Reconciling the surface temperature–surface mass balance relationship in models and ice cores in Antarctica over the last 2 centuries

Ice cores are an important record of the past surface mass balance (SMB) of ice sheets, with SMB mitigating the ice sheets' sea level impact over the recent decades. For the Antarctic Ice Sheet (AIS), SMB is dominated by large-scale atmospheric circulation, which collects warm moist air from further north and releases it in the form of snow as widespread accumulation or focused atmospheric rivers on the continent. This suggests that the snow deposited at the surface of the AIS should record strongly coupled SMB and surface air temperature (SAT) variations. Ice cores use δ18O as a proxy for SAT as they do not record SAT directly. Here, using isotope-enabled global climate models and the RACMO2.3 regional climate model, we calculate positive SMB–SAT and SMB–δ18O annual correlations over ∼90 % of the AIS. The high spatial resolution of the RACMO2.3 model allows us to highlight a number of areas where SMB and SAT are not correlated, and we show that wind-driven processes acting locally, such as foehn and katabatic effects, can overwhelm the large-scale atmospheric contribution in SMB and SAT responsible for the positive SMB–SAT annual correlations. We focus in particular on Dronning Maud Land, East Antarctica, where the ice promontories clearly show these wind-induced effects. However, using the PAGES2k ice core compilations of SMB and δ18O of Thomas et al. (2017) and Stenni et al. (2017), we obtain a weak annual correlation, on the order of 0.1, between SMB and δ18O over the past ∼150 years. We obtain an equivalently weak annual correlation between ice core SMB and the SAT reconstruction of Nicolas and Bromwich (2014) over the past ∼50 years, although the ice core sites are not spatially co-located with the areas displaying a low SMB–SAT annual correlation in the models. To resolve the discrepancy between the measured and modeled signals, we show that averaging the ice core records in close spatial proximity increases their SMB–SAT annual correlation. This increase shows that the weak measured annual correlation partly results from random noise present in the ice core records, but the change is not large enough to match the annual correlation calculated in the models. Our results thus indicate a positive correlation between SAT and SMB in models and ice core reconstructions but with a weaker value in observations that may be due to missing processes in models or some systematic biases in ice core data that are not removed by a simple average.

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