Local scale assessment of climate change and its impacts in the Highlands and Islands of Scotland

The global climate is warming and there is consensus that recent warming trends will amplify, as the present century progresses, in response to a continued build up of atmospheric greenhouse gas (GHG) concentrations. However, there are limitations associated with Global Climate Model (GCM) and Regional Climate Model (RCM) outputs for topographically diverse regions. Strategic management decisions relating to maritime upland communities require locally resolved projections of change across a range of elevations, which are not supplied by the present generation of models. Here, some of these challenges are addressed via a series of distinctive analyses. Quality controlled baseline station data are used to assess performance outputs for seasonal mean values of temperature and precipitation from an RCM at representative locations across the region. In the case of temperature these inter-comparisons indicate a warm bias in the RCM-simulated seasonal minima for the transition seasons of spring and autumn, whereas for summer maxima there is a cold bias in RCM-simulated values. RCM-generated outputs of future changes to temperature and precipitation are then variably combined with station data to model altitudinal changes at western and eastern upland locations. These analyses indicate a substantial upward migration in key seasonal temperature isotherms associated with present vegetation zones for the climate change scenarios used. This approach is then extended by applying selected outputs to conduct Climate Change Impact Assessments (CCIAs) for the scenarios used in a series of upland case studies. The analyses flag a number of remaining research challenges. Principally, these are that scale-dependent controls on local topo-climates are not adequately captured in the GCM driven RCM projection. While the approach delivers more refined local-scale projections of possible change across a range of elevations than has hitherto been available, residual uncertainties associated with the use of GCM/RCM outputs remain

Current and future vulnerabilities to climate change in Ireland : (2010-CCRP-DS-2.3) End of Project Report

Vulnerability assessment is a key aspect of anchoring the potential impacts of climate change to present development planning. In developing a national climate change strategy for Ireland, an assessment of vulnerability at an early stage is essential in order to inform subsequent stages of the process. The key goal of this assessment is to identify first generation vulnerabilities for Ireland based on a sensitivity analysis across the key sectors. Strengthened by input from stakeholders with considerable expertise across the sectors, the results characterise the most vulnerable sectors ahead of a fuller climate change risk assessment which can inform subsequent adaptation options. The assessment also recognises the shift in emphasis away from better defining exposure and potential impacts to a better understanding of the factors that affect societies’ and systems’ sensitivity to those impacts and their capacity to adapt. This reflects the increasing recognition of the importance of considering social vulnerability alongside biophysical vulnerability. In essence this is a reflection of the shift in conceptual thinking away from a top-down scenario and impacts first approach to a bottom-up vulnerability and thresholds-first approach

Impact of missing data on the efficiency of homogenisation: experiments with ACMANTv3

The impact of missing data on the efficiency of homogenisation with ACMANTv3 is examined with simulated monthly surface air temperature test datasets. The homogeneous database is derived from an earlier benchmarking of daily temperature data in the USA, and then outliers and inhomogeneities (IHs) are randomly inserted into the time series. Three inhomogeneous datasets are generated and used, one with relatively few and small IHs, another one with IHs of medium frequency and size, and a third one with large and frequent IHs. All of the inserted IHs are changes to the means. Most of the IHs are single sudden shifts or pair of shifts resulting in platform-shaped biases. Each test dataset consists of 158 time series of 100 years length, and their mean spatial correlation is 0.68–0.88. For examining the impacts of missing data, seven experiments are performed, in which 18 series are left complete, while variable quantities (10–70%) of the data of the other 140 series are removed. The results show that data gaps have a greater impact on the monthly root mean squared error (RMSE) than the annual RMSE and trend bias. When data with a large ratio of gaps is homogenised, the reduction of the upper 5% of the monthly RMSE is the least successful, but even there, the efficiency remains positive. In terms of reducing the annual RMSE and trend bias, the efficiency is 54–91%. The inclusion of short and incomplete series with sufficient spatial correlation in all cases improves the efficiency of homogenisation with ACMANTv3

Winners and Losers: Climate Change Impacts on Biodiversity in Ireland

The climate envelope modelling described in this report represents a staged investigation into the possible impacts of climate change on the nature conservation resources of Ireland. It represents a significant piece of original research applying state-ofthe- art methods for the first time in Ireland, and is an important step in trying to understand the complex interactions between climate, climate change, and species and habitats across the island. The work is one part of the wider research programme Co-ordination, Communication and Adaptation for Climate Change in Ireland: an Integrated Approach (COCOADAPT) funded by the Environmental Protection Agency (EPA)

An application of HOMER and ACMANT for homogenising monthly precipitation records in Ireland.

Climate change studies based only on raw long-term data are potentially flawed due to the many breaks introduced from non-climatic sources, consequently quality controlled and homogenised climate data is desirable for basing climate related decision making on. Seasonal cycles of precipitation in Ireland and the UK are projected to become more marked as the climate changes, and regional extremes in summer dry spells and winter precipitation have been recorded in recent years. Therefore to analyse and monitor the evolution of precipitation patterns across Ireland, quality controlled and homogenous climate series are needed

An application of HOMER and ACMANT for homogenising monthly precipitation records in Ireland.

Climate change studies based only on raw long-term data are potentially flawed due to the many breaks introduced from non-climatic sources, consequently quality controlled and homogenised climate data is desirable for basing climate related decision making on. Seasonal cycles of precipitation in Ireland and the UK are projected to become more marked as the climate changes, and regional extremes in summer dry spells and winter precipitation have been recorded in recent years. Therefore to analyse and monitor the evolution of precipitation patterns across Ireland, quality controlled and homogenous climate series are needed

On the degrees of freedom of a semi-Riemannian metric

A semi-Riemannian metric in a n-manifold has n(n-1)/2 degrees of freedom, i.e. as many as the number of components of a differential 2-form. We prove that any semi-Riemannian metric can be obtained as a deformation of a constant curvature metric, this deformation being parametrized by a 2-for

Observation of Libron-Libron Interactions in Solid Hydrogen

The anharmonic interactions between librational waves in solid hydrogen are found to lead to significant perturbations in the single-libron spectrum. This large anharmonicity is also responsible for two-libron processes whose frequencies and Raman intensities are calculated. Our results for the one- and two-libron spectra are in excellent agreement with, and hence explain, the optical data

Sensitivity of ferry services to the Western Isles of Scotland to changes in wave and wind climate

PublishedJournal ArticleThis is the final version of the article. Available from AMS via the DOI in this record.The roughness of the seas is rarely mentioned as a major factor in the economic or social welfare of a region. In this study, the relationship between the ocean wave climate and the economy of the Western Isles of Scotland is examined. This sparsely populated region has a high dependency on marine activities, and ferry services provide vital links between communities. The seas in the region are among the roughest in the world during autumn and winter, however, making maintenance of a reliable ferry service both difficult and expensive. A deterioration in wave and wind climate either in response to natural variability or as a regional response to anthropogenic climate change is possible. Satellite altimetry and gale-frequency data are used to analyze the contemporary response of wave and wind climate to the North Atlantic Oscillation (NAO). The sensitivity of wave climate to the NAO extends to ferry routes that are only partially sheltered and are exposed to ocean waves; thus, the reliability of ferry services is sensitive to NAO. Any deterioration of the wave climate will result in a disproportionately large increase in ferry-service disruption. The impacts associated with an unusually large storm event that affected the region in January 2005 are briefly explored to provide an insight into vulnerability to future storm events. © 2013 American Meteorological Society.This research was largely supported by the Tyndall Centre for Climate Change Research project “Toward a vulnerability assessment for the UK coastline” (IT 1.15)

The Youth Comprehensive Risk Assessment (YCRA) as a Treatment Guidance Tool for Adolescents with Behavioral and Developmental Challenges

This chapter describes the evolution of the Youth Comprehensive Risk Assessment (YCRA) by first describing the need, then the evolution of the assessment tool, and finally studies that provide validation