Friday, September 10
Natural Hazards
Friday, September 10
9:00 am - 10:00 am
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Session Six

Abstracts for each presentation are below and the feedback link. Please take the time to fill out the form. Your feedback will be used to identify the best poster and best oral presentation as well as providing valuable comments for the presenters.

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9:00 Rachel Hunt: Tsunami Early Warnings and Responses in New Zealand: Communication and Public Education

Individuals and communities are known to respond in different ways to official tsunami warnings and natural tsunami warning signs. This interdisciplinary research seeks to understand how official warnings are decided upon and communicated and the ways in which warnings can be tailored through education measures to improve tsunami awareness and preparedness. Online social research methods were used to investigate tsunami early warnings and responses in New Zealand.

Documents and archives were studied to examine the nature and content of official tsunami information and the methods currently used to communicate these warnings, these resources were provided by the interview participants. Semi-structured interviews were conducted with tsunami researchers, warning specialists, and emergency managers to gain an understanding of the opinions held on the effectiveness of official warnings and public education. Participants were recruited from research institutes, national agencies, and regional groups in New Zealand, Australia, the Pacific Islands, the UK, and the USA.

Preliminary content and thematic analyses show numerous recurring themes emerging from the qualitative data. The identified themes concern the responsibilities of the various research institutes, national agencies, and regional groups involved in monitoring, disseminating, and responding to official tsunami early warnings. These warnings are communicated on a national scale, whilst the responses vary between regions, similarly national education campaigns are used in conjunction with regional awareness and preparedness measures. Differences in the management of distally and locally generated tsunami events are also apparent, with the capability to communicate official warnings for distal events but a reliance on educating the public to observe natural warning signs for local events.

This research aims to improve the understanding of tsunami responses to official warnings and natural warning signs, as well as to strengthen the design of education materials for preparedness methods, contributing to the development of tsunami resilient communities in New Zealand.


9:15 Devon Francis: What Happens if the Information Used in Predicting the Weather is Biased?

To predict the weather, we rely on many satellite observations. Satellites allow us to take more observations of the Earth, which means much more of the Earth, especially areas that are difficult to access, can be observed more regularly.

However, satellites contain large non-random errors, known as biases, which need to be corrected before they can be used in weather prediction. Biases can also occur in the dynamical models which relate the observation measurements to the atmospheric variables we are interested in, such as temperature, wind speed and humidity. To correct the observation biases to the true atmosphere (and not to the biased models) we use unbiased observations to anchor the system to the truth. As we increase the number of satellite observations available for use in numerical weather prediction, the proportion of unbiased to biased observations decreases. In my research, we look at how we can use the unbiased observations most effectively to reduce the effect of model bias on the observation bias correction.

In this study we look at the importance of the location of the unbiased observations in their ability to reduce the effect of model bias in the observation bias correction. We derive analytical expressions to show the sensitivity of the observation bias correction to the unbiased observations. We find that the ability of the unbiased observations to correct the model bias is dependent on the information shared between the biased and the unbiased observations via the background error correlations. We show that it is necessary for the unbiased observations to observe the regions of significant model bias, in order to reduce the effect of model bias on the estimate of the observation bias.


9:30 Hannah Croad: Structure and Mechanisms of Summer-time Arctic Cyclones 

The rapid decline of Arctic sea ice extent is allowing human activity (e.g. shipping) to expand into the summer-time Arctic, where it will be exposed to the risks of Arctic weather. Arctic cyclones are synoptic-scale low pressure systems in the Arctic, and they produce some of the most impactful Arctic weather in summer, associated with strong winds and atmospheric forcings that have large impacts on the sea ice. Hence, there is a demand for accurate forecasts. However, Arctic cyclones are ~1-2 days less predictable than mid-latitude cyclones in current numerical weather prediction models. Improvements in forecasts of summer-time Arctic cyclones can be achieved through a better understanding of their structural evolution and mechanisms, since our current knowledge is based on a limited number of case studies. In this work, the 2020 Arctic summer season is analysed and used to illustrate the wide variety of cyclone structures in the Arctic. Cyclones can be classified as low-level dominant (warm core) or upper-level dominant (cold core). Furthermore, some cyclones develop as part of a baroclinic wave at maximum intensity (like typical mid-latitude cyclones), whilst others develop as separate anomalies far from a jet stream (with tropopause polar vortices), looking quite different from mid-latitude cyclones. This initial classification will motivate the formulation of conceptual models for Arctic cyclones. Examination of Arctic cyclone case studies highlights these structures, and also the role of frictional and diabatic processes (surface drag, surface heat fluxes, and atmospheric moisture) in the development of these cyclones over land, ocean, and sea ice.


9:45 Wilson Chan: Current and future risk of unprecedented UK droughts

Hydrological droughts, extended periods of below-normal river flow or groundwater levels threaten water resources availability. The UK has experienced recurring periods of hydrological droughts in the past and their frequency and severity are predicted to increase under future warming. Quantifying current and future chance of extreme droughts are challenging given the short observational record.

Here, we apply the UNSEEN (Unprecedented Simulation of Extreme Events using ENsembles) method to estimate current and future risk of unprecedented droughts and identify the meteorological conditions associated with their occurrence. The EC-Earth large ensemble, consisting of 2000 years each of present day, 2°C and 3°C conditions, is used to estimate current and future risk of low rainfall and to drive the GR6J hydrological model at 100 UK catchments to estimate risk of droughts.

Averaged across the UK, we find a 13% and 4% chance in summer and winter respectively with rainfall lower than the observed seasonal minimum in present day climate. Under future warming, the risk increases significantly in summer (average 38% at 3°C) but slightly decreases in winter due to projections of wetter winters. Simulated river flow show that mean annual minimum flow is projected to reduce across the UK under future warming. There is a low chance (~1%) of unprecedented droughts exceeding the worst observed drought in present day climate (notably difficult to exceed the 1975-76 drought) but the risk increases with future warming. Despite low current risk, it remains possible for worse droughts to develop across the UK in present day climate with the worst drought in the large ensemble being significantly longer with greater deficit and higher intensity compared to the worst observed drought. These results can improve risk estimates of extreme droughts and further research will focus on the occurrence of different drought types (e.g. heatwave-driven droughts and multi-year droughts).

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