Description: On the MOSES cruise Sternfahrt_1 water samples were taken on 8 stations. Samples were exchanged between the institutes Alfred-Wegener-Institut Zentrum für Polar- und Meeresforschung (AWI), Helmholtz Centre for Ocean Research Kiel (Geomar) and Helmholtz-Zentrum Geesthacht Centre for Materials and Coastal Research (HZG) for later comparison of the nutrient data.

Description: The dataset is about temporal variability of dissolved methane along the freshwater-sea continuum in northern Germany. Sensors were installed at fixed stations at in total three sites at different water depths. This dataset is from the station in Cuxhaven (53.8771 N, 8.7048 E) taken at about 2-7m depth (depending on the tide). The data was obtained between 11 April and 28 August 2021 in high frequency measurements (1 min) with a methane sensor from Kongsberg (4H Jena model CONTROS HydroC CH4). Methane concentrations were calculated according to manufacturer's instructions, based on temperature and salinity values from COSYNA Container Cuxhaven. For the quality control of the data a local range of 0.1 – 1000 nmol/L was set, a technical range for the pump power 2 – 8 Watt, a spike and gradient value of 1. Due to heavy biofouling the external pump of the sensor failed, resulting in data gaps. For a more detailed description see the article cited in References.

Description: During Stern_1 single water samples were taken either from a rosette (vertical) or from on the way systems. For the on the way systems a time off-set was calculated and subtracted from the original time. The aim of the sampling was mainly to counter-check with different sensors. More details can be found in the expedition report (Fahrtbericht Moses Sternfahrten). For methane concentrations, water was transferred to glass bottles (120 ml) and stored cold (4°). In the home laboratory a 20 ml head space was created and analyzed via gas chromatography. Magen et al. (2014).

Description: A thorough and reliable assessment of changes in sea surface water temperatures (SSWTs) is essential for understanding the effects of global warming on long-term trends in marine ecosystems and their communities. The first long-term temperature measurements were established almost a century ago, especially in coastal areas, and some of them are still in operation. However, while in earlier times these measurements were done by hand every day, current environmental long-term observation stations (ELTOS) are often fully automated and integrated in cabled underwater observatories (UWOs). With this new technology, year-round measurements became feasible even in remote or difficult to access areas, such as coastal areas of the Arctic Ocean in winter, where measurements were almost impossible just a decade ago. In this context, there is a question over what extent the sampling frequency and accuracy influence results in long-term monitoring approaches. In this paper, we address this with a combination of lab experiments on sensor accuracy and precision and a simulated sampling program with different sampling frequencies based on a continuous water temperature dataset from Svalbard, Arctic, from 2012 to 2017. Our laboratory experiments showed that temperature measurements with 12 different temperature sensor types at different price ranges all provided measurements accurate enough to resolve temperature changes over years on a level discussed in the literature when addressing climate change effects in coastal waters. However, the experiments also revealed that some sensors are more suitable for measuring absolute temperature changes over time, while others are more suitable for determining relative temperature changes. Our simulated sampling program in Svalbard coastal waters over 5 years revealed that the selection of a proper sampling frequency is most relevant for discriminating significant long-term temperature changes from random daily, seasonal, or interannual fluctuations. While hourly and daily sampling could deliver reliable, stable, and comparable results concerning temperature increases over time, weekly sampling was less able to reliably detect overall significant trends. With even lower sampling frequencies (monthly sampling), no significant temperature trend over time could be detected. Although the results were obtained for a specific site, they are transferable to other aquatic research questions and non-polar regions.

Description: A thorough and reliable assessment of changes in sea surface water temperatures (SSWTs) is essential for understanding the effects of global warming on long-term trends in marine ecosystems and their communities. The first long-term temperature measurements were established almost a century ago, especially in coastal areas, and some of them are still in operation. However, while in earlier times these measurements were done by hand every day, current environmental long-term observation stations (ELTOS) are often fully automated and integrated in cabled underwater observatories (UWOs). With this new technology, year-round measurements became feasible even in remote or difficult to access areas, such as coastal areas of the Arctic Ocean in winter, where measurements were almost impossible just a decade ago. In this context, there is a question over what extent the sampling frequency and accuracy influence results in long-term monitoring approaches. In this paper, we address this with a combination of lab experiments on sensor accuracy and precision and a simulated sampling program with different sampling frequencies based on a continuous water temperature dataset from Svalbard, Arctic, from 2012 to 2017. Our laboratory experiments showed that temperature measurements with 12 different temperature sensor types at different price ranges all provided measurements accurate enough to resolve temperature changes over years on a level discussed in the literature when addressing climate change effects in coastal waters. However, the experiments also revealed that some sensors are more suitable for measuring absolute temperature changes over time, while others are more suitable for determining relative temperature changes. Our simulated sampling program in Svalbard coastal waters over 5 years revealed that the selection of a proper sampling frequency is most relevant for discriminating significant long-term temperature changes from random daily, seasonal, or interannual fluctuations. While hourly and daily sampling could deliver reliable, stable, and comparable results concerning temperature increases over time, weekly sampling was less able to reliably detect overall significant trends. With even lower sampling frequencies (monthly sampling), no significant temperature trend over time could be detected. Although the results were obtained for a specific site, they are transferable to other aquatic research questions and non-polar regions.