• Energy Research
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This dataset presents trends in man-made emissions of major greenhouse gases and emissions by gas.

This Excel file contains two spread sheets. The first contains observed and projected climate data for Gavin Prairie, the site of the Cypripedium candidum and C. parviflorum populations, while the second contains observed and projected climate data for the Ophrys sphegodes site. Legends are included on each spreadsheet.

It shows point estimates for national GHG emissions (total emissions and seven agricultural activities) from 1978 to 2016 in China.

The Media and Climate Change Observatory Data monitors 74 sources (across newspapers, radio and TV) in 38 countries in seven different regions around the world. Data is assembled by accessing archives through the Lexis Nexis, Proquest and Factiva databases via the University of Colorado libraries. More information may be found at: http://sciencepolicy.colorado.edu/icecaps/research/media_coverage/index.html. These data include Excel (.xlsx), CSV (.csv), and plain-text README files (.txt).

Data set and R script for the published article: Altered winter conditions impair plant development and yield in oilseed rape. The data set includes data about plant development, growth and yield resulting from an experiment where plants were grown at different winter conditions and exposed to an extreme weather event.The R script contains the analyses used for obtaining the results in the published article. Linear models within the R package glmmTMB are used for alla analyses.

We include one dataset with demographic data for birds, called RCW_demo_data. Each row in this csv file represents an individual x year combination, and columns include information about individual and territory characteristics in that year, as well as various vital rates. For reproductive vital rates, we include these rates only for female breeders. Thus, reproductive vital rates such as “successfirstnest” will be NA (indicating missing data) for all males and for female non-breeders. Each row includes a climate reference number (“clim.group”) that allows the demographic data to be matched with the climate data in the climate files (see below for more description about these climate data). Below we list each column individually. Year: year in which data were collected Surtonext: did this individual survive to the next breeding season (1) or not (0)? Nohelp: how many helpers were present in this territory? Firstnestattempt_bin: did this breeding female initiate a nest in that year’s breeding season? 1 indicates yes, 0 indicates no. Morenestattempt_bin: did this breeding female initiate more than one nest in that years breeding season? 1 indicates yes, 0 no. Fledgedfirstnest: how many fledged from the first nest. Fledgedlaternest: how many fledged from any later nests. Eggsfirstattempt: how many eggs in the first nest. Eggslaterattempt: how many eggs in the first nest. Clim.group: a grouping variable that matches the clim.group variable in the climate datasets. Note that the demographic data contains a space, the climate datasets a period, but SH 146 is the same climate grouping as SH.146. Site: one of SH, EG, or CL, representing Sandhills, Eglin, or Camp Lejeune Numericage: age of the bird Binned status: one of Breeder, Helper or Floater (B, H, or F). Sex: F or M Numericmalemateage: age of the male breeder which which a female bred. Only recorde for breeding females. Successfirstnest_bin: was the first nest successful? 1 indicates yes, 0 no. Frsurvivingfirst: what fraction of eggs survived to fledging from the first nest?Successmorenest_bin: were any later (i.e., 2nd or later) nests successful? 1 indicates yes, 0 no. Frsurvivinglater: what fraction of eggs survived to fledging from all later nests? We have included five datasets corresponding to the five climate variables. The name of the csv file indicates the climate variable that the dataset contains. Each dataset contains information on the date, the climate group (clim.grp, corresponds to the climate groups in the demographic dataset), and the value of the climate signal for that date. Units are indicated in the main text for this paper.

Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.ScenarioMIP.CAMS.CAMS-CSM1-0.ssp119' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The CAMS-CSM 1.0 climate model, released in 2016, includes the following components: atmos: ECHAM5_CAMS (T106; 320 x 160 longitude/latitude; 31 levels; top level 10 mb), land: CoLM 1.0, ocean: MOM4 (tripolar; 360 x 200 longitude/latitude, primarily 1deg latitude/longitude, down to 1/3deg within 30deg of the equatorial tropics; 50 levels; top grid cell 0-10 m), seaIce: SIS 1.0. The model was run by the Chinese Academy of Meteorological Sciences, Beijing 100081, China (CAMS) in native nominal resolutions: atmos: 100 km, land: 100 km, ocean: 100 km, seaIce: 100 km.

This dataset contains 2012 national level land occupation totals by North American Industry Classification System (NAICS) 2012 6-digit codes. This dataset was created in FLOWSA, a publicly available python package that generates standardized environmental flows by industry.

As part of global efforts to reduce dependence on carbon-based energy sources there has been a rapid increase in the installation of renewable energy devices. The installation and operation of these devices can result in conflicts with wildlife. In the marine environment, mammals may avoid wind farms that are under construction or operating. Such avoidance may lead to more time spent travelling or displacement from key habitats. A paucity of data on at-sea movements of marine mammals around wind farms limits our understanding of the nature of their potential impacts. Here, we present the results of a telemetry study on harbour seals Phoca vitulina in The Wash, south-east England, an area where wind farms are being constructed using impact pile driving. We investigated whether seals avoid wind farms during operation, construction in its entirety, or during piling activity. The study was carried out using historical telemetry data collected prior to any wind farm development and telemetry data collected in 2012 during the construction of one wind farm and the operation of another. Within an operational wind farm, there was a close-to-significant increase in seal usage compared to prior to wind farm development. However, the wind farm was at the edge of a large area of increased usage, so the presence of the wind farm was unlikely to be the cause. There was no significant displacement during construction as a whole. However, during piling, seal usage (abundance) was significantly reduced up to 25 km from the piling activity; within 25 km of the centre of the wind farm, there was a 19 to 83% (95% confidence intervals) decrease in usage compared to during breaks in piling, equating to a mean estimated displacement of 440 individuals. This amounts to significant displacement starting from predicted received levels of between 166 and 178 dB re 1 μPa(p-p). Displacement was limited to piling activity; within 2 h of cessation of pile driving, seals were distributed as per the non-piling scenario. Synthesis and applications. Our spatial and temporal quantification of avoidance of wind farms by harbour seals is critical to reduce uncertainty and increase robustness in environmental impact assessments of future developments. Specifically, the results will allow policymakers to produce industry guidance on the likelihood of displacement of seals in response to pile driving; the relationship between sound levels and avoidance rates; and the duration of any avoidance, thus allowing far more accurate environmental assessments to be carried out during the consenting process. Further, our results can be used to inform mitigation strategies in terms of both the sound levels likely to cause displacement and what temporal patterns of piling would minimize the magnitude of the energetic impacts of displacement. Wash_diagWash_diag.xlsx is the historic location data (pre windfarm construction) for the 19 individuals used in the analysis described in Russell et al.