Projections of severe heat waves in the United Kingdom

Sanderson, Michael; Ford, G. P.

doi:10.3354/cr01428

Cited by 9 publications

(8 citation statements)

References 25 publications

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“…-It is characterized by high summertime T e (and θ e ), and in situ data have indicated trends in T e exceed those in T alone (Schoof et al, 2015). Further, the region is largely congruent with an area of coherence for extreme T events in prior research (McKinnon et al, 2016).…”

Section: Study Regionmentioning

confidence: 73%

Modeling the contributions of global air temperature, synoptic-scale phenomena and soil moisture to near-surface static energy variability using artificial neural networks

Pryor¹,

Sullivan²,

Schoof³

2017

Preprint

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Abstract. The static energy content of the atmosphere is increasing at the global scale, but exhibits important sub-global and sub-regional scales of variability and is a useful parameter for integrating the net effect of changes in the partitioning of energy at the surface and for improving understanding of the causes of so-called <q>warming-holes</q> (i.e. locations with decreasing daily maximum air temperatures (T) or increasing trends of lower magnitude than the global mean). Further, measures of the static energy content (herein the equivalent potential temperature, &#952;e) are more strongly linked to excess human mortality and morbidity than air temperature alone, and have great relevance in understanding causes of past heat-related excess mortality and making projections of possible future events that are likely to be associated with negative human health and economic consequences. A new non-linear statistical model for summertime daily maximum and minimum &#952;e is developed and used to advance understanding of drivers of historical change and variability over the eastern USA. It is shown that soil moisture (SM) is particularly important in determining the magnitude of &#952;e over regions that have previously been identified as exhibiting <q>warming holes</q> confirming the key importance of SM in dictating the partitioning of net radiation into sensible and latent heat and dictating trends in near-surface T and &#952;e. Consistent with our a priori expectations, models built using Artificial Neural Networks (ANN) out-perform linear models that do not permit interaction of the predictor variables (global T, synoptic-scale meteorological conditions and SM). This is particularly marked in regions with high variability in min- and max-&#952;e, where more complex models built using ANN with multiple hidden layers are better able to capture the day-to-day variability in &#952;e and the occurrence of extreme max-&#952;e. Over the entire domain the ANN with 3 hidden layers exhibits high accuracy in predicting max-&#952;e&#8201;>&#8201;347&#8201;K. The median hit rate for max-&#952;e&#8201;>&#8201;347&#8201;K is >&#8201;0.60, while the median false alarm rate&#8201;&#8776;&#8201;0.08.

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Section: Study Regionmentioning

confidence: 73%

Modeling the contributions of global air temperature, synoptic-scale phenomena and soil moisture to near-surface static energy variability using artificial neural networks

Pryor¹,

Sullivan²,

Schoof³

2017

Preprint

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“…Fischer and Schar, ; Christidis et al ., ). By the 2040s, the heatwave season is expected to expand from July–August to May–September, and by the 2080s, over half the UK will experience heatwave conditions at some point every year (Sanderson and Ford, ). This has major implications for the long‐term infrastructure planning undertaken by Network Rail, and for the long‐term resilience of the whole of the UK transport network.…”

Section: Discussionmentioning

confidence: 99%

The hottest July day on the railway network: insights and thoughts for the future

Ferranti

Chapman

Lee

et al. 2017

Meteorological Applications

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The 1 July 2015 was the hottest July day on record (37.5 ∘ C recorded at Heathrow Airport) in the United Kingdom (UK), and record-breaking temperatures were recorded across England. This short-duration heatwave (30 June-1 July 2015) affected railway services both directly, by causing asset failure or malfunction, and indirectly, by necessitating the use of emergency speed restrictions (ESRs) to reduce the likelihood of track buckling. Incidents caused by heat and lightning were recorded across the British railway network, and knock-on delays affected rail travel in regions where extreme weather did not have a direct impact. Over both days there were more than 220 000 delay-minutes. Many of these could not be attributed specifically to the weather, but 23 700 delay-minutes were due to emergency speed restrictions (ESRs) as a preventative measure to reduce the likelihood of rail buckling, 12 800 to heat and 4000 to lightning incidents. All regions experienced more than twice the daily average delay-minutes on one or both days, costing an estimated £16 million to the national economy. Incidents on critical routes (e.g. London North Eastern connecting London and Scotland) or near critical transport nodes such as Manchester Piccadilly caused the longest delays. Under future warmer climatic conditions, heatwaves and extreme temperatures are projected to occur more frequently and the railway operator has several measures to adapt or update existing infrastructure in order to reduce the impact of heat and lightning. Alternative solutions such as low-cost sensors for real-time condition monitoring or green infrastructure for increased asset resilience should also be considered.

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“…Transport stakeholders must have a clear understanding of the risk that climate change poses to their infrastructure assets, and the processes controlling the risk and any associated uncertainties [40]. For example, climate change projections suggest an increase in the frequency and duration of heatwaves in the UK [41], which may increase the occurrence rail delays associated with track-buckling in the future [14], but the exact future maximum temperature or the location of rail buckling is uncertain. Moreover, there must be a clear understanding of the definition of "risk" as risk perception varies between individuals and organisations [42].…”

Section: Stakeholder Engagmentmentioning

confidence: 99%

Adaptation Becoming Business as Usual: A Framework for Climate-Change-Ready Transport Infrastructure

et al. 2018

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Extreme weather damages and disrupts transport infrastructure in a multitude of ways. Heavy rainfall and ensuing landslides or flooding may lead to road or rail closures; extreme heat can damage road surfaces, or cause tracks, signalling or electronic equipment to overheat, or thermal discomfort for passengers. As extreme weather is expected to occur more frequently in the future, transport infrastructure owners and operators must increase their preparedness in order to reduce weather-related service disruption and the associated financial costs. This article presents a two-sided framework for use by any organisation to develop climate-change-ready transport infrastructure, regardless of their current level of knowledge or preparedness for climate change. The framework is composed of an adaptation strategy and an implementation plan, and has the overarching ambition to embed climate change adaptation within organisational procedures so it becomes a normal function of business. It advocates adaptation pathways, i.e., sequential adaptive actions that do not compromise future actions. The circular, iterative structure ensures new knowledge, or socioeconomic changes may be incorporated, and that previous adaptations are evaluated. Moreover, the framework aligns with existing asset management procedures (e.g., ISO standards) or governmental or organisational approaches to climate change adaptation. By adopting this framework, organisations can self-identify their own level of adaptation readiness and seek to enhance it.

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Projections of severe heat waves in the United Kingdom

Cited by 9 publications

References 25 publications

Modeling the contributions of global air temperature, synoptic-scale phenomena and soil moisture to near-surface static energy variability using artificial neural networks

Modeling the contributions of global air temperature, synoptic-scale phenomena and soil moisture to near-surface static energy variability using artificial neural networks

The hottest July day on the railway network: insights and thoughts for the future

Adaptation Becoming Business as Usual: A Framework for Climate-Change-Ready Transport Infrastructure

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