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Modification of the SUNFLUX solar radiation scheme with a new aerosol parameterization and its validation using observation network data

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Abstract

SUNFLUX is a fast parameterization scheme for determination of the solar radiation at the Earth’s surface. In this paper, SUNFLUX is further modified in the treatment of aerosols. A new aerosol parameterization scheme is developed for five aerosol species. Observational data from Baseline Surface Radiation Network (BSRN), Surface Radiation Budget Network (SURFRAD) and Aerosol Robotic Network (AERONET) stations are used to evaluate the accuracy of the original and modified SUNFLUX schemes. General meteorological data are available at SURFRAD stations, but not at BSRN stations. Therefore, the total precipitable water content and aerosol data are obtained from AERONET stations. Fourteen stations are selected from both BSRN and AERONET. Cloud fraction data from MODIS are further used to screen the cloud. Ten-year average aerosol mixing ratios simulated by the CAM-chem system are used to calculate the fractions of aerosol optical depth for each aerosol species, and these fractions are further used to convert the observed total aerosol optical depth into the components of individual species for use in the evaluations. The proper treatment of multiple aerosol types in the model is discussed. The evaluation results using SUNFLUX with the new aerosol scheme, in terms of the BSRN dataset, are better than those using the original aerosol scheme under clear-sky conditions. However, the results using the SURFRAD dataset are slightly worse, attributable to the differences in the input water vapor and aerosol optical depth. Sensitivity tests are conducted to investigate the error response of the SUNFLUX scheme to the errors in the input variables.

摘 要

SUNFLUX 是地面太阳辐射参数化快速计算方案. 本文对 SUNFLUX 在气溶胶方面做了改进, 开发了针对 5 种气溶胶的参数化方案. 并使用了Baseline Surface Radiation Network (BSRN), Surface Radiation Budget Network (SURFRAD) 和 Aerosol Robotic Network (AERONET) 数据对改进前和改进后 SUNFLUX 方案的精度进行了评估. 由于, 常规气象数据无法在BSRN网站上获取, 需要从SURFRAD网站上获取, 而大气可降水量及气溶胶数据可从AERONET网站上获取. 本文选取了 BSRN 和 AERONET 共有的 14 个站的数据进行评估, 使用 modis 云量数据剔除云的影响, 保证数据质量. 在评估中, 使用 CAM—chem 系统模拟的 10 年平均气雾混合比例, 用于计算每种气溶胶的光学厚度比值, 并使用这些比值将观测的气溶胶光学厚度转化为需要的气溶胶光学厚度. 本文讨论了模型中各种类型的气溶胶处理方法. 评估的结果表明, 晴空条件下, 使用新的气溶胶方案的 SUNFLUX 计算的 BSRN 数据要好于原先的方案. 但是, 计算的 SURFRAD 数据结果稍差, 主要因为输入水汽和气溶胶光学厚度的差异. 同时, 我们也进行了灵敏度测试, 分析SUNFLUX误差相对于输入变量误差的响应.

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Acknowledgements

Jingmiao LIU is supported by the joint research projects entitled “Observing and Modelling Study on Spatial and Temporal Variation of Radiation Budget and PAR in Regional Scale” and “Evaluation on Detailed Agro-climatic Potential Productivity and Effects of Climate Change in Northeast China” (Grant No. CCSF201313). The BSRN data were downloaded from http://www.bsrn.awi.de/. The AERONET data were downloaded from http://aeronet.gsfc.nasa.gov/. The MODIS data were downloaded from http://modis.gsfc.nasa.gov/. The SURFRAD data were downloaded from http://www.esrl.noaa.gov/gmd/grad/surfrad/. The CAM-chem aerosol data were downloaded from ftp://ftp-ipcc.fzjuelich. de/pub/emissions/gridded netcdf/.

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Authors and Affiliations

  1. Institute of Geographic and Remote Sensing, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Yongjian He & Guoping Shi

  2. Environment & Research Division, Australian Bureau of Meteorology, Melbourne, 3001, Australia

    Zhi’an Sun

  3. Institute of Atmospheric Environment, China Meteorological Administration, Shenyang, 110016, China

    Jingmiao Liu

  4. Chinese Academy of Meteorological Sciences, Beijing, 100081, China

    Jingmiao Liu

  5. State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China

    Jiandong Li

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  1. Yongjian He
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Correspondence to Zhi’an Sun.

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He, Y., Sun, Z., Shi, G. et al. Modification of the SUNFLUX solar radiation scheme with a new aerosol parameterization and its validation using observation network data. Adv. Atmos. Sci. 34, 1301–1315 (2017). https://doi.org/10.1007/s00376-016-6262-0

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