Search

Using object stores, and cloud based storage utilising the Amazon S3 HTTP API, presents a number of opportunities and challenges in the storing of climate data. The distributed nature of the objects allows large data sets to be broken into "fragments", each fragment containing a subset of the data. This allows for parallel access to the fragments, improving the performance of reading the data across a network. However, this presents a number of problems. Firstly, determining the fragment size and the optimum method of splitting multi-dimensional data. Secondly, enabling meaningful search of data, when the data may be widely distributed. This talk will present a new method of splitting netCDF files into fragments and storing each fragment as an object in an S3 compatible object store. The location of the fragments, the metadata and dimensions for each climate variable are stored in a master file, which can be written to a location not within the object store, for example a POSIX file system on a SSD. This allows fast search of the metadata without having to reassemble the fragments. Each fragment is stored as a self-contained netCDF file allowing reconstruction of the data if the master file is lost., Previously curated at: http://cedadocs.ceda.ac.uk/1361/ Event type: workshop. This item was not refereed before the publication Main files in this record: RCUK_cloud_2018_nrmassey.pptx Item originally deposited with Centre for Environmental Data Analysis (CEDA) document repository by Dr Neil Massey. Transferred to CEDA document repository community on Zenodo on 2022-11-24

Large data sets used to study the impact of anthropogenic climate change on the 2013/14 floods in the UK are provided. The data consist of perturbed initial conditions simulations using the Weather@Home regional climate modelling framework. Two different base conditions, Actual, including atmospheric conditions (anthropogenic greenhouse gases and human induced aerosols) as at present and Natural, with these forcings all removed are available. The data set is made up of 13 different ensembles (2 actual and 11 natural) with each having more than 7500 members. The data is available as NetCDF V3 files representing monthly data within the period of interest (1st Dec 2013 to 15th February 2014) for both a specified European region at a 50 km horizontal resolution and globally at N96 resolution. The data is stored within the UK Natural and Environmental Research Council Centre for Environmental Data Analysis repository.

Hydro-meteorological extremes such as drought and heavy precipitation can have large impacts on society and the economy. With potentially increasing risks associated with such events due to climate change, properly assessing the associated impacts and uncertainties is critical for adequate adaptation. However, the application of risk-based approaches often requires large sets of extreme events, which are not commonly available. Here, we present such a large set of hydro-meteorological time series for recent past and future conditions for the United Kingdom based on weather@home 2, a modelling framework consisting of a global climate model (GCM) driven by observed or projected sea surface temperature (SST) and sea ice which is downscaled to 25 km over the European domain by a regional climate model (RCM). Sets of 100 time series are generated for each of (i) a historical baseline (1900–2006), (ii) five near-future scenarios (2020–2049) and (iii) five far-future scenarios (2070–2099). The five scenarios in each future time slice all follow the Representative Concentration Pathway 8.5 (RCP8.5) and sample the range of sea surface temperature and sea ice changes from CMIP5 (Coupled Model Intercomparison Project Phase 5) models. Validation of the historical baseline highlights good performance for temperature and potential evaporation, but substantial seasonal biases in mean precipitation, which are corrected using a linear approach. For extremes in low precipitation over a long accumulation period ( > 3 months) and shorter-duration high precipitation (1–30 days), the time series generally represents past statistics well. Future projections show small precipitation increases in winter but large decreases in summer on average, leading to an overall drying, consistently with the most recent UK Climate Projections (UKCP09) but larger in magnitude than the latter. Both drought and high-precipitation events are projected to increase in frequency and intensity in most regions, highlighting the need for appropriate adaptation measures. Overall, the presented dataset is a useful tool for assessing the risk associated with drought and more generally with hydro-meteorological extremes in the UK., Hydrology and Earth System Sciences, 22 (1), ISSN:1027-5606, ISSN:1607-7938