New paper on Gaia and planetary habitability

New paper out – well done Arwen!

Arwen E Nicholson, David M Wilkinson, Hywel T P Williams, Timothy M Lenton; Gaian bottlenecks and planetary habitability maintained by evolving model biospheres: The ExoGaia model, Monthly Notices of the Royal Astronomical Society, , sty658,

The search for habitable exoplanets inspires the question – how do habitable planets form? Planet habitability models traditionally focus on abiotic processes and neglect a biotic response to changing conditions on an inhabited planet. The Gaia hypothesis postulates that life influences the Earth’s feedback mechanisms to form a self-regulating system, and hence that life can maintain habitable conditions on its host planet. If life has a strong influence, it will have a role in determining a planet’s habitability over time. We present the ExoGaia model – a model of simple ‘planets’ host to evolving microbial biospheres. Microbes interact with their host planet via consumption and excretion of atmospheric chemicals. Model planets orbit a ‘star’ which provides incoming radiation, and atmospheric chemicals have either an albedo, or a heat-trapping property. Planetary temperatures can therefore be altered by microbes via their metabolisms. We seed multiple model planets with life while their atmospheres are still forming and find that the microbial biospheres are, under suitable conditions, generally able to prevent the host planets from reaching inhospitable temperatures, as would happen on a lifeless planet. We find that the underlying geochemistry plays a strong role in determining long-term habitability prospects of a planet. We find five distinct classes of model planets, including clear examples of ‘Gaian bottlenecks’ – a phenomenon whereby life either rapidly goes extinct leaving an inhospitable planet, or survives indefinitely maintaining planetary habitability. These results suggest that life might play a crucial role in determining the long-term habitability of planets.

PhD opportunity: Online (mis)information and climate change

Fully funded PhD position to start in September 2018 – apply now!

Online (mis)information and climate change: Using network analysis and machine learning to understand environmental debate

Despite widespread scientific consensus, climate change remains a controversial and politicised topic. On one side, environmentalists push for greater action to prevent and mitigate the effects of climate change. On the other, a well-funded climate denial lobby promote doubt and confuse public opinion. This debate is actively pursued in online news and social media, where denialist blogs and commentators attempt to discredit the scientific viewpoint with a steady stream of contrarian articles and social media posts.

This PhD project will apply advanced computational methods to understand the online media ecosystem around climate change. In particular, it will seek to characterise the role of misinformation in online climate debates, looking in particular at social media accounts, bots and fake news sites linked to the climate denial viewpoint. Within this topic area there is considerable scope for the student to shape the project towards their own interests. The methods utilised will depend on the exact research question chosen, but are likely to combine complex network analysis, machine learning and text mining.

Find out more and apply here:

Deadline: 8th March 2018

New paper: Walding et al (2018) A comparison of the US National Fire Danger Rating System (NFDRS) with recorded fire occurrence and final fire size.

New paper in International Journal of Wildland Fire – well done Nick! Find it here:

An analysis of the National Fire Danger Rating System for the conterminous US (2006–13). Fire danger indices are correlated with measures of fire activity in order to identify spatial patterns and discrepancies across the US and identify different aspects of wildfire activity along several fire danger spectrums.


New publication: Social sensing of floods in the UK

“Social sensing” is a form of crowd-sourcing that involves systematic analysis of digital communications to detect real-world events. Here we consider the use of social sensing for observing natural hazards. In particular, we present a case study that uses data from a popular social media platform (Twitter) to detect and locate flood events in the UK. In order to improve data quality we apply a number of filters (timezone, simple text filters and a naive Bayes ‘relevance’ filter) to the data. We then use place names in the user profile and message text to infer the location of the tweets. These two steps remove most of the irrelevant tweets and yield orders of magnitude more located tweets than we have by relying on geo-tagged data. We demonstrate that high resolution social sensing of floods is feasible and we can produce high-quality historical and real-time maps of floods using Twitter.

Floodiness grid, 64 × 64, over England and Wales on 5/12/2015 using (r, α, T) = (1.0, 0.15, 0.1).
Using tweets collected in 1 hour windows. White indicates no tweets. Colour bar units are floodiness relative to daily max. Top left: 10am-11am. Top Right: 1pm-2pm. Bottom Left: 4pm-5pm. Bottom Right: 9pm-10pm.

Read this article online.