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Mesocosm experiments have been fundamental to investigate the effects of elevated CO2 and ocean acidification (OA) on planktic communities. However, few of these experiments have been conducted using naturally nutrient-limited waters and/or considering the combined effects of OA and ocean warming (OW). Coccolithophores are a group of calcifying phytoplankton that can reach high abundances in the Mediterranean Sea, and whose responses to OA are modulated by temperature and nutrients. We present the results of the first land-based mesocosm experiment testing the effects of combined OA and OW on an oligotrophic Eastern Mediterranean coccolithophore community. Coccolithophore cell abundance drastically decreased under OW and combined OA and OW (greenhouse, GH) conditions. Emiliania huxleyi calcite mass decreased consistently only in the GH treatment; moreover, anomalous calcifications (i.e. coccolith malformations) were particularly common in the perturbed treatments, especially under OA. Overall, these data suggest that the projected increase in sea surface temperatures, including marine heatwaves, will cause rapid changes in Eastern Mediterranean coccolithophore communities, and that these effects will be exacerbated by OA. In order to allow full comparability with other ocean acidification data sets, the R package seacarb (Gattuso et al, 2021) was used to compute a complete and consistent set of carbonate system variables, as described by Nisumaa et al. (2010). In this dataset the original values were archived in addition with the recalculated parameters (see related PI). The date of carbonate chemistry calculation by seacarb is 2021-05-11.

[Experimental design: thermal performance experiments] All experiments were run in climate-controlled incubation facilities of the Institut Mediterrani d’Estudis Avançats (Mallorca, Spain). Following 48 hrs under ambient (collection site) conditions, samples were transferred to individual experimental aquaria, which consisted of a double layered transparent plastic bag filled with 2 L of filtered seawater (60 μm) (following Savva et al. 2018). 16 experimental bags were suspended within 80L temperature-controlled baths. In total, ten baths were used, one for each experimental temperature treatment. Bath temperatures were initially set to the acclimatization temperature (i.e. in situ temperatures) and were subsequently increased or decreased by 1 °C every 24 hours until the desired experimental temperature was achieved. Experimental temperatures were: 15, 18, 21, 24, 26, 28, 30, 32, 34 and 36°C (Table S2). For each species, four replicate aquarium bags were used for each temperature treatment with three individually marked seagrass shoots or three algal fragments placed into each bag. For P. oceanica, each marked plant was a single shoot including leaves, vertical rhizome and roots. For C. nodosa, each marked individual consisted of a 10 cm fragment of horizontal rhizome containing three vertical shoots. Individually marked seaweeds contained the holdfast, and 4-5 fronds of P. pavonica (0.98 ± 0.06 g FW; mean ± SE) or a standardised 5-8 cm fragment with meristematic tip for C. compressa (3.67 ± 0.1 g FW; mean ± SE). Experimental plants were cleaned of conspicuous epiphytes. Once the targeted temperatures were reached in all of the baths, experiments ran for 14 days for the algal species and 21 days for seagrasses to allow for measurable growth in all species at the end of the experiment. Experiments were conducted inside a temperature-controlled chamber at constant humidity and air temperature (15 °C). Bags were arranged in a 4x4 grid within each bath, enabling four species/population treatments to be run simultaneously. Bags were mixed within each bath so that one replicate bag was in each row and column of the grid, to minimise any potential within bath effects of bag position. Replicate bags were suspended with their surface kept open to allow gas exchange and were illuminated with a 14h light:10h dark photoperiod through fluorescent aquarium growth lamps. The water within the bags were mixed with aquaria pumps. The light intensity within each bag was measured via a photometric bulb sensor (LI-COR) and ranged between 180-258 μmol m-2 s-1. Light intensity was constant between experiments and did not significantly differ between experimental treatments (p > 0.05). The temperature in the baths was controlled and recorded with an IKS-AQUASTAR system, which was connected to heaters and thermometers. The seawater within the bags was renewed every 72 hrs and salinity was monitored daily with an YSI multi-parameter meter. Distilled water was added when necessary to ensure salinity levels remained within the range of 36-39 PSU, typical of the study region. Carbon and Nitrogen concentrations in the leaf tissue were measured at the end of the experiment for triplicates of the 24ºC treatment for each species and location (Fig. S2) at Unidade de Técnicas Instrumentais de Análise (University of Coruña, Spain) with an elemental analyser FlashEA112 (ThermoFinnigan). [Species description and distribution] The species used in this study are all common species throughout the Mediterranean Sea, although differ in their biological traits, evolutionary histories and thermo-geographic affinities (Fig. S1). P. oceanica is endemic to the Mediterranean Sea with the all other Posidonia species found in temperate Australia (Aires et al. 2011). The distribution of P. oceanica is restricted to the Mediterranean, spanning from Gibraltar in the west to Cyprus in the east and north into the Aegean and Adriatic seas (Telesca et al. 2015) (Fig. S1A). C. nodosa distribution extends across the Mediterranean Sea and eastern Atlantic Ocean, where it is found from south west Portugal, down the African coast to Mauritania and west to Macaronesia (Alberto et al. 2008) (Fig. S1B). Congeneric species of C. nodosa are found in tropical waters of the Red Sea and Indo-Pacific, suggesting origins in the region at least prior to the closure of the Suez Isthmus, approximately 10Mya. Like C. nodosa, Cystoseira compressa has a distribution that extends across the Mediterranean and into the eastern Atlantic, where it is found west to Macaronesia and south to northwest Africa (Fig. S1C). The genus Cystoseira has recently been reclassified to include just four species with all congeneric Cystoseira spp. having warm-temperate distributions from the Mediterranean to the eastern Atlantic (Orellana et al. 2019). The distribution of Padina pavonica is conservatively considered to resemble C. nodosa and C. compressa, spanning throughout the Mediterranean and into the eastern Atlantic. We considered the poleward distribution limit of P. pavonica to be the British Isles 50ºN (Herbert et al. 2016). P. pavonica was previously thought to have a global distribution, but molecular analysis of the genus has found no evidence to support this (Silberfeld et al. 2013). Instead it has been suggested that P. pavonica was potentially misclassified outside of the Mediterranean, due to morphological similarity with congeneric species (Silberfeld et al. 2013). Padina is a monophyletic genus with a worldwide distribution from tropical to cold temperate waters (Silberfeld et al. 2013). Most species have a regional distribution, with few confirmed examples of species spanning beyond a single marine realm (sensu Spalding et al. 2007). [Metabolic rates] Net production (NP), gross primary production (GPP) and respiration (R) were measured for all species from the four sites for five different experimental temperatures containing the in-situ temperature during sampling up to a 6ºC warming (see SM Table S3 for details). Individuals of the different species were moved to methacrylate cylinders containing seawater treated with UV radiation to remove bacteria and phytoplankton, in incubation tanks at the 5 selected temperatures. Cylinders were closed using gas-tight lids that prevent gas exchange with the atmosphere, containing an optical dissolved oxygen sensor (ODOS® IKS), with a measuring range from 0-200 % saturation and accuracy at 25ºC of 1% saturation, and magnetic stirrers inserted to ensure mixing along the height of the core. Triplicates were measured for each species and location, along with controls consisting in cylinders filled with the UV-treated seawater, in order to account for any residual production or respiration derived from microorganisms (changes in oxygen in controls was subtracted from treatments). Oxygen was measured continuously and recorded every 15 minutes for 24 hours. Changes in the dissolved oxygen (DO) were assumed to result from the biological metabolic processes and represent NP. During the night, changes in DO are assumed to be driven by R, as in the absence of light, no photosynthetic production can occur. R was calculated from the rate of change in oxygen at night, from half an hour after lights went off to half an hour before light went on (NP in darkness equalled R). NP was calculated from the rate of change in DO, at 15 min intervals, accumulated over each 24 h period. Assuming that daytime R equals that during the night, GPP was estimated as the sum of NP and R. To derive daily metabolic rates, we accumulated individual estimates of GPP, NP, and R resolved at 15 min intervals over each 24 h period during experiments and reported them in mmol O2 m−3 day−1. A detailed description of calculation of metabolic rates can be found at Vaquer-Sunyer et al. (Vaquer-Sunyer et al. 2015). [Thermal distribution and thermal safety margins] We estimated the realised thermal distribution for the four experimental species by downloading occurrence records from the Global Biodiversity Information Facility (GBIF.org (11/03/2020) GBIF Occurrence Download). Occurrence records from GBIF were screened for outliers and distributions were verified from the primary literature (Alberto et al. 2008, Draisma et al. 2010, Ni-Ni-Win et al. 2010, Silberfeld et al. 2013, Telesca et al. 2015, Orellana et al. 2019) and Enrique Ballesteros (pers. comms) (Fig. S1). Mean, 1st and 99th percentiles of daily SST’s were downloaded for each occurrence site for the period between 1981-2019 using the SST products described above (Table S4). Thermal range position of species at each experimental site were standardised by their global distribution using a Range Index (RI; Sagarin & Gaines 2002). Median SST at the experimental collection sites were standardized relative to the thermal range observed across a species realized distribution, using the equation: RI = 2(SM- DM)/DB where SM = the median temperature at the experimental collection site, Dm = the thermal midpoint of the species global thermal distribution and DB = range of median temperatures (ºC) that a species experiences across its distribution. The RI scales from -1 to 1, whereby ‘-1’ represents the cool, leading edge of a species distribution, ‘0’ represents the thermal midpoint of a species distribution and ‘1’ represents the warm, trailing edge of a species distribution (Sagarin & Gaines 2002). Thermal safety margins for each population were calculated as the difference between empirically derived upper thermal limits for each population and the maximum long term habitat temperatures recorded at collection sites. Each population’s thermal safety margin was plotted against its range position to examine patterns in thermal sensitivity across a species distribution. [Growth measurements and statistical analyses] Net growth rate of seagrass shoots was measured using leaf piercing-technique (Short & Duarte 2001). At the beginning of the experiment seagrass shoots were pierced just below the ligule with a syringe needle and shoot growth rate was estimated as the elongation of leaf tissue in between the ligule and the mark position of all leaves in a shoot at the end of the experiment, divided by the experimental duration. Net growth rate of macroalgae individuals was measured as the difference in wet weight at the end of the experiment from the beginning of the experiment divided by the duration of the experiment. Moisture on macroalgae specimens was carefully removed before weighing them. Patterns of growth in response to temperature were examined for each experimental population using a gaussian function: g = ke[-0.5(TMA-μ)2/σ2], where k = amplitude, μ = mean and σ = standard deviation of the curve. Best fit values for each parameter were determined using a non-linear least squares regression using the ‘nlstools’ package (Baty et al. 2015) in R (Team 2020). 95% CI for each of the parameters were calculated using non-parametric bootstrapping of the mean centred residuals. The relationship between growth metrics and the best-fit model was determined by comparing the sum of squared deviations (SS) of the observed data from the model, to the SS of 104 randomly resampled datasets. Growth metrics were considered to display a significant relationship to the best-fit model if the observed SS was smaller than the 5th percentile of randomised SS. Upper thermal limits were defined as the optimal temperature + 2 standard deviations (95th percentile of curve) or where net growth = 0. Samples that had lost all pigment or structural integrity by the end of the experiment were considered dead and any positive growth was treated as zero. Comparative patterns in thermal performance between populations have fundamental implications for a species thermal sensitivity to warming and extreme events. Despite this, within-species variation in thermal performance is seldom measured. Here we compare thermal performance between-species variation within communities, for two species of seagrass (Posidonia oceanica and Cymodocea nodosa) and two species of seaweed (Padina pavonica and Cystoseira compressa). Experimental populations from four locations spanning approximately 75% of each species global distribution and a 6ºC gradient in summer temperatures were exposed to 10 temperature treatments (15ºC to 36ºC), reflecting median, maximum and future temperatures. Experimental thermal performance displayed the greatest variability between species, with optimal temperatures differing by over 10ºC within the same location. Within-species differences in thermal performance were also important for P. oceanica which displayed large thermal safety margins within cool and warm-edge populations and small safety margins within central populations. Our findings suggest patterns of thermal performance in Mediterranean seagrasses and seaweeds retain deep ‘pre-Mediterranean’ evolutionary legacies, suggesting marked differences in sensitivity to warming within and between benthic marine communities. [Sample collection] Sample collections were conducted at two sites, separated by approximately 1 km, within each location. Collections were conducted at the same depth (1-3 m) at each location and were spaced across the reef or meadow to try and minimise relatedness between shoots or fragments. Upon collection, fragments were placed into a mesh bag and transported back to holding tanks in cool, damp, dark conditions (following Bennett et al. 2021). Fragments were kept in aerated holding tanks in the collection sites at ambient seawater temperature and maintained under a 14:10 light-dark cycle until transport back to Mallorca, where experiments were performed. Prior to transport, P. oceanica shoots were clipped to 25 cm length (from meristem to tip), to standardise initial conditions and remove old tissue for transport. For transport back to Mallorca, fragments were packed in layers within cool-boxes. Cool-packs were wrapped in damp tea towels (rinsed in seawater) and placed between layers of samples. Samples from Catalonia, Crete and Cyprus experienced approximately 12hrs of transit time. On arrival at the destination, samples were returned to holding tanks with aerated seawater and a 14:10 light-dark cycle. [Sea temperature measurements and reconstruction] Sea surface temperature data for each collection site were based on daily SST maps with a spatial resolution of 1/4°, obtained from the National Center for Environmental Information (NCEI, https://www.ncdc.noaa.gov/oisst (Reynolds et al. 2007). These maps have been generated through the optimal interpolation of Advanced Very High Resolution Radiometer (AVHRR) data for the period 1981-2019. Underwater temperature loggers (ONSET Hobo pro v2 Data logger) were deployed at each site and recorded hourly temperatures throughout one year. In order to obtain an extended time series of temperature at each collection site, a calibration procedure was performed comparing logger data with sea surface temperature from the nearest point on SST maps. In particular, SST data were linearly fitted to logger data for the common period. Then, the calibration coefficients were applied to the whole SST time series to obtain corrected-SST data and reconstruct daily habitat temperatures from 1981-2019. [Field collections] Thermal tolerance experiments were conducted on two seagrass species (P. oceanica and Cymodocea nodosa) and two brown seaweed species (Cystoseira compressa and P. pavonica) from four locations spanning 8 degrees in latitude and 30 degrees in longitude across the Mediterranean (Fig. 1, Table S1). These four species were chosen as they are dominant foundation species and cosmopolitan across the Mediterranean Sea. Thermal performance experiments from Catalonia and Mallorca were conducted simultaneously in June 2016 for seaweeds (P. pavonica and C. compressa) and in August 2016 for seagrasses (P. oceanica and C. nodosa). Experiments for all four species were conducted in July 2017 for Crete and in September 2017 for Cyprus. Horizon 2020 Framework Programme, Award: 659246; Juan de la Cierva Formacion, Award: FJCI-2016-30728; Spanish Ministry of Economy, Industry and Competitiveness, Award: MedShift, CGL2015-71809-P; Spanish Ministry of Science, Innovation and Universities, Award: SUMAECO, RTI2018-095441-B-C21

Associated data and R code for the paper Epstein & Roberts 2022 - Identifying priority areas to manage mobile bottom fishing on seabed carbon in the UK. This repository contains the primary output data from a desk-based investigation of seabed sediment organic carbon (OC) and mobile demeresal fishing in the UKEEZ. Best available published datasets were combined to produce unified maps of predicted seabed OC stocks, mean annual mobile bottom fishing disturbance, mean value of fish landed by mobile bottom fishing, and mean annual cummulative disturbance to seabed carbon from mobile bottom fishing. This data was combined with modeling of estimated fishing displacement to idenitfy priority areas for managmement and/or future research. For further methodological information please refer to the full paper published at PLOS Climate.

Le site d’essai d’énergie marine de l’Atlantique à l’échelle complète (AMETS) fournit des données d’observation de 30 minutes à partir de deux bouées d’ondes directionnelles connues sous le nom de Belmullet A et Belmullet B observant et mesurant la hauteur des vagues, la direction des vagues et la période des vagues. Les bouées AMETS sont situées dans les eaux de l’océan Atlantique Nord au large des côtes de la péninsule d’Erris à Co. Mayo à 50 m et 100 m de profondeur bathymétrique. Amets collecte des données depuis 2012. Un Waverider directionnel est une plate-forme stabilisée par capteur de mouvement d’onde qui peut mesurer les propriétés des ondes, y compris la hauteur, la direction et la période. Le programme AMETS a été géré conjointement par le Marine Institute et la Sustainable Energy Authority of Ireland. Couverture des données à 100 % pour le moment où les bouées ont été opérationnelles. Toute lacune de données au cours de la période de temps indique que la ou les bouées n’ont pas été opérationnelles et ont été en cours d’entretien. Η περιοχή δοκιμής θαλάσσιας ενέργειας πλήρους κλίμακας (AMETS) παρέχει 30 λεπτά παρατηρητικά στοιχεία από δύο κατευθυντικούς σημαντήρες που είναι γνωστοί ως Belmullet Α και Belmullet Β παρατηρώντας και μετρώντας το ύψος κύματος, την κατεύθυνση κύματος και την περίοδο κύματος. Οι σημαντήρες AMETS βρίσκονται στα ύδατα του Βόρειου Ατλαντικού Ωκεανού στα ανοικτά των ακτών της χερσονήσου Erris στο Co. Mayo σε βάθος βαθυμετρίας 50 m και 100 m. Η Amets συλλέγει δεδομένα από το 2012. Μια κατευθυντική Waverider είναι μια σταθεροποιημένη πλατφόρμα αισθητήρα κίνησης κύματος που μπορεί να μετρήσει τις ιδιότητες των κυμάτων συμπεριλαμβανομένου του ύψους, της κατεύθυνσης και της περιόδου. Το πρόγραμμα AMETS διοικείται από κοινού από το Ναυτικό Ινστιτούτο και την Αρχή Βιώσιμης Ενέργειας της Ιρλανδίας. Κάλυψη δεδομένων 100 % για όταν οι σημαντήρες έχουν τεθεί σε λειτουργία. Τυχόν κενά δεδομένων σε χρονική περίοδο δείχνουν ότι ο σημαντήρας ή οι σημαντήρες ήταν μη λειτουργικοί και τελούσαν υπό συντήρηση. El sitio de prueba de energía marina marina a escala completa (AMETS) proporciona datos observacionales de 30 minutos de dos boyas de onda direccionales conocidas como Belmullet A y Belmullet B observando y midiendo la altura de las olas, la dirección y el período de onda. Las boyas AMETS están ubicadas en las aguas del Océano Atlántico Norte frente a la costa de la Península de Erris en Co. Mayo a 50 m y 100 m de profundidades de batimetría. Amets ha estado recopilando datos desde 2012. Un Waverider direccional es una plataforma estabilizada del sensor de movimiento de onda que puede medir las propiedades de las ondas, incluyendo altura, dirección y período. El programa AMETS ha sido gestionado conjuntamente por el Instituto Marino y la Autoridad de Energía Sostenible de Irlanda. Cobertura de datos 100 % para cuando las boyas han estado operativas. Cualquier laguna de datos en el período de tiempo indica que la(s) Buoy(s) no han sido operativas y han estado en mantenimiento. Is-Sit tat-Test tal-Enerġija Marina tal-Atlantiku fuq Skala Sħiħa (AMETS) jipprovdi data ta’ osservazzjoni ta’ 30 minuta minn żewġ bagi ta’ waverider direzzjonali magħrufa bħala Belmullet A u Belmullet B li josservaw u jkejlu l-għoli tal-mewġ, id-direzzjoni tal-mewġ u l-perjodu tal-mewġ. Il-bagi AMETS jinsabu fl-ilmijiet tal-Oċean Atlantiku tat-Tramuntana ‘l barra mill-kosta tal-Peniżola Erris f’Co. Mayo f’fond ta’ 50 m u 100 m ta’ batimetrija. Amets ilha tiġbor id-data mill-2012. Waverider direzzjonali huwa pjattaforma stabbilizzata tas-sensur tal-moviment tal-mewġ li tista ‘tkejjel il-proprjetajiet tal-mewġ inklużi l-għoli, id-direzzjoni u l-perjodu. Il-programm AMETS ġie ġestit b’mod konġunt mill-Istitut Marittimu u l-Awtorità għall-Enerġija Sostenibbli tal-Irlanda. Kopertura tad-data 100 % għal meta l-bagi kienu operattivi. Kwalunkwe nuqqas ta’ data fil-perjodu ta’ żmien jindika li l-Buoy(s) ma kienx(u) operattiv(i) u kien(u) taħt manutenzjoni. Il Full Scale Atlantic Marine Energy Test Site (AMETS) fornisce dati osservazionali di 30 minuti da due boe direzionali di waverider conosciute come Belmullet A e Belmullet B che osservano e misurano l'altezza delle onde, la direzione dell'onda e il periodo d'onda. Le boe AMETS si trovano nelle acque dell'Oceano Atlantico settentrionale al largo della costa della penisola di Erris in Co. Mayo a 50 m e 100 m di profondità di batimetria. Amets raccoglie dati dal 2012. Un Waverider direzionale è una piattaforma stabilizzata del sensore di movimento d'onda che può misurare le proprietà delle onde tra cui altezza, direzione e periodo. Il programma AMETS è stato gestito congiuntamente dal Marine Institute e dalla Sustainable Energy Authority of Ireland. Copertura dei dati al 100 % per quando le boe sono state operative. Eventuali lacune di dati nel periodo di tempo indicano che i Buoy non sono stati operativi e sono stati sottoposti a manutenzione. O sítio de ensaio de energia marinha atlântica da escala completa (AMETS) fornece dados observacionais de 30 minutos a partir de duas boias de onda direcionais conhecidas como Belmullet A e Belmullet B que observam e medem a altura das ondas, a direção das ondas e o período de onda. As boias AMETS estão localizadas nas águas do Oceano Atlântico Norte ao largo da costa da Península de Erris, em Co. Mayo, a 50 m e 100 m de profundidade de batimetria. A Amets recolhe dados desde 2012. Um Waverider direcional é um sensor de movimento de onda estabilizado que pode medir as propriedades das ondas, incluindo altura, direção e período. O programa AMETS foi gerido conjuntamente pelo Instituto Marítimo e pela Autoridade para a Energia Sustentável da Irlanda. Cobertura de dados 100 % para quando as boias estão operacionais. Quaisquer lacunas de dados no período de tempo indicam que a(s) Buoy(s) não foram operacionais e estiveram em manutenção. Site-ul de testare a energiei marine la scară completă (AMETS) oferă date observaționale de 30 de minute de la două geamanduri de undă direcționale cunoscute sub numele de Belmullet A și Belmullet B care observă și măsoară înălțimea undelor, direcția undei și perioada de undă. Balizele AMETS sunt situate în apele Atlanticului de Nord, în largul coastei Peninsulei Erris, în Co. Mayo, la 50 m și 100 m adâncime de baie. Amets colectează date din 2012. Un Waverider direcțional este o platformă stabilizată a senzorului de mișcare de undă care poate măsura proprietățile undelor, inclusiv înălțimea, direcția și perioada. Programul AMETS a fost gestionat în comun de Institutul Marine și Autoritatea pentru Energie Durabilă din Irlanda. Acoperirea datelor 100 % pentru perioada în care geamandurile au fost operaționale. Orice lacune de date în perioada de timp indică faptul că geamurile au fost neoperaționale și au fost în curs de întreținere. De Full Scale Atlantic Marine Energy Test Site (AMETS) biedt 30 minuten observatiegegevens van twee gerichte golfridersboeien die bekend staan als Belmullet A en Belmullet B die golfhoogte, golfrichting en golfperiode observeren en meten. De AMETS boeien bevinden zich in de wateren van de Noord-Atlantische Oceaan voor de kust van het schiereiland Erris in Co. Mayo op 50 m en 100 m bathymetrie diepten. Amets verzamelt sinds 2012 gegevens. Een directionele Waverider is een golfbewegingssensor gestabiliseerd platform dat de eigenschappen van golven met inbegrip van hoogte, richting en periode kan meten. Het AMETS-programma wordt gezamenlijk beheerd door het Marine Institute en de Sustainable Energy Authority of Ireland. Gegevensdekking 100 % voor wanneer de boeien operationeel zijn geweest. Eventuele gegevenslacunes in de tijdsperiode wijzen erop dat de boeien niet-operationeel zijn geweest en in onderhoud zijn geweest. Пълният атлантически обект за изпитване на морската енергия (AMETS) осигурява 30 минути наблюдателни данни от две насочени вълнови шамандури, известни като Belmullet A и Belmullet B, които наблюдават и измерват височината на вълната, посоката на вълната и периода на вълната. Шамандурите AMETS се намират във водите на Северния Атлантически океан край бреговете на полуостров Erris в Co. Mayo на 50 m и 100 m батиметрична дълбочина. Amets събира данни от 2012 г. насам. Насочена Waverider е вълнов сензор за движение стабилизирана платформа, която може да измерва свойствата на вълните, включително височина, посока и период. Програмата AMETS се управлява съвместно от Морския институт и Органа за устойчива енергия на Ирландия. Обхват на данните 100 % за времето, когато шамандурите са били в експлоатация. Всички пропуски в данните във времето показват, че буйът(ите) не е(са) експлоатационен(и) и е(са) в процес на поддръжка. Die Full Scale Atlantic Marine Energy Test Site (AMETS) liefert 30 Minuten Beobachtungsdaten von zwei gerichteten Wellenreiterbojen, die als Belmullet A und Belmullet B bekannt sind, die Wellenhöhe, Wellenrichtung und Wellenperiode beobachten und messen. Die AMETS Bojen befinden sich in den Gewässern des Nordatlantiks vor der Küste der Halbinsel Erris in Co. Mayo in 50 m und 100 m Badetiefe. Amets sammelt seit 2012 Daten. Ein gerichteter Waverider ist eine Wellenbewegungssensor stabilisierte Plattform, die die Eigenschaften von Wellen einschließlich Höhe, Richtung und Periode messen kann. Das AMETS-Programm wurde gemeinsam vom Marine Institute und der Sustainable Energy Authority of Ireland verwaltet. Datenabdeckung 100 % für den Zeitpunkt, an dem die Bojen in Betrieb waren. Etwaige Datenlücken im Zeitraum deuten darauf hin, dass die Buoy(s) nicht betriebsbereit waren und sich in Wartung befanden.

Project: GCOS Earth Heat Inventory - A study under the Global Climate Observing System (GCOS) concerted international effort to update the Earth heat inventory (EHI), and presents an updated international assessment of ocean warming estimates, and new and updated estimates of heat gain in the atmosphere, cryosphere and land over the period from 1960 to present. Summary: The file “GCOS_EHI_1960-2020_Earth_Heat_Inventory_Ocean_Heat_Content_data.nc” contains a consistent long-term Earth system heat inventory over the period 1960-2020. Human-induced atmospheric composition changes cause a radiative imbalance at the top-of-atmosphere which is driving global warming. Understanding the heat gain of the Earth system from this accumulated heat – and particularly how much and where the heat is distributed in the Earth system - is fundamental to understanding how this affects warming oceans, atmosphere and land, rising temperatures and sea level, and loss of grounded and floating ice, which are fundamental concerns for society. This dataset is based on a study under the Global Climate Observing System (GCOS) concerted international effort to update the Earth heat inventory published in von Schuckmann et al. (2020), and presents an updated international assessment of ocean warming estimates, and new and updated estimates of heat gain in the atmosphere, cryosphere and land over the period 1960-2020. The dataset also contains estimates for global ocean heat content over 1960-2020 for different depth layers, i.e., 0-300m, 0-700m, 700-2000m, 0-2000m, 2000-bottom, which are described in von Schuckmann et al. (2022). This version includes an update of heat storage of global ocean heat content, where one additional product (Li et al., 2022) had been included to the initial estimate. The Earth heat inventory had been updated accordingly, considering also the update for continental heat content (Cuesta-Valero et al., 2023).

Dataset on dispersal of alien species in relation to the historic development of hydropower generation and Navigation along the River Danube.

Quantitative reliability, availability, and maintainability (RAM) assessments are of fundamental importance at the early design stages, as well as planning and operation of marine renewable energy systems. This paper presents an RAM framework adaptable to different offshore renewable technologies, conceived to provide support in the choice of the device components and subsequent planning of the O&M strategies. A case study, characterizing a pilot farm of oscillating water column (OWC) wave energy converters (WECs), is illustrated together with the method used to obtain reliable estimate of its key performance indicators (KPIs). Based on a fixed feed-in-tariff for the project, economic figures are estimated, showing a direct relationship with the availability of the farm and the cost of maintenance interventions. Consequently, the probability distributions of the most relevant output variables are presented, and the mutual correlations between them investigated using principal components analysis (PCA) with the aim of discovering the relationships influencing the performance of the offshore farm. In this way, the contributions of the individual factors on the profitability of the project are quantified, and generic guidelines to support the decision-making process are derived. It is shown how this type of analysis provides important insights not only to ocean energy farm operators after the deployment of the devices, but also to device developers at the early design stage of wave energy concepts.