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19 results for “climate science”
Climatological data from mechanistic model experiments of Boljka and Birner (2022/3; npj Climate and Atmospheric Science)
<p>Some climatological output data from mechanistic dry dynamical core model experiments used for the paper of Boljka and Birner (2022/3): "Potential impact of tropopause sharpness on the structure and strength of the general circulation", npj Climate and Atmospheric Science. For more details see the manuscript. </p>
Using Hydroclimate Modeling and Social Science to Enhance Flood Resilience on Lake Ontario through the Climate Smart Communities Program
<p>This repository contains several data products associated with the New York Sea Grant project R/CHD-15 entitled <em>Using Hydroclimate Modeling and Social Science to Enhance Flood Resilience on Lake Ontario through the Climate Smart Communities Program.</em><strong><em> </em></strong>These products include:</p> <p>1. Estimates of the 25-year, 50-year, and 100-year flood across the New York coastline of Lake Ontario. These design events (reported in feet) are for still water levels that take into account both average water levels across the lake as well as local variations in water level due to storm surge. Wave setup and wave run-up are not considered in these design events. The design events incorporate the effects of water level regulation and the potential impacts of climate change on water supplies to Lake Ontario, and they are tailored for 79 unique locations along the shoreline (identified based on longitude and latitude). These flood levels are presented in an online flood risk assessment tool at: https://kts48.users.earthengine.app/view/lake-ontario-water-level-scenarios</p> <p>2. Protocols and summary of results for a series of focus groups and structured telephone interviews with local officials from communities along the Lake Ontario shoreline to assess barriers to participation in the New York State Climate Smart Communities Program.</p> <p>3. A Crosswalk between activities and administrative requirements of the New York State Climate Smart Communities Program and other federal and state flood resiliency programs. </p> <p>4. A final report summarizing the products above. </p>
Data from Citizen science data reveal regional heterogeneity in phenological response to climate in the large milkweed bug, Oncopeltus fasciatus
These data include annotations for life stage, mating behavior, and plant part occupancy of large milkweed bug observations in North America as well as information about climate and environment.
Data supporting "Large-scale citizen science programs can support ecological and climate change assessments"
<p>Text file of phenology observations pulled from the USA National Phenology Network's database (www.usanpn.org) and used in this analysis. </p>
Mirror of data from NOAA U.S. Climate Reference Network for Research Computing in Earth Science
<p>This is a mirror of data from the NOAA U.S. Climate Reference Network (https://www.ncei.noaa.gov/products/land-based-station/us-climate-reference-network).</p> <p>It was created because outbound FTP access is not allowed from some cloud-based JupyterHub setups.</p>
Community-science reveals delayed fall migration of waterfowl and spatiotemporal effects of a changing climate
<p>Climate change has well-documented, yet variable, influences on the annual movements of migratory birds. The effects of climate change on fall migration remains understudied compared to spring, but appears to be less consistent among species, regions, and years. Changes in the pattern and timing of waterfowl migration in particular may result in cascading effects on ecosystem function, and socioeconomic and cultural outcomes. We investigated changes in the migration of 15 waterfowl species along a major flyway corridor of continental importance in northeastern North America using 43 years of community-science data. We built spatially- and temporally-explicit hierarchical generative additive models for each species and demonstrated that climate, specifically the interaction between minimum temperature and precipitation, significantly influences migration phenology for most species. Certain species' migratory movements responded to specific temperature thresholds (climate migrants) and others reacted more to the interaction of temperature and precipitation (extreme event migrants). There are already significant changes in the fall migration phenology of common waterfowl species with high ecological and economic importance, which may simply increase in the context of a changing climate. If not addressed, climate change could induce mismatches in management, regulations, and population surveys which would negatively impact the hunting industry. Our findings highlight the importance of considering species-specific spatiotemporal scales of effect on climate on migration and our methods can be widely adapted to quantify and forecast climate-driven changes in wildlife migration.</p>
Data from: Graduate student reflections on learning and implementing community science methods to tackle compounding climate extremes
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Phil Trans B special issue: Climate change adaptation needs a science of culture
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Data from: A pan-European citizen science study shows population size, climate and land use are related to biased morph ratios in the heterostylous plant Primula veris
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Community-science reveals delayed fall migration of waterfowl and spatiotemporal effects of a changing climate
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Provisioning forest and conservation science with European tree species distribution models under climate change
<p>Estimating shifts in the current range of forest tree species is crucial for formulating adaptive management strategies such as assisted migration. Ecological niche models have been the most widely used tools to estimate the potential climatic suitability of species worldwide. The reliability of such estimations depends on the model algorithm and the input data such as climate and species occurrence. We developed a dataset of the potential distribution of seven ecologically and economically important tree species of Europe in terms of their climatic suitability with an ensemble approach while accounting for uncertainty due to model algorithms. The distribution models shall be the basis for follow-up studies in forest and conservation science.</p>
Nunataryuk Session "Indigenous Peoples, science and climate change in the Arctic: lessons learned and a way forward" at ICASS X
<p>Permafrost coasts in the whole Arctic represent 34% of the world's coasts (Lantuit et al., 2012) and a key interface for human-environmental interactions. These coasts provide essential ecosystem services, exhibit high biodiversity and productivity, and support indigenous lifestyles. At the same time, this coastal zone is a dynamic and vulnerable zone of expanding infrastructure investment and growing health concerns. Climate change is affecting this fragile environment by triggering coastal landscape instability and increased hazard exposure (Forbes et al., 2011). Permafrost thaw in combination with increasing sea level and changing sea-ice cover expose the Arctic coastal and nearshore areas to rapid changes (Fritz et al. 2017). Since 2017, scientists from the Nunataryuk Project are working in cooperation with local communities in order to identify the impacts of thawing land, coast and subsea permafrost on the global climate and on humans in the Arctic and to develop targeted and co-designed adaptation and mitigation strategies.</p>
Community science validates climate suitability projections from ecological niche modeling
<p><span>Climate change poses an intensifying threat to many bird species, and projections of future climate suitability provide insight into how species may shift their distributions in response. Climate suitability is characterized using ecological niche models (ENMs), which correlate species occurrence data with current environmental covariates and project future distributions using the modeled relationships together with climate predictions. Despite their widespread adoption, ENMs rely on several assumptions that are rarely validated <i>in situ </i>and can be highly sensitive to modeling decisions, precluding their reliability in conservation decision-making. Using data from a novel, large-scale community science program, we developed dynamic occupancy models to validate near-term climate suitability projections for bluebirds and nuthatches in summer and winter. We estimated occupancy, colonization, and extinction dynamics across species' ranges in the United States in relation to projected climate suitability in the 2020s, and used a Gibbs variable selection approach to quantify evidence of species-climate relationships. We also included a Bird Conservation Region strata-level random effect to examine among-strata variation in occupancy that may be attributable to land-use and ecoregional differences. Across species and seasons, we found strong evidence that initial occupancy and colonization were positively related to 2020 climate suitability, illustrating an independent validation of projections from ENMs across a large geographic area. </span><span>Random strata effects revealed that occupancy probabilities were generally higher than average in core areas and lower than average in peripheral areas of species' ranges, and served as a first step in identifying spatial patterns of occupancy from these community science data. </span><span>Our findings lend much-needed support to the use of ENM projections for addressing questions about potential climate-induced changes in species' occupancy dynamics. More broadly, </span>our work highlights the value of community scientist observations for ground-truthing projections from statistical models and for refining our understanding of the processes shaping species' distributions under a changing climate.</p>
Data from: Historical citizen science to understand and predict climate-driven trout decline
Historical species records offer an excellent opportunity to test the predictive ability of range forecasts under climate change, but researchers often consider that historical records are scarce and unreliable, besides the datasets collected by renowned naturalists. Here, we demonstrate the relevance of biodiversity records developed through citizen-science initiatives generated outside the natural sciences academia. We used a Spanish geographical dictionary from the mid-nineteenth century to compile over 10 000 freshwater fish records, including almost 4 000 brown trout (Salmo trutta) citations, and constructed a historical presence–absence dataset covering over 2 000 10 × 10 km cells, which is comparable to present-day data. There has been a clear reduction in trout range in the past 150 years, coinciding with a generalized warming. We show that current trout distribution can be accurately predicted based on historical records and past and present values of three air temperature variables. The models indicate a consistent decline of average suitability of around 25% between 1850s and 2000s, which is expected to surpass 40% by the 2050s. We stress the largely unexplored potential of historical species records from non-academic sources to open new pathways for long-term global change science.
Model data for "The GERB Obs4MIPs Radiative Flux Dataset: A new tool for climate model evaluation", submitted to Earth System Science Data
<p>© Crown Copyright, Met Office</p><p>The E1hrClimMon files contain the monthly mean diurnal cycles of TOA radiative fluxes (all-sky and clear-sky) for amip experiment of two configurations of HadGEM3: GC3.1 and GC5.0. The monthly mean diurnal cycle is constructed by averaging each UTC hourly mean over the entire month. The HadGEM3 OLR diagnostics used in this study differ from those submitted to CFMIP3. The OLR diagnostics submitted to CFMIP3 contain a correction that accounts for the surface temperature adjustment by the boundary layer scheme in model time steps between radiation time steps. This OLR diagnostic adjustment is introduced to conserve energy, but it significantly distorts the diurnal cycle of OLR. For comparison with the GERB obs4MIPs products, the OLR without this correction is recommended.</p><p>The COSP file contains the average monthly climatologies for the variables cfadLidarsr532 and clisccp for the amip simulations of GC3.1 and GC5.0.</p>
Data from: Historical citizen science to understand and predict climate-driven trout decline
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Data from: Community science validates climate suitability projections from ecological niche modeling
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A data science approach to climate change risk assessment applied to pluvial flood occurrences for the United States and Canada (Supplementary Material)
<p>This resource is tied to manuscript "A data science approach to climate change risk assessment applied to pluvial flood occurrences for the United States and Canada".</p> <p>It contains: (1) a PDF with additional details on the implementation of GLM, GAM and RF, summarized outputs for two models, a bias analysis of the CRCM and extensive tables from Section 5.2; (2) full outputs for two models; (3) high-resolution figures, and; (4) rasters for selected figures.</p> <p>**</p> <p>Version 2 adds a log-log version of Figure 9 and changes the main PDF file to remove blue section titles.</p>
COMPLEX NETWORKS IN CLIMATE SCIENCE: PROGRESS, OPPORTUNITIES AND CHALLENGES
COMPLEX NETWORKS IN CLIMATE SCIENCE: PROGRESS, OPPORTUNITIES AND CHALLENGES KARSTEN STEINHAEUSER, NITESH V. CHAWLA, AND AUROOP R. GANGULY Abstract. Networks have been used to describe and model a wide range of complex systems, both natural as well as man-made. One particularly interesting application in the earth sciences is the use of complex networks to represent and study the global climate system. In this paper, we motivate this general approach, explain the basic methodology, report on the state of the art (including our contributions), and outline open questions and opportunities for future research.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.