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An Assessment of the Tropical Pacific Latent Heat Flux Simulated by BCC_CSM 1.1(m)
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摘要: 利用1979—2005年OAFlux (Objectively Analyzed air-sea Fluxes) 观测资料以及CMIP5的15个耦合模式的模拟结果，评估了BCC_CSM1.1(m) 模式对热带太平洋年平均潜热通量气候态和变化趋势的模拟能力，并分析造成趋势偏差的可能原因。结果表明：BCC_CSM1.1(m) 模式模拟热带太平洋年平均潜热通量气候态在各纬度上差异较大, 其中在赤道的模拟能力较佳，而在10°N和8°S附近模拟偏差较大；BCC_CSM1.1(m) 模式对热带太平洋年平均潜热通量趋势的模拟能力一般，造成趋势偏差的主要原因是该模式低估了风速对潜热通量的局地贡献以及它对风速的非局地贡献的模拟存在较大偏差。此外，该模式未能较好地模拟出风速对全球变暖响应。因此，BCC_CSM1.1(m) 模式对热带太平洋年平均潜热通量趋势模拟的改进需加强其对风速模拟的改进。
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关键词:
- BCC_CSM1.1(m);
- 潜热通量;
- 热带太平洋
Abstract: 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.-
Key words:
- BCC_CSM1.1(m);
- latent heat flux;
- tropical Pacific
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图 1 BCC_CSM1.1(m) 模式模拟的年平均潜热通量与观测偏差空间分布
(a) 潜热通量气候态的偏差，(b) 潜热通量方差的偏差
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
图 2 热带太平洋纬向平均的年平均潜热通量的模拟结果对比 (灰色曲线表示其他14个模式结果) (a) BCC_CSM1.1(m) 模式纬向平均潜热通量与多模式的比较，(b) CMIP5模式纬向平均潜热通量与观测偏差
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))
图 3 CMIP5模式的热带太平洋年平均潜热通量均方根误差
Fig. 3 The root mean square error of annual latent heat flux over the tropical Pacific in CMIP5 models
图 4 观测 (a) 和BCC_CSM1.1(m) 模式模拟 (b) 的热带太平洋年平均潜热通量、海表温度、风速和比湿的重置序列的3年滑动平均
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)
图 5 1979—2005年观测 (a) 和BCC_CSM1.1(m) 模式模拟 (b) 的热带太平洋年平均潜热通量f′LHF,
Fig. 5 Slope distributions of annual mean f′LHF,
图 6 观测 (a) 和BCC_CSM1.1(m) 模式模拟 (b) 的热带太平洋年平均潜热通量、海表温度、风速和Tg指数变化
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)
表 1 15个CMIP5模式信息
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° 
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表 2 1979—2005年BCC_CSM1.1(m) 模式模拟和观测热带太平洋年平均潜热通量及各因子相关系数
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。
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表 3 1979—2005年观测和BCC_CSM1.1(m) 模式模拟热带太平洋年平均潜热通量及各因子趋势的斜率
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。
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