A 1/4°-Spatial-Resolution Daily Sea Surface Temperature Climatology Based on a Blended Satellite and in situ Analysis
A 1/4°-Spatial-Resolution Daily Sea Surface Temperature Climatology Based on a Blended Satellite and in situ Analysis
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DOI：
10.1175/jcli-d-14-00293.1
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发表时间：
2014-11-01
影响因子：
4.9
通讯作者：
Xue, Yan
中科院分区：
文献类型：
--
作者：
Banzon, Viva F.;Reynolds, Richard W.;Xue, Yan
利用NOAA逐日最佳插值（OI）海温的前30年（1982-2011）数据，构建了一个新的海温气候平均值。每日分析在1/40度（类似于25公里）的空间网格上混合了现场和卫星数据。使用分析可以计算所有海洋网格点的气候学值，即使是那些没有观测的点。与国家环境预测中心每月制作的1度空间分辨率气候学进行比较，主要是由NOAA每周OISST计算得出的。这两种气候学都很好地反映了主要的海洋特征和年温度循环。日气候学表现为沿西边界流梯度较紧，沿海岸线分辨率较好。在高海温梯度地区，由于分辨率差异，两种气候学差异超过0.6℃。这两种气候学在非常高纬度地区也不同，在那里OISST产品的海冰处理不同。在持续多云的地区，新的气候学通常较低约0.4摄氏度，这可能反映了两种分析的输入卫星海温之间的差异。由于新的气候学代表了与每日分析相匹配的尺度上的平均条件，因此计算相应的每日异常更为合适。
A new sea surface temperature (SST) climatological mean was constructed using the first 30 years (1982-2011) of the NOAA daily optimum interpolation (OI) SST. The daily analysis blends in situ and satellite data on a 1/40 degrees (similar to 25 km) spatial grid. Use of an analysis allows computation of a climatological value for all ocean grid points, even those without observations. Comparisons were made with a monthly, 1 degrees-spatial-resolution climatology produced by the National Centers for Environmental Prediction, computed primarily from the NOAA weekly OISST. Both climatologies were found to provide a good representation of major oceanic features and the annual temperature cycle. However, the daily climatology showed tighter gradients along western boundary currents and better resolution along coastlines. The two climatologies differed by over 0.6 degrees C in high-SST-gradient regions because of resolution differences. The two climatologies also differed at very high latitudes, where the sea ice processing differed between the OISST products. In persistently cloudy areas, the new climatology was generally cooler by approximately 0.4 degrees C, probably reflecting differences between the input satellite SSTs to the two analyses. Since the new climatology represents mean conditions at scales that match the daily analysis, it would be more appropriate for computing the corresponding daily anomalies.