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Understanding the bias in surface latent and sensible heat fluxes in contemporary AGCMs over tropical oceans
Climate Dynamics ( IF 3.8 ) Pub Date : 2020-08-27 , DOI: 10.1007/s00382-020-05431-y
Xin Zhou , Pallav Ray , Bradford S. Barrett , Pang-Chi Hsu

The performance of 20 models participating in the atmospheric model intercomparison project (AMIP) is evaluated concerning surface latent (QLH) and sensible (QSH) heat flux over the tropical oceans (30°S–30°N). Biases were calculated by comparing model fluxes to observations from moored buoys and the objectively analyzed air–sea fluxes (OAFlux) database. All 20 AMIP models overestimate QLH with an ensemble mean bias of 20 W m−2, and 18 of the 20 models overestimate QSH with an ensemble mean bias of 5 W m−2 when compared to OAFlux, implying a systematic positive bias over the tropical oceans. A comparison with buoy observations also showed similar biases. To obtain insights into the causes behind model bias, we quantified the contribution from near-surface winds, specific humidity, and temperatures. It is found that near-surface humidity contributes more to the bias in QLH than wind speed, while air temperature contributes more to bias in QSH than wind speed. On the other hand, the root mean squared error (RMSE) in QLH has contributions from both near-surface humidity and wind. The contribution from humidity to the mean bias in QLH is 13 W m−2, with RMSE of 15 W m−2, suggesting a systematic overestimation of sea-air humidity difference in models. The model ensemble, in general, simulates QLH and QSH better than individual models. Models with higher horizontal and vertical resolutions perform better than coarse resolution models.



中文翻译:

了解当代AGCM在热带海洋上的表面潜热通量和显热通量的偏差

对20个参与大气模型比对项目(AMIP)的模型的性能进行了评估,涉及热带海洋(30°S–30°N)上的表面潜热(Q LH)和显热(Q SH)。通过将模型通量与系泊浮标的观测值和客观分析的海气通量(OAFlux)数据库进行比较,可以计算偏差。所有20个AMIP模型都以集合平均偏差为20 W m -2高估了Q LH,而20个模型中的18个模型以集合平均偏差为5 W m -2高估了Q SH与OAFlux相比,意味着对热带海洋有系统的积极偏见。与浮标观测值的比较也显示出类似的偏差。为了深入了解模型偏差背后的原因,我们量化了近地表风,比湿和温度的影响。发现近地表湿度比风速对Q LH的影响更大,而气温比风速对Q SHH的影响更大。另一方面,Q LH中的均方根误差(RMSE)来自近地表湿度和风。湿度对Q LH中平均偏差的贡献为13 W m -2,RMSE为15 W m -2,这表明系统地高估了模型中的海洋空气湿度差。通常,模型集成比单个模型更好地模拟Q LHQ SH。水平和垂直分辨率较高的模型比粗分辨率模型的性能更好。

更新日期:2020-10-19
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