Light Charles X, Arbic Brian K, Martin Paige E, Brodeau Laurent, Farrar J Thomas, Griffies Stephen M, Kirtman Ben P, Laurindo Lucas C, Menemenlis Dimitris, Molod Andrea, Nelson Arin D, Nyadjro Ebenezer, O'Rourke Amanda K, Shriver Jay F, Siqueira Leo, Small R Justin, Strobach Ehud
Electrical Engineering and Computer Science Department, University of Michigan, Ann Arbor, MI USA.
Department of Earth and Environmental Sciences, University of Michigan, Ann Arbor, MI USA.
Clim Dyn. 2022;59(9-10):2887-2913. doi: 10.1007/s00382-022-06257-6. Epub 2022 Mar 29.
High-frequency precipitation variance is calculated in 12 different free-running (non-data-assimilative) coupled high resolution atmosphere-ocean model simulations, an assimilative coupled atmosphere-ocean weather forecast model, and an assimilative reanalysis. The results are compared with results from satellite estimates of precipitation and rain gauge observations. An analysis of irregular sub-daily fluctuations, which was applied by Covey et al. (Geophys Res Lett 45:12514-12522, 2018. 10.1029/2018GL078926) to satellite products and low-resolution climate models, is applied here to rain gauges and higher-resolution models. In contrast to lower-resolution climate simulations, which Covey et al. (2018) found to be lacking with respect to variance in irregular sub-daily fluctuations, the highest-resolution simulations examined here display an irregular sub-daily fluctuation variance that lies closer to that found in satellite products. Most of the simulations used here cannot be analyzed via the Covey et al. (2018) technique, because they do not output precipitation at sub-daily intervals. Thus the remainder of the paper focuses on frequency power spectral density of precipitation and on cumulative distribution functions over time scales (2-100 days) that are still relatively "high-frequency" in the context of climate modeling. Refined atmospheric or oceanic model grid spacing is generally found to increase high-frequency precipitation variance in simulations, approaching the values derived from observations. Mesoscale-eddy-rich ocean simulations significantly increase precipitation variance only when the atmosphere grid spacing is sufficiently fine (< 0.5°). Despite the improvements noted above, all of the simulations examined here suffer from the "drizzle effect", in which precipitation is not temporally intermittent to the extent found in observations.
在12种不同的自由运行（非数据同化）耦合高分辨率大气 - 海洋模式模拟、一种同化耦合大气 - 海洋天气预报模式以及一种同化再分析中，计算了高频降水方差。将结果与降水的卫星估计值和雨量计观测结果进行了比较。对不规则的亚日波动进行了分析，Covey等人（《地球物理研究快报》45:12514 - 12522，2018. 10.1029/2018GL078926）将其应用于卫星产品和低分辨率气候模型，本文将其应用于雨量计和高分辨率模型。与Covey等人（2018年）发现的在不规则亚日波动方差方面存在不足的低分辨率气候模拟不同，这里研究的最高分辨率模拟显示出的不规则亚日波动方差更接近卫星产品中的值。这里使用的大多数模拟无法通过Covey等人（2018年）的技术进行分析，因为它们不是以亚日时间间隔输出降水。因此，本文的其余部分重点关注降水的频率功率谱密度以及在气候建模背景下仍相对“高频”的时间尺度（2 - 100天）上的累积分布函数。一般发现，精细化的大气或海洋模型网格间距会增加模拟中的高频降水方差，接近观测得出的值。只有当大气网格间距足够精细（< 0.5°）时，富含中尺度涡旋的海洋模拟才会显著增加降水方差。尽管有上述改进，但这里研究的所有模拟都存在“毛毛雨效应”，即降水中的时间间歇性程度不如观测中发现的那样。
