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Preliminary Evaluations of FGOALS-g2 for Decadal Predictions


doi: 10.1007/s00376-012-2084-x

  • The Flexible Global Ocean-Atmosphere-Land System model, Grid-point Version 2 (FGOALS-g2) for decadal predictions, is evaluated preliminarily, based on sets of ensemble 10-year hindcasts that it has produced. The results show that the hindcasts were more accurate in decadal variability of SST and surface air temperature (SAT), particularly in that of Nino3.4 SST and China regional SAT, than the second sample of the historical runs for 20th-century climate (the control) by the same model. Both the control and the hindcasts represented the global warming well using the same external forcings, but the control overestimated the warming. The hindcasts produced the warming closer to the observations. Performance of FGOALS-g2 in hindcasts benefits from more realistic initial conditions provided by the initialization run and a smaller model bias resulting from the use of a dynamic bias correction scheme newly developed in this study. The initialization consists of a 61-year nudging-based assimilation cycle, which follows on the control run on 01 January 1945 with the incorporation of observation data of upper-ocean temperature and salinity at each integration step in the ocean component model, the LASG IAP Climate System Ocean Model, Version 2 (LICOM2). The dynamic bias correction is implemented at each step of LICOM2 during the hindcasts to reduce the systematic biases existing in upper-ocean temperature and salinity by incorporating multi-year monthly mean increments produced in the assimilation cycle. The effectiveness of the assimilation cycle and the role of the correction scheme were assessed prior to the hindcasts.
  • [1] CUI Limei, SUN Jianhua, QI Linlin, LEI Ting, 2011: Application of ATOVS Radiance-Bias Correction to Typhoon Track Prediction with Ensemble Kalman Filter Data Assimilation, ADVANCES IN ATMOSPHERIC SCIENCES, 28, 178-186.  doi: 10.1007/s00376-010-9145-9
    [2] LI Shan, RONG Xingyao, LIU Yun, LIU Zhengyu, Klaus FRAEDRICH, 2013: Dynamic Analogue Initialization for Ensemble Forecasting, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 1406-1420.  doi: 10.1007/s00376-012-2244-z
    [3] Lei HAN, Mingxuan CHEN, Kangkai CHEN, Haonan CHEN, Yanbiao ZHANG, Bing LU, Linye SONG, Rui QIN, 2021: A Deep Learning Method for Bias Correction of ECMWF 24–240 h Forecasts, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 1444-1459.  doi: 10.1007/s00376-021-0215-y
    [4] Donglei SHI, Guanghua CHEN, Ke WANG, Xinxin BI, Kexin CHEN, 2020: Evaluation of Two Initialization Schemes for Simulating the Rapid Intensification of Typhoon Lekima (2019), ADVANCES IN ATMOSPHERIC SCIENCES, 37, 987-1006.  doi: 10.1007/s00376-020-2038-7
    [5] CHEN Hong, LIN Zhaohui, 2006: A Correction Method Suitable for Dynamical Seasonal Prediction, ADVANCES IN ATMOSPHERIC SCIENCES, 23, 425-430.  doi: 10.1007/s00376-006-0425-3
    [6] Banglin ZHANG, Vijay TALLAPRAGADA, Fuzhong WENG, Jason SIPPEL, Zaizhong MA, 2016: Estimation and Correction of Model Bias in the NASA/GMAO GEOS5 Data Assimilation System: Sequential Implementation, ADVANCES IN ATMOSPHERIC SCIENCES, 33, 659-672.  doi: 10.1007/ s00376-015-5155-y
    [7] Chenwei SHEN, Qingyun DUAN, Chiyuan MIAO, Chang XING, Xuewei FAN, Yi WU, Jingya HAN, 2020: Bias Correction and Ensemble Projections of Temperature Changes over Ten Subregions in CORDEX East Asia, ADVANCES IN ATMOSPHERIC SCIENCES, 37, 1191-1210.  doi: 10.1007/s00376-020-0026-6
    [8] Keon-Tae SOHN, H. Joe KWON, Ae-Sook SUH, 2003: Prediction of Typhoon Tracks Using Dynamic Linear Models, ADVANCES IN ATMOSPHERIC SCIENCES, 20, 379-384.  doi: 10.1007/BF02690796
    [9] Jiawen ZHU, Xiaodong ZENG, Minghua ZHANG, Yongjiu DAI, Duoying JI, Fang LI, Qian ZHANG, He ZHANG, Xiang SONG, 2018: Evaluation of the New Dynamic Global Vegetation Model in CAS-ESM, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 659-670.  doi: 10.1007/s00376-017-7154-7
    [10] Lei ZHU, Zhiyong MENG, Yonghui WENG, Fuqing ZHANG, 2022: Assimilation of All-sky Geostationary Satellite Infrared Radiances for Convection-Permitting Initialization and Prediction of Hurricane Joaquin (2015), ADVANCES IN ATMOSPHERIC SCIENCES, 39, 1859-1872.  doi: 10.1007/s00376-022-2015-4
    [11] XUE Hai-Le, SHEN Xue-Shun, CHOU Ji-Fan, 2013: A Forecast Error Correction Method in Numerical Weather Prediction by Using Recent Multiple-time Evolution Data, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 1249-1259.  doi: 10.1007/s00376-013-2274-1
    [12] Lili LEI, Yangjinxi GE, Zhe-Min TAN, Yi ZHANG, Kekuan CHU, Xin QIU, Qifeng QIAN, 2022: Evaluation of a Regional Ensemble Data Assimilation System for Typhoon Prediction, ADVANCES IN ATMOSPHERIC SCIENCES, 39, 1816-1832.  doi: 10.1007/s00376-022-1444-4
    [13] Yuanyuan WANG, Zhenghui XIE, Binghao JIA, 2016: Incorporation of a Dynamic Root Distribution into CLM4.5: Evaluation of Carbon and Water Fluxes over the Amazon, ADVANCES IN ATMOSPHERIC SCIENCES, 33, 1047-1060.  doi: 10.1007/s00376-016-5226-8
    [14] Keon-Tae SOHN, Deuk-Kyun RHA, Young-Kyung SEO, 2003: The 3-Hour-Interval Prediction of Ground-Level Temperature in South Korea Using Dynamic Linear Models, ADVANCES IN ATMOSPHERIC SCIENCES, 20, 575-582.  doi: 10.1007/BF02915500
    [15] MA Jiehua, WANG Huijun, FAN Ke, 2015: Dynamic Downscaling of Summer Precipitation Prediction over China in 1998 Using WRF and CCSM4, ADVANCES IN ATMOSPHERIC SCIENCES, 32, 577-584.  doi: 10.1007/s00376-014-4143-y
    [16] Abebe Kebede, Kirsten Warrach-sagi, Thomas Schwitalla, Volker Wulfmeyer, Tesfaye Amdie, Markos Ware, 2024: Assessment of Seasonal Rainfall Prediction in Ethiopia: Evaluating a Dynamic Recurrent Neural Network to Downscale ECMWF-SEAS5 Rainfall, ADVANCES IN ATMOSPHERIC SCIENCES.  doi: 10.1007/s00376-024-3345-1
    [17] HAN Leqiong, LI Shuanglin, LIU Na, 2014: An Approach for Improving Short-Term Prediction of Summer Rainfall over North China by Decomposing Interannual and Decadal Variability, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 435-448.  doi: 10.1007/s00376-013-3016-0
    [18] Jianping LI, Tiejun XIE, Xinxin TANG, Hao WANG, Cheng SUN, Juan FENG, Fei ZHENG, Ruiqiang DING, 2022: Influence of the NAO on Wintertime Surface Air Temperature over East Asia: Multidecadal Variability and Decadal Prediction, ADVANCES IN ATMOSPHERIC SCIENCES, 39, 625-642.  doi: 10.1007/s00376-021-1075-1
    [19] Na LI, Lingkun RAN, Dongdong SHEN, Baofeng JIAO, 2021: An Experiment on the Prediction of the Surface Wind Speed in Chongli Based on the WRF Model: Evaluation and Calibration, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 845-861.  doi: 10.1007/s00376-021-0201-4
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Manuscript History

Manuscript received: 25 April 2012
Manuscript revised: 09 August 2012
通讯作者: 陈斌, bchen63@163.com
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Preliminary Evaluations of FGOALS-g2 for Decadal Predictions

    Corresponding author: WANG Bin; 
  • 1. State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029
  • 2. Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System Science, Tsinghua University, Beijing 100084

Abstract: The Flexible Global Ocean-Atmosphere-Land System model, Grid-point Version 2 (FGOALS-g2) for decadal predictions, is evaluated preliminarily, based on sets of ensemble 10-year hindcasts that it has produced. The results show that the hindcasts were more accurate in decadal variability of SST and surface air temperature (SAT), particularly in that of Nino3.4 SST and China regional SAT, than the second sample of the historical runs for 20th-century climate (the control) by the same model. Both the control and the hindcasts represented the global warming well using the same external forcings, but the control overestimated the warming. The hindcasts produced the warming closer to the observations. Performance of FGOALS-g2 in hindcasts benefits from more realistic initial conditions provided by the initialization run and a smaller model bias resulting from the use of a dynamic bias correction scheme newly developed in this study. The initialization consists of a 61-year nudging-based assimilation cycle, which follows on the control run on 01 January 1945 with the incorporation of observation data of upper-ocean temperature and salinity at each integration step in the ocean component model, the LASG IAP Climate System Ocean Model, Version 2 (LICOM2). The dynamic bias correction is implemented at each step of LICOM2 during the hindcasts to reduce the systematic biases existing in upper-ocean temperature and salinity by incorporating multi-year monthly mean increments produced in the assimilation cycle. The effectiveness of the assimilation cycle and the role of the correction scheme were assessed prior to the hindcasts.

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