Abstract. Offline forcing methods for ice-sheet models often make use of an index
approach in which temperature anomalies relative to the present are calculated by
combining a simulated glacial–interglacial climatic anomaly field,
interpolated through an index derived from the Greenland ice-core temperature
reconstruction, with present-day climatologies. An important drawback of this
approach is that it clearly misrepresents climate variability at millennial
timescales. The reason for this is that the spatial glacial–interglacial
anomaly field used is associated with orbital climatic variations, while it
is scaled following the characteristic time evolution of the index, which
includes orbital and millennial-scale climate variability. The spatial
patterns of orbital and millennial variability are clearly not the same, as
indicated by a wealth of models and data. As a result, this method can be
expected to lead to a misrepresentation of climate variability and thus of
the past evolution of Northern Hemisphere (NH) ice sheets. Here we illustrate
the problems derived from this approach and propose a new offline climate
forcing method that attempts to better represent the characteristic pattern
of millennial-scale climate variability by including an additional spatial
anomaly field associated with this timescale. To this end, three different
synthetic transient forcing climatologies are developed for the past
120 kyr following a perturbative approach and are applied to an ice-sheet
model. The impact of the climatologies on the paleo-evolution of the NH ice
sheets is evaluated. The first method follows the usual index approach in which temperature anomalies relative to the present are calculated by combining a simulated glacial–interglacial climatic anomaly field, interpolated through an index derived from ice-core data, with present-day climatologies.
In the second approach the representation of millennial-scale climate
variability is improved by incorporating a simulated stadial–interstadial
anomaly field. The third is a refinement of the second one in which the
amplitudes of both orbital and millennial-scale variations are tuned to
provide perfect agreement with a recently published absolute temperature
reconstruction over Greenland. The comparison of the three climate forcing
methods highlights the tendency of the usual index approach to overestimate
the temperature variability over North America and Eurasia at millennial
timescales. This leads to a relatively high NH ice-volume variability on
these timescales. Through enhanced ablation, this results in too low an ice
volume throughout the last glacial period (LGP), below or at the lower end of
the uncertainty range of estimations. Improving the representation of
millennial-scale variability alone yields an important increase in ice volume
in all NH ice sheets but especially in the Fennoscandian Ice Sheet (FIS).
Optimizing the amplitude of the temperature anomalies to match the Greenland
reconstruction results in a further increase in the simulated ice-sheet
volume throughout the LGP. Our new method provides a more realistic
representation of orbital and millennial-scale climate variability and
improves the transient forcing of ice sheets during the LGP. Interestingly,
our new approach underestimates ice-volume variations on millennial
timescales as indicated by sea-level records. This suggests that either the
origin of the latter is not the NH or that processes not represented in our
study, notably variations in oceanic conditions, need to be invoked to
explain millennial-scale ice-volume fluctuations. We finally provide here
both our derived climate evolution of the LGP using the three methods as well
as the resulting ice-sheet configurations. These could be of interest for
future studies dealing with the atmospheric or/and oceanic consequences of
transient ice-sheet evolution throughout the LGP and as a source of climate
input to other ice-sheet models.
Supplementary material to "Dust record in an ice core from tropical Andes (Nevado Illimani – Bolivia), potential for climate variability analyses in the Amazon basin"
