周晨 - 南京大学 - 大气科学学院

个人简介

2024- 南京大学大气科学学院，教授 2017-2023 南京大学大气科学学院，副教授 2014-2017 美国劳伦斯利弗莫尔国家实验室，博士后研究员 2010-2014 美国德州农工大学，博士 2003-2010 北京大学，学士、硕士

研究领域

云会通过反射太阳短波辐射冷却地球，也会通过温室效应加热地球，在地球的能量收支中起着重要的作用。云的辐射效应不但决定了当前的天气和气候，还间接决定了未来地表平均温度、降水、风能和太阳能等重要要素的变化。然而，云的观测和模拟都存在巨大的不确定性，因此相关的研究有着重要的意义。以下是本人的研究方向： 1. 云的辐射与遥感 (a) 云粒子的后向散射特性与激光雷达反演激光雷达能够遥感探测薄云的垂直结构，而激光雷达的反演算法需要预设云粒子的后向散射特性。我们发现并论证了云粒子的后向散射相干增强效应，并将该效应应用于卷云的激光雷达反演当中。 (b) 基于深度学习算法和红外辐射的云属性反演尽管夜间云属性对地表温度起着重要作用，大多数被动式卫星产品却不提供完整的夜间云产品，这是因为基于可见光的云反演算法无法被用于夜间云反演，而传统的红外云属性反演算法无法得到厚云光学厚度。我们使用深度卷积神经网络建立了一套红外云属性反演算法,并使用多源卫星产品对神经网络进行了训练，实现了全天候的云光学厚度、云有效粒径、云顶高度、云底高度和云相态反演，反演精度优于传统算法。 2. 云的辐射反馈 (a) 云、海温/海冰的水平空间分布与地球系统的能量收支在全球变暖的背景下，云属性对地表温度的响应会影响全球能量收支，并进一步影响地表温度。过去的研究只考虑全球平均温度变化产生的云反馈，而我们则基于覆盖全球的格林函数海温扰动实验提出了海温/海冰的水平分布式样影响云反馈的机制，并量化了这个效应对全球能量收支、温度变化和水循环的影响。在我们的研究的影响下，国外学者建立了格林函数模式比较计划（GFMIP），目前已有来自5个不同国家的多个实验室参与了这个计划。 (b) 云的辐射效应与局地地表温度之间的相互作用局地云辐射效应对陆表温度和海表温度的变化和变率都有着重要的影响。我们的研究表明，除去陆表温度与近地面温度之间的相互作用以后，云辐射效应的变率是陆表温度变率的最重要因素；在海洋表面，局地云反馈强度的分布对海表温度变化和变率的水平分布式样也都有着重要的影响。

近期论文

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Liu, Y., Huang, Y., Yuan, J., Xie, Y., & Zhou*, C. (2024). Contribution of surface radiative effects, heat fluxes and their interactions to land surface temperature variability. Journal of Geophysical Research: Atmospheres, 129, e2023JD039495. Wang, Q., C. Zhou*, H. Letu, Y. Zhu, X. Zhuge, C. Liu, F. Weng, and M. Wang, 2023: Obtaining Cloud Base Height and Phase From Thermal Infrared Radiometry Using a Deep Learning Algorithm. IEEE Transactions on Geoscience and Remote Sensing, 61, doi: 10.1109/TGRS.2023.3317532 Zhou, C.*, X. Han, and L. Bi, 2023: Quantifying the coherent backscatter enhancement of non-spherical particles with discrete dipole approximation. Opt. Express, 31, 24183-24193. Zhang, S., P. Stier, G. Dagan, C. Zhou, and M. Wang, 2023: Sea surface warming patterns drive hydrological sensitivity uncertainties. Nature Climate Change, 13, 545–553, https://doi.org/10.1038/s41558-023-01678-5. Zhou, C.*, Wang, M., Zelinka, M. D., Liu, Y., Dong, Y., and Armour, K. C., 2023: Explaining forcing efficacy with pattern effect and state dependence. Geophysical Research Letters, 50, e2022GL101700. Wang, Q., C. Zhou*, X. Zhuge, C. Liu, F. Weng, and M. Wang, 2022: Retrieval of cloud properties from thermal infrared radiometry using convolutional neural network. Remote Sensing of Environment, 278, https://doi.org/10.1016/j.rse.2022.113079. Zhou, C.*, Y. Liu, and Q. Wang, 2022: Calculating the climatology and anomalies of surface cloud radiative effect using cloud property histograms and cloud radiative kernels. Adv. Atmos. Sci., doi: 10.1007/s00376-021-1166-z. Samset, B.H., Zhou, C., Fuglestvedt, J.S. et al. Earlier emergence of a temperature response to mitigation by filtering annual variability. Nature Communications, 13, 1578 (2022). https://doi.org/10.1038/s41467-022-29247-y Zhou, C.*, M. D. Zelinka, A. E. Dessler, and M. Wang, 2021: Greater committed warming after accounting for the pattern effect. Nature Climate Change, doi: https://doi.org/10.1038/s41558-020-00955-x. Zhou, C.*, J. Lu, Y. Hu, and M.D. Zelinka, 2020: Responses of the Hadley Circulation to Regional Sea Surface Temperature Changes. J. Climate, 33, 429–441, https://doi.org/10.1175/JCLI-D-19-0315.1. Zhou C. *(2018), Coherent backscatter enhancement in single scattering, Optics Express, 26(10), A508-A519, doi: 10.1364/OE.26.00A508. Zhou, C.*, M. D. Zelinka, and S. A. Klein, 2017: Analyzing the dependence of global cloud feedback on the spatial pattern of sea surface temperature change with a Green's function approach, J. Adv. Model. Earth Syst., 9, doi:10.1002/2017MS001096. Zhou, C.*, M. D. Zelinka, and S. A. Klein, 2016: Impact of decadal cloud variations on the Earth’s energy budget. Nature Geoscience, 9, 871–874, doi: 10.1038/ngeo2828. Zelinka, M. D., C. Zhou and S. A. Klein, 2016: Insights from a Refined Decomposition of Cloud Feedbacks. Geophysical Research Letters, 43, doi:10.1002/2016GL069917. Zhou, C., and P. Yang, 2015: Backscattering peak of ice cloud particles. Optics Express, 23(9), 11995-12003. Zhou, C.*, M. D. Zelinka, A. E. Dessler, and S. A. Klein, 2015: The relationship between inter-annual and long-term cloud feedbacks, Geophysical Research Letters, 42, doi:10.1002/2015GL066698. Zhou, C., A. E. Dessler, M. D. Zelinka, P. Yang, and T. Wang, 2014: Cirrus feedback on interannual climate fluctuations, Geophysical Research Letters, 41, 9166–9173, doi:10.1002/2014GL062095. Zhou, C., M. D. Zelinka, A. E. Dessler, and P. Yang, 2013a: An Analysis of the Short-Term Cloud Feedback Using MODIS Data. Journal of Climate, 26, 4803–4815. Zhou, C., P. Yang, A. E. Dessler and F. Liang, 2013b: Statistical properties of horizontally oriented plates in optically thick clouds from satellite observations, IEEE Geoscience and Remote Sensing Lett., 10, 986-990. Zhou, C., P. Yang, A. E. Dessler, Y.-X. Hu and B. A. Baum, 2012: Study of horizontally oriented ice crystals with CALIPSO observations and comparison with Monte Carlo Radiative Transfer Simulations, Journal of Applied Meteorology and Climatology, 51, 1426-1439. Hu, Y.-Y., C. Zhou and J. Liu, 2011: Observational evidence for poleward expansion of the Hadley Circulation, Advances in Atmospheric Sciences, 28, 33-44.