Tang Huiqing, Zeng Gang, Huang Yue. An assessment of the tropical pacific latent heat flux simulated by BCC_CSM 1.1(m). J Appl Meteor Sci, 2016, 27(4): 463-472. DOI:  10.11898/1001-7313.20160409.
Citation: Tang Huiqing, Zeng Gang, Huang Yue. An assessment of the tropical pacific latent heat flux simulated by BCC_CSM 1.1(m). J Appl Meteor Sci, 2016, 27(4): 463-472. DOI:  10.11898/1001-7313.20160409.

An Assessment of the Tropical Pacific Latent Heat Flux Simulated by BCC_CSM 1.1(m)

DOI: 10.11898/1001-7313.20160409
  • Received Date: 2015-11-19
  • Rev Recd Date: 2016-05-11
  • Publish Date: 2016-07-31
  • The simulated tropical Pacific annual mean latent heat flux by BCC_CSM1.1(m) as well as 14 other CMIP5 models are analyzed and compared with observations from objectively analyzed air-sea fluxes (OAFlux). Some possible causes for annual latent heat flux trend biases in BCC_CSM1.1(m) are investigated.Biases of annual average latent heat flux between observations and BCC_CSM1.1(m) in the tropical ocean and west boundary current area is larger, while in mid-high latitudes is smaller. Annual average latent heat flux is larger than observations, and annual mean latent heat flux variance is smaller than observations. The tropical Pacific annual and zonal mean latent heat flux is quite different in different latitudes. Simulation results of BCC_CSM1.1(m) near 10°N and 8°S have relatively large biases, while the biases are rather small in equator. So BCC_CSM1.1(m) needs to focus on improving the simulation of Pacific latent heat flux near 10° in each hemisphere.Among 15 CMIP5 models, NorESM1_M gives the best simulation result, and the root mean square error is the smallest, while the root mean square error of GISS_E2_R result is the largest. The root mean square error of BCC_CSM1.1(m) result is 22.9 W·m-2, ranking eighth among all models, which indicates a moderate simulating ability.The trend of the tropical Pacific annual mean latent heat flux in BCC_CSM1.1(m) has biases comparing with the observation, and the cause can be concluded in 3 aspects. First, the local contribution horizontal wind speed to latent heat flux trend is underestimated in BCC_CSM1.1(m). Second, there are large biases for simulated non-local contribution of horizontal wind speed in BCC_CSM1.1(m). Finally, the response to the global warming of horizontal wind speed in BCC_CSM1.1(m) has large biases as well. Therefore, the main cause for trend biases of tropical Pacific annual mean latent heat flux is the large simulation deviation of horizontal wind speed in BCC_CSM1.1(m), and therefore the model needs improving in horizontal wind speed simulation.
  • Fig. 1  Spatial patterns of annual latent heat flux biases in BCC_CSM1.1(m) relative to the observation

    (a) bias of climate mean latent heat flux, (b) bias of latent heat flux variance

    Fig. 2  Zonal-mean annual latent heat flux over the tropical Pacific of 15 CMIP5 models (a), and differences between the simulated and the observation (b)

    (grey lines indicate results in other 14 models except BCC_CSM1.1(m))

    Fig. 3  The root mean square error of annual latent heat flux over the tropical Pacific in CMIP5 models

    Fig. 4  3-year moving averages of annual latent heat flux, SST, wind speed and air specific humidity reconstructed series over the tropical Pacific by the observation (a) and BCC_CSM1.1(m)(b)

    Fig. 5  Slope distributions of annual mean f′LHF, , , , over the tropical Pacific by the observation (a) and BCC_CSM1.1(m)(b) from 1979 to 2005

    Fig. 6  Normalized time series of the annual latent heat flux, sea surface temperature and wind speed over the tropical Pacific and Tg index from the observation (a) and BCC_CSM1.1(m)(b)

    Table  1  Information of 15 CMIP5 models

    模式 国家 水平分辨率
    BCC_CSM1.1(m) 中国 1.12°×1.12°
    CCSM4 美国 1.25°×1.25°
    CanESM2 加拿大 2.79°×2.79°
    CSIRO_MK3-6-0 澳大利亚 1.87°×1.87°
    FGOALS-g2 中国 3.00°×3.00°
    GISS_E2_H 美国 2.00°×2.50°
    GISS_E2_R 美国 2.00°×2.50°
    HadCM3 英国 2.50°×3.71°
    HadGEM2_ES 英国 1.25°×1.87°
    INM_CM4 俄国 1.50°×2.00°
    IPSL_CM5A_LR 法国 1.89°×3.71°
    IPSL_CM5A_MR 法国 1.27°×2.48°
    MPI_ESM_LR 德国 1.86°×1.87°
    MPI_ESM_MR 德国 1.86°×1.87°
    NorESM1_M 挪威 1.89°×2.48°
    DownLoad: Download CSV

    Table  2  Correlation coefficients of 1979-2005 annual mean latent heat flux to its impact factors over the tropical Pacific in BCC_CSM1.1(m) and observations

    因子 原始数据 重置序列
    潜热通量 0.38 0.78*
    近海表风速 0.03 0.60*
    海表温度 0.65* 0.87*
    比湿 0.52* 0.80*
    注:*表示达到0.05的显著性水平。
    DownLoad: Download CSV

    Table  3  Trend slopes of annual latent heat flux and its impact factors over the tropical and subtropical Pacific by the observation and BCC_CSM1.1(m) from 1979 to 2005

    因子 观测数据 观测重置序列 BCC_CSM数据 BCC_CSM重置序列
    潜热通量/(W·m-2/(10 a)) 2.95±0.83* 3.02±0.19* 0.52±0.20* 0.66±0.08*
    近海表风速/(m·s-1/(10 a)) 0.08±0.06 0.08±0.02* 0.01±0.01 0.01±0.002*
    海表温度/(℃/(10 a)) 0.11±0.02* 0.07±0.02* 0.20±0.07* 0.23±0.07*
    比湿/(g·kg-1/(10 a)) 0.06±0.03* 0.03±0.01* 0.22±0.07* 0.23±0.06*
       注:*表示趋势达到0.05显著性水平。
    DownLoad: Download CSV
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    • Received : 2015-11-19
    • Accepted : 2016-05-11
    • Published : 2016-07-31

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