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A realistic estimation of uncertainty is an essential requirement for all analytical measurements. It is common practice, however, for the uncertainty estimate of a chemical measurement to be based on the instrumental precision associated with the analysis of a single or multiple samples, which can lead to underestimation. Within the context of chemical oceanography such an underestimation of uncertainty could lead to an over interpretation of the result(s) and hence impact on, e.g., studies of biogeochemical cycles, and the outputs from oceanographic models. Getting high quality observational data with a firm uncertainty assessment is therefore essential for proper model validation. This paper describes and compares two recommended approaches that can give a more holistic assessment of the uncertainty associated with such measurements, referred to here as the “bottom up” or modeling approach and the “top down” or empirical approach. “Best practice” recommendations for the implementation of these strategies are provided. The “bottom up” approach combines the standard uncertainties associated with each stage of the entire measurement procedure. The “top down” approach combines the uncertainties associated with day to day reproducibility and possible bias in the complete data set and is easy to use. For analytical methods that are routinely used, laboratories will have access to the information required to calculate the uncertainty from archived quality assurance data. The determination of trace elements in seawater is a significant analytical challenge and iron is used as an example for the implementation of both approaches using real oceanographic data. Relative expanded uncertainties of 10 – 20% were estimated for both approaches compared with a typical short term precision (rsd) of less than or equal to 5%. Refereed 14.A 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) Best Practice 2018-04-19

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