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Abstract:
The Arctic is one of the regions most vulnerable to climate change. Its climate
is governed by self-reinforcing feedback mechanisms that amplify temperature
variability and trends. As a result, Arctic surface temperatures have increased
almost four times faster than the global average in recent decades and, alongside,
the Arctic sea ice is declining. Not only the trends but also the variability of these
two key variables are closely linked. In this dissertation, I examine two aspects of
the internal variability of sea ice and surface air temperature in a warming Arctic
climate using the powerful tool of large ensemble climate simulations.
The first part of this thesis focuses on the persistence/memory of Arctic sea
ice on seasonal to inter-annual timescales. This is relevant for sea-ice predictions,
which are of growing socio-economic interest due to the retreat of sea ice in the
warming Arctic. Previous studies have identified a substantial gap between the
operational forecast skill and model-based estimates of the potential predictability
of Arctic sea ice. By analyzing lagged correlations of sea-ice area anomalies in
large model ensembles and multiple observational products, I show that climate
models significantly overestimate the memory of pan-Arctic sea-ice area from the
summer months into the following year, which cannot be explained by internal
variability. I further show that the overestimation arises from how the seasonal ice zone "remembers" preceding summer sea-ice area anomalies. My results suggest
that there is likely a misrepresentation of processes related to the memory of sea
ice in climate models, which could explain part of the gap between potential and
operational forecast skill of Arctic sea-ice area.
The second part of this thesis addresses the response of daily Arctic surface
air temperature to global warming. While the average temperature is rapidly
increasing, previous studies have shown that the variability and the amplitude of
the seasonal cycle of Arctic surface air temperature are decreasing, all of which
can alter temperature extremes. I provide the first quantitative assessment of the
projected changes in the distribution of daily Arctic surface air temperatures as a
function of global warming in multiple large ensembles. Thereby, I show that the
reduction in daily temperature variations throughout the year is mainly caused
by the weakened seasonal temperature cycle and complemented by decreasing
sub-seasonal temperature variability in the cold seasons (autumn, winter, spring). I
further show that the reduced temperature variations dampen the increase in warm
extreme temperatures that would be caused only through mean warming by nearly
50% in the cold seasons and amplify the decrease in cold extreme temperatures at
even higher rates. My results show that a warmer Arctic climate will be subject to
fewer temperature variations and less extreme relative to its new mean temperature, which may ease adaptation to a new Arctic climate state.
Overall, this dissertation contributes to a better understanding of climate vari-
ability in the Arctic, its representation in climate models, and its changes under
global warming.
