Technical note: Seamless gas measurements across the land–ocean aquatic continuum – corrections and evaluation of sensor data for CO<sub>2</sub>, CH<sub>4</sub> and O<sub>2</sub> from field deployments in contrasting environments

Abstract

Abstract. The ocean and inland waters are two separate regimes, with concentrations in greenhouse gases differing on orders of magnitude between them. Together, they create the land–ocean aquatic continuum (LOAC), which comprises itself largely of areas with little to no data with regards to understanding the global carbon system. Reasons for this include remote and inaccessible sample locations, often tedious methods that require collection of water samples and subsequent analysis in the lab, and the complex interplay of biological, physical and chemical processes. This has led to large inconsistencies, increasing errors and has inevitably lead to potentially false upscaling. A set-up of multiple pre-existing oceanographic sensors allowing for highly detailed and accurate measurements was successfully deployed in oceanic to remote inland regions over extreme concentration ranges. The set-up consists of four sensors simultaneously measuring pCO2, pCH4 (both flow-through, membrane-based non-dispersive infrared (NDIR) or tunable diode laser absorption spectroscopy (TDLAS) sensors), O2 and a thermosalinograph at high resolution from the same water source. The flexibility of the system allowed for deployment from freshwater to open ocean conditions on varying vessel sizes, where we managed to capture day–night cycles, repeat transects and also delineate small-scale variability. Our work demonstrates the need for increased spatiotemporal monitoring and shows a way of homogenizing methods and data streams in the ocean and limnic realms.

Autonomous methane seep site monitoring offshore Western Svalbard: Hourly to seasonal variability and associated oceanographic parameters

“…Our findings highlight the advantage of towed or autonomous instrumentation capable of providing continuous CH 4 data giving a considerably better coverage and representation of the CH 4 distribution in a much shorter time frame (e.g. Sommer et al, (2015) and Canning et al, (2021)). They also stress the importance of having a systematic grid when collecting water samples for inventory estimates in intense seep areas, thereby allowing the law of large numbers to apply.…”

The highest methane concentrations in an Arctic river are linked to local terrestrial inputs

“…The calibration and validation of the data were done following Canning et al (2021a). Drift and response time corrections were not applied because we assume sufficient exposure of the water to the sensor at the low sailing speeds.…”

“…However, the advantage of the high-spatial-resolution data allowed for a surface coverage that help identify high methane concentration areas. For more in-depth corrections see Canning et al (2021a). 240…”

Comment on gi-2021-28

“…This process is mainly driven by the difference in partial pressure between the two media and can be relatively slow. This results in high sensor RTs, leading to unwanted spatial and temporal ambiguities in recorded signals for sensors used in profiling (Miloshevich et al, 2004), on moving platforms (Bittig et al, 2014;Canning et al, 2021) or deployed in dynamic environments (Atamanchuk et al, 2015). Herein, we refer to sensors with this particular design as Equilibrium Based (EB) sensors and we seek to establish a robust, simple and predictable method for correcting high RT induced errors in data from these sensors.…”

“…Miloshevich et al, (2004) counteracts this using an iterative algorithm that minimizes third derivatives to obtain locally smooth (noise-free) time-series prior to the reconstruction of u a (t). While this and similar methods seems to usually work well in practice (Bittig et al, 2014;Canning et al, 2021;Fiedler et al, 2013;Miloshevich et al, 2004), it is difficult to determine the uncertainty of the estimate, as the iterative scheme does not model error behavior. Predicting the expected solution of the iterative estimator is also difficult and there is no straightforward way of choosing suitable smoothing parameters.…”