The impact of melt ponds on summertime microwave brightness temperatures and sea-ice concentrations
The impact of melt ponds on summertime microwave brightness temperatures and sea-ice concentrations
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DOI：
10.5194/tc-10-2217-2016
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发表时间：
2016-09
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抽象的。在夏季，从卫星微波亮度温度得出的海冰浓度不太准确。在北冰洋，缺乏准确性主要是由融化池造成的，但也是由雪和海冰表面本身的性质变化造成的。我们调查的敏感性8海冰浓度反演算法融化池比较海冰浓度与融化池分数。我们从2009年夏季的微波亮度温度网格每日海冰浓度。我们推导出每天的部分融化池，浮冰之间的开放水域，和冰表面分数从当代中分辨率光谱仪（MODIS）反射率数据。我们只使用网格单元的MODIS海冰浓度，这是融池部分加上冰面部分，超过90%。对于一组算法，例如，布里斯托和Comiso自举频率模式（Bootstrap_f），海冰浓度的线性相关的MODIS融池分数相当明显的6月后。对于其他算法，例如，在90GHz附近和Comiso自举极化模式（Bootstrap_p）下，这种关系较弱，且在夏季发展较晚。我们属性的变化的敏感性，整个算法的融化池分数的亮度温度的雪属性变化的不同的敏感性。我们发现，对于100%海冰和40%的融池分数，海冰浓度低估了14%（Bootstrap_f）和26%（Bootstrap_p）。对于20%的熔池分数，低估减少到0%。在浮冰之间存在真实的开放水域的情况下，海冰浓度在60%海冰浓度下被高估26%（Bootstrap_f）和14%（Bootstrap_p）之间，在80%海冰浓度下，所有算法都被高估20%。根据我们对2009年夏季数据的调查，没有一种算法表现最好。我们建议，这些算法是更敏感的融化池可以更容易地优化，因为未知的雪和海冰表面特性变化的影响不太明显。
Abstract. Sea-ice concentrations derived from satellite microwave brightness temperatures are less accurate during summer. In the Arctic Ocean the lack of accuracy is primarily caused by melt ponds, but also by changes in the properties of snow and the sea-ice surface itself. We investigate the sensitivity of eight sea-ice concentration retrieval algorithms to melt ponds by comparing sea-ice concentration with the melt-pond fraction. We derive gridded daily sea-ice concentrations from microwave brightness temperatures of summer 2009. We derive the daily fraction of melt ponds, open water between ice floes, and the ice-surface fraction from contemporary Moderate Resolution Spectroradiometer (MODIS) reflectance data. We only use grid cells where the MODIS sea-ice concentration, which is the melt-pond fraction plus the ice-surface fraction, exceeds 90 %. For one group of algorithms, e.g., Bristol and Comiso bootstrap frequency mode (Bootstrap_f), sea-ice concentrations are linearly related to the MODIS melt-pond fraction quite clearly after June. For other algorithms, e.g., Near90GHz and Comiso bootstrap polarization mode (Bootstrap_p), this relationship is weaker and develops later in summer. We attribute the variation of the sensitivity to the melt-pond fraction across the algorithms to a different sensitivity of the brightness temperatures to snow-property variations. We find an underestimation of the sea-ice concentration by between 14 % (Bootstrap_f) and 26 % (Bootstrap_p) for 100 % sea ice with a melt-pond fraction of 40 %. The underestimation reduces to 0 % for a melt-pond fraction of 20 %. In presence of real open water between ice floes, the sea-ice concentration is overestimated by between 26 % (Bootstrap_f) and 14 % (Bootstrap_p) at 60 % sea-ice concentration and by 20 % across all algorithms at 80 % sea-ice concentration. None of the algorithms investigated performs best based on our investigation of data from summer 2009. We suggest that those algorithms which are more sensitive to melt ponds could be optimized more easily because the influence of unknown snow and sea-ice surface property variations is less pronounced.