Melting of the Greenland ice sheet (GrIS) in response to anthropogenic global warming poses a severe threat in terms of global sea level rise of more than 7~m for its complete loss (Allan et al., 2021), but also in terms of North Atlantic freshening that in turn destabilises the Atlantic Overturning Circulation (Boers, 2021). Modelling and paleoclimate evidence suggest that rapidly increasing temperatures in the Arctic can trigger positive feedback mechanisms, and the GrIS is hypothesised to exhibit multiple stable states (Gregory et al., 2020). Consequently, critical transitions are expected when the global mean surface temperature crosses specific thresholds, and there is substantial hysteresis between the alternative stable states (Robinson et al., 2012). Here, we investigate the impact of different overshoot scenarios with varying peak and convergence temperatures for a broad range of warming and subsequent cooling rates. Our results show that both the maximum GMT and the time span of overshooting given global mean temperature (GMT) targets are critical in determining GrIS stability. We find a threshold GMT around 4.5°C above pre-industrial levels for an abrupt loss of the ice sheet, preceded by several intermediate stable states. We show that overshoots of up to 7.5°C GMT above pre-industrial levels might be tolerable if GMTs are subsequently reduced below 1.5°C GMT above pre-industrial levels within a few centuries. However, our results also show that even temporarily overshooting the temperature threshold, without a transition to a new ice sheet state, still leads to dramatic sea-level rise up to several metres during the time span before returning to moderate GMTs.

<p>The rapid loss of Arctic Sea Ice (ASI) in the last decades is one of the most evident manifestations of anthropogenic climate change. A transition to an ice-free Arctic during summer would impact climate and ecosystems, both regionally and globally. The identification of Early-Warning Signals (EWSs) for the loss of the summer ASI could provide important insights into the state of the Arctic region.</p> <p>We collect and analyze CMIP6 model runs that reach ASI-free conditions (area below 10<sup>6</sup> km<sup>2</sup>) in September. Despite the high inter-model spread, with the range for the date of an ice-free summer spanning around 100 years, the evolution of the summer ASI area right before reaching ice-free conditions is strikingly similar across the CMIP6 models.</p> <p>When looking for EWSs for summer ASI loss, we observe a significant increase in the variance of the ASI area before reaching ice-free conditions. This behavior is detected in the majority of the models and also averaged over the ensemble. We find no increase in the 1-year-lag autocorrelation in model data, possibly due to the multiscale characteristics of climate variability, which can mask changes in serial correlations. However, in the satellite-inferred observations, increases in both variance and 1-year-lag autocorrelation have recently been revealed. </p>

The rapid loss of Arctic Sea Ice (ASI) in the last decades is one of the most evident manifestations of anthropogenic climate change. A transition to an ice-free Arctic during summer would impact climate and ecosystems, both regionally and globally. The identification of Early-Warning Signals (EWSs) for the loss of the summer ASI could provide important insights into the state of the Arctic region. We collect and analyze CMIP6 model runs that reach ASI-free conditions (area below 10^6 km^2) in September. Despite the high inter-model spread, with the range for the date of an ice-free summer spanning around 100 years, the evolution of the summer ASI area right before reaching ice-free conditions is strikingly similar across the CMIP6 models. When looking for EWSs for summer ASI loss, we observe a significant increase in the variance of the ASI area before reaching ice-free conditions. This behavior is detected in the majority of the models, and also averaged over the ensemble. We find no increase in the 1-year-lag autocorrelation in model data, possibly due to the multiscale characteristics of climate variability, which can mask changes in serial correlations. However, in the satellite-inferred observations, increases in both variance and 1-year-lag autocorrelation have recently been revealed.