# Cloud Radiative Feedbacks during the ENSO Cycle Simulated by CAMS-CSM

• Corresponding author: Lijuan HUA, hualj@cma.gov.cn
• Funds:

Supported by the National Key Research and Development Program (2018YFC1506002); National Natural Science Foundation of China (41606011, 41705059, 41630423, and 41420104002); Basic Scientific Research and Operation Foundation of Chinese Academy of Meteorological Sciences (2017Y007); National Science Foundation AGS-1565653; National (Key) Basic Research and Development (973) Program of China (2015CB453200); Startup Foundation for Introducing Talent of NUIST, LASG Open Project; open fund of State Key Laboratory of Loess and Quartary Geology (SKLLQG1802), and NUIST Excellent Bachelor Dissertation Funding (1241591901003). This is the Earth System Modeling Center (ESMC) contribution (No. 247)

• doi: 10.1007/s13351-019-8104-3
• This study evaluated the simulated cloud radiative feedbacks (CRF) during the El Niño–Southern Oscillation (ENSO) cycle in the latest version of the Chinese Academy of Meteorological Sciences climate system model (CAMS-CSM). We conducted two experimental model simulations: the Atmospheric Model Intercomparison Project (AMIP), forced by the observed sea surface temperature (SST); and the preindustrial control (PIcontrol), a coupled run without flux correction. We found that both the experiments generally reproduced the observed features of the shortwave and longwave cloud radiative forcing (SWCRF and LWCRF) feedbacks. The AMIP run exhibited better simulation performance in the magnitude and spatial distribution than the PIcontrol run. Furthermore, the simulation biases in SWCRF and LWCRF feedbacks were linked to the biases in the representation of the corresponding total cloud cover and precipitation feedbacks. It is interesting to further find that the simulation bias originating in the atmospheric component was amplified in the PIcontrol run, indicating that the coupling aggravated the simulation bias. Since the PIcontrol run exhibited an apparent mean SST cold bias over the cold tongue, the precipitation response to the SST anomaly (SSTA) changes during the ENSO cycle occurred towards the relatively warmer western equatorial Pacific. Thus, the corresponding cloud cover and CRF shifted westward and showed a weaker magnitude in the PIcontrol run versus observational data. In contrast, the AMIP run was forced by the observational SST, hence representing a more realistic CRF. Our results demonstrate the challenges of simulating CRF in coupled models. This study also underscores the necessity of realistically representing the climatological mean state when simulating CRF during the ENSO cycle.
• Fig. 1.  The response of SWCRF to SSTA changes (W m−2 K−1) during the ENSO cycle for the (a) observation, (b) AMIP run, and (c) PIcontrol run. Spatial pattern correlation coefficients between the two simulations and observational data are shown in Figs. 1b, c.

Fig. 2.  As in Fig. 1, but for the response of LWCRF to SSTA changes (W m−2 K−1) during the ENSO cycle.

Fig. 3.  From left to right, bars show the SWsfc feedback (${\alpha _{{\rm{SW}}}}$; m−2 K−1), DYF, RHF, and LWPF from the reanalyses (green bars), AMIP run (blue), and PIcontrol run (red). Note that the feedbacks shown here were calculated by using only the positive Niño 3 SSTAs (SSTA > 0 K).

Fig. 4.  As in Fig. 1, but for the response of CLD to SSTA changes (% K−1) during the ENSO cycle.

Fig. 5.  The cloud fraction response to SSTA changes (% K−1) during the ENSO cycle along the equator (averaged between 5°S and 5°N) for (a) AMIP and (b) PIcontrol runs.

Fig. 6.  As in Fig. 1, but for the response of precipitation to SSTA changes (mm day−1 K−1) during the ENSO cycle.

Fig. 7.  The vertical velocity response to SSTA changes (hPa day−1 K−1) during the ENSO cycle along the equator (averaged between 5°S and 5°N) for the (a) observation, (b) AMIP run, and (c) PIcontrol run.

Fig. 8.  Climatological SST (°C) for the (a) observational data and (b) PIcontrol run. (c) The climatological SST profile along the equator (averaged between 5°S and 5°N), and the y-axis denotes the unit of temperature (°C).

•  [1] Bo LU, Hong-Li REN. ENSO Features, Dynamics, and Teleconnections to East Asian Climate as Simulated in CAMS-CSM. Journal of Meteorological Research, 2019, 33(1): 46-65.  doi: 10.1007/s13351-019-8101-6. [2] ZHANG Qin, DING Yihui, YI Lan. THE DECADAL CLIMATE VARIABILITY AND THE ANOMALOUS ENSO DEVELOPMENTS IN 1990S*. Journal of Meteorological Research, 2002, 16(1): 1-20. [3] GUAN Zhaoyong, LIU Xuanfei. NUMERICAL STUDY ON THE ROLE OF VORTICAL AND DIVERGENT COMPONENTS OF WIND STRESS IN ENSO CYCLE*. Journal of Meteorological Research, 2000, 14(3): 361-372. [4] YIN Yonghong, SHI Li, NI Yunqi. A DIAGNOSIS OF THE INTERANNUAL VARIABILITY OF SEA SURFACE TEMPERATURE AND SURFACE WIND FIELD IN THE TROPICAL OCEANS AND ITS CORRELATIVE CHARACTERS WITH ENSO CYCLE*. Journal of Meteorological Research, 2002, 16(1): 21-36. [5] Zeng-Zhen HU, Arun KUMAR, Bohua HUANG, Jieshun ZHU, Hong-Li REN. Interdecadal Variations of ENSO around 1999/2000. Journal of Meteorological Research, 2017, 31(1): 73-81.  doi: 10.1007/s13351-017-6074-x. [6] Hong-Li REN, Fei ZHENG, Jing-Jia LUO, Run WANG, Minghong LIU, Wenjun ZHANG, Tianjun ZHOU, Guangqing ZHOU. A Review of Research on Tropical Air–Sea Interaction, ENSO Dynamics, and ENSO Prediction in China. Journal of Meteorological Research, 2020, 34(1): 43-62.  doi: 10.1007/s13351-020-9155-1. [7] LONG Zhenxia, LI Chongyin. NUMERICAL SIMULATION OF LAG INFLUENCE OF ENSO ON EAST-ASIAN MONSOON. Journal of Meteorological Research, 2001, 15(1): 59-70. [8] ZHANG Yongsheng, JIANG Shangcheng. RETRIEVAL OF THE TROPICAL DIVERGENT WIND FROM OLR AND ITS APPLICATION IN ENSO DIAGNOSIS. Journal of Meteorological Research, 2000, 14(1): 61-81. [9] Shuanglin LI, Zhe HAN, Huopo CHEN. A Comparison of the Effects of Interannual Arctic Sea Ice Loss and ENSO on Winter Haze Days: Observational Analyses and AGCM Simulations. Journal of Meteorological Research, 2017, 31(5): 820-833.  doi: 10.1007/s13351-017-7017-2. [10] Chih-Pei CHANG, Tim LI, Song YANG. Seasonal Prediction of Boreal Winter Rainfall over the Western Maritime Continent during ENSO. Journal of Meteorological Research, 2020, 34(2): 294-303.  doi: 10.1007/s13351-020-9181-z. [11] ZHANG Zuqiang, DING Yihui, ZHAO Zongci. ON WESTERLY WIND BURSTS IN EQUATORIAL WESTERN PACIFIC BEFORE AND DURING THE ONSET AND INITIAL DEVELOPMENT PHASES OF ENSO*. Journal of Meteorological Research, 2000, 14(4): 385-401. [12] DENG Li, LI Tim, LIU Jia, PENG Melinda. Factors Controlling the Interannual Variations of MJO Intensity. Journal of Meteorological Research, 2016, 30(3): 328-340.  doi: 10.1007/s13351-016-5113-3. [13] ZHOU Tianjun, CHEN Xiaolong, DONG Lu, WU Bo, MAN Wenmin, ZHANG Lixia, LIN Renping, YAO Junchen, SONG Fengfei, ZHAO Chongbo. Chinese Contribution to CMIP5：An Overview of Five Chinese Models’Performances. Journal of Meteorological Research, 2014, 28(4): 481-509.  doi: 10.1007/s13351-014-4001-y. [14] Xu Jianjun, Zhu Qiangen, Sun Zhaobo. INTERRELATION BETWEEN EAST-ASIAN WINTER MONSOON AND INDIAN/PACIFIC SST WITH THE INTERDECADAL VARIATION*. Journal of Meteorological Research, 1998, 12(3): 275-287. [15] Lijuan HUA, Lin CHEN, Xinyao RONG, Jian LI, Guo ZHANG, Lu WANG. An Assessment of ENSO Stability in CAMS Climate System Model Simulations. Journal of Meteorological Research, 2019, 33(1): 80-88.  doi: 10.1007/s13351-018-8092-8. [16] Tim LI, Bin WANG, Bo WU, Tianjun ZHOU, Chih-Pei CHANG, Renhe ZHANG. Theories on Formation of an Anomalous Anticyclone in Western North Pacific during El Niño: A Review. Journal of Meteorological Research, 2017, 31(6): 987-1006.  doi: 10.1007/s13351-017-7147-6. [17] Qian Weihong. THE UNDERSTANDING OF ENSO CYCLE MECHANISM AND ENSO POTENTIAL PREDICTION ABILITY*. Journal of Meteorological Research, 1997, 11(1): 105-118. [18] HUA Lijuan, YU Yongqiang. Nonlinear Responses of Oceanic Temperature to Wind Stress Anomalies in Tropical Pacific and Indian Oceans: A Study Based on Numerical Experiments with an OGCM. Journal of Meteorological Research, 2015, 29(4): 608-626.  doi: 10.1007/s13351-015-4115-x. [19] Li Peiji. A PRELIMINARY STUDY OF SNOW MASS VARIATIONS IN CHINA OVER THE PAST 30 YEARS*. Journal of Meteorological Research, 1992, 6(2): 231-237. [20] LI Chongyin, LING Jian, SONG Jie, PAN Jing, TIAN Hua, CHEN Xiong. Research Progress in China on the Tropical Atmospheric Intraseasonal Oscillation. Journal of Meteorological Research, 2014, 28(5): 671-692.  doi: 10.1007/s13351-014-4015-5.
###### 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

## Cloud Radiative Feedbacks during the ENSO Cycle Simulated by CAMS-CSM

###### Corresponding author: Lijuan HUA, hualj@cma.gov.cn;
• 1. Key Laboratory of Meteorological Disaster, Ministry of Education/Joint International Research Laboratory of Climate and Environmental Change/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology (NUIST), Nanjing 210044
• 2. State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029
• 3. State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi’an 710061
• 4. State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, China Meteorological Administration, Beijing 100081
Funds: Supported by the National Key Research and Development Program (2018YFC1506002); National Natural Science Foundation of China (41606011, 41705059, 41630423, and 41420104002); Basic Scientific Research and Operation Foundation of Chinese Academy of Meteorological Sciences (2017Y007); National Science Foundation AGS-1565653; National (Key) Basic Research and Development (973) Program of China (2015CB453200); Startup Foundation for Introducing Talent of NUIST, LASG Open Project; open fund of State Key Laboratory of Loess and Quartary Geology (SKLLQG1802), and NUIST Excellent Bachelor Dissertation Funding (1241591901003). This is the Earth System Modeling Center (ESMC) contribution (No. 247)

Abstract: This study evaluated the simulated cloud radiative feedbacks (CRF) during the El Niño–Southern Oscillation (ENSO) cycle in the latest version of the Chinese Academy of Meteorological Sciences climate system model (CAMS-CSM). We conducted two experimental model simulations: the Atmospheric Model Intercomparison Project (AMIP), forced by the observed sea surface temperature (SST); and the preindustrial control (PIcontrol), a coupled run without flux correction. We found that both the experiments generally reproduced the observed features of the shortwave and longwave cloud radiative forcing (SWCRF and LWCRF) feedbacks. The AMIP run exhibited better simulation performance in the magnitude and spatial distribution than the PIcontrol run. Furthermore, the simulation biases in SWCRF and LWCRF feedbacks were linked to the biases in the representation of the corresponding total cloud cover and precipitation feedbacks. It is interesting to further find that the simulation bias originating in the atmospheric component was amplified in the PIcontrol run, indicating that the coupling aggravated the simulation bias. Since the PIcontrol run exhibited an apparent mean SST cold bias over the cold tongue, the precipitation response to the SST anomaly (SSTA) changes during the ENSO cycle occurred towards the relatively warmer western equatorial Pacific. Thus, the corresponding cloud cover and CRF shifted westward and showed a weaker magnitude in the PIcontrol run versus observational data. In contrast, the AMIP run was forced by the observational SST, hence representing a more realistic CRF. Our results demonstrate the challenges of simulating CRF in coupled models. This study also underscores the necessity of realistically representing the climatological mean state when simulating CRF during the ENSO cycle.

Reference (55)

/