The Arctic Predictability and Prediction on Seasonal-to-Interannual TimEscales (APPOSITE) data set

Recent decades have seen significant developments in seasonal-to-interannual timescale climate prediction capabilities. However, until recently the potential of such systems to predict Arctic climate had not been assessed. This paper describes a multi-model predictability experiment which was run as part of the Arctic Predictability and Prediction On Seasonal to Inter-annual Timescales (APPOSITE) project. The main goal of APPOSITE was to quantify the timescales on which Arctic climate is predictable. In order to achieve this, a coordinated set of idealised initial-value predictability experiments, with seven general circulation models, was conducted. This was the first model intercomparison project designed to quantify the predictability of Arctic climate on seasonal to inter-annual timescales. Here we present a description of the archived data set (which is available at the British Atmospheric Data Centre) and an update of the project's results. Although designed to address Arctic predictability, this data set could also be used to assess the predictability of other regions and modes of climate variability on these timescales, such as the El Niño Southern Oscillation.

Twenty Years of Data Management in the British Atmospheric Data Centre

The British Atmospheric Data Centre (BADC) has existed in its present form for 20 years, having been formally created in 1994. It evolved from the GDF (Geophysical Data Facility), a SERC (Science and Engineering Research Council) facility, as a result of research council reform where NERC (Natural Environment Research Council) extended its remit to cover atmospheric data below 10km altitude. With that change the BADC took on data from many other atmospheric sources and started interacting with NERC research programmes. The BADC has now hit early adulthood. Prompted by this milestone, we examine in this paper whether the data centre is creaking at the seams or is looking forward to the prime of its life, gliding effortlessly into the future. Which parts of it are bullet proof and which parts are held together with double-sided sticky tape? Can we expect to see it in its present form in another twenty years’ time? To answer these questions, we examine the interfaces, technology, processes and organisation used in the provision of data centre services by looking at three snapshots in time, 1994, 2004 and 2014, using metrics and reports from the time to compare and contrasts the services using BADC. The repository landscape has changed massively over this period and has moved the focus for technology and development as the broader community followed emerging trends, standards and ways of working. The incorporation of these new ideas has been both a blessing and a curse, providing the data centre staff with plenty of challenges and opportunities. We also discuss key data centre functions including: data discovery, data access, ingestion, data management planning, preservation plans, agreements/licences and data policy, storage and server technology, organisation and funding, and user management. We conclude that the data centre will probably still exist in some form in 2024 and that it will most likely still be reliant on a file system. However, the technology delivering this service will change and the host organisation and funding routes may vary.

A vector auto-regressive model for onshore and offshore wind synthesis incorporating meteorological model information

The growth of wind power production in the electricity portfolio is striving to meet ambitious targets set, for example by the EU, to reduce greenhouse gas emissions by 20% by 2020. Huge investments are now being made in new offshore wind farms around UK coastal waters that will have a major impact on the GB electrical supply. Representations of the UK wind field in syntheses which capture the inherent structure and correlations between different locations including offshore sites are required. Here, Vector Auto-Regressive (VAR) models are presented and extended in a novel way to incorporate offshore time series from a pan-European meteorological model called COSMO, with onshore wind speeds from the MIDAS dataset provided by the British Atmospheric Data Centre. Forecasting ability onshore is shown to be improved with the inclusion of the offshore sites with improvements of up to 25% in RMS error at 6 h ahead. In addition, the VAR model is used to synthesise time series of wind at each offshore site, which are then used to estimate wind farm capacity factors at the sites in question. These are then compared with estimates of capacity factors derived from the work of Hawkins et al. (2011). A good degree of agreement is established indicating that this synthesis tool should be useful in power system impact studies.

Rainfall and outbreaks of drinking water related disease and in England and Wales

A case-crossover study compared rainfall in the 4 weeks before drinking water related outbreaks with that in the five previous control years. This included public and private drinking water related outbreaks in England and Wales from 1910 to 1999. Of 111 outbreaks, 89 met inclusion criteria and the implicated pathogens included Giardia, Cryptosporidium, E. coli, S. Typhi, S. Paratyphi, Campylobacter and Streptobacillus moniliformis. Weather data was derived from the British Atmospheric Data Centre There was a significant association between excess cumulative rainfall in the previous 7 days and outbreaks (p=0.001). There was an excess of rainfall below 20 mm for the three weeks previous to this in outbreak compared to control weeks (p=0.002). Cumulative rainfall exceedances were associated with outbreak years. This study provides evidence that both low rainfall and heavy rain precede many drinking water outbreaks and assessing the health impacts of climate change should examine both.

Appendix: Data management and data archive for the HYREX Programme

Since the mid 1980s, changes in political imperatives plus technological changes in computer hardware and software have heightened the awareness of the economic value and importance of quality datasets to scientific research. The Natural Environment Research Council’s (NERC) interdisciplinary Thematic and Special Topic Programmes have highlighted the need for a coherent data management policy to provide and preserve these quality datasets for posterity. The Hydrological Radar EXperiment (HYREX) Special Topic Programme brought together multi-disciplinary researchers from UK public sector laboratories and universities. In this paper, the HYREX data management strategy, its problems and its solutions are discussed. The HYREX data archive, situated at NERC’s British Atmospheric Data Centre, is described. Keywords: radar, data, archive, web, storm, flood