  The Early Anthropogenic Hypothesis considers that greenhouse gas
concentrations should have declined during the Holocene in absence of humankind
activity, leading to glacial inception around the present. It partly relies on
the fact that present levels of northern summer incoming solar radiation are
close to those that, in the past, preceded a glacial inception phenomenon,
associated to declines in greenhouse gas concentrations. However, experiments
with various numerical models of glacial cycles show that next glacial
inception may still be delayed by several ten thousands of years, even with the
assumption of greenhouse gas concentration declines during the Holocene.
Furthermore, as we show here, conceptual models designed to capture the gross
dynamics of the climate system as a whole suggest also that small disturbances
may sometimes cause substantial delays in glacial events, causing a fair level
of unpredictability on ice age dynamics. This suggests the need of a validated
mathematical description of the climate system dynamics that allows us to
quantify uncertainties on predictions. Here, it is proposed to organise our
knowledge about the physics and dynamics of glacial cycles through a Bayesian
inference network. Constraints on the physics and dynamics of climate can be
encapsulated into a stochastic dynamical system. These constraints include, in
particular, estimates of the sensitivity of the components of climate to
external forcings, inferred from plans of experiments with large simulators of
the atmosphere, oceans and ice sheets. On the other hand, palaeoclimate
observations are accounted for through a process of parameter calibration. We
discuss promises and challenges raised by this programme.
