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Tropical Indian Ocean surface salinity bias in Climate Forecasting System coupled models and the role of upper ocean processes

机译:热带印度洋地表盐度偏差在气候预测系统中的耦合模型和上层海洋过程的作用

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In the present study sea surface salinity (SSS) biases and seasonal tendency over the Tropical Indian Ocean (TIO) in the coupled models [Climate Forecasting System version 1 (CFSv1) and version 2 (CFSv2)] are examined with respect to observations. Both CFSv1 and CFSv2 overestimate SSS over the TIO throughout the year. CFSv1 displays improper SSS seasonal cycle over the Bay of Bengal (BoB), which is due to weaker model precipitation and improper river runoff especially during summer and fall. Over the southeastern Arabian Sea (AS) weak horizontal advection associated with East Indian coastal current during winter limits the formation of spring fresh water pool. On the other hand, weaker Somali jet during summer results for reduced positive salt tendency in the central and eastern AS. Strong positive precipitation bias in CFSv1 over the region off Somalia during winter, weaker vertical mixing and absence of horizontal salt advection lead to unrealistic barrier layer during winter and spring. The weaker stratification and improper spatial distribution of barrier layer thickness (BLT) in CFSv1 indicate that not only horizontal flux distribution but also vertical salt distribution displays large discrepancies. Absence of fall Wyrtki jet and winter equatorial currents in this model limit the advection of horizontal salt flux to the eastern equatorial Indian Ocean. The associated weaker stratification in eastern equatorial Indian Ocean can lead to deeper mixed layer and negative Sea Surface Temperature (SST) bias, which in turn favor positive Indian Ocean Dipole bias in CFSv1. It is important to note that improper spatial distribution of barrier layer and stratification can alter the air-sea interaction and precipitation in the models. On the other hand CFSv2 could produce the seasonal evolution and spatial distribution of SSS, BLT and stratification better than CFSv1. However CFSv2 displays positive bias in evaporation over the whole domain and negative bias in precipitation over the BoB and equatorial Indian Ocean, resulting net reduction in the fresh water availability. This net reduction in fresh water forcing and the associated weaker stratification lead to deeper (than observed) mixed layer depth and is primarily responsible for the cold SST bias in CFSv2. However overall improvement of mean salinity distribution in CFSv2 is about 30 % and the mean error has reduced by more than 1 psu over the BoB. This improvement is mainly due to better fresh water forcing and model physics. Realistic run off information, better ocean model and high resolution in CFSv2 contributed for the improvement. Further improvement can be achieved by reducing biases in the moisture flux and precipitation.
机译:在本研究中,结合观测资料,研究了耦合模型[气候预测系统版本1(CFSv1)和版本2(CFSv2)]中热带印度洋(TIO)的海表盐度(SSS)偏差和季节性趋势。全年中,CFSv1和CFSv2都高估了TIO上的SSS。 CFSv1在孟加拉湾(BoB)上显示不适当的SSS季节性周期,这是由于模型降水不足和河流径流不当所致,尤其是在夏季和秋季。在阿拉伯东南海(AS)上,冬季冬季与东印度洋沿海海流相关的弱水平对流限制了春季淡水池的形成。另一方面,夏季期间索马里急流的减弱导致中部和东部AS的正盐趋势降低。冬季,索马里以外区域CFSv1的强降水偏正,垂直混合较弱以及水平盐对流的缺乏导致冬季和春季的屏障层不切实际。 CFSv1中较弱的分层和不适当的势垒层厚度(BLT)空间分布表明,不仅水平通量分布而且垂直盐分布也显示出较大的差异。该模型缺乏秋季Wyrtki射流和冬季赤道洋流,限制了水平盐通量向赤道东印度洋的平流。赤道东印度洋相关的较弱分层可能导致更深的混合层和负海表温度(SST)偏差,进而有利于CFSv1的正印度洋偶极子偏差。重要的是要注意,屏障层的空间分布不当和分层会改变模型中的海-气相互作用和降水。另一方面,与CFSv1相比,CFSv2可以更好地产生SSS,BLT和分层的季节演变和空间分布。但是,CFSv2在整个区域的蒸发量显示正偏差,而在BoB和赤道印度洋的降水量上显示负偏差,从而导致淡水净减少。淡水强迫的净减少以及相关的较弱的分层导致混合层深度更深(比所观察到的更深),并且主要是造成CFSv2中冷SST偏差的原因。但是,CFSv2中平均盐度分布的总体改善约为30%,并且平均误差比BoB降低了1 psu以上。这种改进主要归因于更好的淡水强迫和模型物理。实际的径流信息,更好的海洋模型和CFSv2中的高分辨率为改进做出了贡献。通过减少湿气通量和降水的偏差可以实现进一步的改进。

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