Drivers and Impacts of Extreme Weather Events in Antarctica (ExtAnt)
Lead Research Organisation:
British Antarctic Survey
Department Name: Science Programmes
Abstract
ExtAnt will provide the first comprehensive assessment of present day and future high impact extreme weather events in Antarctica, and associated risks. Key risks include impacts of extreme weather on vulnerable ice shelves, the breakup of which can speed up flow of grounded ice and affect global sea level, and on the highly specialised Antarctic biodiversity. This ambitious programme brings together leading UK and international scientists to use new modelling resources and methods to elucidate drivers of extreme events. New modelling capability will be developed to quantify impacts of extreme events on surface melt of ice shelves. These advances will bring a step change over current knowledge of extremes. ExtAnt's legacy will include a dataset for advancing research into broader impacts, for example on ecosystems.
The ExtAnt programme of research has been developed around three core research aims (RAs) and specific objectives to achieve these, outlined as follows.
RA1. Quantify the relative contributions of key drivers of Antarctic extreme events and determine the role of anthropogenic forcing in specific observed cases.
A wealth of new high resolution regional climate model (RCM) reanalysis-driven hindcasts that have been and are being created will be used, along with observations, to establish the most comprehensive record to date of Antarctic weather extremes. This will provide a foundation for assessing drivers of extreme events by quantifying the roles of specific large-scale and synoptic phenomena, including cyclones and atmospheric rivers. New diagnostic model capabilities will provide unprecedented quantitative information on lower-latitude sources of moisture. Anthropogenic drivers will be assessed using a multi-method approach to extreme event attribution in rapid (use existing simulations) or delayed (generating new ensemble simulations) mode.
RA2. Resolve present day trends and variability of extreme events and their impacts: in particular, assess the roles of the ozone hole, GHG concentrations and modes of internal climate variability.
Antarctica exhibits the largest internal climate variability on earth, therefore in order answer questions relating to trends and variability of the occurrence / severity of extreme events, we will use large ensembles (LEs) of climate model simulations. LEs are created by running a climate model many times (often 10s of times) with the same external forcing (e.g. greenhouse gases (GHGs) or ozone). This will allow us to resolve the phases of internal climate variability most conducive to the occurrence of extremes and over which parts of Antarctica, alongside responses to anthropogenic forcing from GHGs and stratospheric ozone.
RA3. Quantify the severity and frequency of future Antarctic extreme events, and associated risks related to their impacts on vulnerable ice shelves, and provide information relevant for assessing impacts on ecosystems.
Projections of 21st century behaviour of extreme events will be developed to explore a range of possible futures associated with both internal variability and external forcing. A statistical RCM-emulator will be developed and used to help translate output from LE climate model simulations to local impact-relevant scales. This will provide input/forcing for a newly integrated melt lake model allowing impacts of different realisations of internal variability and different forcing scenarios on ice shelf stability to be assessed. Advanced statistical methods, including machine learning (ML) / artificial intelligence (AI), will be used to select a representative sample of LE projections for downscaling. Additionally, information on extremes relevant to ecosystems will be provided for ongoing and future research into ecological/broader impacts.
The ExtAnt programme of research has been developed around three core research aims (RAs) and specific objectives to achieve these, outlined as follows.
RA1. Quantify the relative contributions of key drivers of Antarctic extreme events and determine the role of anthropogenic forcing in specific observed cases.
A wealth of new high resolution regional climate model (RCM) reanalysis-driven hindcasts that have been and are being created will be used, along with observations, to establish the most comprehensive record to date of Antarctic weather extremes. This will provide a foundation for assessing drivers of extreme events by quantifying the roles of specific large-scale and synoptic phenomena, including cyclones and atmospheric rivers. New diagnostic model capabilities will provide unprecedented quantitative information on lower-latitude sources of moisture. Anthropogenic drivers will be assessed using a multi-method approach to extreme event attribution in rapid (use existing simulations) or delayed (generating new ensemble simulations) mode.
RA2. Resolve present day trends and variability of extreme events and their impacts: in particular, assess the roles of the ozone hole, GHG concentrations and modes of internal climate variability.
Antarctica exhibits the largest internal climate variability on earth, therefore in order answer questions relating to trends and variability of the occurrence / severity of extreme events, we will use large ensembles (LEs) of climate model simulations. LEs are created by running a climate model many times (often 10s of times) with the same external forcing (e.g. greenhouse gases (GHGs) or ozone). This will allow us to resolve the phases of internal climate variability most conducive to the occurrence of extremes and over which parts of Antarctica, alongside responses to anthropogenic forcing from GHGs and stratospheric ozone.
RA3. Quantify the severity and frequency of future Antarctic extreme events, and associated risks related to their impacts on vulnerable ice shelves, and provide information relevant for assessing impacts on ecosystems.
Projections of 21st century behaviour of extreme events will be developed to explore a range of possible futures associated with both internal variability and external forcing. A statistical RCM-emulator will be developed and used to help translate output from LE climate model simulations to local impact-relevant scales. This will provide input/forcing for a newly integrated melt lake model allowing impacts of different realisations of internal variability and different forcing scenarios on ice shelf stability to be assessed. Advanced statistical methods, including machine learning (ML) / artificial intelligence (AI), will be used to select a representative sample of LE projections for downscaling. Additionally, information on extremes relevant to ecosystems will be provided for ongoing and future research into ecological/broader impacts.
