It is commonly thought that the results of inversions are unique to the model they are calculated in. This is because there are many complexities in ice flow modelling, and different choices are made in different models which can impact the outputs. There are dozens of different ice flow models used in our field of study, and it would be useful to know whether values calculated from inversion in one model are valid in others.
Across the PROPHET project, three models are being used. The modelling group here at Northumbria uses Úa, while our collaborators at other universities use models called STREAMICE and ISSM. All of these use inversion to calculate distributions of basal friction and the flow rate factor which represents internal ice properties in the equations. We are investigating the differences between the inversion processes, and whether the values calculated can be transferred between models for forward runs. This will depend on the extent to which the inversion outputs represent the underlying physics in the model equations, and how much they are affected by the individual numerical behaviour of the models.
Additionally, we are investigating the level of correlation between our inversion outputs and observations of reflectivity under ice derived from airborne radar measurements.
Among the equations which models use to calculate ice flow is the sliding law. This describes the relationship between velocity and basal stress, and contains the basal friction coefficient which is calculated by inversions.
There are several sliding laws which can be chosen, and the modelling community has not agreed upon which performs best. We are investigating the differences between model outputs, both inversions and forward runs, using different sliding laws. We hope to analyse the effects that each of these sliding laws has on ice flow, particularly how they each respond to changes in melt rates.
There are several processes from outside the ice sheet system which have an effect on Thwaites glacier, particularly where it meets the ocean. We call these external drivers of change.
We are running experiments to determine which of the external drivers have the greatest influence on the glacier. We are investigating the impacts of processes such as calving, ice shelf thinning rates and grounding line movement.
As described above, coupled modelling often produces the most realistic melt rates for an ice shelf. However, the process of coupling is far more computationally expensive and time-consuming. For many applications it is more useful to use a melt rate parameterisation, a simpler mathematical equation which can be directly included within an ice flow model.
As with many aspects of modelling, there are several options. We are using a parameterisation based on a plume model which calculates melt rates from information about the base of the ice shelf and depth profiles of ocean temperature and salinity. We are also investigating the potential of machine learning in predicting melt rates
Ice flow models rely on several input parameters which can introduce uncertainty into the outputs. We are working to quantify the uncertainty introduced by many of these parameters by using a surrogate model - a simplified version of the full model - to test thousands of combinations and see which produce results closest to those observed over the last few decades.
The results of this work will allow us to pick the best possible combinations of model inputs to improve our future predictions of how Thwaites will affect sea levels.
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