ABSTRACT
Large ensembles of global temperature are provided for three climate scenarios: historical (2006-16), 1.5 °C and 2.0 °C above pre-industrial levels. Each scenario has 700 members (70 simulations per year for ten years) of 6-hourly mean temperatures at a resolution of 0.833° ´ 0.556° (longitude ´ latitude) over the land surface. The data was generated using the climateprediction.net (CPDN) climate simulation environment, to run HadAM4 Atmosphere-only General Circulation Model (AGCM) from the UK Met Office Hadley Centre. Biases in simulated temperature were identified and corrected using quantile mapping with reference temperature data from ERA5. The data is stored within the UK Natural and Environmental Research Council Centre for Environmental Data Analysis repository as NetCDF V4 files.
ABSTRACT
Regional data from the UK Government's Department for Transport has been analyzed to produce a forecasted dataset of the uptake of electric vehicles (EVs) within Counties of England to the first quarter of the year 2100 using an S-curve methodology. This data includes all vehicles, not just cars. The historic proportion of electric vehicles in the fleets of these regions is calculated using data from 2011 Q4 to 2021 Q1. This data is then analyzed using SCATE, the S-Curve Adoption Tool for EVs to forecast the future proportion of electric vehicles in these Counties. Two data tables are presented: the reformatted historic data and the data from the S-curve analysis. Data is also presented for the collective UK.
ABSTRACT
This meta-analysis quantifies the changes in greenhouse gas (GHG) emissions from dairy farms, caused by anaerobically digesting (AD) cattle manure. As this is a novel quantifiable synthesis of the literature, a database of GHG emissions from dairy farms is created. Each case in the database consists of a baseline (reference with no AD system) and an AD scenario. To enable interstudy comparison, emissions are normalized by calculating relative changes (RCs). The distributions of RCs are reported by specific GHGs and operation units. Nonparametric tests are applied to the RCs in order to identify a statistical difference of AD with respect to baseline scenarios (Wilcoxon rank test), correlations (Spearman test), and best estimation for changes in emissions (Kernel density distribution estimator). From 749 studies identified, 30 papers yield 89 independent cases. The median reductions in emissions from the baseline scenarios, according to operation units, are -43.2% (n.s.) for storage, -6.3% for field application of slurries, -11.0% for offset of energy from fossil fuel, and +0.4% (n.s.) for offset of inorganic fertilizers. The leaks from digesters are found to significantly increase the emissions from baseline scenarios (median = +1.4%).
