Sea-ice edge phytoplankton bloom

Satellite-derived sea-ice retreat timing (tR) and maximum chlorophyll-a concentration in the ice edge zone between 1998 and 2018. Sea ice concentration (SIC) was obtained from the National Snow and Ice Data Center. It is based on daily passive microwave radiometry processed using the Bootstrap algorithm (Comiso, 2000) at 25 km resolution. The Bootstrap technique clusters the multichannel passive microwave sensors: Scanning Multi- channel Microwave Radiometer on the Nimbus-7 satellite, Special Sensor Microwave/Imager and Special Sensor Microwave Imager/Sounder from the Defense Meteorological Satellite Program’s satellites, and the Advanced Microwave Scanning Radiometer (Comiso et al., 1997). SIC was interpolated onto the same Chla grid using the nearest neighborhood scheme implemented in Matlab.

Multi-sensor merged clorophyll-a concentration (Chla) Level-3 (i.e., binned and mapped) 8-day composites from the Globcolour Project (http://www.globcolour.info/) were used as a proxy for phytoplankton biomass. Globcolour products have a spa- tial resolution of 4.63 km and cover the 1998–2018 period. The merged product was selected to improve the spatial-temporal coverage diminishing gaps due to cloud cover and sea-ice coverage (Maritorena et al., 2010). The binning methodology combines the normalized water- leaving radiances from different ocean color sensors whenever they are available, which includes SeaWiFS (1998–2010), MODIS-Aqua (2002–2018), Medium- Resolution Imaging Spectrometer (MERIS: 2002–2011), and Visible Infrared Imaging Radiometer Suite (VIIRS: 2012–2018). [Chla] was estimated from normalized water-leaving radiances merged using the Garver-Siegel- Maritorena (GSM) semi-analytical model (Garver and Siegel, 1997; Maritorena et al., 2002).

To assess the impacts of sea-ice retreat timing on marginal ice zone phytoplankton blooms (also refers to phytoplankton spring blooms or ice-edge blooms), we analyzed both Chla and SIC variability in parallel. The method is similar to that of Perrette et al. (2011), which was also adopted by Lowry et al. (2014) and Renaut et al. (2018). The sea-ice retreat, tR, is defined as the day at which SIC is below 10% for at least 24 days. This time interval is longer than the 20 days applied by Perrette et al. (2011) and Renaut et al. (2018) and the 14 days by Lowry et al. (2014) because we used 8-day composites instead of daily maps. However, to avoid sub-pixel contamination in ice-infested regions near the ice edge (Be´langer et al., 2013), we opted to be more conservative by applying a 10% threshold on SIC, as did Perrette et al. (2011) and Renaut et al. (2018) instead of 50% as applied by Lowry et al. (2014). The maximum Chla observed in the ice edge zone was extracted for each pixel for each year, yielding one map of MIZ Chla per year.

本数据集记录了1998年至2018年间通过卫星获取的海冰退缩时间（tR）及冰缘区最大叶绿素a浓度。海冰浓度（SIC）数据源自国家冰雪数据中心，基于每日被动微波辐射测量，采用Bootstrap算法（Comiso, 2000）以25公里分辨率进行处理。Bootstrap技术将多通道被动微波传感器进行聚类，包括Nimbus-7卫星上的扫描多通道微波辐射计、国防气象卫星计划卫星的专用微波/成像仪和专用微波成像仪/测音仪，以及先进微波扫描辐射计（Comiso et al., 1997）。SIC数据通过最近邻方案插值至相同的叶绿素a网格中，该方案在Matlab中实现。

Globcolour项目（http://www.globcolour.info/）的多传感器融合叶绿素a浓度（Chla）Level-3（即分箱并绘制）8日复合数据被用作浮游植物生物量的替代指标。Globcolour产品具有4.63公里的空间分辨率，覆盖1998年至2018年期间。选定的融合产品旨在改善空间-时间覆盖，减少云层和海冰覆盖导致的间隙（Maritorena et al., 2010）。分箱方法结合了不同海洋颜色传感器可用的归一化水反照率，包括SeaWiFS（1998–2010）、MODIS-Aqua（2002–2018）、中分辨率成像光谱仪（MERIS：2002–2011）和可见光-红外成像辐射仪套件（VIIRS：2012–2018）。[Chla]是通过使用Garver-Siegel-Maritorena（GSM）半解析模型（Garver and Siegel, 1997；Maritorena et al., 2002）合并归一化水反照率进行估计的。

为评估海冰退缩时间对边缘冰区浮游植物 blooms（亦指浮游植物春季 blooms 或冰缘 blooms）的影响，我们对Chla和SIC的变异性进行了并行分析。该方法与Perrette et al.（2011）的方法类似，也被Lowry et al.（2014）和Renaut et al.（2018）采用。海冰退缩时间，tR，定义为SIC低于10%且连续24天的那一天。这个时间间隔长于Perrette et al.（2011）和Renaut et al.（2018）应用的20天，以及Lowry et al.（2014）应用的14天，因为我们使用了8日复合数据而非每日地图。然而，为了避免冰缘附近冰覆盖区域内的亚像素污染（Be´langer et al., 2013），我们采取了更为保守的策略，将SIC的阈值设定为10%，与Perrette et al.（2011）和Renaut et al.（2018）的做法一致，而非Lowry et al.（2014）应用的50%。每年从冰缘区每个像素中提取观察到的最大Chla，从而每年生成一张边缘冰区Chla图。