Sensitivity of phytoplankton primary production estimates to available irradiance under heterogeneous sea ice conditions - Tremblay, Jean-Éric; Peeken, Ilka; Massicotte, Philippe; Katlein, Christian; Babin, Marcel; Flores, Hauke; Huot, Yannick; Castellani, Giulia; Arnd, Stefanie; Lange, B. (Benjamin) (Journal of Geophysical Research: Oceans, 2020)
The Arctic ice scape is composed by a mosaic of ridges, hummocks, melt ponds, leads, and snow. Under such heterogeneous surfaces, drifting phytoplankton communities are experiencing a wide range of irradiance conditions and intensities that cannot be sampled representatively using single‐location measurements. Combining experimentally derived photosynthetic parameters with transmittance measurements acquired at spatial scales ranging from hundreds of meters (using a remotely operated vehicle, ROV) to thousands of meters (using a surface and underice trawl, SUIT), we assessed the sensitivity of water column primary production estimates to multiscale underice light measurements. Daily primary production calculated from transmittance from both the ROV and the SUIT ranged between 0.004 and 939 mgC·m−2·day−1. Upscaling these estimates at larger spatial scales using satellite‐derived sea ice concentration reduced the variability by 22% (0.004–731 mgC·m−2·day−1). The relative error in primary production estimates was two times lower when combining remote sensing and in situ data compared to ROV‐based estimates alone. These results suggest that spatially extensive in situ measurements must be combined with large‐footprint sea ice coverage sampling (e.g., remote sensing, aerial imagery) to accurately estimate primary production in ice‐covered waters. Also, the results indicated a decreasing error of primary production estimates with increasing sample size and the spatial scale at which in situ measurements are performed. Conversely, existing estimates of spatially integrated phytoplankton primary production in ice‐covered waters derived from single‐location light measurements may be associated with large statistical errors. Considering these implications is important for modeling scenarios and interpretation of existing measurements in a changing Arctic ecosystem.