Oxygen fluxes beneath Arctic land-fast ice and pack ice: towards estimates of ice productivity

Abstract

Sea-ice ecosystems are among the most extensive of Earth's habitats; yet its autotrophic and heterotrophic activities remain poorly constrained. We employed the in situ aquatic eddy-covariance (AEC) O 2 flux method and laboratory incubation techniques (H 14 CO -deplete meltwater and changes in water flow velocity masked potential biological-mediated activity. AEC estimates of primary productivity were only possible at one study location. Here, productivity rates of 1.3 ± 0.9 mmol O 2 m −2 day −1 , much larger than concurrent laboratory tracer estimates (0.03 mmol C m −2 day −1 ), indicate that ice algal production and its importance within the marine Arctic could be underestimated using traditional approaches. Given careful flux interpretation and with further development, the AEC technique represents a promising new tool for assessing oxygen dynamics and sea-ice productivity in ice-covered regions.

Aquatic Biogeochemical Eddy Covariance Fluxes in the Presence of Waves

“…These large RSME (37%–315%, Table ) are comparable to individual maximum flux errors reported for sensor separations (67%; Berg et al., 2015), analog to digital resolution (64%; McGinnis et al., 2011), coordinate rotations with low‐frequency waves (±50%; Reimers et al., 2012), nonsteady state conditions (100%; Holtappels et al., 2013), stirring‐sensitivity (100%, Holtappels et al., 2015), low‐frequency waves, stirring sensitivity and sensor separation (150%; Reimers et al., 2016a), and motion corrections for moving platforms (200%, Long & Nicholson 2018). In comparison, the NRSME are more consistent with studies that report average error estimates for slow response times (5%–10%; Lorrai et al., 2010; McGinnis et al., 2011; Chipman et al., 2012), stirring sensitivity (<15%; Attard et al., 2014, 2015, 2018; Volaric et al., 2018), spatial variability of footprints (<5%; Attard et al., 2015), and storage corrections (<10%, Plew 2019). Thus, the NRSME estimates provide better error comparisons between studies as the errors are not inflated by diminishingly small values and errors are normalized by the flux range, which has been determined to vary by at least four orders of magnitude from deep sea (Berg et al., 2009) to coral reef (Long et al., 2013) ecosystems.…”

In situ response of Antarctic under-ice primary producers to experimentally altered pH

“…Several variables measured daily were analysed as responses, for example, DO fluxes were indicative of net photosynthetic oxygen production by the under ice community 66 , ΔpH (outflow pH minus inflow pH) was indicative of CO 2 loss, incorporating both biological (photosynthesis) and non-biological losses (diffusion into the ice above). Fluxes of DO were calculated as Concentration outflow minus Concentration inflow , multiplied by seawater supply rate 67 and standardised by the area of under ice algal habitat enclosed by each chamber (units of μmol O 2 m −2 h −1 ).…”

First of an Arctic sea ice meiofauna food web analysis based on abundance, biomass and stable isotope ratios

“…Although recent progress has been made in regards to e.g. remotely sensing ice algal biomass (Cimoli et al 2017 or the non-destructive determination of ice algal primary productivity using eddy correlation approaches (Attard et al 2018), destruction of the habitat is still applied and required for estimation of, e.g., community diversity or activity and biomass measurements in different ice layers , McMinn et al 2009.…”