Advanced Search
Article Contents

Estimation and long-term trend analysis of surface solar radiation in Antarctica: A case study of Zhongshan Station

Fund Project:

This work was supported by the National Natural Science Foundation of China (41941010 and 41771064), the National Basic Research Program of China (2016YFC1400303), and the Basic Fund of the Chinese Academy of Meteorological Sciences (2018Z001). We greatly appreciate the help from the Polar Research Institute of China and the Antarctic expeditioners at the Chinese Zhongshan Station.


doi:  10.1007/s00376-021-0386-6

  • Long-term, ground-based daily global solar radiation (DGSR) at Zhongshan Station in Antarctica can quantitatively reveal the basic characteristics of Earth’s surface radiation balance and validate satellite data for the Antarctic region. The fixed station was established in 1989 and conventional radiation observations started much later in 2008. In this study, a random forest (RF) model for estimating DGSR was developed using ground meteorological observation data, and a high-precision, long-term DGSR dataset was constructed. Then, the trend of DGSR from 1990 to 2019 at Zhongshan Station, Antarctica, was analyzed. The RF model, which performs better than other models, shows a desirable performance of DGSR hindcast estimation with an R2 of 0.984, root-mean-square error of 1.377 MJ/m2, and mean absolute error of 0.828 MJ/m2. The trend of DGSR annual anomalies increased during 1990-2004 and then began to decrease after 2004. Note that the maximum value of annual anomalies occurred during approximately 2004/05 and is mainly related to the days with precipitation (especially those related to good weather during the polar day period) at this station. In addition to clouds and water vapor, bad weather conditions (such as snowfall, which can result in low visibility and then decreased sunshine duration and solar radiation) were the other major factors affecting solar radiation at this station. The high-precision, long-term estimated DGSR dataset enables us to further study and understand the role of Antarctica in global climate change and the interactions between snow, ice and atmosphere.
  • [1] Zhao Ping, Chen Longxun, 2000: Calculation of Solar Albedo and Radiation Equilibrium over the Qinghai-Xizang Plateau and Analysis of Their Climatic Features, ADVANCES IN ATMOSPHERIC SCIENCES, 17, 140-156.  doi: 10.1007/s00376-000-0050-5
    [2] HU Bo, WANG Yuesi, LIU Guangren, 2010: Long-Term Trends in Photosynthetically Active Radiation in Beijing, ADVANCES IN ATMOSPHERIC SCIENCES, 27, 1380-1388.  doi: 10.1007/s00376-010-9204-2
    [3] Chen Yingyi, Chao Jiping, 1984: A TWO-DIMENSIONAL ENERGY BALANCE CLIMATE MODEL INCLUDING RADIATION AND ICE CAPS-ALBEDO FEEDBACK, ADVANCES IN ATMOSPHERIC SCIENCES, 1, 234-255.  doi: 10.1007/BF02678136
    [4] WEI Ke, CHEN Wen, HUANG Ronghui, 2006: Long-Term Changes of the Ultraviolet Radiation in China and its Relationship with Total Ozone and Precipitation, ADVANCES IN ATMOSPHERIC SCIENCES, 23, 700-710.  doi: 10.1007/s00376-006-0700-3
    [5] LIN Pengfei, YU Yongqiang, LIU Hailong, 2013: Long-term Stability and Oceanic Mean State Simulated by the Coupled Model FGOALS-s2, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 175-192.  doi: 10.1007/s00376-012-2042-7
    [6] ZHU Zhilin, SUN Xiaomin, ZHANG Renhua, 2003: Statistical Analysis and Comparative Study of Energy Balance Components Estimated Using Micrometeorological Techniques during HUBEX/IOP 1998/99, ADVANCES IN ATMOSPHERIC SCIENCES, 20, 285-291.  doi: 10.1007/s00376-003-0014-7
    [7] T. C. LEE, H. S. CHAN, E. W. L. GINN, M. C. WONG, 2011: Long-Term Trends in Extreme Temperatures in Hong Kong and Southern China, ADVANCES IN ATMOSPHERIC SCIENCES, 28, 147-157.  doi: 10.1007/s00376-010-9160-x
    [8] Lei LIU, Fei HU, 2019: Long-term Correlations and Extreme Wind Speed Estimations, ADVANCES IN ATMOSPHERIC SCIENCES, 36, 1121-1128.  doi: 10.1007/s00376-019-9031-z
    [9] Tomio Asai, Yasumasa Kodama, Ji-Cang Zhu, 1988: LONG-TERM VARIATIONS OF CYCLONE ACTIVITIES IN EAST ASIA, ADVANCES IN ATMOSPHERIC SCIENCES, 5, 149-158.  doi: 10.1007/BF02656777
    [10] Susannah M. BURROWS, Aritra DASGUPTA, Sarah REEHL, Lisa BRAMER, Po-Lun MA, Philip J. RASCH, Yun QIAN, 2018: Characterizing the Relative Importance Assigned to Physical Variables by Climate Scientists when Assessing Atmospheric Climate Model Fidelity, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 1101-1113.  doi: 10.1007/s00376-018-7300-x
    [11] WANG Yuesi, HU Bo, LIU Guangren, 2005: A Primary Study of the Variations of Vertical Radiation with the Beijing 325-m Meteorological Tower, ADVANCES IN ATMOSPHERIC SCIENCES, 22, 172-180.  doi: 10.1007/BF02918507
    [12] HU Shujuan, CHOU Jifan, 2004: Uncertainty of the Numerical Solution of a Nonlinear System's Long-term Behavior and Global Convergence of the Numerical Pattern, ADVANCES IN ATMOSPHERIC SCIENCES, 21, 767-774.  doi: 10.1007/BF02916373
    [13] Yong ZHANG, Lejian ZHANG, Jianping GUO, Jinming FENG, Lijuan CAO, Yang WANG, Qing ZHOU, Liangxu LI, Bai LI, Hui XU, Lin LIU, Ning AN, Huan LIU, 2018: Climatology of Cloud-base Height from Long-term Radiosonde Measurements in China, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 158-168.  doi: 10.1007/s00376-017-7096-0
    [14] FENG Lin, WU Dexing, LIN Xiaopei, MENG Xiangfeng, 2010: The Effect of Regional Ocean-Atmosphere Coupling on the Long-term Variability in the Pacific Ocean, ADVANCES IN ATMOSPHERIC SCIENCES, 27, 393-402.  doi: 10.1007/s00376-009-8195-3
    [15] Kyu Rang KIM, Tae Heon KWON, Yeon-Hee KIM, Hae-Jung KOO, Byoung-Cheol CHOI, Chee-Young CHOI, 2009: Restoration of an Inner-City Stream and Its Impact on Air Temperature and Humidity Based on Long-Term Monitoring Data, ADVANCES IN ATMOSPHERIC SCIENCES, 26, 283-292.  doi: 10.1007/s00376-009-0283-x
    [16] Ping LIANG, Yihui DING, 2017: The Long-term Variation of Extreme Heavy Precipitation and Its Link to Urbanization Effects in Shanghai during 1916-2014, ADVANCES IN ATMOSPHERIC SCIENCES, 34, 321-334.  doi: 10.1007/s00376-016-6120-0
    [17] Zhen LI, Zhongwei YAN, Lijuan CAO, Phil D. JONES, 2018: Further-Adjusted Long-Term Temperature Series in China Based on MASH, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 909-917.  doi: 10.1007/s00376-018-7280-x
    [18] Wang Duo, Zhang Tan, 1989: The Band Characteristics of Long-Term Variation of Precipitation and Water Resources in East Asia Monsoon Region, ADVANCES IN ATMOSPHERIC SCIENCES, 6, 347-356.  doi: 10.1007/BF02661540
    [19] Jin Long, LuoYing, Lin Zhenshan, 1997: Comparison of Long-Term Forecasting of June-August Rainfall over Changjiang-Huaihe Valley, ADVANCES IN ATMOSPHERIC SCIENCES, 14, 87-92.  doi: 10.1007/s00376-997-0047-4
    [20] G. B. Pant, K. Rupa Kumar, B. Parthasarathy, H. P. Borgaonkar, 1988: LONG-TERM VARIABILITY OF THE INDIAN SUMMER MON-SOON AND RELATED PARAMETERS, ADVANCES IN ATMOSPHERIC SCIENCES, 5, 469-481.  doi: 10.1007/BF02656792

Get Citation+

Export:  

Share Article

Manuscript History

Manuscript received: 17 November 2020
Manuscript revised: 09 March 2021
Manuscript accepted: 06 April 2021
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Estimation and long-term trend analysis of surface solar radiation in Antarctica: A case study of Zhongshan Station

Abstract: Long-term, ground-based daily global solar radiation (DGSR) at Zhongshan Station in Antarctica can quantitatively reveal the basic characteristics of Earth’s surface radiation balance and validate satellite data for the Antarctic region. The fixed station was established in 1989 and conventional radiation observations started much later in 2008. In this study, a random forest (RF) model for estimating DGSR was developed using ground meteorological observation data, and a high-precision, long-term DGSR dataset was constructed. Then, the trend of DGSR from 1990 to 2019 at Zhongshan Station, Antarctica, was analyzed. The RF model, which performs better than other models, shows a desirable performance of DGSR hindcast estimation with an R2 of 0.984, root-mean-square error of 1.377 MJ/m2, and mean absolute error of 0.828 MJ/m2. The trend of DGSR annual anomalies increased during 1990-2004 and then began to decrease after 2004. Note that the maximum value of annual anomalies occurred during approximately 2004/05 and is mainly related to the days with precipitation (especially those related to good weather during the polar day period) at this station. In addition to clouds and water vapor, bad weather conditions (such as snowfall, which can result in low visibility and then decreased sunshine duration and solar radiation) were the other major factors affecting solar radiation at this station. The high-precision, long-term estimated DGSR dataset enables us to further study and understand the role of Antarctica in global climate change and the interactions between snow, ice and atmosphere.

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return