Projecting the Reoccurrence of Major Caribbean Hurricanes under the Paris Agreement Goals

Student thesis: Master's ThesisMaster of Science by Research (MScR)


Hurricanesareamongthemostdestructiveextremeweathereventsaffecting humanity, in both social and economic terms. Hurricane Maria devastated Puerto Rico in 2017 with the most rainfall to hit the country from a hurricane in 40 years, whilst secondary impacts such as flooding, landslides and disease were estimated to have claimed over a thousand lives. Since a large proportion of the Caribbean’s coastal communities are affected by these systemsandparticularlyvulnerabletotheirimpact,itiscriticalthatwedevelop an understanding of whether hurricane activity and associated impacts will change as a result of a warming climate – and if so, how – such that these countriescanbeinformedwhenpreparingfortheimpactsofclimatechange. This thesis explores the influence of a 1.5◦C and 2◦C global warming above the pre-industrial average (the Paris Agreement scenarios) on hurricane rainfall using a dynamical hurricane model applied to future projection simulations from four global circulation models (GCMs). Results indicatethatextremehurricanerainfalleventsaffectingtheCaribbeanregionare more likely in the Paris Agreement scenarios. The Eastern Caribbean region displays a strong global warming signal for example, a rainfall event consistent with hurricane Maria is 57% more likely in the Paris Agreement goal of 2◦C compared to the present climate. Overall, rainfall events resonant with hurricanes,Irma,GeorgesandMatthewbecomemorelikelyunderbothParis Agreement scenarios compared to the present climate. The likelihood of a hurricane with rainfall matching or exceeding that of hurricane Ivan, which hit Jamaica in 2004, does not largely differ between scenarios. Itshouldbenotedthatalargebiaswaspresentintherainfallestimations. Though bias was corrected by applying a correction factor to fit estimations to observed return periods of hurricane rainfall events, readers should be aware of reduced confidence in the results.
Date of Award28 Nov 2019
Original languageEnglish
Awarding Institution
  • The University of Bristol
SupervisorPaul J Valdes (Supervisor) & Dann M Mitchell (Supervisor)

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