Quantifying sources of uncertainty in regional climate model scenarios for Ireland

This thesis develops a novel framework for model skill assessment and the generation of probabilistic future climate scenarios. Traditional approaches to model validation assume that skill in simulating the mean climate is a valid indicator of skill in modelling the climate system. However, without information about how errors arise, conclusions cannot be drawn about whether models are genuinely skilful. Initially, verification statistics are used to assess model skill in simulating seasonal means and variability of Irish climate for 1961-1990. Significant biases were identified, however without further analysis, these biases cannot be attributed to a cause. Therefore, a spatial analysis, including EOF analysis, was undertaken which indicated that biases may be either spatially consistent (systematic) or inconsistent (random), an important distinction. Next, representation of a key large-scale driver of Irish climate, the North Atlantic Oscillation, was examined for a representative subsample of models. Skill in simulating the NAO was found to vary considerably between models. Therefore, assessing statistics of mean climate may not be the optimum way to characterize model skill, as deficiencies in the representation of large-scale drivers may not be detected. Both quantitative and qualitative information from the skill assessments was used to inform probabilistic ensemble projections of future climate using Bayesian Model Averaging. In some cases, weighting scheme variation affects the ensemble PDF shape. In other cases, PDFs are similar when different weights are used, but the relative contributions of ensemble members vary. This is a crucial finding, as this underlying variation may not be immediately apparent, but may affect the confidence attached to the PDF. Therefore, robustness of ensemble generation methods must be considered when determining the level of confidence attached to a projection. Finally, the implications of these results for climate decision-making are discussed and recommendations for the use of climate models in decision-making are presented

Long-term trends in large-scale circulation behaviour and wind storms for North Atlantic islands: a multi-data analysis using ERA-20C and meteorological station data

This research examines the role that large-scale circulation plays in local storminess for the North Atlantic islands of Orkney and Shetland, using the ERA-20C (1900–2009) reanalysis dataset. Automatic Lamb weather type classification is applied to daily mean sea level pressure (MSLP) data at 0.125° × 0.125° resolution to typify large-scale circulation patterns and calculate measures of storminess (frequencies of severe gale days, very severe gale days, and gale days that are not severe), calibrated using available observations from meteorological stations on the islands, which were made available by the UK Met Office. Analysis of the reanalysis-based gale day record indicates that while the frequency of cyclonic (C) weather type days does not vary over the study period, this weather type is coinciding more often with gale days and especially very severe gale days, which may indicate an increase in the intensity of cyclonic weather types in the region

Model skill measures in probabilistic regional climate projections for Ireland.

In the present study, a range of regional climate models have been used to test approaches to Bayesian model averaging (BMA), particularly the quantification of model weights/Bayesian priors. The results of skill assessments were used to inform probabilistic future projections of Irish climate using a BMA approach in order to evaluate how different approaches to skill assessment, based on representation of climate means, or of a large-scale driver (the NAO), or a combination thereof, may influence the final climate projection. Results indicate that meansbased skill assessments may not always provide a useful indication of model skill and that further analyses are required to assess a model’s ability to simulate the dynamics of the climate system. While this research illustrates that the use of metrics derived from the model predicted NAO impacts on the regional projection, it also supports the inclusion of other large-scale model diagnostics. When used to weight model projections to produce ensemble climate projections, the choice of skill metric may have an impact on the shape of the probability distribution and the most probable outcome of future climate predictions. The present study demonstrates that when working with probabilistic outputs of ensemble climate modelling experiments, awareness of the approaches used to evaluate models and the techniques used to combine them to formulate ensemble projections are integral in enabling robust responses to the potential changes in climate represented by models

Running as a catalyst for environmental data inquiry: closing the distance between ‘everyday’ and ‘expert’ knowledges

Public misunderstanding of environmental data is often framed as a skills deficit on the part of the audience, but in truth, ‘data’ is multifarious and manifold in everyday life. Drawing on my experiences as an exceedingly average runner with a geographer’s appreciation for maps, this essay charts how running has catalysed my own inquiry with environmental data, acting as an embodied methodology for thinking about place-based environmental change and risks, like flooding. I argue that communication of such risks could be enhanced by integrating data into the everyday spaces and activities where people encounter maps, aiding them to make connections between familiar forms of data and new knowledge

Climate impact assessment and ‘islandness’: Challenges and opportunities of knowledge production and decision-making for Small Island Developing States

Purpose: Climate data, including historical climate observations and climate model outputs, are often utilised in climate impact assessments, to explore potential climate futures. However, characteristics often associated with ‘islandness’, such as smallness, land boundedness and isolation, may mean that climate impact assessment methods applied at broader scales cannot simply be downscaled to island settings. This paper discusses information needs and the limitations of climate models and datasets in the context of small islands, and explores how such challenges might be addressed. Design/methodology/approach: Reviewing existing literature, the paper explores challenges of islandness in top-down, model-led climate impact assessment, and bottom-up, vulnerability-led approaches. It examines how alternative forms of knowledge production can play a role in validating models and in informing adaptation actions at the local level, and highlights decision-making techniques that can support adaptation even when data is uncertain. Findings: Small island topography is often too detailed for global or even regional climate models to resolve, but equally, local meteorological station data may be absent or uncertain, particularly in island peripheries. However, rather than viewing the issue as decision-making with big data at the regional/global scale versus with little or no data at the small island scale, a more productive discourse can emerge by conceptualising strategies of decision-making with unconventional types of data. Originality/value: This paper provides a critical overview and synthesis of issues relating to climate models, datasets and impact assessment methods as they pertain to islands, which can benefit decision-makers and other end-users of climate data in island communities

EURO-CORDEX regional climate model simulation of precipitation on Scottish islands (1971-2000): Model performance and implications for decision-making in topographically complex regions

Due to their scale and complex topography, islands such as the Hebrides and Shetland Islands are not fully resolved by global climate models, which may impact the quality of data that can be provided about future climate in such locations. In principle, dynamical downscaling may provide helpful additional detail about future local climate. However, there is also the potential for error and uncertainty to cascade through to the regional simulation. Here, we evaluate the simulative skill of the EURO-CORDEX regional climate model ensemble on regional and local scales in the Hebrides and Shetland Islands, and consider the potential for such models to aid decision-making in island settings, and other locations characterised by complex topography. Several precipitation indices (accumulated precipitation amount, mean daily precipitation amount, max 1-day and 5-day precipitation amounts, simple daily intensity, number of heavy and very heavy precipitation days) are used to assess model performance and identify bias relative to observations. Models are compared regionally, and at specific locations, namely Stornoway in the Hebrides and Lerwick in Shetland, for the period 1971-2000. Regional evaluation utilises the UKCP09 gridded observational dataset and local evaluation at Stornoway Airport and Lerwick utilises observed mean precipitation and extreme indices from the European Climate Assessment & Dataset project. While no models perform skilfully across all the metrics studied, some models capture aspects of the precipitation climate at each location particularly well. Differences in model performance between the two case study sites highlight the value of evaluating models on multiple spatial scales. The implications of model uncertainty for decision-making are also discussed

Guest editorial introduction : critical reflections on governance and ‘resilience’ in small island contexts

Climate policy documents and national plans of small island states and subnational jurisdictions frequently reference the need for ‘resilience’. Yet, definitions of ‘resilience’ vary across disciplines, and depend on one’s cultural lens. Furthermore, climatic trends and events are often not the only challenges facing island communities; they occur alongside political, economic, social, and cultural change and events, giving rise to context- specific and interlinked vulnerabilities, which in turn require tailored and thoughtful solutions. This special section seeks to reflect on what the concept of 'resilience' means in island contexts, how it is deployed, and the dynamics of governance and decision making for 'resilience'. Drawing on the papers in this special section, we suggest that there are several points of ‘creative tension’ in resilience discourse. Identifying the gaps between ‘resilience’ as currently conceptualised, and what could be, helps us move towards more equitable and just resilience.peer-reviewe

Sustainable futures in a changing climate: an open education resource

This Open Education Resource Syllabus focuses on climate change using different disciplinary perspectives to explore the topic. We have endeavoured to make this syllabus accessible and adaptable for a range of potential users – including schoolteachers, university faculty not ordinarily engaged in the themes and wanting to integrate climate change into their usual courses, and facilitators of staff workshops on climate change. We hope it all who explore its resources will find something of use for their own practices

Carbon emission savings and short-­term health care impacts from telemedicine: An evaluation in epilepsy

Objective: Health systems make a sizeable contribution to national emissions of greenhouse gases that contribute to global climate change. The UK National Health Service is committed to being a net zero emitter by 2040, and a potential contribution to this target could come from reductions in patient travel. Achieving this will require actions at many levels. We sought to determine potential savings and risks over the short term from telemedicine through virtual clinics. Methods: During the severe acute respiratory syndrome coronavirus 2 (SARS-2-CoV) pandemic, scheduled face-to-face epilepsy clinics at a specialist site were replaced by remote teleclinics. We used a standard methodology applying conversion factors to calculate emissions based on the total saved travel distance. A further conversion factor was used to derive emissions associated with electricity consumption to deliver remote clinics from which net savings could be calculated. Patients’ records and clinicians were interrogated to identify any adverse clinical outcomes. Results: We found that enforced telemedicine delivery for over 1200 patients resulted in the saving of ~224 000 km of travel with likely avoided emissions in the range of 35 000–40 000 kg carbon dioxide equivalent (CO2e) over a six and half month period. Emissions arising directly from remote delivery were calculated to be <200 kg CO2e (~0.5% of those for travel), representing a significant net reduction of greenhouse gas emissions. Only one direct adverse outcome was identified, with some additional benefits identified anecdotally. Significance: The use of telemedicine can make a contribution toward reduced emissions in the health care sector and, in the delivery of specialized epilepsy services, had minimal adverse clinical outcomes over the short term. However, these outcomes will likely vary with clinic locations, medical specialties and conditions