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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

2020-04-20: "Depth, water" values corrected for BYK17_ICE27 and BYK17_ICE28 (correction submitted by authors)2020-04-20: Latitude and Longitude for events corrected (due to editing error in PANGAEA, wrong coordinates were presented between 2019-02-21 and 2020-04-20)2020-10-06: Latitude and Longitude (wronly reported in expedition report) corrected In April 2017, four ice cores were drilled offshore of the Bykovsky Peninsula in Siberia to measure sea ice thickness, snow thickness, and water depth.

This dataset contains the dissolved organic matter (DOM) quantification and optical characterisation results from a KOSMOS mesocosm experiment carried out in the framework of the Ocean Artificial Upwelling project. The experiment was carried out in the autumn of 2018 in the oligotrophic waters of Gran Canaria. During the 39 days of experiment nutrient-rich deep water was added to the mesocosms in two modes (singular vs recurring additions), with four levels of intensity. Dissolved organic carbon, nitrogen and phosphorus were quantified with a Shimadzu TOC-5000 and a QuAAtro AutoAnalyzer. The absorption and fluorescence proprieties of DOM were determined making use of an Ocean Optics USB2000+UV-VIS-ES Spectrometer and a Jobin Yvon Horiba Fluoromax-4 spectrofluorometer, respectively. The aim of this dataset was to study the effect of artificial upwelling on the dissolved organic matter pool and its potential implications for carbon sequestration.

Times are given in UTC

Permafrost thaw and ice wedge degradation lead to drastic landscape changes in the permafrost region. With this data set we investigated the cliff retreat of the Sobo-Sise Cliff (SSC), a high ice-bearing yedoma cliff in the Lena River Delta. The 1,660 m long cliff SSC extends from 72°32'34 N / 128°15'59 E to 72°32'06 N / 128°18'21 E and is located on the Sardakhskaya channel, which is one of the main Lena river branches in the Lena River Delta. Erosion rates for the SSC were determined based on satellite images from different sensors (Corona, Hexagon, Landsat, Planet cube-sat) for the period 1965-2018. Cliff front lines were manually digitized and erosion rates were calculated with the Digital Shoreline Analysis System (DSAS) tool (Himmelstoos et al. 2018). The study Fuchs et al. (2020) (DOI:10.3389/feart.2020.00336) shows that the up to 27.7 m high SSC erodes in average 15.7 m yr-1 (2015-2018). During the entire observed time period from 1965-2018, the SSC retreated in average 484 m (ranging from 322 - 680 m). This dataset includes the mean annual erosion rates of the yedoma SSC for the time periods 1965-1975, 1975-2000, 2000-2005, 2005-2010, 2010-2015, and 2015-2018, as well as the absolute cliff retreat rates over the entire period 1965-2018, which are derived from remote sensing imagery analyzed with the DSAS tool (doi:10.1594/PANGAEA.918505). Related trend data for this region, based on Landsat trend analysis are available at doi:10.1594/PANGAEA.884136 (Nitze, 2018).

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.

This file presents the detailed description of two deep permafrost cores from the Yukechi Alas landscape in Central Yakutia, Russia. These data were recorded during subsampling of the cores and are based on visual and haptic impression. The core "Alas1" represents an adjacent alas deposit. Core was obtained during field work in March 2015.