Quantifying Arctic storm risk in a changing climate

A dramatic reduction in Arctic sea ice has opened up opportunities for business in diverse sectors such as fossil fuel & mineral extraction, shipping and tourism. Industrial activities in the Arctic are expected to be subject to high levels of investment over the coming decades. As a result, there has been an increase in the exposure of humans and infrastructure to extreme Arctic weather. This is particularly true in summer, when large swathes of ice-free water have opened up. Unlike the mid-latitude storm track, which is most active in winter, cyclones within the Arctic Ocean are more common in summer, during months of low sea ice cover.
This project will seek to quantify the risk posed by Arctic summer cyclones to offshore infrastructure, such as oil platforms, and shipping. It will examine the strength of surface winds and waves associated with such storms and how often extreme conditions, which pose a risk to infrastructure, occur. The project will be in two parts:

1. Assessing risk from Arctic cyclones in Reanalysis products, and understanding their shortcomings for long term analysis and trends: The remoteness of the Arctic resulted in low levels of observations before the 1980s. The impact of the lack of observations on the ability of atmospheric re-analysis products to represent long-term trends in Arctic storminess will be investigated by performing an Arctic observing system sensitivity study with the ERA-Interim reanalysis. This will be done in conjunction with European Centre for Medium Range Weather Forecasts (ECMWF).

2. Understanding interanual-to-decadal variability in storm frequency and its drivers using reanlaysis and model experiments: resently, little is known about how the frequency of Arctic weather extremes changes on interannual-to-decadal timescales. In the second half of the project, the student will combine observational analysis with the use of state-of-the-art climate model experiments to investigate the links between decadal shifts in key variables, such as sea ice cover, and Arctic storminess.

The successful student will spend a 3 month placement within XL Catlin’s risk management team, in London, to develop skills in risk analysis within a business setting. As part of collaboration with ECMWF, the student will spend 1 month at ECMWF where they will receive training in the use of ECMWF’s PrepIFS modeling package.

The project is supervised by Kevin Hodges (University of Reading), and co-supervised by Jonny Day (University of Reading), Len Shaffrey (University of Reading), and Tom Philp (XL Catlin).

The full project description is available at: http://www.met.reading.ac.uk/nercdtp/home/available/desc/SC201615.pdf