Ocean Sample Clauses

Ocean.ย Vegetation. Some sources state with certainty that eelgrass (Zosteraceae) beds are found throughout coastal areas of the North Pacific from the Gulf of Alaska to Coos Bay, Oregon. (NatureServe Explorer, 2014) Birds. The western half of the state of Washington is located along the Pacific Flyway๐Ÿ•ˆ. Concentrations of sea/waterbirds, such as brown pelican (Pelecanus occidentalis) are frequently observed in the surf and near-shore area, as are terns (spp. Sternidae) cormorants (spp. Phalacrocoracidae), and gulls (spp. Laridae). Surveys of the near-shore and offshore areas observe higher concentrations of sea/water birds in winter than summer or fall.
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Ocean.ย For CMEMS applications, there are two different data gaps: ๏‚ท Observations needed do not exist. This kind of gap can be roughly identified by comparing the requirements and spatiotemporal distribution of the observations. ๏‚ท Observations exist but: o do not fit with CMEMS purposes. As CMEMS is an operational program, most of the applications have a strict requirement for timeliness. For example, near real time forecast and validation need observations in near real time; interim reanalysis needs observations in interim scale, i.e., 1-12 months before present time. Observations for CMEMS use will also need to reach certain quality standards. This can be especially true for calibrating satellite retrieval algorithms in TACs which may need observations with very high quality. o Data are not freely available. This can be caused by different reasons, e.g. data policy, research publication, economic benefit, technological confidentiality and even political issues. Besides the data gaps, lack of in situ observations can be caused by technological gaps and sustainability gaps. The former relates to the technology capacity in providing operational and cost- effective monitoring for a given parameter, while the latter is determined by economic, policy, organizational and infrastructure-related issues in support for maintaining the monitoring activities. In the following subsections, we will analyse the data -, technology - and sustainability gaps in Arctic Ocean in situ observations for CMEMS. 4.3.1 Data gaps and adequacy analysis 3.1. All 106 PSMSL tide gauges above 68o โ–‡. โ–‡โ–‡โ–‡โ–‡โ–‡ dots mark the 69 gauges with at least 5 years of data and trends within ~2 cm/year, while red dots mark rejected gauges (Source: โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡ et al., 2015). 3.1). However only a part of the Norwegian data is included in CMEMS and only monthly data are used by ARC MFC. For SL TAC, monthly mean sea level data from 10 GLOSS stations SST: in SST TAC, in situ SST is mainly used for calibrating and validating the satellite products. Currently only SST from about 132 surface drifters and 80 Argo profilers are used. There will be more high- quality data added from EUMETSAT/Copernicus Trusted projects. There are no significant data gaps identified for this purpose. However, many other in-situ SST datasets have been identified from the Ferrybox and moorings. For ARC MFC, regional error statistics of forecast and reanalysis are becoming important. Hence more SST data are need for validation. For data assimil...
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Ocean.ย In situ marine observations in the Arctic are mainly made by Arctic countries (Canada, Denmark, Norway, Russia and USA) for national waters and by all countries with an interest in the Arctic and some international organizations for Arctic open waters. Depending on purpose and resource availability, some monitoring activities are regular, operational and sustainable (e.g. for purposes of operational services, environment and marine resource assessment and climate change) while the others are short term, irregular and less sustainable (e.g. for purposes of research and commercial interests). The observations are collected by leading institutes/scientists of projects and programs, national, regional and global oceanographic data centres. A non-exhaustive list of the Arctic in-situ data collectors are given below: ๏‚ท Marine Environment Data, Fisheries and Oceans Canada, โ–‡โ–‡โ–‡โ–‡://โ–‡โ–‡โ–‡.โ–‡โ–‡โ–‡โ–‡-โ–‡โ–‡โ–‡โ–‡.โ–‡โ–‡โ–‡- โ–‡โ–‡โ–‡.โ–‡โ–‡.โ–‡โ–‡/โ–‡โ–‡โ–‡โ–‡-โ–‡โ–‡โ–‡โ–‡/โ–‡โ–‡โ–‡โ–‡โ–‡-โ–‡โ–‡โ–‡.โ–‡โ–‡โ–‡โ–‡ ๏‚ท ECDS - Environment Climate Data Sweden. โ–‡โ–‡โ–‡โ–‡โ–‡://โ–‡โ–‡โ–‡โ–‡.โ–‡โ–‡/ ๏‚ท Ifremer Coriolis. โ–‡โ–‡โ–‡โ–‡://โ–‡โ–‡โ–‡.โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡.โ–‡โ–‡.โ–‡โ–‡โ–‡/ ๏‚ท Russian Research institute of Hydrometeorological Information โ€“ World Data Centre, โ–‡โ–‡โ–‡โ–‡://โ–‡โ–‡โ–‡.โ–‡โ–‡โ–‡โ–‡โ–‡.โ–‡โ–‡ ๏‚ท National Oceanographic Data Centre, USA. http//โ–‡โ–‡โ–‡.โ–‡โ–‡โ–‡โ–‡.โ–‡โ–‡โ–‡โ–‡.โ–‡โ–‡โ–‡ ๏‚ท Norwegian Polar Data Centre at the Norwegian Polar Institute (โ–‡โ–‡โ–‡โ–‡โ–‡://โ–‡โ–‡โ–‡โ–‡.โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡.โ–‡โ–‡/home/ ๏‚ท Chinese National Arctic and Antarctic data centre. โ–‡โ–‡โ–‡โ–‡://โ–‡โ–‡โ–‡.โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡.โ–‡โ–‡โ–‡.โ–‡โ–‡/index/ ๏‚ท Korean Polar Data Centre โ–‡โ–‡โ–‡โ–‡โ–‡://โ–‡โ–‡โ–‡โ–‡.โ–‡โ–‡โ–‡โ–‡โ–‡.โ–‡โ–‡.โ–‡โ–‡/about/intro ๏‚ท Arctic Data Archive System, Japan. โ–‡โ–‡โ–‡โ–‡โ–‡://โ–‡โ–‡โ–‡.โ–‡โ–‡โ–‡โ–‡.โ–‡โ–‡.โ–‡โ–‡/ ๏‚ท Arctic Office at British Antarctic Survey, UK. โ–‡โ–‡โ–‡โ–‡โ–‡://โ–‡โ–‡โ–‡.โ–‡โ–‡โ–‡.โ–‡โ–‡.โ–‡โ–‡/data/our-data/data- systems/ ๏‚ท Oceanographic Databases, Bedford Institute of Oceanography. โ–‡โ–‡โ–‡โ–‡://โ–‡โ–‡โ–‡.โ–‡โ–‡โ–‡.โ–‡โ–‡.โ–‡โ–‡/science/data-donnees/base/index-en.php ๏‚ท ArcticNet: โ–‡โ–‡โ–‡โ–‡://โ–‡โ–‡โ–‡.โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡.โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡.โ–‡โ–‡/data-management ๏‚ท The Greenland Ecosystem Monitoring (GEM) Database, โ–‡โ–‡โ–‡โ–‡โ–‡://โ–‡โ–‡โ–‡โ–‡.โ–‡-โ–‡-โ–‡.โ–‡โ–‡/ ๏‚ท Department of Bioscience, Aarhus University, โ–‡โ–‡โ–‡โ–‡://โ–‡โ–‡โ–‡โ–‡.โ–‡โ–‡.โ–‡โ–‡/en/consultancy/scienticdatacentres/ ๏‚ท Data portal of the โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡ โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡ Institute (AWI) Helmholtz Centre. โ–‡โ–‡โ–‡โ–‡โ–‡://โ–‡โ–‡โ–‡โ–‡.โ–‡โ–‡โ–‡.โ–‡โ–‡/?site=home ๏‚ท UDASH โ€“ Unified Database for Arctic and Subarctic Hydrography, โ–‡โ–‡โ–‡โ–‡โ–‡://โ–‡โ–‡โ–‡โ–‡.โ–‡โ–‡โ–‡.โ–‡โ–‡/id/eprint/47869/ ๏‚ท Integrated Climate Data Center - ICDC. โ–‡โ–‡โ–‡โ–‡โ–‡://โ–‡โ–‡โ–‡โ–‡.โ–‡โ–‡โ–‡.โ–‡โ–‡โ–‡-โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡.โ–‡โ–‡/1/daten/ocean.html ๏‚ท International Arctic Science Committee (IASC) Arctic Data Committee. โ–‡โ–‡โ–‡โ–‡โ–‡://โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡.โ–‡โ–‡โ–‡/ ๏‚ท JCOMM operational observations (JCOMMOPS). โ–‡โ–‡โ–‡โ–‡://โ–‡โ–‡โ–‡.โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡.โ–‡โ–‡โ–‡/board ๏‚ท Norwegian Marine Data Centre, โ–‡โ–‡โ–‡โ–‡://โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡.โ–‡โ–‡โ–‡โ–‡.โ–‡โ–‡...
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Ocean.ย In-situ ocean observations are mandatory for CMEMS but also critical for C3S and CSS. Main source of concern at the moment is the lack of an adequate network of biogeochemical and deep (> 2000 m) measurements. Internationally, sustained ocean observation is coordinated through the UNESCO-IOC Global Ocean Observing System (GOOS) as well as the Global Climate Observing System (GCOS). The current ocean observing system remains fragmented especially for in situ observations. In addition, several of the networks and individual contributions are not well resourced and lack long term funding perspectives. There is therefore a strong rationale to advance the current system components towards a more sustainable, better-coordinated and more comprehensive ocean observing system to jointly deliver integrated ocean information to assess current trends and predict future scenarios. The importance of ocean observations has recently attracted great attention especially after the G7 Ministerโ€™s statements of June 2016. In Europe, several programs and projects focus on design and implementation of effective ocean observing capacities, improving their efficiency and their timely and high-quality information delivery for climate, health of the ocean, operational services, security and science. It is recommended that โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡ actively engage in a dialog with the individual projects consortia to secure that requirements from the Copernicus Services are taken into account in the planning and design of observations systems. Up to 2030, Europe is building an end-to-end, integrated and sustained European Ocean Observing System (EOOS) 1 under the leadership of EuroGOOS and European Marine Board, which is a coordinating framework designed to: - align and integrate Europeโ€™s ocean observing capacity; - promote a systematic and collaborative approach to collecting information on the state and variability of our seas; - underpin sustainable management of the marine environment and its resources EOOS will need to perform an extensive network assessment and optimization. It is foreseen that the main stream of such studies will have to use โ€œsimulated observationsโ€. Therefore, it is important to develop an efficient โ€œEOOS Simulatorโ€ which can easily conduct various kinds of OSSEs. The future EOOS assessment and optimal design framework can be built up by using the โ€œEOOS Simulatorโ€. Considering that OSSEs are more or less model dependent, a multi-model approach will be needed.
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Ocean.ย The lack of a widely-approved policy for open and free exchange of ocean data means that national and/or institutional policies govern the data exchange in the marine community. The development over the past two decades has been towards a more open and free exchange of data; there are however still huge amounts of data that are not available to the Copernicus Services. It is therefore important to continue to trace data and negotiate with data owners to release their data for the benefit of society. It is recommended to implement some strengthening of data exchange agreements within the ocean community to the benefit of all Copernicus Services needing ocean data. It could be a task of EuroGOOS to address this problem.
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