A new approach for simulating the paleo-evolution of the Northern Hemisphere ice sheets

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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 and oceanic consequences of transient ice-sheet evolution throughout the LGP and as a source of climate input to other ice-sheet models.
© Author(s) 2018. This work is distributed under the Creative Commons Attribution 4.0 License. This work was funded by the Spanish Ministerio de Economía y Competitividad through project MOCCA (Modelling Abrupt Climate Change, grant CGL2014-59384-R). Rubén Banderas was funded by a PhD thesis grant of the Universidad Complutense de Madrid. Alexander Robinson is funded by the Marie Curie Horizon2020 project CONCLIMA (Grant 703251). Part of the computations of this work were performed in EOLO, the HPC of Climate Change of the International Campus of Excellence of Moncloa, funded by MECD and MICINN. This is a contribution to CEI Moncloa. We would like to thank the two anonymous reviewers for their suggestions and comments, which have contributed to improve the paper.
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