Advanced Search
Article Contents

Evaluation of Cloud Vertical Structure Simulated by Recent BCC_AGCM Versions through Comparison With CALIPSO-GOCCP Data

Fund Project:

doi: 10.1007/s00376-013-3099-7

  • The abilities of BCC_AGCM2.1 and BCC_AGCM2.2 to simulate the annual-mean cloud vertical structure (CVS) were evaluated through comparison with GCM-Oriented CALIPSO Cloud Product (CALIPSO-GOCCP) data. BCC_AGCM2.2 has a dynamical core and physical processes that are consistent with BCC_AGCM2.1, but has a higher horizontal resolution. Results showed that both BCC_AGCM versions underestimated the global-mean total cloud cover (TCC), middle cloud cover (MCC) and low cloud cover (LCC), and that BCC_AGCM2.2 underestimated the global-mean high cloud cover (HCC). The global-mean cloud cover shows a systematic decrease from BCC_AGCM2.1 to BCC_AGCM2.2, especially for HCC. Geographically, HCC is significantly overestimated in the tropics, particularly by BCC_AGCM2.1, while LCC is generally overestimated over extra-tropical lands, but significantly underestimated over most of the oceans, especially for subtropical marine stratocumulus clouds. The leading EOF modes of CVS were extracted. The BCC_AGCMs perform well in reproducing EOF1, but with a larger variance explained. The two models also capture the basic features of EOF3, except an obvious deficiency in eigenvector peaks. EOF2 has the largest simulation biases in both position and strength of eigenvector peaks. Furthermore, we investigated the effects of CVS on relative shortwave and longwave cloud radiative forcing (RSCRF and RLCRF). Both BCC_AGCM versions successfully reproduce the sign of regression coefficients, except for RLCRF in PC1. However, the RSCRF relative contributions from PC1 and PC2 are overestimated, while the relative contribution from PC3 is underestimated in both BCC_AGCM versions. The RLCRF relative contribution is underestimated for PC2 and overestimated for PC3.
    摘要: The abilities of BCC_AGCM2.1 and BCC_AGCM2.2 to simulate the annual-mean cloud vertical structure (CVS) were evaluated through comparison with GCM-Oriented CALIPSO Cloud Product (CALIPSO-GOCCP) data. BCC_AGCM2.2 has a dynamical core and physical processes that are consistent with BCC_AGCM2.1, but has a higher horizontal resolution. Results showed that both BCC_AGCM versions underestimated the global-mean total cloud cover (TCC), middle cloud cover (MCC) and low cloud cover (LCC), and that BCC_AGCM2.2 underestimated the global-mean high cloud cover (HCC). The global-mean cloud cover shows a systematic decrease from BCC_AGCM2.1 to BCC_AGCM2.2, especially for HCC. Geographically, HCC is significantly overestimated in the tropics, particularly by BCC_AGCM2.1, while LCC is generally overestimated over extra-tropical lands, but significantly underestimated over most of the oceans, especially for subtropical marine stratocumulus clouds. The leading EOF modes of CVS were extracted. The BCC_AGCMs perform well in reproducing EOF1, but with a larger variance explained. The two models also capture the basic features of EOF3, except an obvious deficiency in eigenvector peaks. EOF2 has the largest simulation biases in both position and strength of eigenvector peaks. Furthermore, we investigated the effects of CVS on relative shortwave and longwave cloud radiative forcing (RSCRF and RLCRF). Both BCC_AGCM versions successfully reproduce the sign of regression coefficients, except for RLCRF in PC1. However, the RSCRF relative contributions from PC1 and PC2 are overestimated, while the relative contribution from PC3 is underestimated in both BCC_AGCM versions. The RLCRF relative contribution is underestimated for PC2 and overestimated for PC3.
  • 加载中
  • Bodas-Salcedo, A., and Coauthors, 2011: COSP: Satellite simulation software for model assessment. Bull. Amer. Meteor. Soc., 92, 1023-1043.
    Bony, S., and Coauthors, 2006: How well do we understand and evaluate climate change feedback processes? J. Climate, 19, 3445-3482.
    Bony, S., and J. L. Dufresne, 2005: Marine boundary layer clouds at the heart of tropical cloud feedback uncertainties in climate models. Geophys. Res. Lett., 32, L20806, doi: 10.1029/2005GL023851.
    Cesana, G., and H. Chepfer, 2012: How well do climate models simulate cloud vertical structure? A comparison between CALIPSO-GOCCP satellite observations and CMIP5 models. Geophys. Res. Lett., 39, L20803, doi: 10.1029/2012GL 053153.
    Cess, R. D., and Coauthors, 1990: Intercomparison and interpretation of climate feedback processes in 19 atmospheric general circulation models. J. Geophys. Res., 95, 16 601-16 615.
    Cess, R. D., and Coauthors, 1996: Cloud feedback in atmospheric general circulation models: An update. J. Geophys. Res., 101, 12 791-12 794.
    Chen, T., Y. C. Zhang, and W. B. Rossow, 2000a: Sensitivity of atmospheric radiative heating rate profiles to variations of cloud layer overlap. J. Climate, 13, 2941-2959.
    Chen, T., W. B. Rossow, and Y. C. Zhang, 2000b: Radiative effects of cloud-type variations. J. Climate, 13, 264-286.
    Chepfer, H., S. Bony, D. Winker, G. Cesana, J. L. Dufresne, P. Minnis, C. J. Stubenrauch, and S. Zeng, 2010: The GCM-Oriented CALIPSO cloud product (CALIPSO-GOCCP). J. Geophys. Res., 115, D00H16, doi: 10.1029/2009JD012251.
    Chepfer, H., S. Bony, D. Winker, M. Chiriaco, J. L. Dufresne, and G. S#cod#232;ze, 2008: Use of CALIPSO lidar observations to evaluate the cloudiness simulated by a climate model. Geophys. Res. Lett., 35, L15704, doi: 10.1029/2008GL034207.
    Clement, A. C., R. Burgman, and J. R. Norris, 2009: Observational and model evidence for positive low-level cloud feedback. Science, 325, 460-464.
    Collins, W. D., and Coauthors, 2006: The formulation and atmospheric simulation of the community atmosphere model version 3 (CAM3). J. Climate, 19, 2144-2161.
    Hack, J. J., J. M. Caron, G. Danabasoglu, K. W. Oleson, C. Bitz, and J. E. Truesdale, 2006: CCSM-CAM3 climate simulation sensitivity to changes in horizontal resolution. J. Climate, 19, 2267-2289.
    Hannay, C., D. L. Williamson, J. J. Hack, J. T. Kiehl, J. G. Olson, S. A. Klein, C. S. Bretherton, and M. K#cod#x0014d;hler, 2009: Evaluation of forecasted southeast Pacific stratocumulus in the NCAR, GFDL, and ECMWF models. J. Climate, 22, 2871-2889.
    Harrison E. F., P. Minnis, B. R. Barkstrom, V. Ramanathan, R. D. Cess, and G. G. Gibson, 1990: Seasonal variation of cloud radiative forcing derived from the Earth radiation budget experiment. J. Geophys. Res., 95, 18 687-18 703.
    Hurrell, J. W., J. J. Hack, D. Shea, J. M. Caron, and J. Rosinski, 2008: A new sea surface temperature and sea ice boundary dataset for the community atmosphere model. J. Climate, 21, 5145-5153.
    Jiang, J. H., and Coauthors, 2012: Evaluation of cloud and water vapor simulations in CMIP5 climate models using NASA "A-Train" satellite observations. J. Geophys. Res., 117, D14105, doi: 10.1029/2011JD017237.
    Johnson, R. H., and P. E. Ciesielski, 2000: Rainfall and radiative heating rates from TOGA COARE atmospheric budgets. J. Atmos. Sci., 57, 1497-1514.
    Kato, S., N. G. Loeb, F. G. Rose, D. R. Doelling, D. A. Rutan, T. E. Caldwell, L. S. Yu, and R. A. Weller, 2013: Surface irradiances consistent with CERES-derived top-of-atmosphere shortwave and longwave irradiances. J. Climate, 26, 2719-2740, doi: 10.1175/JCLID-12-00436.1.
    Kay, J. E., and Coauthors, 2012: Exposing global cloud biases in the community atmosphere model (CAM) using satellite observations and their corresponding instrument simulators. J. Climate, 25, 5190-5207.
    Klein, S. A., and D. L. Hartmann, 1993: The seasonal cycle of low stratiform clouds. J. Climate, 6, 1587-1606.
    Kokhanovsky, A., M. Vountas, and J. P. Burrows, 2011: Global distribution of cloud top height as retrieved from SCIAMACHY onboard ENVISAT spaceborne observations. Remote Sensing, 3(5),836-844.
    L'Ecuyer, T. S., and J. H. Jiang, 2010: Touring the atmosphere aboard the A-Train. Phys. Today, 63, 36-41.
    Li, J. D., Y. M. Liu, and G. X. Wu, 2009: Cloud radiative forcing in Asian monsoon region simulated by IPCC AR4 AMIP models. Adv. Atmos. Sci., 26(5),923-939, doi: 10.1007 /s00376-009-8111-x.
    Lin, W. Y., and M. H. Zhang, 2004: Evaluation of clouds and their radiative effects simulated by the NCAR community atmospheric model against satellite observations. J. Climate, 17, 3302-3318.
    Liu, Y., W. Wu, M. P. Jensen, and T. Toto, 2011: Relationship between cloud radiative forcing, cloud fraction and cloud albedo, and new surface-based approach for determining cloud albedo. Atmos. Chem. Phys., 11, 7155-7170.
    Nam, C. C. W., and J. Quaas, 2012: Evaluation of clouds and precipitation in the ECHAM5 general circulation model using CALIPSO and CloudSat satellite data. J. Climate, 25, 4975-4992.
    Pincus, R., C. P. Batstone, R. J. P. Hofmann, K. E. Taylor, and P. J. Glecker, 2008: Evaluating the present-day simulation of clouds, precipitation, and radiation in climate models. J. Geophys. Res., 113, D14209, doi: 10.1029/2007JD009334.
    Probst, P., R. Rizzi, E. Tosi, V. Lucarini, and T. Maestri, 2012: Total cloud cover from satellite observations and climate models. Atmospheric Research, 107, 161-170.
    Qian, Y., C. N. Long, H. Wang, J. M. Comstock, S. A. McFarlane, and S. Xie, 2012: Evaluation of cloud fraction and its radiative effect simulated by IPCC AR4 global models against ARM surface observations. Atmos. Chem. Phys., 12, 1785-1810.
    Roeckner, E., and Coauthors, 2006: Sensitivity of simulated climate to horizontal and vertical resolution in the ECHAM5 atmosphere model. J. Climate, 19, 3771-3791.
    Rossow, W. B., and R. A. Schiffer, 1999: Advances in understanding clouds from ISCCP. Bull. Amer. Meteor. Soc., 80, 2261-2288.
    Slingo, J. M., 1987: The development and verification of a cloud prediction scheme for the ECMWF model. Quart. J. Roy. Meteor. Soc., 113, 899-927.
    Stephens, G. L., 2005: Cloud feedbacks in the climate system: A critical review. J. Climate, 18, 237-273.
    Stephens, G. L., and Coauthors, 2002: The CloudSat Mission and the A-Train. Bull. Amer. Meteor. Soc., 83, 1771-1790.
    Stubenrauch, C. J., and Coauthors, 2013: Assessment of global cloud datasets from satellites: Project and database initiated by the GEWEX radiation panel. Bull. Amer. Meteor. Soc., 94, 1031-1049, doi: 10.1175/BAMS-D-12-00117.1.
    Taylor, K. E., R. J. Stouffer, and G. A. Meehl, 2012: An overview of CMIP5 and the experiment design. Bull. Amer. Meteor. Soc., 93, 485-498.
    Wang, F., Y. H. Ding, and Y. Xu, 2007: Cloud and radiation processes simulated by a coupled atmosphere-ocean model. Acta Meteor. Sinica, 21(4),397-408.
    Wood, R., 2012: Stratocumulus clouds. Mon. Wea. Rev., 140, 2373-2423.
    Wu, T. W., 2012: A mass-flux cumulus parameterization scheme for large-scale models: Description and test with observations. Climate Dyn., 38, 725-744.
    Wu, T. W., R. C. Yu, and F. Zhang, 2008: A modified dynamic framework for the atmospheric spectral model and its application. J. Atmos. Sci., 65, 2235-2253.
    Wu, T. W., and Coauthors, 2010: The Beijing Climate Center atmospheric general circulation model: description and its performance for the present-day climate. Climate Dyn., 34, 123-147.
    Xu, K. M., and S. K. Krueger, 1991: Evaluation of cloudiness parameterizations using a cumulus ensemble model. Mon. Wea. Rev., 119, 342-367.
    Yao, M. S., and Y. Cheng, 2012: Cloud simulations in response to turbulence parameterizations in the GISS model E GCM. J. Climate, 25, 4963-4974.
    Young, S. A., M. A. Vaughan, R. E. Kuehn, and D. M. Winker, 2013: The retrieval of profiles of particulate extinction from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) data: Uncertainty and error sensitivity analyses. J. Atmos. Oceanic Technol., 30, 395-428.
    Yu, R. C., Y. Q. Yu, and M. H. Zhang, 2001: Comparing cloud radiative properties between the eastern China and the Indian Monsoon Region. Adv. Atmos. Sci., 18(6),1090-1102.
    Zhang, M. H., and Coauthors, 2005: Comparing clouds and their seasonal variations in 10 atmospheric general circulation models with satellite measurements. J. Geophys. Res., 110, D15S02, doi: 10.1029/2004JD005021.
  • [1] ZHANG Jinqiang, CHEN Hongbin, BIAN Jianchun, XUAN Yuejian, DUAN Yunjun, Maureen CRIBB, 2012: Development of Cloud Detection Methods Using CFH, GTS1, and RS80 Radiosondes, ADVANCES IN ATMOSPHERIC SCIENCES, 29, 236-248.  doi: 10.1007/s00376-011-0215-4
    [2] Lei WANG, Qing BAO, Wei-Chyung WANG, Yimin LIU, Guo-Xiong WU, Linjiong ZHOU, Jiandong LI, Hua GONG, Guokui NIAN, Jinxiao LI, Xiaocong WANG, Bian HE, 2019: LASG Global AGCM with a Two-moment Cloud Microphysics Scheme: Energy Balance and Cloud Radiative Forcing Characteristics, ADVANCES IN ATMOSPHERIC SCIENCES, , 697-710.  doi: 10.1007/s00376-019-8196-9
    [3] Xixun ZHOU, Bing XIE, Hua ZHANG, Jingyi HE, Qi CHEN, 2022: Decomposition of Fast and Slow Cloud Responses to Quadrupled CO2 Forcing in BCC–AGCM2.0 over East Asia, ADVANCES IN ATMOSPHERIC SCIENCES, 39, 2188-2202.  doi: 10.1007/s00376-022-1441-7
    [4] Hui XU, Jianping GUO, Jian LI, Lin LIU, Tianmeng CHEN, Xiaoran GUO, Yanmin LYU, Ding WANG, Yi HAN, Qi CHEN, Yong ZHANG, 2021: The Significant Role of Radiosonde-measured Cloud-base Height in the Estimation of Cloud Radiative Forcing, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 1552-1565.  doi: 10.1007/s00376-021-0431-5
    [5] LI Xiangshu, GUO Xueliang, FU Danhong, 2013: TRMM-retrieved Cloud Structure and Evolution of MCSs over the Northern South China Sea and Impacts of CAPE and Vertical Wind Shear, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 77-88.  doi: 10.1007/s00376-012-2055-2
    [6] WU Chunqiang, ZHOU Tianjun, SUN De-Zheng, BAO Qing, 2011: Water Vapor and Cloud Radiative Forcings over the Pacific Ocean Simulated by the LASG/IAP AGCM: Sensitivity to Convection Schemes, ADVANCES IN ATMOSPHERIC SCIENCES, 28, 80-98.  doi: 10.1007/s00376-010-9205-1
    [7] LI Jiandong, LIU Yimin, WU Guoxiong, 2009: Cloud Radiative Forcing in Asian Monsoon Region Simulated by IPCC AR4 AMIP Models, ADVANCES IN ATMOSPHERIC SCIENCES, 26, 923-939.  doi: 10.1007/s00376-009-8111-x
    [8] ZHANG Yi, and LI Jian, 2013: Shortwave Cloud Radiative Forcing on Major Stratus Cloud Regions in AMIP-type Simulations of CMIP3 and CMIP5 Models, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 884-907.  doi: 10.1007/s00376-013-2153-9
    [9] Chen ZHOU, Yincheng LIU, Quan WANG, 2022: Calculating the Climatology and Anomalies of Surface Cloud Radiative Effect Using Cloud Property Histograms and Cloud Radiative Kernels, ADVANCES IN ATMOSPHERIC SCIENCES, 39, 2124-2136.  doi: 10.1007/s00376-021-1166-z
    [10] Yuan WANG, Jonathan M. VOGEL, Yun LIN, Bowen PAN, Jiaxi HU, Yangang LIU, Xiquan DONG, Jonathan H. JIANG, Yuk L. YUNG, Renyi ZHANG, 2018: Aerosol Microphysical and Radiative Effects on Continental Cloud Ensembles, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 234-247.  doi: 10.1007/s00376-017-7091-5
    [11] SHOU Yixuan, LI Shenshen, SHOU Shaowen, ZHAO Zhongming, 2006: Application of a Cloud-Texture Analysis Scheme to the Cloud Cluster Structure Recognition and Rainfall Estimation in a Mesoscale Rainstorm Process, ADVANCES IN ATMOSPHERIC SCIENCES, 23, 767-774.  doi: 10.1007/s00376-006-0767-x
    [12] Shi LUO, Chunsong LU, Yangang LIU, Yaohui LI, Wenhua GAO, Yujun QIU, Xiaoqi XU, Junjun LI, Lei ZHU, Yuan WANG, Junjie WU, Xinlin YANG, 2022: Relationships between Cloud Droplet Spectral Relative Dispersion and Entrainment Rate and Their Impacting Factors, ADVANCES IN ATMOSPHERIC SCIENCES, 39, 2087-2106.  doi: 10.1007/s00376-022-1419-5
    [13] Zhang Minghua, Yu Rucong, Yu Yongqiang, 2001: Comparing Cloud Radiative Properties between the Eastern China and the Indian Monsoon Region, ADVANCES IN ATMOSPHERIC SCIENCES, 18, 1090-1102.  doi: 10.1007/s00376-001-0025-1
    [14] Yan XIA, Yongyun HU, Jiping LIU, Yi HUANG, Fei XIE, Jintai LIN, 2020: Stratospheric Ozone-induced Cloud Radiative Effects on Antarctic Sea Ice, ADVANCES IN ATMOSPHERIC SCIENCES, 37, 505-514.  doi: 10.1007/s00376-019-8251-6
    [15] Fei WANG, Hua ZHANG, Qi CHEN, Min ZHAO, Ting YOU, 2020: Analysis of Short-term Cloud Feedback in East Asia Using Cloud Radiative Kernels, ADVANCES IN ATMOSPHERIC SCIENCES, 37, 1007-1018.  doi: 10.1007/s00376-020-9281-9
    [16] Kong Fanyou, Qin Yu, 1994: The Vertical Transport of Air Pollutants by Connective Clouds, Part III: Transport Features of Different Cloud Systems, ADVANCES IN ATMOSPHERIC SCIENCES, 11, 13-26.  doi: 10.1007/BF02656989
    [17] Kong Fanyou, Qin Yu, 1993: The Vertical Transport of Air Pollutants by Convective Clouds. Part I: A Non-Reactive Cloud Transport Model, ADVANCES IN ATMOSPHERIC SCIENCES, 10, 415-427.  doi: 10.1007/BF02656966
    [18] Sun-Hee SHIN, Ok-Yeon KIM, Dongmin KIM, Myong-In LEE, 2017: Cloud Radiative Effects and Changes Simulated by the Coupled Model Intercomparison Project Phase 5 Models, ADVANCES IN ATMOSPHERIC SCIENCES, 34, 859-876.  doi: 10.1007/s00376-017-6089-3
    [19] Lijun ZHAO, Yuan WANG, Chuanfeng ZHAO, Xiquan DONG, Yuk L. YUNG, 2022: Compensating Errors in Cloud Radiative and Physical Properties over the Southern Ocean in the CMIP6 Climate Models, ADVANCES IN ATMOSPHERIC SCIENCES, 39, 2156-2171.  doi: 10.1007/s00376-022-2036-z
    [20] Xiaoning XIE, He ZHANG, Xiaodong LIU, Yiran PENG, Yangang LIU, 2018: Role of Microphysical Parameterizations with Droplet Relative Dispersion in IAP AGCM 4.1, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 248-259.  doi: 10.1007/s00376-017-7083-5

Get Citation+

Export:  

Share Article

Manuscript History

Manuscript received: 14 May 2013
Manuscript revised: 17 July 2013
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

Evaluation of Cloud Vertical Structure Simulated by Recent BCC_AGCM Versions through Comparison With CALIPSO-GOCCP Data

    Corresponding author: WANG Fang; 
  • 1. National Climate Center, China Meteorological Administration, Beijing 100081 ; 
  • 2. Sun Yat-sen University, Guangzhou 510275
Fund Project:  This work was jointly supported by the National Natural Science Foundation of China (Grant Nos. 41275077 and 41105054), the National Basic Research Program of China (973 Program: 2010CB951902), the China Meteorological Administration (Grant Nos. GYHY201106022 and GYHY201306048), and the Sun Yat-sen University 985 Project, Phase 3.

Abstract: The abilities of BCC_AGCM2.1 and BCC_AGCM2.2 to simulate the annual-mean cloud vertical structure (CVS) were evaluated through comparison with GCM-Oriented CALIPSO Cloud Product (CALIPSO-GOCCP) data. BCC_AGCM2.2 has a dynamical core and physical processes that are consistent with BCC_AGCM2.1, but has a higher horizontal resolution. Results showed that both BCC_AGCM versions underestimated the global-mean total cloud cover (TCC), middle cloud cover (MCC) and low cloud cover (LCC), and that BCC_AGCM2.2 underestimated the global-mean high cloud cover (HCC). The global-mean cloud cover shows a systematic decrease from BCC_AGCM2.1 to BCC_AGCM2.2, especially for HCC. Geographically, HCC is significantly overestimated in the tropics, particularly by BCC_AGCM2.1, while LCC is generally overestimated over extra-tropical lands, but significantly underestimated over most of the oceans, especially for subtropical marine stratocumulus clouds. The leading EOF modes of CVS were extracted. The BCC_AGCMs perform well in reproducing EOF1, but with a larger variance explained. The two models also capture the basic features of EOF3, except an obvious deficiency in eigenvector peaks. EOF2 has the largest simulation biases in both position and strength of eigenvector peaks. Furthermore, we investigated the effects of CVS on relative shortwave and longwave cloud radiative forcing (RSCRF and RLCRF). Both BCC_AGCM versions successfully reproduce the sign of regression coefficients, except for RLCRF in PC1. However, the RSCRF relative contributions from PC1 and PC2 are overestimated, while the relative contribution from PC3 is underestimated in both BCC_AGCM versions. The RLCRF relative contribution is underestimated for PC2 and overestimated for PC3.

摘要: The abilities of BCC_AGCM2.1 and BCC_AGCM2.2 to simulate the annual-mean cloud vertical structure (CVS) were evaluated through comparison with GCM-Oriented CALIPSO Cloud Product (CALIPSO-GOCCP) data. BCC_AGCM2.2 has a dynamical core and physical processes that are consistent with BCC_AGCM2.1, but has a higher horizontal resolution. Results showed that both BCC_AGCM versions underestimated the global-mean total cloud cover (TCC), middle cloud cover (MCC) and low cloud cover (LCC), and that BCC_AGCM2.2 underestimated the global-mean high cloud cover (HCC). The global-mean cloud cover shows a systematic decrease from BCC_AGCM2.1 to BCC_AGCM2.2, especially for HCC. Geographically, HCC is significantly overestimated in the tropics, particularly by BCC_AGCM2.1, while LCC is generally overestimated over extra-tropical lands, but significantly underestimated over most of the oceans, especially for subtropical marine stratocumulus clouds. The leading EOF modes of CVS were extracted. The BCC_AGCMs perform well in reproducing EOF1, but with a larger variance explained. The two models also capture the basic features of EOF3, except an obvious deficiency in eigenvector peaks. EOF2 has the largest simulation biases in both position and strength of eigenvector peaks. Furthermore, we investigated the effects of CVS on relative shortwave and longwave cloud radiative forcing (RSCRF and RLCRF). Both BCC_AGCM versions successfully reproduce the sign of regression coefficients, except for RLCRF in PC1. However, the RSCRF relative contributions from PC1 and PC2 are overestimated, while the relative contribution from PC3 is underestimated in both BCC_AGCM versions. The RLCRF relative contribution is underestimated for PC2 and overestimated for PC3.

1. Introduction
  • Climate modelers have been bedeviled by uncertainties in climate sensitivity among models for decades when projecting future climate in response to increasing concentrations of greenhouse gases (e.g., Cess et al., 1990, 1996; Stephens, 2005). Among those key climate feedbacks, cloud feedback has been deemed as the primary source of inter-model differences in equilibrium climate sensitivity, with low cloud being the largest contributor (e.g., Bony et al., 2006, Clement et al., 2009). As a main way to modulate the Earth's climate, radiative heating from clouds has been proven to be an important contributor to the vertical profile of atmospheric diabatic heating, which in turn influences atmospheric stratification and general circulation (Johnson and Ciesielski, 2000, Stephens et al., 2002). This feature motivates a critical evaluation of the representation of cloud vertical structure (CVS) in current climate models, which is crucial for improving our understanding of cloud and radiative processes and their representation in climate models.

    In climate models, cloud radiative properties are a combination of macro- (e.g., cloud amount and cloud height) and micro- (e.g., cloud water phase, cloud optical depth and cloud droplet size) physical properties of clouds. When a model does not produce enough cloud, we can compensate by making clouds optically thicker, enabling us to achieve the correct cloud radiative forcing (CRF) at the top of the atmosphere (TOA) (Kay et al., 2012). However, besides TOA radiative fluxes, both macro- and micro-physical properties should also be simulated reasonably within the atmosphere, especially for cloud cover, which directly reflects the position where cloud occurs (i.e., where cloud optical properties should take effect). Therefore, a comprehensive evaluation of macrophysical cloud features (particularly CVS) is very important before their microphysical properties are considered.

    Previous studies have evaluated the ability of climate models to simulate cloud cover (or fraction) (Zhang et al., 2005; Wang et al., 2007; Pincus et al., 2008; Nam and Quaas, 2012), and these studies have mostly focused on total cloud cover (TCC) or three-layer vertical clouds (high, middle, and low cloud cover; hereafter HCC, MCC and LCC, respectively) as defined by the International Satellite Cloud Climatology Project (ISCCP). Different CVSs may result in the same TCC due to different cloud overlap schemes that are used in models (Chen et al., 2000a). The ISCCP D-series products that are used to derive HCC, MCC, and LCC depict CVS with a coarse resolution on the basis of seven vertical levels of cloud from 50 to 1000 hPa (Rossow and Schiffer, 1999). The Atmospheric Radiation Measurement (ARM) datasets are commonly used to evaluate CVS and its radiative effect [e.g., (Qian et al., 2012)], but are restricted to the ARM sites only.

    Recently, we have seen the emergence of the National Aeronautics and Space Administration (NASA) A-Train products, named as such because the constellation of Earth observation satellites from which they derive cross the Equator each day at around 1330 solar time (the "afternoon train"). These satellites move around the Earth in a sun-synchronous orbit and carry radar (CloudSat) and lidar (CALIPSO; Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observations) sensors. Vertical cloud profiles with high vertical resolution can be acquired by taking advantage of the complimentary cloud sensing capabilities of CloudSat and CALIPSO (L'Ecuyer and Jiang, 2010). By using CloudSat and CALIPSO measurements, previous studies have evaluated the vertical structure of cloud and its properties in climate models (Chepfer et al., 2008; Cesana and Chepfer, 2012; Nam and Quaas, 2012; Jiang et al., 2012), greatly improving our knowledge of cloud vertical characteristics.

    In the present reported study, we focused on evaluating CVS and its radiative impact in the recent versions of the Beijing Climate Center (BCC) atmospheric general circulation model (AGCM) by comparing with satellite-based observations. The impact of an increase in horizontal resolution on simulations of cloud and its radiative effect was also investigated, which is helpful for improving cloud parameterization in the BCC_AGCM. Descriptions of the models, experiments and data used are given in section 2. The basic features of ISCCP-defined cloud types are compared between the BCC_AGCMs and observations in section 3.1. The results of an EOF analysis, applied to extract the leading CVS modes, are reported in section 3.2, followed by an analysis of the effect of CVS on radiation in section 3.3. Finally, a discussion and conclusions are presented in section 4.

2. Methodology
  • The latest two versions of the BCC_AGCM (BCC_ AGCM2.1 and BCC_AGCM2.2) were used for this study. BCC_AGCM2.1 was developed from BCC_AGCM2.0, which originated from version three of the Community Atmospheric Model (CAM3) (Collins et al., 2006), developed at the National Center for Atmospheric Research (NCAR), with a horizontal resolution of T42 (#cod#8764;2.8#cod#x000b0;). The dynamics in BCC_AGCM2.0 were different from the Eulerian spectral formulation of the dynamical equations in CAM3. The dynamical core of BCC_AGCM2.0 originated from the Eulerian dynamic framework of CAM3. The main differences were in the use of a reference atmospheric temperature and a reference surface pressure (Wu et al., 2008). Several new physical parameterizations (Wu et al., 2010) were applied to replace the corresponding original ones. As compared to BCC_AGCM2.0, BCC_AGCM2.1 adopts a new cumulus convective scheme (Wu, 2012) based on a bulk-cloud approach. To provide better resolved regional climate simulations, BCC_AGCM2.2 has a further increase in horizontal resolution to T106 (#cod#8764;1#cod#x000b0;), but remains the same in the vertical direction with 26 levels. However, necessary tunings in the physics package have been applied in the T106 version, to keep the radiative balance at the TOA.

    Diagnostic methods for the cloud cover scheme are used to calculate cloud cover in both models, although total cloud condensation is determined with a bulk microphysical parameterization. Three types of clouds are considered in the current cloud cover scheme: marine stratocumulus clouds, convective clouds, and layered clouds. Marine stratocumulus clouds are diagnosed following (Klein and Hartmann, 1993) by representing marine stratocumulus cloud cover as a function of the difference in potential temperature between 700 hPa and the surface, which is an effective indicator of the static stability of the lower atmosphere. Convective clouds are composed of shallow and deep convective clouds, which are parameterized as a function of updraft mass fluxes from shallow and deep cumulus schemes, respectively (Xu and Krueger, 1991). The remaining layered clouds are diagnosed based on relative humidity and a pressure-dependent relative humidity threshold following (Slingo, 1987). The total cloud cover is then diagnosed as the maximum value of cloud cover among the three cloud types for each model volume (i.e., at one level of one model cell).

    To enable simulations of data from several satellite-borne sensors from model variables, some software called the CFMIP Observation Simulator Package (COSP) has been developed under the Cloud Feedback Model Intercomparison Project (CFMIP), including simulators for both passive instruments [ISCCP, the Multi-angle Imaging Spectroradiometer (MISR), and the Moderate Resolution Imaging Spectroradiometer (MODIS)] and active instruments (CloudSat radar and CALIPSO lidar) (Bodas-Salcedo et al., 2011). The COSP acts as an intermediate to ensure consistency in the algorithm between model output and satellite retrieval. In BCC_AGCM 2.1 and BCC_AGCM2.2, COSP has been applied to generate cloud and radiation variables in a way similar to relevant simulators for model evaluation against satellite observations and intercomparison of CFMIP models.

    According to the design of phase five of the Coupled Model Intercomparison Project (CMIP5) (Taylor et al., 2012), two sets of Atmospheric Model Intercomparison Project (AMIP) runs were conducted for the period 1979-2008 using BCC_AGCM2.1 and BCC_AGCM2.2, respectively. These runs included three members and changing conditions were imposed consistent with observations. The monthly-varying boundary conditions, including SST and sea-ice data were from observations. The recommended SST and sea-ice dataset is a merged product based on version one of the monthly mean Hadley Centre sea ice and SST dataset (HadISST1) and version two of the National Oceanic and Atmospheric Administration (NOAA) weekly optimum interpolation (OI) SST analysis (Hurrell et al., 2008). The external forcing changing with time included well-mixed greenhouse gases, ozone, aerosols, solar radiation, and volcanos. These boundary and external forcing data, except the volcanic data, were derived from the CMIP5 website, and are the common forcing data for the climate models included in the CMIP5 experiment, thus facilitating comparisons between models.

  • The model data used in the study were the AMIP runs of BCC_AGCM2.1 and BCC_AGCM2.2, which served as the AMIP output of the Beijing Climate Center Climate System Model version 1.1 (BCC_CSM1.1) and BCC_CSM1.1m (same as BCC_CSM1.1 but with a moderate resolution) participating in CMIP5. The data that have been submitted to CMIP5/CFMIP-2 can be downloaded from the CMIP5 website (http://pcmdi9.llnl.gov/esgf-web-fe/). The integration was from January 1979 to December 2008. The variables covered 3D cloudiness, surface radiative fluxes and relevant circulation variables. All the cloud variables used in the study were additionally outputted by the lidar simulator embedded in the BCC_AGCM, which is one of the COSP simulators specifically for CALIPSO lidar and is convenient for intercomparison between climate models and CALIPSO because of the consistency in the physics.

    Total cloud cover measures the column-integrated cloud cover over a model grid cell based on some cloud overlap assumptions, regardless of CVS. Therefore, different cloud vertical distributions may result in similar TCC. To obtain a coarse vertical perspective of cloud, a commonly-used three-level categorization of cloud type was defined, similar to ISCCP, according to cloud top pressure (P t) or approximate cloud top height above sea level (z), given by the following hydrostatic equilibriums:

    Low clouds : Pt#cod#x02265;680 hPa, z#cod#x02264;3.36 km,

    Middle clouds : 440hPa<Pt<680hPa, or 3.36|km <z <6.72 km, (1)

    High clouds : Pt#cod#x02264;440 hPa, z#cod#x02265;6.72 km.

    The GCM-orientedCALIPSO Cloud Product (CALIPSO-GOCCP, hereafter GOCCP) was designed to evaluate the cloudiness simulated by GCMs under the CFIMP framework (Chepfer et al., 2010), and covers the time period from June 2006 to December 2010. It includes cloud variables such as global 3D cloud cover at 40 vertical levels and high, middle and low cloud cover in terms of the ISCCP definition. The data have a vertical resolution of 480 m and a horizontal resolution of 2#cod#x000b0;#cod#215;2#cod#x000b0;, which is recommended for model evaluation. For more details, readers are referred to the website of CALIPSO-GOCCP: http://climserv.ipsl.polytechnique.fr/cfmip-obs/Calipso_goccp.html.

    The Cloud and Earth's Radiant Energy System (CERES) Energy Balanced and Filled (EBAF)-Surface and -TOA L3B Ed2.6r data products (Kato et al., 2013) were used to study the effects of cloud on surface shortwave (SW) radiation and TOA outgoing longwave (LW) radiation, which are provided to CMIP5 for intercomparison between satellite-based estimates and climate model simulations of radiative fluxes. The monthly-mean datasets include surface downwelling and upwelling SW radiation at the Earth's surface and upwelling LW radiation at the TOA for both clear- and all-sky conditions. They have the same spatial resolution of 1#cod#x000b0;#cod#215; 1#cod#x000b0;, and the time periods are from March 2000 to February 2010 for the surface dataset, and from March 2000 to June 2012 for the TOA dataset. It should be noted that the CERES clear-sky irradiances are averaged through a weighting by the clear-sky fraction, while climate models compute clear-sky irradiance by removing clouds. This discrepancy in definition may produce more global-mean downwelling SW irradiance (about 0.88 W m-2) than in climate models (Kato et al., 2013). However, this difference is small compared to the bias between CERES and our models, so we ignored its impact.

    For temporal consistency, datasets during the common time period (June 2006 to December 2008) were used in the study. Furthermore, we only considered the annual mean for all the data in our analysis.

    Figure 1.  Global distribution of CALIPSO annual (a) TCC, (b) HCC, (c) MCC, and (d) LCC (units: %).

3. Results
  • Before evaluating the models' simulations, we analyze the basic distribution features of GOCCP cloud (Fig. 1). The annual-mean TCC covers about 67% of the Earth's surface in GOCCP, slightly larger than the 64% given by (Cesana and Chepfer, 2012). The GOCCP TCC is also comparable with that observed by other spaceborne sensors (Stubenrauch et al., 2013). In addition, the spatial distributions are consistent between different observational platforms. Higher TCC in GOCCP is mainly located in the mid-latitude storm tracks and the ITCZ, with a peak at around 60#cod#x000b0; in the Southern Hemisphere (SH) from the zonal-mean perspective. Lower TCC mainly occurs over the subsidence region of the Hadley cell, especially the deserts over subtropical lands (Fig. 1a). HCC (Fig. 1b) contributes greatly to higher TCC along the ITCZ, where three highest-HCC regions exist. Consistent with the ITCZ, the maximum of zonal-mean HCC occurs a little north of the Equator. LCC is generally composed of optically-thick stratocumulus cloud over the oceans (Fig. 1d). Besides the above-mentioned mid-latitude storm tracks, subtropical stratocumulus decks can also be found anchored to the west coasts of continents, playing a crucial role in the surface radiation budget (Wood, 2012). Among the three types of cloud, MCC is the lowest (Fig. 1c), which has a similar distribution to HCC in the tropics and with LCC over the mid-latitude oceans, indicating that MCC may be seen as a downward (upward) extension of the HCC (LCC) in these regions.

    Figure 2.  BCC_AGCM2.1 (left) and BCC_AGCM2.2 (right) simulated (a, b) TCC, (c, d) HCC, (e, f) MCC, and (g, h) LCC biases as compared to GOCCP (units: %).

    The biases of total and the three types of cloud cover between GOCCP and those simulated by BCC_AGCM2.1 and BCC_AGCM2.2 are shown in Fig. 2 and Table 1. Both BCC_AGCM2.1 and BCC_AGCM2.2 underestimate the global-mean TCC, with biases of about -11.8% and -19.0% for BCC_AGCM2.1 and BCC_AGCM2.2, respectively (Figs. 2a and b; also see Table 1), which is a common bias in climate models when compared to ISCCP observations (Zhang et al., 2005; Probst et al., 2012), and still exists in CMIP5 models compared with GOCCP data (Cesana and Chepfer, 2012). Underestimations also exist for HCC, MCC and LCC, except the HCC in BCC_AGCM2.1 (Figs. 2c-h). Further examination of the bias distribution in BCC_AGCM2.1 indicates two significant features. Firstly, HCC is significantly overestimated over the tropics, especially over equatorial eastern Africa to the tropical western Indian Ocean, and on both sides of the ITCZ in the central and eastern Pacific. Meanwhile, HCC is underestimated over western equatorial Africa and the Amazon basin where higher HCC can be observed (Figs. 2c and d; also see Fig. 1b), which is similar to other models such as NCAR CAM (Lin and Zhang, 2004; Kay et al., 2012) and ECHAM (Nam and Quaas, 2012). Clearly, the overestimation (underestimation) of HCC primarily arises from excessive (deficient) transport of the convective mass flux over tropical deep convective regions, which is closely associated with the performance of the convective scheme in the BCC_AGCM models. Secondly, LCC is generally overestimated over extra-tropical lands but underestimated over most of the oceans. The largest negative biases are associated with subtropical marine stratocumulus clouds (Figs. 2g and h), which mainly result from the large-scale subsidence in subtropical oceans and coastal-upwelling-induced cold SST, and controlled by planetary boundary layer (PBL) feedbacks between radiative driving, turbulence, surface fluxes, latent heat release and entrainment (Wood, 2012). Typically, oceanic stratocumulus clouds are not well represented in current climate models; they are frequently severely underestimated and the PBL depth is too shallow (Bony and Dufresne, 2005; Hannay et al., 2009). In addition, it should be noted that compensation between HCC and LCC may reduce the TCC bias, especially in the tropics, in spite of the unreasonable vertical structure (Zhang et al., 2005). In addition, we can see that the biases are larger over oceans than over land for TCC and LCC, but the MCC biases are smaller over oceans than over land in both models (Table 1).

    Figure 3.  Latitude-altitude cross section of annual cloud cover observed by (a) CALIPSO and simulated by (b) BCC_AGCM2.1 and (c) BCC_AGCM2.2 (units: %). Dashed lines denote boundaries of ISCCP cloud types at 3.36 and 6.72 km, respectively.

    A significant systematic decrease in global-mean cloud for all cloud types can be found in BCC_AGCM2.2 as compared to BCC_AGCM2.1, and the maximum reduction is in HCC. This feature is mainly due to the increase in horizontal resolution from BCC_AGCM2.1 to BCC_AGCM2.2. HCC and MCC decrease almost globally, and the decrease in HCC is especially remarkable over the tropics where a reduction by over 15% can be found. In contrast, LCC moderately increases in the low latitudes and obviously decreases in the middle to higher latitudes (not shown). Further analysis reveals that the simulated zonal-mean air temperature in BCC_AGCM2.2 is generally higher in the troposphere than that in BCC_AGCM2.1 (not shown). Particularly in the upper troposphere, the warming can reach over 1 K, with a maximum over the tropics. Such a change in temperature tends to depress convection, leading to less convective mass flux transported to the upper troposphere and thus less HCC (Figs. 2c and d), and also a small increase of LCC between 30#cod#x000b0;N and 30#cod#x000b0;S. These responses of clouds to the increase in horizontal resolution have also been reported for other models (e.g., Hack et al., 2006; Roeckner et al., 2006). This implies that an increase in horizontal resolution only is not enough to improve simulations of cloud cover, indicating that refining both the horizontal and vertical resolution is needed for improvement (Roeckner et al., 2006).

  • In the previous section, CVS was investigated by dividing clouds into three levels in terms of the cloud-top height. In this section, CVS is analyzed in more detail based on the finer-stratified GOCCP clouds. First, we examine the zonal mean of cloud cover as a function of latitude and altitude from the data presented in Fig. 3. We can see that three obvious high-value areas exist for GOCCP HCC, with the maximum at 12-15 km in the tropics and the other two areas located at lower altitudes of the mid-latitudes. Below 3 km, strong marine stratocumulus can be found over southern oceans and northern high latitudes. These features are also evident in horizontal HCC and LCC (Figs. 1b and d). Both BCC_AGCM versions can successfully capture an overall structure of zonal-mean cloud cover. However, large biases are characterized by an overestimation of high cloud, with maximum biases at 7-12 km in the mid-high latitudes and above 10 km in the tropics, and an underestimation of low cloud around 1.5 km with a maximum over southern oceans centered at 45#cod#x000b0;S (not shown). In BCC_AGCM2.2, the high-cloud bias decreases remarkably due to the great decrease in HCC in response to the improvement of horizontal resolution. A slight increase in cloud cover also can be found below 2 km between 60#cod#x000b0;N and 60#cod#x000b0;S, which provides further evidence for the above results for HCC, MCC and LCC.

    Figure 3 depicts a zonal-mean cloud vertical profile and limited information is provided for the spatial variability of CVS. An EOF analysis or principal component analysis (PCA) was employed to extract the major modes along the vertical dimension. Then, the 3D cloud cover was decomposed into vertical and horizontal components by treating the horizontal distribution as time sampling in ordinary EOF analysis. Let Xmn be the cloud cover matrix with m vertical levels (m=40 in this study) and n horizontal grid points. Then Xmn can be decomposed as follows:

    Xmn= VmmTmn, (2)

    where Vmm denotes the eigenvector matrix which contains m eigenvectors at most, and Tmn represents the time coefficient matrix. It should be noted that Tmn does not mean the time coefficients in the traditional sense, but the horizontal loading distribution of vertical cloud cover modes.

    Figure 4 shows the first three leading modes of vertical cloud cover, and it can be seen that the accumulated explained variances (EV) are 80.3%, 85.8% and 84.9% for GOCCP, BCC_AGCM2.1 and BCC_AGCM2.2, respectively. All the modes are significant at the 95% confidence level, through which the basic characteristics of annual cloud cover can be interpreted.

    Figure 4.  First three leading EOF modes of cloud cover for CALIPSO (solid line), BCC_AGCM2.1 (dashed line) and BCC_AGCM2.2 (dotted line). Dashed lines denote boundaries of ISCCP cloud types at 3.36 and 6.72 km, respectively.

    Figure 5.  Time coefficients of the first (upper row), second (middle row) and third (bottom row) EOFs for CALIPSO (left column), BCC_AGCM2.1 (central column) and BCC_AGCM2.2 (right column).

    The first mode (EOF1) accounts for 42.1%, 53.3% and 51.7% of the total EV for GOCCP, BCC_AGCM2.1 and BCC_AGCM2.2, respectively (Fig. 4a). Both BCC_AGCM versions simulate EOF1 quite well, although their EVs are about 10% larger. It can be clearly seen that vertical cloud cover changes with an opposite sign above and below about 10 km, which means more (less) cloud cover above 10 km and less (more) cloud cover below 10 km. Combined with the spatial distribution of time coefficient (Figs. 5a-c), this mode indicates a significant discrimination of cloud top height between the tropics and the mid-latitudes (Kokhanovsky et al., 2011). In the tropics, EOF1 corresponds to high-thin cirrus detrained from deep convection, while in the mid-latitudes low and middle clouds prevail. In spite of the great consistency of eigenvectors between GOCCP and the two versions of BCC_AGCM, the horizontal loading of the vertical mode in the BCC_AGCMs is generally overestimated in the tropics, except for central Africa and the Amazon basin, indicating weaker deep convection in these regions.

    The second mode (EOF2) shows a sign-consistent variation of eigenvector above 3 km, but with an opposite sign near the surface in GOCCP (Fig. 4b). However, in the BCC_AGCMs, the simulated eigenvectors have significant deficiencies. Firstly, the peak near 7 km is simulated about 2 km lower and weaker. Secondly, the models cannot reproduce the opposite-sign variation between the low and middle-high clouds, failing to capture the PBL cloud feature around 1-2 km. In addition, the BCC_AGCMs imitate an abnormal peak at around 18 km. The simulated EVs are 20.7% and 22.0% for BCC_AGCM2.1 and BCC_AGCM2.2 respectively, about 5% less than GOCCP. It is noteworthy that horizontal loading distributions are simulated somewhat reasonably (Figs. 5d-f), although there are large deficiencies in eigenvectors.

    The third mode (EOF3) reflects the GOCCP eigenvector having a primary positive peak at around 10 km and a secondary positive peak below 3 km (Fig. 4c). On the contrary, two negative eigenvector peaks occur at around 16 km and 3 km. EOF3 indicates the coexistence of high and mid-low clouds in the main deep convective regions over the tropics and a 10-km-height high-cloud belt at around 45#cod#x000b0; latitude (Fig. 5g). The BCC_AGCMs simulate comparable EVs to GOCCP. However, the positive high-cloud peak is simulated more strongly, while the low cloud peak cannot be captured. The peak at around 16 km is also simulated at a higher altitude, and weakly by the BCC_AGCMs.

  • Cloud radiative forcing (CRF) is commonly used to quantify the impact of clouds on the Earth's radiation budget at both the TOA and the surface (e.g., Harrison et al., 1990). However, CRF varies largely with location. Meanwhile, different heights generally correspond to different cloud types with distinct radiative properties (Chen et al., 2000b). It is more intuitive to introduce the relative SW cloud radiative forcing (RSCRF) to better represent the effect of CVS on surface SW radiation (Liu et al., 2011). RSCRF excludes the radiative effects of other factors such as surface albedo and seasonally- and geographically-varying incoming SW radiation. RSCRF is a measurement of a cloud's extinction (meaning absorption and backscattering of SW radiation by clouds) to incident SW irradiance at the surface, which may be defined as follows:

    RSCRF = (Sdn,all−Sdn,clr)/Sdn,clr, (3)

    where Sdn,all and Sdn,clr denote the all-sky and clear-sky surface downwelling SW radiation fluxes respectively, with positive values being indicative of downward fluxes.

    Similarly, the relative longwave cloud radiative forcing (RLCRF) can be defined to illustrate the relative effects of clouds on TOA outgoing LW radiation, which is given as follows:

    RLCRF = (Lup,all−Lup,clr)/Lup,clr, (4)

    where Lup,all and Lup,clr denote the all-sky and clear-sky upwelling LW radiation fluxes at the TOA respectively, with positive values being indicative of upward fluxes.

    Figure 6 presents the RSCRF and RLCRF calculated from the CERES observation. Clearly, clouds always exert a negative RSCRF at the surface and a negative RLCRF at the TOA. Maximum RSCRF is situated at the extra-tropical oceans, especially in the SH, generally exceeding 40% due to the large amount and optically-thick properties of low stratocumulus. On the other hand, minimum RSCRF mainly occurs in the subtropical subsidence regions, Greenland and the Antarctic (Fig. 6a). For RLCRF (Fig. 6b), the maximum is located in the ITCZ, indicating the important effect of high clouds on outgoing LW radiation. Overall, both BCC_AGCM versions tend to overestimate the global-mean RSCRF, by 5.5% and 3.9% for BCC_AGCM2.1 and BCC_AGCM2.2, respectively (Figs. 7a and b). That is, the BCC_AGCMs have stronger SW extinction ability, although they have less cloud in most cases (Fig. 2). This overestimation of RSCRF is also presented in the geographical distribution. Particularly in the high latitudes of the Northern Hemisphere, RSCRF is significantly overestimated, by about 20% (Figs. 7a and b). In addition, an apparent underestimation of RSCRF can be seen clearly in East Asia, particularly in BCC_AGCM2.2, corresponding to the significant cloud decrease. CRF simulation bias in East Asia commonly exists in climate models, due mainly to our limited understanding of the distinctive monsoon climate and its poor representation in climate models (Yu et al., 2001; Li et al., 2009). The bias of RLCRF is also overestimated by the BCC_AGCMs, but is much smaller than RSCRF (Figs. 7c and d). The RLCRF overestimation is about 1.3% and 0.8% for BCC_AGCM2.1 and BCC_AGCM2.2, respectively.

    Figure 6.  (a)RSCRFand(b)RLCRFobservedbyCERES(units:\,%).

    Figure 7.  BCC_AGCM2.1 (left) and BCC_AGCM2.2 (right) simulated biases of RSCRF (upper row) and RLCRF (bottom row) as compared to CERES (units: %).

    To what extent RSCRF and RLCRF can be interpreted by CVS is now investigated in both observations and the models. The commonly-used method for this is multiple linear regression analysis; however, there is always strong collinearity between cloud covers at different vertical layers, and thus the exploratory variables are not independent and may cause the regression coefficients to be unstable if 40-level vertical clouds are directly used in the regression analysis. As an alternative, the principal component regression (PCR) method is a more effective way to avoid the problem. First, we used previously-obtained eigenvectors (namely EOFs) to construct the principal components (PC), which are independent from each other, and can be expressed as follows:

    where Vmi and Xin denote EOF eigenvectors and original 3D cloud cover field, with m, n and i representing the number of PC, horizontal spatial point and vertical level, respectively. Then, multiple linear regression was conducted (m=3) to obtain the following equation:

    where a indicates regression coefficients and the other symbols have the same meanings as previously defined. Here, RCRF can refer to RSCRF or RLCRF. The regression coefficients are given in Table 2. Statistically, both the equation and the individual variable pass the 99% confidence level (F-test). The coefficients measure the variation of RSCRF and RLCRF with one unit change in PC when keeping the other two PCs constant. In GOCCP, PC1 and PC2 tend to reduce the RSCRF (note RSCRF is negative), and PC3 tends to increase RSCRF, corresponding to one unit PC change. Meanwhile, PC1 and PC3 tend to increase the RLCRF and PC2 tends to reduce the RLCRF.

    Both BCC_AGCMs reproduce the sign of the coefficients successfully for RSCRF. In GOCCP, the largest RSCRF variation with unit PC change is from PC2. The coefficients of PC1 (a1) simulated by the BCC-AGCMs are substantially larger than those in CALIPSO, especially for BCC_ AGCM2.2, indicating that this cloud vertical mode of BCC_AGCM overestimates the SW extinction ability corresponding to unit change of PC1. Considering the good agreement in eigenvectors between CALIPSO and the BCC_AGCMs, it can be inferred that the extinction bias may primarily come from the difference in cloud microphysical optical properties. This feature may partly arise from the simulated bias of cloud water content (CWC), which can be interpreted by the underestimation of ice cloud water content (IWC) in the tropical upper troposphere and the overestimation of CWC in the extra-tropical middle and low troposphere as simulated by a coupled version of BCC_AGCM2.1 (i.e., BCC_CSM1.0) when compared to A-Train observations (Jiang et al., 2012). For PC2, the relative SW extinction ability is also overestimated for both BCC_AGCM versions. For PC3, the relative SW extinction ability is underestimated in both BCC_AGCM versions. Due to an existence of a deficiency in eigenvectors, the bias of the coefficients (a2 and a3) cannot be explained by either the eigenvector or an individual difference in optical properties, but rather by a combination of the two. It is important to note that the BCC_AGCMs tend to overestimate the extinction ability for unit cloud cover, which may be compensated for by inadequate cloud in the models resulting in less bias in CRF at the TOA. BCC_AGCM2.2 has stronger extinction with unit cloud cover change than the BCC_AGCM2.1, indicating that there may be an automatic compensation between cloud cover and extinction ability considering the systematic decrease in cloud cover with an increase in the horizontal resolution of BCC_AGCM2.2, which is proven by an overall increase in ice cloud water content in the low-middle latitudes and an increase in liquid cloud water content in the tropics (not shown).

    Multiple regression coefficients for CALIPSO, BCC_ AGCM2.1 and BCC_AGCM2.2.

    a0 a1 a2 a3
    RSCRF CALIPSO -8.11 0.34 0.43 -0.21
    #cod#160; BCC_AGCM2.1 -10.07 0.46 0.49 -0.11
    #cod#160; BCC_AGCM2.2 -11.42 0.60 0.53 -0.19
    RLCRF CALIPSO 2.22 0.03 -0.28 0.08
    #cod#160; BCC_AGCM2.1 0.58 -0.03 -0.16 0.17
    #cod#160; BCC_AGCM2.2 0.62 -0.02 -0.27 0.22

    As for RLCRF, both BCC_AGCM versions reproduce the correct sign of the coefficients for PC2 and PC3, but give the wrong sign for PC1, although its contribution is very small. The largest relative contribution to RLCRF is from PC2, which is underestimated by both the BCC_AGCM models. Meanwhile, the contribution from PC3 is overestimated in the BCC_AGCMs. Unlike SW radiation, cloud exerts its effect on RLCRF mainly through its absorption and re-emission to LW radiation. Apart from cloud cover and cloud water content, the RLCRF greatly depends upon the cloud-top temperature, which determines the LW emissivity to a large degree (i.e., the higher the cloud top, the larger the RLCRF). This is why PC2 has the smaller relative contribution to RLCRF if the eigenvector bias is considered in EOF2.

    Equation (6) was used to calculate the actual contribution of PCs on RSCRF and RLCRF by multiplying the PC by the relevant coefficient. First, we analyze the global-mean contributions of PCs to RSCRF and RLCRF, as presented in Table 3. It can be seen that the largest global-mean RSCRF and RLCRF are both contributed by PC2. It is noteworthy that PC1 possesses the largest EV but does not make the largest contribution to RSCRF and RLCRF. This feature can be explained through the global distribution of actual RSCRF and RLCRF contribution by different PCs, where PC1 has an obvious opposite-sign contribution to RSCRF or RLCRF between the tropics and the middle latitudes, other than the consistent negative contribution by PC2 (not shown). As for the simulated biases (Figs. 8 and 9), the RSCRF and RLCRF contributions from PC1 are generally underestimated by the BCC_AGCMs between 30#cod#x000b0;N and 30#cod#x000b0;S and overestimated in the mid-latitudes, leading to a small bias in global-mean RSCRF and RLCRF from PC1. Combined with the simulated cloud bias relevant to PC1, this may imply that SW extinction by high cloud may be inadequate, while excessive SW radiation is absorbed or reflected by middle and low clouds, which is also the case for the LW radiation if the LW emission is considered rather than the extinction. Meanwhile, the RSCRF contribution from PC2 is overestimated in the tropics and underestimated mainly between 30#cod#x000b0; to 60#cod#x000b0; in both hemispheres, which may primarily come from the downward shift of the eigenvector peak in the BCC_AGCMs and failure to capture the PBL cloud features (Fig. 4b). The RLCRF from PC2 is generally underestimated in the extra-tropics and overestimated in some areas of the tropics. For PC3, the BCC_AGCMs cannot capture the spatial variation of the RSCRF and RLCRF sign in GOCCP, other than the consistent negative contribution for RSCRF and positive contribution for RLCRF (not shown), resulting in an overestimation of about 3% for RSCRF and #cod#62;4% for RLCRF.

    Figure 8.  Biases of actual RSCRF contributions from PC1 (upper row), PC2 (middle row) and PC3 (bottom row) simulated by BCC_AGCM2.1 (left) and BCC_AGCM2.2 (right) as compared to GOCCP (units: %).

    Figure 9.  Biases of actual RLCRF contributions from PC1 (upper row), PC2 (middle row) and PC3 (bottom row) simulated by BCC_AGCM2.1 (left) and BCC_AGCM2.2 (right) as compared to GOCCP (units: %).

4. Discussion and conclusions
  • In this study, CVS in two recent versions of the BCC_ AGCM was evaluated against CALIPSO-GOCCP data. Results showed that BCC_AGCM2.1 and BCC_AGCM2.2 underestimate global-mean TCC by 11.8% and 19.0%, respectively. Underestimation of global-mean cloud cover in the BCC_AGCMs also exists for HCC, MCC and LCC, except for HCC in BCC_AGCM2.1. Geographically, HCC is significantly overestimated in the tropics, particularly for BCC_AGCM2.1, and LCC is generally overestimated over extra-tropical lands but greatly underestimated over most of the oceans, especially for subtropical marine stratocumulus clouds. Besides, MCC is also underestimated.

    The GOCCP data were used to obtain detailed cloud vertical features. Results showed that significant overestimation occurs at 7-12 km in the mid-high latitudes and above 10 km in the tropics, accompanied by an underestimation of low cloud around 1.5 km, particularly over the southern oceans around 45#cod#x000b0;S (Fig. 3). Further EOF analysis indicated that both BCC_AGCM versions perform well in reproducing EOF1, except that the EV is about 10% larger than for GOCCP. For EOF3, the BCC_AGCMs also capture the basic features, except for an obvious deficiency in strength of eigenvector peaks. The largest simulation bias occurs in EOF2, characterized by a downward shift and a weakening of the 7-km eigenvector peak and a spurious negative maximum around 18 km, as well as a lack of the PBL positive peak below 2 km. We addressed the impacts of CVS on RSCRF and RLCRF by conducting a PCR analysis using PCs constructed in terms of the aforementioned EOFs. Both BCC_AGCM versions successfully reproduce the sign of regression coefficients, except for PC1 in RLCRF, compared to GOCCP. For RSCRF, the relative SW extinction ability (corresponding to unit change of PCs) from PC1 and PC2 is overestimated, while it is underestimated in both BCC_AGCM versions for PC3. As for RLCRF, the relative contribution is underestimated for PC2 and overestimated for PC3. It is interesting that PC1 does not make the largest contribution to RSCRF and RLCRF, although it does possess the largest explained variance, which can be interpreted by the cancellation of the opposite-sign contribution between the tropics and extra-tropics.

    Also investigated was how the simulated cloud cover has responded to the increase in horizontal resolution in the more recent version of BCC_AGCM. The most remarkable change is that global-mean cloud cover has systematic decreased from BCC_AGCM2.1 to BCC_AGCM2.2, especially for HCC. This change is also seen in other models (e.g., Hack et al., 2006; Roeckner et al., 2006). A possible explanation is that the remarkable warming response to the resolution increase in the upper troposphere depresses convection and leads to less convective mass flux transported to the upper troposphere. However, we still cannot determine which version of the model is better based solely on the increase in horizontal resolution. Results also showed that the simulated biases in some fields, such as zonal-mean high cloud (Fig. 3) and global-mean relative cloud radiative forcing (Fig. 7), have improved, while the bias of TCC has grown. It is suggested that simultaneously refining the horizontal and vertical resolution is necessary in order to improve simulations.

    As mentioned above, the models tend to overestimate the SW extinction ability for unit cloud cover, and this overestimation may be compensated for by inadequate cloud. This compensation is also found between BCC_AGCM2.1 and BCC_AGCM2.2 as a response to the increase in horizontal resolution, i.e., the latter has fewer clouds but stronger relative extinction ability. This compensation may reduce simulated CRF bias at the TOA, where the radiation equilibrium is of most concern to climate modelers. However, this may hide the large deficiencies in both cloud cover and cloud optical properties. In addition, considering the close link of oceanic stratocumulus to PBL, the large biases in marine low stratocumulus may be partly caused by the deficiency in PBL simulation (Hannay et al., 2009). An improvement in PBL turbulence parameterization may be an effective way to obtain better simulation of PBL clouds (Yao and Cheng, 2012).

    Finally, it should be noted that satellite observations still contain uncertainties both in the retrieval algorithm and in spatial representation (e.g., Young et al., 2013). We also noticed that biases between different platforms may be much smaller than those among models, and even between observations and models. Therefore, improving simulations of climate models based on evaluations against satellite observations remains a very important method.

Reference

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint