| 常数和变化积雪密度方案诊断计算积雪厚度的敏感性研究 |
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| 引用本文： | 张慧敏,金梅兵,祁第.常数和变化积雪密度方案诊断计算积雪厚度的敏感性研究[J].海洋学报,2022,44(7):47-57. |
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| 作者姓名： | 张慧敏 金梅兵 祁第 |
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| 作者单位： | 1.南京信息工程大学 海洋科学学院，江苏 南京 210044 |
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| 基金项目： | 国家重点研发计划（2019YFE0114800，2018YFA0605900）。 |
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| 摘 要： | 海冰上积雪的分布是影响海冰与大气能量交换以及气候变化的重要因素。当前的CMIP6气候模式（如CESM2和NESM3）采用定常的积雪密度，而专注于模拟雪厚度和密度变化的模式（如SnowModel-LG）则采用经验的变化雪密度公式。对比CryoSat-2卫星观测的积雪厚度发现，从积雪厚度的空间分布与平均值难以判断出变化雪密度对北冰洋积雪厚度模拟产生何种影响，对于变化雪密度模拟积雪厚度的改进及机制有待进一步研究。本文采用随气温、风速等因子变化的雪密度经验公式模型，并利用SNOTEL单站的长时间序列观测资料，对不同影响因子设计如下敏感性实验:A. 考虑所有气象因子的变化雪密度模型；B. 常数雪密度模型；C. 在A中不考虑风对密实化的影响；D. 在A中不考虑气温对密实化的影响。实验A、B、C和D诊断计算的2018年11月1日至2019年5月10日积雪厚度的均方根误差分别为4.2 cm、4.8 cm、25.9 cm和4.2 cm。结果表明，变化雪密度方案A模拟的积雪密度、厚度在平均值上与常数雪密度的结果接近，但其模拟的积雪厚度均方根误差最小，并且能够模拟出积雪厚度在几天到十几天时间尺度上的高频变化，同时减小了这种高频变化对应时段雪厚模拟结果的相对误差，二者具有一定的相关性。此外，还发现气温变化对积雪密实化的影响远小于风。
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| 关 键 词： | 气候模式 北极 积雪厚度 积雪密度 |
| 收稿时间： | 2021-07-29 |
Sensitivity study of constant and variable snow density schemes in diagnosing and calculating snow depth |
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| Institution: | 1.School of Marine Science, Nanjing University of Information Science and Technology, Nanjing 210044, China2.Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519080, China3.Key Laboratory of Global Change and Marine Atmospheric Chemistry, Ministry of Natural Resources, Third Institute of Oceanography, Ministry of Natural Resources, Xiamen 361005, China4.Polar and Marine Research Institute, Jimei University, Xiamen 361021, China |
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| Abstract: | Current CMIP6 climate models (such as CESM2 and NESM3) use constant snow density, while those models that focus on snow depth and density changes (such as SnowModel-LG) use empirical snow density formulas. Comparing the modeled snow depth with those observed by the CryoSat-2 satellite, it is found that from the perspective of the spatial distribution and average value of the snow depth, it is difficult to detect the effects of varying snow density on the simulation of snow depth in the Arctic Ocean. The model improvement and its mechanism from varying snow depth is still to be further studied. Here an empirical snow density model considering meteorological factors such as air temperature, wind etc., is applied to the SNOTEL observational site to carry out the following sensitivity experiments for different factors: A. snow density model considering all meteorological factors; B. constant snow density model; C. same as A but the influence of wind on the densification is not considered and D. same as A but the influence of temperature on the densification is not considered. The root mean square error of snow depth simulated by experiments A, B, C and D from November 1, 2018 to May 10, 2019 are 4.2 cm, 4.8 cm, 25.9 cm, and 4.2 cm, respectively. The results show that the mean snow density and depth simulated by the varying snow density model are close to the results using constant snow density, but the root mean square error of the simulated snow depth from Case A is the smallest, and the Case A simulation can reproduce the high frequency variations of snow depth on the time scale of several days to ten days. In the meantime, the relative errors in the period with high-frequency snow depth variations are also reduced as they are found to be related. In addition, it is also found that the influence of temperature on snow densification is much smaller than that of wind. |
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