Any experiment with climate models relies on a potentially large set of spatiotemporal
boundary conditions. These can represent both the initial state of the
system and/or forcings driving the model output throughout the experiment. These
boundary conditions are typically fixed using available reconstructions in climate
modelling ...
Any experiment with climate models relies on a potentially large set of spatiotemporal
boundary conditions. These can represent both the initial state of the
system and/or forcings driving the model output throughout the experiment. These
boundary conditions are typically fixed using available reconstructions in climate
modelling studies; however, in reality they are highly uncertain, that uncertainty is
unquantified, and the e↵ect on the output of the experiment can be considerable.
We develop efficient quantification of these uncertainties that combines relevant data
from multiple models and observations. Starting from the coexchangeability model,
we develop a coexchangeable process model to capture multiple correlated spatiotemporal
fields of variables. We demonstrate that further exchangeability judgements
over the parameters within this representation lead to a Bayes linear analogy of
a hierarchical model. We use the framework to provide a joint reconstruction of
sea-surface temperature and sea-ice concentration boundary conditions at the last
glacial maximum (23–19 kya) and use it to force an ensemble of ice-sheet simulations
using the FAMOUS-Ice coupled atmosphere and ice-sheet model. We demonstrate
that existing boundary conditions typically used in these experiments are implausible
given our uncertainties and demonstrate the impact of using more plausible boundary
conditions on ice-sheet simulation.