Snow depth and sea ice thickness derived from the measurements of SIMBA buoy 2020T75

The Snow and Ice Mass Balance Array (SIMBA) is a thermistor string type IMB (Jackson et al., 2013) which measures the environment temperature SIMBA-ET and temperature change around the thermistors after a weak heating applied to each sensor (SIMBA-HT). Totally, there were 22 SIMBAs deployed in the Arcitic Ocean over the Distributed Network (DN) and the Central Observatory during the Legs 1a, 1 and 3 of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) campaign. The SIMBA thermistor chain is 5.12 m long, and equipped with 256 thermistors (Maxim Integrated DS28EA00) at 0.02 m spacing. Based on a manual identification method, the SIMBA-ET and SIMBA-HT were processed to yield snow depth and ice thickness. Here, we combined the two optimal methods (the ET vertical gradient and HT rise ratio) to reduce the uncertainty. To keep the consistency, we use the snow or ice surface, consequentially the snow depth, determined by the ET vertical gradient. The formations of snow ice and superposed ice are not considered in this data set. That is to say, the value of snow depth includes the layers of snow ice at two sites (2019T56 and 2019T72). The superposed ice was generally negligible. We used the HT rise ratio to determine the ice-water interface, consequentially the ice thickness. Overall, the measurement accuracy was 0.02 m for both the snow depth and ice thickness. After the snow cover melted over, the negative values for the snow depth indicate the onset of ice surface melt.

冰雪质量平衡阵列（Snow and Ice Mass Balance Array，SIMBA）是一种热敏电阻串式冰质量平衡仪（thermistor string type Ice Mass Balance，IMB）[Jackson et al., 2013]，可测量环境温度（SIMBA-ET）及对每个传感器施加弱加热后热敏电阻周围的温度变化（SIMBA-HT）。在北极气候多学科漂流观测站（Multidisciplinary drifting Observatory for the Study of Arctic Climate，MOSAiC）科考航次的1a段、1段和3段期间，共22套SIMBA部署于北冰洋的分布式网络（Distributed Network，DN）和中央观测站区域。SIMBA热敏电阻链长5.12米，配备256个热敏电阻（Maxim Integrated DS28EA00型号），间距为0.02米。基于人工识别方法，对SIMBA-ET和SIMBA-HT数据进行处理以获取雪深和冰厚。本研究结合了两种最优方法（ET垂直梯度法与HT上升比率法）以降低不确定性。为保证一致性，雪面或冰面（进而雪深）的确定采用ET垂直梯度法。本数据集未考虑雪冰和叠加冰的形成；也就是说，两个站点（2019T56和2019T72）的雪深值包含雪冰层。叠加冰的影响通常可忽略不计。冰-水界面（进而冰厚）的确定采用HT上升比率法。总体而言，雪深和冰厚的测量精度均为0.02米。当积雪完全融化后，雪深的负值表示冰面融化的开始。