Abstract. This paper describes a near-surface ocean profiler, which has been designed to precisely measure vertical gradients in the top 10 m of the ocean. Variations in the depth of seawater collection are minimized when using the profiler compared to conventional CTD/rosette deployments. The profiler consists of a remotely operated winch mounted on a tethered yet free-floating buoy, which is used to raise and lower a small frame housing sensors and inlet tubing. Seawater at the inlet depth is pumped back to the ship for analysis. The profiler can be used to make continuous vertical profiles or to target a series of discrete depths. The profiler has been successfully deployed during wind speeds up to 10 m s −1 and significant wave heights up to 2 m. We demonstrate the potential of the profiler by presenting measured vertical profiles of the trace gases carbon dioxide and dimethylsulfide. Trace gas measurements use an efficient microporous membrane equilibrator to minimize the system response time. The example profiles show vertical gradients in the upper 5 m for temperature, carbon dioxide and dimethylsulfide of 0.15 • C, 4 µatm and 0.4 nM respectively.
The Kitikmeot Sea is a semi-enclosed, east-west waterway in the southern Canadian Arctic Archipelago (CAA). In the present work, the ice conditions, stratification and circulation of the Kitikmeot Sea are diagnosed using numerical simulations with a 1/12 • resolution. The physical oceanographic conditions of the Kitikmeot Sea are different from channels in the northern CAA due to the existence of a substantial ice-free period each year. The consequences of such ice conditions are twofold. First, through fluctuations of external forcings, such as solar radiation and wind stress, acting directly or indirectly on the sea surface, the seasonal ice coverage leads to significant seasonal variations in both stratification and circulation. Our simulation results suggest that such variations include freshening and deepening of the surface layer, whose salinity can reach as low as 15 psu during the peak runoff season, and significantly stronger along-shore currents driven directly by the wind stress during the ice-free season.The second consequence is that the sea ice is not landfast but can move freely during the melting season. By analyzing the relative importance of thermodynamic (freezing/melting) and dynamic (ice movement) processes to the ice dynamics, our simulation results suggest that there exists a net inflow of sea ice into the Kitikmeot Sea, which melts locally each summer. The movement of sea ice thus provides a significant freshwater pathway, which contributes ∼ 14 km 3 /year of freshwater to the Kitikmeot Sea on average, equivalent to a third of freshwater input from runoff from the land.
Abstract. In recent years, large datasets of in situ marine carbonate system parameters (partial pressure of CO2 (pCO2), total alkalinity, dissolved inorganic carbon and pH) have been collated, quality controlled and made publicly available. These carbonate system datasets have highly variable data density in both space and time, especially in the case of pCO2, which is routinely measured at high frequency using underway measuring systems. This variation in data density can create biases when the data are used, for example for algorithm assessment, favouring datasets or regions with high data density. A common way to overcome data density issues is to bin the data into cells of equal latitude and longitude extent. This leads to bins with spatial areas that are latitude and projection dependent (e. g. become smaller and more elongated as the poles are approached). Additionally, as bin boundaries are defined without reference to the spatial distribution of the data or to geographical features, data clusters may be divided sub-optimally (e. g. a bin covering a region with a strong gradient). To overcome these problems and to provide a tool for matching surface in situ data with satellite, model and climatological data, which often have very different spatiotemporal scales both from the in situ data and from each other, a methodology has been created to group in situ data into ‘regions of interest’: spatiotemporal cylinders consisting of circles on the Earth’s surface extending over a period of time. These regions of interest are optimally adjusted to contain as many in situ measurements as possible. All surface in situ measurements of the same parameter contained in a region of interest are collated, including estimated uncertainties and regional summary statistics. The same grouping is applied to each of the non-in situ datasets in turn, producing a dataset of coincident matchups that are consistent in space and time. About 35 million in situ data points were matched with data from five satellite sources and five model and re-analysis datasets to produce a global matchup dataset of carbonate system data, consisting of ~286,000 regions of interest spanning 54 years from 1957 to 2020. Each region of interest is 100 km in diameter and 10 days in duration. An example application, the reparameterisation of a global total alkalinity algorithm, is shown. This matchup dataset can be updated as and when in situ and other datasets are updated, and similar datasets at finer spatiotemporal scale can be constructed, for example to enable regional studies. The matchup dataset provides users with a large multiparameter carbonate system dataset containing data from different sources, in one consistent, collated and standardised format suitable for model-data intercomparisons and model evaluations. The OceanSODA-MDB data can be downloaded from https://doi.org/10.12770/0dc16d62-05f6-4bbe-9dc4-6d47825a5931 (Land and Piollé, 2022).
Abstract. In recent years, large datasets of in situ marine carbonate system parameters (partial pressure of CO2 (pCO2), total alkalinity, dissolved inorganic carbon and pH) have been collated, quality-controlled and made publicly available. These carbonate system datasets have highly variable data density in both space and time, especially in the case of pCO2, which is routinely measured at high frequency using underway measuring systems. This variation in data density can create biases when the data are used, for example, for algorithm assessment, favouring datasets or regions with high data density. A common way to overcome data density issues is to bin the data into cells of equal latitude and longitude extent. This leads to bins with spatial areas that are latitude- and projection-dependent (e.g. become smaller and more elongated as the poles are approached). Additionally, as bin boundaries are defined without reference to the spatial distribution of the data or to geographical features, data clusters may be divided sub-optimally (e.g. a bin covering a region with a strong gradient). To overcome these problems and to provide a tool for matching surface in situ data with satellite, model and climatological data, which often have very different spatiotemporal scales both from the in situ data and from each other, a methodology has been created to group in situ data into “regions of interest”: spatiotemporal cylinders consisting of circles on the Earth's surface extending over a period of time. These regions of interest are optimally adjusted to contain as many in situ measurements as possible. All surface in situ measurements of the same parameter contained in a region of interest are collated, including estimated uncertainties and regional summary statistics. The same grouping is applied to each of the non-in situ datasets in turn, producing a dataset of coincident matchups that are consistent in space and time. About 35 million in situ data points were matched with data from five satellite sources and five model and reanalysis datasets to produce a global matchup dataset of carbonate system data, consisting of ∼286 000 regions of interest spanning 54 years from 1957 to 2020. Each region of interest is 100 km in diameter and 10 d in duration. An example application, the reparameterisation of a global total alkalinity algorithm, is presented. This matchup dataset can be updated as and when in situ and other datasets are updated, and similar datasets at finer spatiotemporal scale can be constructed, for example, to enable regional studies. The matchup dataset provides users with a large multi-parameter carbonate system dataset containing data from different sources, in one consistent, collated and standardised format suitable for model–data intercomparisons and model evaluations. The OceanSODA-MDB data can be downloaded from https://doi.org/10.12770/0dc16d62-05f6-4bbe-9dc4-6d47825a5931 (Land and Piollé, 2022).
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.