learning based parameter sensitivity tests, we identified turbulent heat fluxes, wind‐driven mixing, and water transparency as the major processes controlling lake thermal and mixing regimes. Snow density was also important for modeling the ice phenology of high‐latitude lakes. The relative influence of the key processes and the corresponding parameters mainly depended on lake latitude and depth. Turbulent heat fluxes showed a decreasing importance in affecting epilimnion temperature with increasing latitude. Wind‐driven mixing was less influential to lake stratification for deeper lakes while the impact of light extinction, on the contrary, showed a positive correlation with lake depth. Our findings may guide improvements in 1‐D lake model parameterizations to achieve higher fidelity in simulating global lake thermal dynamics.

中文翻译：

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跨全球湖泊的一维湖模型的验证和敏感性分析

湖泊对当地和全球范围的天气和气候都有重要影响。然而，众所周知，使用数值模型进行预测非常困难，因为全球范围内湖泊的异质性很高，但是观测稀疏。在这里，我们遵循部门间影响模型比较项目（ISIMIP2a）的第2a阶段本地湖协议，评估了一维湖模型在模拟具有不同形态和地理特征的58个湖的热结构中的性能。校准后，对于上扬和全廓线温度模拟，湖泊的70％和75％的均方根误差（RMSE）低于2°C，平均分别为1.71°C和1.43°C。模型的性能主要取决于湖泊的形状而不是位置，支持为全球应用按湖泊形状分组模型参数的可能性。此外，通过基于机器学习的参数敏感性测试，我们确定了湍流热通量，风混合和水透明性是控制湖泊热和混合状态的主要过程。雪密度对于模拟高纬度湖泊的冰物候也很重要。关键过程和相应参数的相对影响主要取决于湖泊的纬度和深度。随着纬度的增加，湍流的热通量对影响epi上温度的重要性降低。风的混合对深层湖泊的分层影响较小，而光灭绝的影响则与湖泊深度呈正相关。