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6 results for “SUMMER Project”
REU data set from summer of 2022. Project was designed to understand how crayfish (Faxonius rusticus) respond to chemical cues from largemouth bass predators under different shelter distributions.
Research into predator–prey interactions has focused on the landscape of fear and nonconsumptive effects that result from prey responses. Prey behavior is influenced by predator presence and the location and quality of foraging resources in habitats. These areas have been fruitful, but the role of prey refuges has lagged. We investigated how refuge spatial distribution and quality influence prey behavior. To determine the role of the landscape of safety (LOS) in prey decision-making, we altered spatial relationships between refuges, refuge quality, and predation threats in mesocosms. Mesocosms were constructed such that prey only received predatory chemical cues. We employed a behavioral assay including largemouth bass (Micropterus salmoides (Lacepède, 1802): predator) and virile crayfish (Faxonius rusticus (Girard, 1852): prey). Crayfish shelter use was significantly influenced by quality and spatial relationship of shelters to predatory threats, and the interaction of these two factors. Particularly, crayfish used high-quality shelters more often when located closer to predatory cues than farther away and did not use low-quality shelters more than controls. High-quality shelter usage decreased as threat level (measured by gape ratio) decreased. These results support the idea that prey utilize an LOS, and information contained in these two landscapes may alter behavioral decisions.
Data for "Origins of Uncertainty in Projections of Summer North Pacific Subtropical High"
<p>This dataset contains the results of the numerical experiments used in manuscript "Origins of Uncertainty in Projections of Summer North Pacific Subtropical High"</p>
VineScout EU project - Summer 2020 data
<p>The Excel file contains several tabs (tests T1, T2, T3, T4, T5, T6, and T8 in 2020) with the following columns for each datum: Hour (Spanish summer time. The UTC is that value minus 2 hours), Minutes, Seconds, Latitude, and Longitude of the VineScout robot antenna, East, North, East, and North of the crop (vineyard of the right side), Altitude, Canopy temperature, Ambient temperature, Pressure, and Humidity, Compass heading, NDVI, PRI (averaged), Row, Irrigation rate, Space, and Scholander pressure when it was measured</p>
Storyline Simulations Data for the paper Athanase et al.: Projected amplification of summer marine heatwaves in a warming Northeast Pacific Ocean
<p>Data used for producing the Figures in the paper entitled "Projected amplification of summer marine heatwaves in a warming Northeast Pacific Ocean", Athanase et al. (Communications Earth & Environment).</p> <p>The AWI-CM-1-1-MR free runs are available in the Earth System Grid Federation (ESGF) data nodes (https://esgf-data.dkrz.de/search/cmip6-dkrz/). The ERA5 reanalysis data used in the paper can be accessed from the European Centre for Medium-Range Weather Forecasts (ECMWF; https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5). Here, we provide data from the nudged storyline simulations carried out with the AWI-CM-1-1-MR coupled climate model.</p> <p>Parameters naming convention:</p> <p>- Sea Surface Temperature ("tos").</p> <p>- Radiative Fluxes ("radiations"), including net surface heat flux ("qnet"), net surface thermal radiation ("trads"), net surface solar radiation ("srads"), latent heat flux ("ahfl"), sensible heat flux ("ahfs").</p> <p>- Low Clouds Cover ("lcc").</p> <p>- Mixed Layer Depth ("mlotst").</p> <p>- Surface Air Temperature ("tas").</p> <p>- 10 m winds ("u10","v10").</p> <p>All data is provided as the 5-member ensemble mean from the nudged storyline simulations. Data is provided for the storyline simulations of the summer 2019 Northeast Pacific marine heatwave, in different background climate conditions: preindustrial ("PI"), present-day ("PD"), and +4°C warmer world ("4K"). </p> <p> </p>
Summer Rainfall Scenarios and Climate Change Factor Projections over Wanzhou County, China
<p>This dataset consists of rainfall scenarios and ensemble projections of extreme daily rainfall and mean summer season rainfall over Wanzhou County, China.</p> <p><strong>Precipitation Reference Period (1979-2018)</strong></p> <p>The reference scenario rainfall covers the period of 1979-2018, and is derived from the China Meteorological Forcing Dataset (https://data.tpdc.ac.cn/en/data/8028b944-daaa-4511-8769-965612652c49/). The extreme daily rainfall (in mm/day) is derived from Gumbel distributions fitted to monthly maximum daily rainfall covering the months of June to August. A spatial distribution of return periods from 2, 5, 10 20, 50 and 100 years for this scenario were derived and included in this dataset. The mean seasonal rainfall scenario covers the average daily rainfall (in mm/day) for the months of May to July to represent antecedent rainfall conditions of that could trigger shallow landslides during the summer season.</p> <ul> <li>Spatial extent: Wanzhou County, China</li> <li>Spatial Resolution: 0.1 degrees x 0.1 degrees</li> <li>Time period: 1979-2018</li> <li>Data Format: .csv files (.xyz file extensions)</li> <li>Variable: Rainfall (pr)</li> <li>Units: mm/day </li> </ul> <p><strong>Ensemble Projections and Climate Change Factors</strong></p> <p>The ensemble climate change projections cover two periods: Mid-21st Century (2021-2060) and Late-21st Century (2061-2100). The influence of climate change is assessed through climate change factors that represent a multiplicative factor of change between present and future climate model outputs. The ensemble projections are the mean climate change factor derived from four bias-corrected Regional Climate Model outputs. The ensemble consisted of the results REMO2015 and RegCM4 models that dynamically downscaled HadGEM2-ES, MPI-ESM-ML, and MPI-ESM-MR model outputs (https://esgf-data.dkrz.de/search/cordex-dkrz/). The bias correction was performed using the quantile delta method. An empirical transfer function for daily rainfall was used to derive the mean seasonal rainfall scenario, while a parametric (Gumbel distribution) transfer function was used to derive on the monthly maxima for the extreme daily rainfall scenarios.</p> <ul> <li>Spatial extent: Wanzhou County, China</li> <li>Spatial Resolution: 0.22 degrees x 0.22 degrees</li> <li>Time periods: Mid-21st Century (2021-2060) & Late-21st Century (2061-2100)</li> <li>Data Format: .csv files</li> <li>Variable: Climate Change Factor (ccf)</li> <li>Unit: Dimensionless</li> <li>Included ensemble projection statistics: <ul> <li>Standard deviation (sd)</li> <li>Coefficient of Variation (cv)</li> </ul> </li> </ul>
Summer Draft Project
<p>Summer draft music project </p>
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International Brain Laboratory public data
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OpenNeuro
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