Assessing surface heat fluxes in atmospheric reanalyses with a decade of data from the NOAA Kuroshio Extension Observatory
Assessing surface heat fluxes in atmospheric reanalyses with a decade of data from the NOAA Kuroshio Extension Observatory
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利用 NOAA 黑潮扩展观测站十年来的数据评估大气再分析中的表面热通量
DOI：
10.1002/2016jc011905
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发表时间：
2016
影响因子：
--
通讯作者：
Dai McClurg
中科院分区：
文献类型：
--
作者：
Dongxiao Zhang;M. Cronin;Caihong Wen;Y. Xue;Arun Kumar;Dai McClurg
以往的研究发现，数值天气预报模式再分析的海气通量存在较大偏差和不确定性，必须加以识别和减少，以便在天气和气候预测方面取得进展。本文利用NOAA黑潮扩展观测站（KEO）的海气热通量测量值对NCEP的气候预报系统再分析（CFSR）和ECMWF再分析- interim （ERA-I）两种新一代再分析进行了评估，结果表明这两种新一代再分析有了显著改进。在两次再分析中，所有四个通量分量（感热通量和潜热通量以及净长波和短波辐射）都与观测值高度相关，总净地表热通量的相关系数均在0.96以上。虽然净地表热通量的误差与以往的再分析相比显著减小，但均方根误差（rmse）和偏差仍然很高，特别是CFSR: CFSR和ERA-I的rmse分别减少了25-30%至64和61 W/m2，而偏差减少了40-60%至28和20 W/m2。但CFSR高估了冬季热量释放90 W/m2。误差主要由潜热通量引起，均方根误差主要由潜热通量和短波辐射误差引起。两种重新分析都高估了与冬季风暴相关的风速，低估了夏季的特定湿度。然而，ERA-I潜热通量及其总净地表热通量更接近观测值。CFSR中的bulk算法是造成CFSR冬季热释放高估的主要原因。这篇文章受版权保护。版权所有。
Previous studies have found large biases and uncertainties in the air-sea fluxes from Numerical Weather Prediction model reanalyses, which must be identified and reduced in order to make progress on weather and climate predictions. Here, air-sea heat fluxes from NOAA Kuroshio Extension Observatory (KEO) measurements are used to assess two new reanalyses, NCEP's Climate Forecast System Reanalysis (CFSR) and ECMWF Reanalysis-Interim (ERA-I), suggesting that these two new generation reanalyses have significantly improved. In both reanalyses, all four flux components (sensible and latent heat flux and net longwave and shortwave radiation) are highly correlated with observation, with the correlation of total net surface heat fluxes above 0.96. Although errors of the net surface heat flux have significantly reduced from previous reanalyses, the Root Mean Square Errors (RMSEs) and biases remain high especially for CFSR: the RMSEs of CFSR and ERA-I are reduced by 25-30% to 64 and 61 W/m2 respectively, while biases are reduced by 40-60% to 28 and 20 W/m2. But CFSR overestimates the winter heat release by 90 W/m2. The main cause of biases is the latent heat flux, while RMS errors are primarily due to latent heat flux and shortwave radiation errors. Both reanalyses overestimate the wind speed associated with winter storms and underestimate specific humidity in summer. The ERA-I latent heat flux, and its total net surface heat flux, are however closer to observation. It is the bulk algorithm in CFSR that is found to be mainly responsible for overestimates of winter heat release in CFSR. This article is protected by copyright. All rights reserved.