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The development of phosphate sensors suitable for long-term in situ deployments in natural waters, is essential to improve our understanding of the distribution, fluxes, and biogeochemical role of this key nutrient in a changing ocean. Here, we describe the optimization of the molybdenum blue method for in situ work using a lab-on-chip (LOC) analyzer and evaluate its performance in the laboratory and at two contrasting field sites. The in situ performance of the LOC sensor is evaluated using hourly time-series data from a 56-day trial in Southampton Water (UK), as well as a month-long deployment in the subtropical oligotrophic waters of Kaneohe Bay (Hawaii, USA). In Kaneohe Bay, where phosphate concentrations were characteristic of the dry season (0.13 ± 0.03 μM, n = 704), the in situ sensor accuracy was 16 ± 12% and a potential diurnal cycle in phosphate concentrations was observed. In Southampton Water, the sensor data (1.02 ± 0.40 μM, n = 1,267) were accurate to ±0.10 μM relative to discrete reference samples. Hourly in situ monitoring revealed striking tidal and storm derived fluctuations in phosphate concentrations in Southampton Water that would not have been captured via discrete sampling. We show the impact of storms on phosphate concentrations in Southampton Water is modulated by the spring-neap tidal cycle and that the 10-fold decline in phosphate concentrations observed during the later stages of the deployment was consistent with the timing of a spring phytoplankton bloom in the English Channel. Under controlled laboratory conditions in a 250 L tank, the sensor demonstrated an accuracy and precision better than 10% irrespective of the salinity (0–30), turbidity (0–100 NTU), colored dissolved organic matter (CDOM) concentration (0–10mg/L), and temperature (5–20◦C) of the water (0.3–13 μM phosphate) being analyzed. This work demonstrates that the LOC technology is mature enough to quantify the influence of stochastic events on nutrient budgets and to elucidate the role of phosphate in regulating phytoplankton productivity and community composition in estuarine and coastal regimes. Refereed 14.A Nutrients TRL 8 Actual system completed and "mission qualified" through test and demonstration in an operational environment (ground or space) Manual (incl. handbook, guide, cookbook etc) Standard Operating Procedure

The dataset contains meteorological observations during a measurement campaign with the drone LUCA (Lightweight high Ceiling Aerial System). The measurements were collected between the 25th and the 29th October 2021 at rather erratic launch times, as operations from the military training area Todendorf (Panker) in Northern Germany within the restricted airspace belonging to the training area depend on air traffic control clearance. As parameters, pressure, temperature, humidity and wind direction/speed were observed along with time and position of the drone. Besides the raw measurements, temperature and humidity readings have been corrected with an estimated inverse sensor transfer function. The data allows intercomparing the drone measurements with collocated radiosonde launches which enables a discussion on the possible use of the drone LUCA and similar systems for operational meteorology.

Changes in marine net primary productivity (PP) and export of particulate organic carbon (EP) are projected over the 21st century with four global coupled carbon cycle-climate models. These include representations of marine ecosystems and the carbon cycle of different structure and complexity. All four models show a decrease in global mean PP and EP between 2 and 20% by 2100 relative to preindustrial conditions, for the SRES A2 emission scenario. Two different regimes for productivity changes are consistently identified in all models. The first chain of mechanisms is dominant in the low- and mid-latitude ocean and in the North Atlantic: reduced input of macro-nutrients into the euphotic zone related to enhanced stratification, reduced mixed layer depth, and slowed circulation causes a decrease in macro-nutrient concentrations and in PP and EP. The second regime is projected for parts of the Southern Ocean: an alleviation of light and/or temperature limitation leads to an increase in PP and EP as productivity is fueled by a sustained nutrient input. A region of disagreement among the models is the Arctic, where three models project an increase in PP while one model projects a decrease. Projected changes in seasonal and interannual variability are modest in most regions. Regional model skill metrics are proposed to generate multi-model mean fields that show an improved skill in representing observation-based estimates compared to a simple multi-model average. Model results are compared to recent productivity projections with three different algorithms, usually applied to infer net primary production from satellite observations.

In the field of ocean observing, the term of “observatory” is often used without a unique meaning. A clear and unified definition of observatory is needed in order to facilitate the communication in a multidisciplinary community, to capitalize on future technological innovations and to support the observatory design based on societal needs. In this paper, we present a general framework to define the next generation Marine OBservatory (MOB), its capabilities and functionalities in an operational context. The MOB consists of four interconnected components or “gears” (observation infrastructure, cyberinfrastructure, support capacity, and knowledge generation engine) that are constantly and adaptively interacting with each other. Therefore, a MOB is a complex infrastructure focused on a specific geographic area with the primary scope to generate knowledge via data synthesis and thereby addressing scientific, societal, or economic challenges. Long-term sustainability is a key MOB feature that should be guaranteed through an appropriate governance. MOBs should be open to innovations and good practices to reduce operational costs and to allow their development in quality and quantity. A deeper biological understanding of the marine ecosystem should be reached with the proliferation of MOBs, thus contributing to effective conservation of ecosystems and management of human activities in the oceans. We provide an actionable model for the upgrade and development of sustained marine observatories producing knowledge to support science-based economic and societal decisions. Refereed 14.A Manual (incl. handbook, guide, cookbook etc) 2018-09-07

A critical challenge in paleoclimate data analysis is the fact that the proxy data are heterogeneously distributed in space, which affects statistical methods that rely on spatial embedding of data. In the paleoclimate network approach nodes represent paleoclimate proxy time series, and links in the network are given by statistically significant similarities between them. Their location in space, proxy and archive type is coded in the node attributes. We develop a semi-empirical model for Spatio-Temporally AutocoRrelated Time series, inspired by the interplay of different Asian Summer Monsoon (ASM) systems. We use an ensemble of transition runs of this START model to test whether and how spatio–temporal climate transitions could be detectable from (paleo)climate networks. We sample model time series both on a grid and at locations at which paleoclimate data are available to investigate the effect of the spatially heterogeneous availability of data. Node betweenness centrality, averaged over the transition region, does not respond to the transition displayed by the START model, neither in the grid-based nor in the scattered sampling arrangement. The regionally defined measures of regional node degree and cross link ratio, however, are indicative of the changes in both scenarios, although the magnitude of the changes differs according to the sampling. We find that the START model is particularly suitable for pseudo-proxy experiments to test the technical reconstruction limits of paleoclimate data based on their location, and we conclude that (paleo)climate networks are suitable for investigating spatio–temporal transitions in the dependence structure of underlying climatic fields.

For years, the Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research (AWI), together with the international MOSAiC partners, had been planning and developing the scientific, logistical and financial concept for the implementation of the MOSAiC expedition. The planning and organization of this endeavor was an enormous e˙ort, involving more than 80 institutions from 20 countries. The number of groups and individuals that significantly contributed to the success of the drift observatory goes far beyond the scope of usual polar expeditions.

Strong earthquakes cause large changes in the station positions and velocities of the geodetic reference stations; i.e., the global ITRF (International Terrestrial Reference Frame) and its regional densifications like SIRGAS (Sistema de Referencia Geocéntrico para Las Américas) in Latin America and the Caribbean. To ensure the long-term stability of the geodetic reference frames, the transformation of station positions between different epochs requires the computation of reliable continuous surface deformation (or velocity) models. This data set contains the new continental continuous crustal deformation model VEMOS2015 (Velocity Model for SIRGAS 2015) for Latin America and the Caribbean inferred from GNSS (GPS+GLONASS) measurements gained after the strong earthquakes occurred in 2010 in Chile and Mexico. It is based on a multi-year velocity solution for a network of 456 continuously operating GNSS stations covering a five years period from March 14, 2010 to April 11, 2015. VEMOS2015 is computed using the least square collocation (LSC) approach with empirically determined covariance functions.

Water/ice velocity data and instrument status from a Nortek Aquadopp Profiler 2MHz acoustic doppler current profiler (ADCP) attached to a remotely operated vehicle (ROV) during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition between November 2019 and September 2020. The Aquadopp System Integrator Manual by Nortek AS can be found here: https://sensor.awi.de/rest/sensors/onlineResources/getOnlineResourcesFile/1764/system-integrator-manual_Mar2016.pdf

The data sets under this data collection include detailed snow pit studies of snow temperature, density, specific surface area (SSA), stratigraphy (snow grain type & size and hand hardness), and salinity as well as high-resolution snow penetrometer measurement profiles. The data were collected during the ALERT2018 campaign (Multidisciplinary Arctic Program (MAP) - Last Ice) off Alert, Nunavut, Canada in the Lincoln Sea in May 2018. For more detailed information, please refer to the individual data sets.

This bibliography unites different data types collected by a remotely operated vehicle (ROV) underneath drifting sea ice during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition of the research vessel Polarstern (AWI, 2017) between November 2019 and September 2020 (Nicolaus et al., 2022). The observation class M500 ROV was manufactured by Ocean Modules AB (Åtvidaberg, Sweden) and is owned by the Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung (Bremerhaven, Germany). It was equipped with an extended multidisciplinary sensor payload. To investigate the under-ice light field and bio-optical properties, the ROV was equipped with hyperspectral radiometers, transmissometers and a triplet fluorometer. Oceanographic properties were recorded with a standard CTD package including sensors for dissolved oxygen, nitrate and pH. Single beam and multibeam sonar allowed for a three-dimensional mapping of the ice underside, while an acoustic current profiler measured relative water currents. In addition to different video and still cameras for visual documentation of the under-ice environment, the ROV was equipped with a hyperspectral imager. For sampling of the under-ice ecosystem, a suction sampling system and an under-ice plankton as well as a gypsum net were attached to the ROV. Data were collected for all seasons and for various sea-ice and surface conditions. All data are provided at their original temporal resolution and have a common GPS synchronized timestamp. All times are given in UTC. Horizontal position of the ROV was determined using a long baseline acoustic positioning system and is provided in a floe-fixed, relative coordinate system (X, Y) with the origin (X=0 m, Y=0 m) at the ROV access hole in the MOSAiC central observatory (CO). Operations were conducted in a 300 m radius around the hole. A one function manipulator in combination with the ROV's six degrees of freedom enabled deployment and recovery of equipment (e.g. sediment traps) underneath the ice cover. Details about the respective deployment conditions can be found in the respective cruise reports of MOSAiC. A technical description of the ROV can be found in Katlein et al. (2017). MATLAB codes used for processing the data are published on ZENODO (Anhaus et al., 2023). The data will be described in a respective ROV data paper. When reusing the data, please refer to this data paper (Anhaus et al., in prep.) and the actual datasets listed below (Datasets listed in this bibliography). Please carefully read the comment section below and contact the author(s) before using the data.