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首页> 外文期刊>Journal of Geophysical Research, D. Atmospheres: JGR >Modeled and observed clouds during Surface Heat Budget of the Arctic Ocean (SHEBA)
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Modeled and observed clouds during Surface Heat Budget of the Arctic Ocean (SHEBA)

机译:北冰洋地表热量收支期间的模拟和观测云(SHEBA)

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Observed monthly mean cloud cover from the SHEBA site is found to differ by a substantial amount during winter depending on cloud observing instrument. This makes it difficult for climate modelers to evaluate modeled clouds and improve parameterizations. Many instruments and human observers cannot properly detect the thinnest clouds and count them as clear sky instead, resulting in too low cloud cover. To study the impact from the difficulties in the detection of thin clouds, we compute cloud cover in our model with a filter that removes the thinnest clouds. Optical thickness is used as a proxy to identify thin clouds as we are mainly interested in the impact of clouds on radiation. With the results from a regional climate model simulation of the Arctic, we can reproduce the large variability in wintertime cloud cover between instruments when assuming different cloud detection thresholds. During winter a large fraction of all clouds are optically thin, which causes the large sensitivity to filtering by optical thickness. During summer, most clouds are far above the optical thickness threshold and filtering has no effect. A fair comparison between observed and modeled cloud cover should account for thin clouds that may be present in models but absent in the observational data set. Difficulties with the proper identification of clouds and clear sky also has an effect on cloud radiative forcing. The derived clear-sky longwave flux at the surface can vary by some W m~-2 depending on the lower limit for the optical thickness of clouds. This impacts on the “observed” LW cloud radiative forcing and suggests great care is needed in using satellite-derived cloud radiative forcing for model development.
机译:在冬季,根据SHEBA站点观测到的月平均云量差异很大,这取决于云观测仪器。这使气候建模人员难以评估建模的云并改善参数设置。许多仪器和人类观察者无法正确检测出最薄的云层,而是将它们视为晴朗的天空,从而导致云层覆盖率过低。为了研究薄云检测困难带来的影响,我们在模型中使用去除最薄云的过滤器来计算云量。光学厚度被用作识别薄云的代理,因为我们主要对云对辐射的影响感兴趣。根据北极地区气候模型模拟的结果,当假设不同的云探测阈值时,我们可以重现仪器之间的冬季云量变化较大。在冬季,所有云层中的很大一部分都是光学稀薄的,这导致对通过光学厚度过滤的敏感度很高。在夏季,大多数云层都远远超过光学厚度阈值,并且过滤没有影响。对观测到的和模型化的云量进行合理的比较,应该可以解释模型中可能存在但观测数据集中不存在的薄云。正确识别云层和晴朗天空的困难也会影响云层的辐射强迫。取决于云的光学厚度的下限,在表面上得出的晴空长波通量可能会以W m〜-2左右变化。这会影响“观察到的” LW云辐射强迫,并建议在使用卫星衍生的云辐射强迫进行模型开发时需要格外小心。

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