Storylines of Summer Arctic climate change constrained by Barents-Kara Sea and Arctic tropospheric warming for climate risks assessment
Abstract. While climate models broadly agree on the changes expected to occur over the Arctic with global warming on a pan-Arctic scale (i.e., polar amplification, sea-ice loss, increased precipitation), the magnitude and patterns of those changes at regional and local scales remain uncertain. This limits the usability of climate model projections for risk assessments and their impact on human activities or ecosystems (e.g., fires, permafrost thawing). Whereas any single or ensemble-mean projection may be of limited use to stakeholders, recent studies have shown the value of the storyline approach in providing a comprehensive and tractable set of climate projections that can be used to evaluate changes in environmental or societal risks associated with global warming.
Here, we apply the storyline approach to a large ensemble of CMIP6 models, with the aim of distilling the wide spread in model predictions into four physically plausible outcomes of Arctic summertime climate change. This is made possible by leveraging strong covariability in the climate system, associated with well-known but poorly constrained teleconnections and local processes: specifically, we find that differences in Barents-Kara Sea warming and lower tropospheric warming over polar land regions among CMIP6 models explain most of the inter-model variability in pan-Arctic surface summer climate response to global warming. Based on this novel finding, we compare regional disparities in climate change across the four storylines. Our storyline analysis highlights the fact that, for a given amount of global warming, certain climate risks can be intensified while others may be lessened, relative to a “middle-of-the-road” ensemble mean projection. We find this to be particularly relevant when comparing climate change over terrestrial and marine areas of the Arctic, which can show substantial differences in their sensitivity to global warming. We conclude by discussing potential implications of our findings for modelling climate change impacts on ecosystems and human activities.
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