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The data contains three supporting datasets: 1. Mid-intertidal data 2. Vertical transect data 3. GPS coordinates for all sites

Time series of data corresponding to Evans et al. (2022) "Natural variability and expansion of the nitrogen deficit within the Eastern Tropical North Pacific Oxygen Deficient Zone", containing secondary quality controlled data of 8 cruises in the ETNP ODZ, seven of which on the 110 W line, as well as supplemental sediment core data and CalCOFI oxygen data for comparison. Intermediate data products generated by the code used for this paper are also included, and the code to generate these intermediate products as well as the final outputs has been uploaded onto a separate Zenodo repository, "ETNP_ODZ_time_series_code" at https://doi.org/10.5281/zenodo.6519316. More detailed information is available in the README, but should you have any questions, please reach out to Allan Devol or Natalya Evans.

This dataset contains spatial predictions of the potential environmental spaces for pingos, ice-wedge polygons and rock glaciers across the Northern Hemisphere permafrost areas. The potential environmental spaces, i.e. conditions where climate, topography and soil properties are suitable for landform presence, were predicted with statistical ensemble modelling employing geospatial data on environmental conditions at 30 arc-second resolution (~1 km). In addition to the baseline period (1950-2000), the predictions are provided for 2041-2060 and 2061-2080 using climate-forcing scenarios (Representative Concentration Pathways 4.5 and 8.5). The resulting dataset consists of five spatial predictions for each landform in GeoTIFF format.The data provide new information on 1) the fine-scale spatial distribution of permafrost landforms in the Northern Hemisphere, 2) the potential future alterations in the environmental suitability for permafrost landforms due to climate change, and 3) the circumpolar distribution of various ground ice types, and can 4) facilitate efforts to inventory permafrost landforms in incompletely mapped areas.

Misurazioni orarie SMPS per Malin Head per l'anno 2019 Clima Chimica e Climate Change Network (AC3) Stazione: Testa di Malin (MLH) Le coordinate: 55º 22′ N, 7º 20′ O ALTITUDINE: 22 m s.l.m. Altezza di misura: 5 m Tipo di sito: fondo rurale Strumentazione: PALAS U200 SMPS Gamma di dati: 8-1200 nm (l'intestazione determina l'intervallo inferiore) Unità:[1/cm³] Tempo di consegna: TERMINI E CONDIZIONI Informazioni di base: Malin Head è l'attuale stazione meteorologica sinottica Met Eireann. Numero di serie dello strumento: Informazioni di calibrazione: Calibrato ogni 2 anni Погодинний вимірювання SMPS для Малін Голова на 2019 рік Хімія атмосфери та мережа змін клімату (AC3) Станція метро: Малін Голова (MLH) Географічні координати міста: 55° 22′ N, 7° 20′ ВТ ВИСОТА ПІДЙОМУ: 22 м с. Висота вимірювання: 5 м Тип сайту: сільський фон Вимірювальні прилади: ПАЛАС U200 SMPS Діапазон даних: 8-1200 нм (заголовок визначає нижній діапазон) Одиниці:[1/см³] Час роботи: СТВОРЕННЯ UTC Довідкова інформація: Малін Хед - існуюча синоптична метеорологічна станція Met Eireann. Серійний номер приладу: Інформація про калібрування: Калібрується кожні 2 роки Stundas SMPS mērījumi Malin Head 2019. gadam Atmosfēras ķīmijas un klimata pārmaiņu tīkls (AC3) Atrašanās vieta: Malin Head (MLH) Koordinātas: 55° 22′ ZIEMEĻU PLATUMA, 7° 20′ RIETUMU GARUMA, AUGSTUMS VIRS JŪRAS LĪMEŅA: 22 m asl Mērīšanas augstums: 5 m Vietnes tips: lauku vide Instrumenti: PALAS U200 SMPS Datu diapazons: 8–1200 nm (virsma nosaka zemāko diapazonu) Vienības:[1/cm³] Laiks: UTC UTC Vispārīga informācija: Malin Head ir esošā Met Eireann sinoptiskās meteoroloģijas stacija. Instrumenta sērijas numurs: Kalibrēšanas informācija: Kalibrēts ik pēc 2 gadiem Kejl fis-siegħa tal-SMPS għal Malin Head għas-sena 2019 Netwerk dwar il-Kimika u t-Tibdil fil-Klima (AC3) Stazzjon: Ras ta’ Malin (MLH) Koordinati: 55° 22′ N, 7° 20′ W ALTITUDNI: 22 m asl L-għoli tal-kejl: 5 m Tip ta’ sit: sfond rurali Strumentazzjoni: PALAS U200 SMPS Firxa ta’ dejta: 8–1200 nm (l-intestatura tiddetermina l-firxa aktar baxxa) Unitajiet:[1/cm³] Ħin: UTC Informazzjoni ta’ sfond: Malin Head huwa l-istazzjon eżistenti tal-meteoroloġija sinottika Met Eireann. Numru tas-serje tal-istrument: Informazzjoni ta’ kalibrazzjoni: Kalibrat kull sentejn Valandiniai SMPS matavimai Malin Head 2019 metams Atmosferos chemijos ir klimato kaitos tinklas (AC3) Stotelės: Malin Head (MLH) Koordinatės: 55° 22′ ŠIAURĖS PLATUMOS, 7° 20′ VAKARŲ ILGUMOS AUKŠTIS: 22 m asl Matavimo aukštis: 5 m Svetainės tipas: kaimo fonas Instrumentai: PALAS U200 SMPS Duomenų diapazonas: 8–1200 nm (antraštė nustato žemesnį diapazoną) Vienetų skaičius:[1/cm³] Laikas: UTC Pagrindinė informacija: Malin Head yra esama Met Eireann sinoptinės meteorologijos stotis. Priemonės serijos numeris: Kalibravimo informacija: Kalibruojama kas 2 metus Mediciones por hora de SMPS para Malin Head para el año 2019 Red de Química y Cambio Climático de la Atmósfera (AC3) De la estación: Malin Head (MLH) Coordenadas: 55.º 22′ N, 7.º 20′ O ALTURA: 22 m asl Altura de medición: 5 m Tipo de sitio: fondo rural Instrumentación: PALAS U200 SMPS Rango de datos: 8-1200 nm (la cabecera determina el rango inferior) Unidades:[1/cm³] Hora: UTC Información de antecedentes: Malin Head es la estación de meteorología sinóptica Met Eireann existente. Número de serie del instrumento: Información de calibración: Calibrado cada 2 años Medições horárias SMPS para Malin Head para o ano 2019 Rede de Química da Atmosfera e Alterações Climáticas (AC3) Estação: Cabeça de Malin (MLH) Coordenadas: 55.º 22′ N, 7.º 20′ W ALTITUDE: 22 m/sl Altura da medição: 5 m Tipo do site: contexto rural Instrumentação: PALAS U200 SMPS Gama de dados: 8-1200 nm (o cabeçalho determina o intervalo mais baixo) Unidades:[1/cm³] Hora: UTC Informações gerais: Malin Head é a estação de meteorologia sinóptica Met Eireann existente. Número de série do instrumento: Informações de calibração: Calibrado a cada 2 anos SMPS-metingen per uur voor Malin Head voor het jaar 2019 Netwerk van atmosfeerchemie en klimaatverandering (AC3) Locatie: Malin Hoofd (MLH) Coördinaten: 55° 22′ NOORDERBREEDTE, 7° 20′ WL HOOGTE: 22 m asl De hoogte van de meting: 5 m Soort site: landelijke achtergrond Instrumentatie: PALAS U200 SMPS Gegevensbereik: 8-1200 nm (header bepaalt een lager bereik) Eenheden:[1/cm³] Tijd: UTC Achtergrondinformatie: Malin Head is het bestaande synoptische meteorologisch station Met Eireann. Instrument serienummer: Kalibratie info: Om de 2 jaar gekalibreerd Почасови SMPS измервания за Malin Head за 2019 г. Мрежа за химия на атмосферата и изменение на климата (AC3) Станция: Malin Head (MLH) Координати: 55° 22′ С.Ш., 7° 20′ З.Д. ВИСОЧИНА:22 m asl Височина на измерване:5 м Тип на сайта:произход на селските райони Инструментална апаратура:PALAS U200 SMPS Обхват на данните:8—1200 nm (главата определя долен обхват) Единици:[1/cm³] Време:UTC Основна информация: Malin Head е съществуващата метеорологична станция Met Eireann. Сериен номер на инструмента: Информация за калибриране: Калибрирани на всеки 2 години Mesures horaires SMPS pour Malin Head pour l’année 2019 Réseau de chimie de l’atmosphère et du changement climatique (AC3) Station: Tête de Malin (MLH) Coordonnées: 55° 22′ N, 7° 20′ O ALTITUDE:22 m asl Hauteur de mesure:5 m Type de site:milieu rural Instrumentation:PALAS U200 SMPS Plage de données:8-1200 nm (l’en-tête détermine la plage inférieure) Unités:[1/cm³] Temps:UTC Informations générales: Malin Head est la station météorologique synoptique de Met Eireann. Numéro de série de l’instrument: Informations d’étalonnage: Calibré tous les 2 ans

Herewith we present the extended 1Hz dataset of wind measurements from a Skipheia meteorological station on the island of Frøya on the western coast of Norway, Trondelag. The data binned in 10 min averages can be find at: https://doi.org/10.5281/zenodo.2557500 The site represents an exposed coastal wind climate with open sea, land and mixed fetch from various directions. UTM-coordinates of the Met-mast: 8.34251 E and 63.66638 N. See the map for details (NorwegianMapping Authority): https://www.norgeskart.no/#!?project=norgeskart&layers=1003&zoom=3&lat=7035885.49&lon=539601.41&markerLat=7077031.483032227&markerLon=170902.83203125&panel=searchOptionsPanel&sok=Titranveien Presented data were gathered between years 2009-2016. Data&hardware summary: Years 2009-2016: Mast2 equipped with 6 pairs of 2D sonic anemometers at 10, 16, 25, 40, 70, 100 m above the ground, independent temperature measurements at the same heights and near the ground; pressure and relative humidity from local meteostation (Sula, 20 km away). Years 2014-2016: Mast4 equipped with 2 pairs of 2D sonic anemometers at 40 and 100 m above the ground. The distance between the masts is 79 m. Data is binned in years and months and stored in a ‘*.txt’ tab-separated values file. Data column order is described in SkipheiaMast2_header.txt and SkipheiaMast4_header.txt, where WSx is the wind speed (m/s), WDx is the wind direction (360 deg), ATx is the air temperature (deg C) and x designates the instrument number. The instruments are numbered starting from the ground. Example: For Mast2 (6 pairs of anemometers, ground temperature + 6 temperature sensors on the mast) that means that AT0 is the ground temperature. WS1 and WS2 are wind speed records at 10 m level. WS3 and WS4 are wind speed records at 16 m. For Mast4 (2 pairs of anemometers) that means that WS1 and WS2 are wind speed records at 40 m level. WS3 and WS4 are wind speed records at 100 m. Detailed site description with wind climate description can be found in attached analysis: Site analysys.pdf. Additional information and analysis can be found in listed below works, using data from Frøya site: Bardal, L. M., & Sætran, L. R. (2016, September). Spatial correlation of atmospheric wind at scales relevant for large scale wind turbines. In Journal of Physics: Conference Series (Vol. 753, No. 3, p. 032033). IOP Publishing, doi:10.1088/1742-6596/753/3/032033, https://iopscience.iop.org/article/10.1088/1742-6596/753/3/032033/pdf Bardal, L. M., & Sætran, L. R. (2016). Wind gust factors in a coastal wind climate. Energy Procedia, 94, 417-424, https://doi.org/10.1016/j.egypro.2016.09.207 IEA Wind TCP Task 27 Compendium of IEA Wind TCP Task 27 Case Studies, Technical Report, Prepared by Ignacio Cruz Cruz, CIEMAT, Spain Trudy Forsyth, WAT, United States, October 2018; Chapter 1.8. https://community.ieawind.org/HigherLogic/System/DownloadDocumentFile.ashx?DocumentFileKey=8afc06ec-bb68-0be8-8481-6622e9e95ae7&forceDialog=0 Domagalski, P., Bardal, L. M., & Sætran, L. Vertical Wind Profiles in Non-neutral Conditions-Comparison of Models and Measurements from Froya. Journal of Offshore Mechanics and Arctic Engineering, doi: 10.1115/1.4041816, http://offshoremechanics.asmedigitalcollection.asme.org/article.aspx?articleid=2711333&resultClick=3 Møller, M., Domagalski, P., & Sætran, L. R. (2019, October). Characteristics of abnormal vertical wind profiles at a coastal site. In Journal of Physics: Conference Series (Vol. 1356, No. 1, p. 012030). IOP Publishing. https://iopscience.iop.org/article/10.1088/1742-6596/1356/1/012030 Møller, M., Domagalski, P., and Sætran, L. R.: Comparing Abnormalities in Onshore and Offshore Vertical Wind Profiles, Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2019-40 , in review, 2019.

Model results and data used to make future projections of the effects of climate change on the physiology of tuna in the global ocean ------------------------------------------------- Description: Coupled Model Intercomparison Project Phase 5 (CMIP5) model results were downloaded from here: https://esgf-node.llnl.gov/search/cmip5/ World Ocean Atlas (WOA) 2009 data were downloaded from here: https://www.nodc.noaa.gov/OC5/WOA09/netcdf_data.html The model results and data should only be used to reproduce the analysis described in this publication: Mislan, K. A. S., C. A. Deutsch, R. W. Brill, J. P. Dunne, and J. L. Sarmiento. (2017) Projections of climate driven changes in tuna vertical habitat based on species-specific differences in blood oxygen affinity. Global Change Biology. The Zenodo archive of the code is here: https://doi.org/10.5281/zenodo.808742 ------------------------------------------------- Instructions: Download the tar.gz file, unzip, and put the folders in the data folder of the CMIP5_p50_tuna code.

Species distribution models (SDMs) are becoming an important tool for marine conservation and management. Yet while there is an increasing diversity and volume of marine biodiversity data for training SDMs, little practical guidance is available on how to leverage distinct data types to build robust models. We explored the effect of different data types on the fit, performance and predictive ability of SDMs by comparing models trained with four data types for a heavily exploited pelagic fish, the blue shark (Prionace glauca), in the Northwest Atlantic: two fishery-dependent (conventional mark-recapture tags, fisheries observer records) and two fishery-independent (satellite-linked electronic tags, pop-up archival tags). We found that all four data types can result in robust models, but differences among spatial predictions highlighted the need to consider ecological realism in model selection and interpretation regardless of data type. Differences among models were primarily attributed to biases in how each data type, and the associated representation of absences, sampled the environment and summarized the resulting species distributions. Outputs from model ensembles and a model trained on all pooled data both proved effective for combining inferences across data types and provided more ecologically realistic predictions than individual models. Our results provide valuable guidance for practitioners developing SDMs. With increasing access to diverse data sources, future work should further develop truly integrative modeling approaches that can explicitly leverage strengths of individual data types while statistically accounting for limitations, such as sampling biases.  Please see the README document ("README.md") and the accompanying published article: Braun, C. D., M. C. Arostegui, N. Farchadi, M. Alexander, P. Afonso, A. Allyn, S. J. Bograd, S. Brodie, D. P. Crear, E. F. Culhane, T. H. Curtis, E. L. Hazen, A. Kerney, N. Lezama-Ochoa, K. E. Mills, D. Pugh, N. Queiroz, J. D. Scott, G. B. Skomal, D. W. Sims, S. R. Thorrold, H. Welch, R. Young-Morse, R. Lewison. In press. Building use-inspired species distribution models: using multiple data types to examine and improve model performance. Ecological Applications. Accepted. DOI: < article DOI will be added when it is assigned >

Whether marine fish will grow differently in future high pCO2 environments remains surprisingly uncertain. Long-term and whole-life cycle effects are particularly unknown, because such experiments are logistically challenging, space demanding, exclude long-lived species, and require controlled, restricted feeding regimes—otherwise increased consumption could mask potential growth effects. Here, we report on repeated, long-term, food-controlled experiments to rear large populations (>4,000 individuals total) of the experimental model and ecologically important forage fish Menidia menidia (Atlantic silverside) under contrasting temperature (17°, 24°, and 28°C) and pCO2 conditions (450 vs. 2,200 μatm) from fertilization to a third of this annual species' life span. Quantile analyses of trait distributions showed mostly negative effects of high pCO2 on long-term growth. At 17°C and 28°C, but not at 24°C, high pCO2 fish were significantly shorter [17°C: -5 to -9%; 28°C: -3%] and weighed less [17°C: -6 to -18%; 28°C: -8%] compared to ambient pCO2 fish. Reductions in fish weight were smaller than in length, which is why high pCO2 fish at 17°C consistently exhibited a higher Fulton's k (weight/length ratio). Notably, it took more than 100 days of rearing for statistically significant length differences to emerge between treatment populations, showing that cumulative, long-term CO2 effects could exist elsewhere but are easily missed by short experiments. Long-term rearing had another benefit: it allowed sexing the surviving fish, thereby enabling rare sex-specific analyses of trait distributions under contrasting CO2 environments. We found that female silversides grew faster than males, but there was no interaction between CO2 and sex, indicating that males and females were similarly affected by high pCO2. Because Atlantic silversides are known to exhibit temperature-dependent sex determination, we also analyzed sex ratios, revealing no evidence for CO2-dependent sex determination in this species. In order to allow full comparability with other ocean acidification data sets, the R package seacarb (Gattuso et al, 2020) 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 2020-12-25.