An Evaluation of the Performance of Sea-Bird Scientific's Autonomous SeaFET<sup>TM</sup>: Considerations for the Broader Oceanographic Community

Abstract

The commercially available Sea-Bird SeaFET TM provides an accessible way for a broad 19 community of researchers to study ocean acidification and obtain robust measurements of 20 seawater pH via the use of an in situ autonomous sensor. There are pitfalls, however, that have 21 been detailed in previous best practices for sensor care, deployment, and data handling. Here, we 22 took advantage of two distinctly different coastal settings to evaluate the Sea-Bird SeaFET TM and 23 examine the multitude of scenarios in which problems may arise confounding the accuracy of 24 measured pH. High-resolution temporal measurements of pH were obtained during 3-to 5-month 25 field deployments in three separate locations (two in south-central, Alaska, USA, and one British 26 Columbia, Canada) spanning a broad range of nearshore temperature and salinity conditions. 27Both the internal and external electrodes onboard the SeaFET TM were evaluated against robust 28 benchtop measurements for accuracy utilizing either the factory calibration, an in situ single-29 point calibration, or in situ multi-point calibration. In addition, two sensors deployed in parallel 30 in Kasitsna Bay, AK, USA, were compared for inter-sensor variability in order to quantify other 31 factors contributing to SeaFET TM intrinsic inaccuracies. Based on our results, the multi-point 32 calibration method provided the highest accuracy (< 0.025 difference in pH) of pH when 33 compared against benchtop measurements. Spectral analysis of time series data showed that 34 during spring in Alaskan waters, a range of tidal frequencies dominated pH variability, while 35 seasonal oceanographic conditions were the dominant driver in Canadian waters. Further, it is 36 suggested that spectral analysis performed on initial deployments may be able to act as an a 37 posteriori method to better identify appropriate calibration regimes. Based on this evaluation, we 38 provide a comprehensive assessment of the potential sources of uncertainty associated with 39 accuracy and precision of the SeaFETs TM electrodes. predominately a function of equilibration with atmospheric pCO 2 , thus increasing on yearly and 50 decadal timescales as continued burning of fossil fuels ensues (Hofmann et al., 2011; Orr et al., 51 2005). Coastal acidification, however, can manifest on short time and space scales driven by 52 riverine input and its chemical constituents (e.g., organic carbon, nutrients, and organic 53 alkalinity), community metabolism and organization, tidal cycles, upwelling, and groundwater 54 input (Duarte et al., 2013;Sunda and Cai, 2012; Waldbusser and Salisbury, 2014), all of which 55 can act in conjunction with increasing atmospheric CO 2 , leading to more frequent, intense, and 56 longer-lasting acidification events (Hales et al., 2016;Harris et al., 2013). In the face of rapidly 57 changing coastal conditions, tracking and quantifying the progression of OA requires precise and 58 accurate measurements of carbonate chemistry over long periods of time; these can be achieved 5...

A regional hindcast model simulating ecosystem dynamics, inorganic carbon chemistry and ocean acidification in the Gulf of Alaska

“…Insufficient inorganic carbon data coverage impede our ability to understand the interplay between drivers in different seasons and to determine the relative importance of the various controls of the inorganic carbon system at different locations. Ongoing 5 and historic inorganic carbon system observations in the GOA consist of a limited set of shipboard oceanographic observations, underway measurements from research vessels and scientific sampling systems installed on ferries, and a small number of sensors deployed at fixed nearshore coastal locations (Fabry et al, 2009;Evans et al, 2014Evans et al, , 2015Reisdorph and Mathis, 2014;Miller et al, 2018). These draw an incomplete picture of the spatial and temporal variability.…”