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The emergence of new, statistically robust trends in Antarctic surface mass balance (SMB) requires an understanding of the underlying SMB variability (noise). We show that simple white or AR[1] noise models do not adequately represent the variability of SMB in both the RACMO2.3p2 SMB model output (1979-2017) and composite ice core records (1800-2010), underestimating low-frequency variability. By testing a range of noise models, we find that a Generalized Gauss Markov (GGM) model better approximates the noise around a linear trend. The general preference for GGM noise applies over spatial scales from the total ice sheet down to individual drainage basins. Over the longest timescales considered, trend uncertainties are 1.3-2.3 times larger using a GGM model compared to using an AR1 model at the ice sheet scale. Overall, our results suggest that larger trends or longer periods are required before new SMB trends can be robustly separated from background noise.

Plain Language Summary The rate of snowfall in Antarctica varies over months to millennia. Snowfall is expected to increase over coming decades as the climate warms and evaporates more water from the Southern Ocean and then falls as snow. The question we focus on is "when can we be sure a new trend has emerged?". To help answer this question we examine the variability of snowfall since 1800 as recorded in ice cores and as predicted since 1979 by a weather model. We find that variations in snowfall are largest over longer periods (decades) and that traditional ways of estimating the natural variability underestimate the "noise" at these long periods, by a factor of up to about 10. As a result, it is harder to detect genuine changes in trends in Antarctic snowfall than previously thought-they will need to be larger or persist for longer to confidently detect them. It is clear, nonetheless, that Antarctica is losing overall mass due to increased discharge from key glaciers, and this is expected to dominate any changes to snowfall.