• Energy Research
• Open Access close
• Restricted close
• Open Source close
• CA close

This package generates all of the model runs and plotting code for "Current infrastructure does not yet commit us to 1.5°C warming". See enclosed README file for dependencies and how to run.

These data sets provide the WRF [1] calculated wind data for Pritzwalk (onshore) and FINO3 (offshore) as Python dictionaries. Additionally, the files contain k-means cluster objects derived from these profiles. These data sets were used for power assessment and design exploration of Airborne Wind Energy Systems using the awebox [2] optimization toolbox. WRF setups are described in detail and used in publication [3,4,5]. Wind data are interpolated to fixed heights of: [10, 28, 50, 70, 90, 100, 150, 200, 250, 300, 350, 400, 450, 500, 550, 600, 700, 800, 1000, 1200] meters above ground. Onshore wind data: Location lat: 53° 10.78' N; long: 12° 11.35' E Time: 1 September 2015 - 31 August 2016 Timestep: 10 min Offshore wind data: Location lat: 55° 11.7' N, long: 7° 9.5' E Time: 1 September 2013 - 31 August 2014 Timestep: 10 min The clusters are derived from both horizontal wind velocity components using the scikit-learn’s k-means clustering algorithm [6]. For our purposes, wind vectors were rotated such that the main wind speed always points in the same direction (u_main,u_deviation). [1]: Weather Research and Forecasting Model [2]: awebox [3]: Improving mesoscale wind speed forecasts using lidar-based observation nudging for airborne wind energy systems [4]: Offshore and onshore ground-generation airborne wind energy power curve characterization [5]:Ground-generation airborne wind energy design space exploration [6]: sklearn.cluster.KMeans

Select monthly climate data for provinces in Canada. Monthly data includes mean temperature, maximum temperatures, minimum temperature, snow, precipitation, HDD, CDD, and Trade.

Transmitted solar radiance was measured using an ARC (Advanced-Radiance-Collector) RAMSES hyper-spectral radiometer (TriOS) mounted on the ROV during the ARTofMELT2023 expedition in May and June 2023 and normalized by the incident solar irradiance as measured using an ACC (Advanced-Cosine-Collector) RAMSES hyper-spectral radiometer (TriOS) installed on-board the ship. All times are given in Universal Coordinated Time (UTC).

Current and projected future potential boreal bird densities (4-km resolution) Citation for journal article associated with this dataset: -------------------- Stralberg, D., S. M. Matsuoka, A. Hamann, E. M. Bayne, P. Sólymos, F. K. A. Schmiegelow, X. Wang, S. G. Cumming, and S. J. Song. 2015. Projecting boreal bird responses to climate change: the signal exceeds the noise. Ecological Applications 25:52-69. http://dx.doi.org/10.1890/13-2289.1 Coordinate System ------------------ Projection: Lambert Conformal Conic False Easting: 0.00000000 False Northing: 0.00000000 Central Meridian: -95.00000000 Standard Parallel 1: 49.00000000 Standard Parallel 2: 77.00000000 Latitude Of Origin: 0.00000000 Linear Unit: Meter Datum: D WGS 1984 Summary ------- The boreal forest biome provides a resource-rich environment for breeding birds, supporting high species diversity and bird numbers. These birds are likely to shift their distributions northward in response to rapid climate change over the next century. We used a comprehensive dataset of avian point-count surveys from across boreal Canada and Alaska, combined with interpolated climate data, to develop bioclimatic niche models of current avian distribution and density for 80 boreal-breeding songbird species. We then used a downscaling of projected future climates to assess the potential for these species to change their distribution and abundance in response to climate change. Note that projections represent potential densities based on climatic conditions, land use and topography. They do not account for physiographic barriers such as the northern extent of the Rocky Mountains that may prevent colonization of otherwise suitable habitat. Therefore current species’ distributions may be over-estimated in certain regions, particularly in Alaska. Boosted regression tree models of species distribution were averaged across two sets of covariates (climate-only and climate + land use + topography), 11 bootstrap samples, and four global climate models. Mean projections and uncertainty estimates (coefficient of variation) are available for the current period (based on climate data from 1961-1990) and three future time periods (2011–2040, 2041­–2070, 2071–2100). Climate data layers available at tinyurl.com/ClimateNA. Contact ------- Diana Stralberg, University of Alberta (stralber@ualberta.ca) Boreal Avian Modelling Project (borealbirds.ca) Project sponsors ---------------- Boreal Avian Modelling (BAM) Project Alberta Biodiversity Management and Climate Change Adaptation Project Avian data providers -------------- http://www.borealbirds.ca/index.php/data_partners USGS Breeding Bird Survey Breeding Bird Atlases of Canada BAM founding organisations and funders -------------------------------------- Environment Canada University of Alberta Canadian BEACONs Project Financial supporters -------------------- USFWS Neotropical Migratory Bird Conservation Act Vanier Canada Graduate Scholarships Alberta Biodiversity Monitoring Institute Alberta Innovates Technology Futures Alberta Pacific Forest Industries Inc. Climate Change and Emissions Management Corporation Joint Canada-Alberta Implementation Plan for Oil Sands Monitoring Killam Trusts Landscape Conservation Cooperatives National Fish and Wildlife Foundation Université Laval Species code definitions ------------------------ Code Common name (Scientific name) ALFL Alder Flycatcher (Empidonax alnorum) ‡ AMCR American Crow (Corvus brachyrhynchos) AMGO American Goldfinch (Spinus tristis) AMPI American Pipit (Anthus rubescens) ‡ AMRE American Redstart (Setophaga ruticilla) AMRO American Robin (Turdus migratorius) ‡ ATSP American Tree Sparrow (Spizella arborea) ‡ BAWW Black-and-white Warbler (Mniotilta varia) BBWA Bay-breasted Warbler (Setophaga castanea) BCCH Black-capped Chickadee (Poecile atricapillus) ‡ BHCO Brown-headed Cowbird (Molothrus ater) BHVI Blue-headed Vireo (Vireo solitarius) BLBW Blackburnian Warbler (Setophaga fusca) BLJA Blue Jay (Cyanocitta cristata) BLPW Blackpoll Warbler (Setophaga striata) ‡ BOCH Boreal Chickadee (Poecile hudsonicus) ‡ BRBL Brewer’s Blackbird (Euphagus cyanocephalus) BRCR Brown Creeper (Certhia americana) ‡ BTNW Black-throated Green Warbler (Setophaga virens) CAWA Canada Warbler (Cardellina canadensis) CCSP Clay-colored Sparrow (Spizella pallida) CEDW Cedar Waxwing (Bombycilla cedrorum) CHSP Chipping Sparrow (Spizella passerina) ‡ CMWA Cape May Warbler (Setophaga tigrina) COGR Common Grackle (Quiscalus quiscula) CONW Connecticut Warbler (Oporornis agilis) CORA Common Raven (Corvus corax) ‡ CORE Common Redpoll (Acanthis flammea) ‡ COYE Common Yellowthroat (Geothlypis trichas) CSWA Chestnut-sided Warbler (Setophaga pensylvanica) DEJU Dark-eyed Junco (Junco hyemalis) ‡ EAKI Eastern Kingbird (Tyrannus tyrannus) EAPH Eastern Phoebe (Sayornis phoebe) EVGR Evening Grosbeak (Coccothraustes vespertinus) FOSP Fox Sparrow (Passerella iliaca) ‡ GCKI Golden-crowned Kinglet (Regulus satrapa) ‡ GCTH Gray-cheeked Thrush (Catharus minimus) ‡ GRAJ Gray Jay (Perisoreus canadensis) ‡ HETH Hermit Thrush (Catharus guttatus) ‡ HOLA Horned Lark (Eremophila alpestris) ‡ LCSP Le Conte's Sparrow (Ammodramus leconteii) LEFL Least Flycatcher (Empidonax minimus) LISP Lincoln's Sparrow (Melospiza lincolnii) ‡ MAWA Magnolia Warbler (Setophaga magnolia) MOWA Mourning Warbler (Geothlypis philadelphia) NAWA Nashville Warbler (Oreothlypis ruficapilla) NOWA Northern Waterthrush (Parkesia noveboracensis) ‡ OCWA Orange-crowned Warbler (Oreothlypis celata) ‡ OSFL Olive-sided Flycatcher (Contopus cooperi) ‡ OVEN Ovenbird (Seiurus aurocapilla) PAWA Palm Warbler (Setophaga palmarum) PHVI Philadelphia Vireo (Vireo philadelphicus) PIGR Pine Grosbeak (Pinicola enucleator) ‡ PISI Pine Siskin (Spinus pinus) ‡ PUFI Purple Finch (Carpodacus purpureus) RBGR Rose-breasted Grosbeak (Pheucticus ludovicianus) RBNU Red-breasted Nuthatch (Sitta canadensis) ‡ RCKI Ruby-crowned Kinglet (Regulus calendula) ‡ REVI Red-eyed Vireo (Vireo olivaceus) RUBL Red-winged Blackbird (Agelaius phoeniceus) ‡ RWBL Rusty Blackbird (Euphagus carolinus) ‡ SAVS Savannah Sparrow (Passerculus sandwichensis) ‡ SOSP Song Sparrow (Melospiza melodia) SWSP Swamp Sparrow (Melospiza georgiana) SWTH Swainson's Thrush (Catharus ustulatus) ‡ TEWA Tennessee Warbler (Oreothlypis peregrina) TRES Tree Swallow (Tachycineta bicolor) ‡ VATH Varied Thrush (Ixoreus naevius) ‡ VESP Vesper Sparrow (Pooecetes gramineus) WAVI Warbling Vireo (Vireo gilvus) WCSP White-crowned Sparrow (Zonotrichia leucophrys) ‡ WETA Western Tanager (Piranga ludoviciana) WEWP Western Wood-Pewee (Contopus sordidulus) ‡ WIWA Wilson's Warbler (Cardellina pusilla) ‡ WIWR Winter Wren (Troglodytes hiemalis) WTSP White-throated Sparrow (Zonotrichia albicollis) WWCR White-winged Crossbill (Loxia leucoptera) ‡ YBFL Yellow-bellied Flycatcher (Empidonax flaviventris) YRWA Yellow-rumped Warbler (Setophaga coronata) ‡ YWAR Yellow Warbler (Setophaga petechia) ‡ ‡ symbols denote the 38 species currently breeding in the Alaskan boreal region.

Data used in "Anderson et al. 2023. Unexpected sources of uncertainty in projecting habitat shifts for Arctic shorebirds under climate change". Includes shorebird observations from Environment and Climate Change Canada PRISM dataset, and environmental covariates. See Anderson et al. 2023 methods for more details. Data was funded and collected by Environment and Climate Change Canada.

GENERAL INFORMATION 1. Title of Dataset: ThermalManipulation_Data 2. Date of data collection : Main text data: 2019-06-19 Supplemental Data: 2021-03-20 3. Geographic location of data collection Main article data: April Point, Quadra Island, British Columbia, Canada (50.064067, -125.236816) Supplemental Data: Spanish Banks beach, Vancouver, British Columbia, Canada (49.278124, -123.222027) ---------------------------------------------------------------------------------------------------------------- DATA & FILE OVERVIEW 1. File List: Main text: - 01_Quadra_heatingData.csv: data was collected to evaluate the initial tidepool heating manipulation trial. The data contains information on abiotic parameters for each treatment condition. - 02_Quadra_coolingData.csv : data was collected to evaluate the tidepool cooling temperature manipulation trial. The data contains information on abiotic parameters for each treatment condition throughout the trial. Supporting Materials: - 03_SupplementalData_Trial1_HOBO.csv: data was collected to assess how pool temperatures and heating by the SAUTE may vary between containers of similar volume but different dimensions. This dataset provides high temporal resolution temperature measurements. - 04_SupplementalData_Trial1_SpotMeas.csv: data was collected to assess how pool temperatures and heating by the SAUTE may vary between containers of similar volume but different dimensions. This dataset contains spot measurements at a variety of locations (see diagrams in supplement of publication) throughout each container to better understand patterns of thermal variation within a container. - 05_SupplementalData_Trial2_HOBO.csv: data was collected to evaluate the ability of the SAUTE to heat a large volume of water (100L). HOBO data loggers were placed at various locations (see diagrams in supplement of publication) throughout each container. - 06_SupplementalData_Trial2_SpotMeas: data was collected to evaluate the ability of the SAUTE to heat a large volume of water (100L). Spot measurements were taken at various locations (see diagrams in supplement of publication) throughout each container to assess thermal variation within a container. ---------------------------------------------------------------------------------------------------------------- DATA-SPECIFIC INFORMATION FOR: [01_Quadra_heatingData.csv] 1. Number of variables: 8 2. Number of cases/rows: 135 3. Variable List: - pool_ID: unique identifier for each pool - treatment: level of treatment condition (Ambient/Control/Heated) - time: time of measurement (PST, 24hr format) - temperature: water temperature (degrees celsius) - salinity: water salinity (psu) - pH: water pH - DO_mg_L : Dissolved Oxygen (mg/L) measured in a subset of pools and timepoints - DO_Percent : Dissolved Oxygen (%) measured in a subset of pools and timepoints 4. Missing Values: NA values indicate missing values. Data was not collected for given parameter(s) at these timepoints. ---------------------------------------------------------------------------------------------------------------- DATA-SPECIFIC INFORMATION FOR: [02_Quadra_coolingData.csv] 1. Number of variables: 5 2. Number of cases/rows: 64 3. Variable List: - pool_ID: unique identifier for each pool - treatment: level of treatment condition (Cooled/Control) - time: time of measurement (PST, 24hr format) - temperature: water temperature (degrees celsius) - salinity: water salinity (psu) ---------------------------------------------------------------------------------------------------------------- DATA-SPECIFIC INFORMATION FOR: [03_SupplementalData_Trial1_HOBO.csv] 1. Number of variables: 5 2. Number of cases/rows: 1600 3. Variable List: - Date-Time (PST): Date and time (PST & 24hr format) of temperature measurement - heating_treatment: level of treatment condition (Control/Heated) - container_type: type of experimental container used (bucket/tray) - container_ID: Identifier within each heating_treatment*container_type combination - temperature: water temperature (degrees celsius) ---------------------------------------------------------------------------------------------------------------- DATA-SPECIFIC INFORMATION FOR: [04_SupplementalData_Trial1_SpotMeas.csv] 1. Number of variables: 6 2. Number of cases/rows: 486 3. Variable List: - container_type: type of experimental container used (bucket/tray) - container_ID: Identifier within each heating_treatment*container_type combination - heating_treatment:level of treatment condition (Control/Heated) - measurement_location:measurement locations within a container - corresponding diagram of locations in the supplemental materials for the accompanying article (a,b,c,d,e) - time: time of measurement (PST, 24hr format) - temperature: water temperature (degrees celsius) ---------------------------------------------------------------------------------------------------------------- DATA-SPECIFIC INFORMATION FOR: [05_SupplementalData_Trial2_HOBO.csv] 1. Number of variables: 5 2. Number of cases/rows: 1190 3. Variable List: - Date-Time (PST): Date and time (PST & 24hr format) of temperature measurement - heating_treatment: level of treatment condition (Control/Heated) - logger_depth: relative depth of data logger within each experimental container (shallow/bottom) - logger_location: relative logger location within each depth (corner/centre) * Note: Control treatment does not have data for the [shallow, corner] location * - temperature: water temperature (degrees celsius) ---------------------------------------------------------------------------------------------------------------- DATA-SPECIFIC INFORMATION FOR: [06_SupplementalData_Trial2_SpotMeas] 1. Number of variables: 6 2. Number of cases/rows: 20 3. Variable List: - container_type: type of experimental container used (tub) - heating_treatment:level of treatment condition (Control/Heated) - measurement_location: measurement location within container based on cardinal directions (NE, NW, centre, SE, SW) - measurement_depth: relative depth of temperature measurement within each experimental container (shallow/ bottom) - temperature: water temperature (degrees celsius) - time: time of measurement (PST, 24hr format) There is a growing need to better understand the potential impacts of altered thermal regimes on biodiversity and ecosystem function as mean temperatures, and the likelihood of extreme temperatures, continue to increase. One valuable approach to identify mechanisms and pathways of thermally-driven change at the community level is through the manipulation of temperature in the field. However, where methods exist, they are often costly or unable to produce ecologically relevant changes in temperature. Here, we present a low cost, easily assembled, and readily customizable thermal manipulation system for tide pools or other small bodies of water – the Seaside Array for Understanding Thermal Effects (SAUTE) – and demonstrate its ability to effectively alter the temperature in tide pools. During our three-hour heating manipulation, heated pools reached temperatures 4°C warmer than unmanipulated pools. During the cooling manipulation, cooled pools remained on average 1.8°C cooler than control pools. The novel SAUTE system can be used to alter the temperature of tide pools in situ. Further, it could be modified to heat other environments such as freshwater vernal pools and settlement tiles in a realistic and meaningful manner, serving as a useful tool to test questions surrounding the relationship between climate warming, thermal variability, and ecological processes in natural aquatic communities. Please see the linked publication and supporting materials for full methodological details.

Please cite as: Olivia Graham, Tiffany Stephens, Brendan Rappazzo, Corinne Klohmann, Sukanya Dayal, Emily Adamczyk, Angeleen Olson, Margot Hessing-Lewis, Morgan Eisenlord, Bo Yang, Colleen Burge, Carla Gomes, Drew Harvell. (2022) Data and code from: Deeper habitats and cooler temperatures moderate a climate-driven disease in an essential marine habitat [dataset] Cornell University eCommons Repository. https://doi.org/10.7298/6ybh-w566 ; These files contain data and R code supporting all results reported in Graham et al. "Deeper habitats and cooler temperatures moderate a climate-driven disease in an essential marine habitat." In Graham et al., we found: Eelgrass creates critical coastal habitats worldwide and fulfills essential ecosystem functions as a foundation seagrass. Warming and disease threaten eelgrass meadows with mass mortalities and cascading ecological impacts, even in pristine locations. Although deeper, subtidal meadows are valuable fish nursery grounds and may also provide refuge from the climate-fueled seagrass wasting disease, nothing is known about differences in disease levels across remote locations in northern latitudes and between tidal zones (intertidal and subtidal meadows). From cross-boundary surveys on 5,761 eelgrass leaves from Alaska to Washington assisted with a machine-language algorithm, we measured outbreak conditions with average disease prevalence over 66% in intertidal and 50% in subtidal. In field surveys, disease was consistently lower in subtidal compared to adjacent intertidal meadows; remotely-sensed temperatures revealed significant associations between spring temperature anomalies and disease. While new studies show links between warm temperature anomalies and increased disease, our work detects beneficial effects of cooling in colder water anomalies. Disease was reduced in all regions except Puget Sound in the cooler summer of 2017. Pooled across both years, predicted disease prevalence was nearly 40% lower for subtidal than intertidal leaves, but in both tidal zones, ...