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This paper explores the concept of co-stewardship in the Arctic through the lens of the Study of Environmental Arctic Change’s Human Wellbeing (HWB) team. Rooted in Indigenous knowledge and collaborative science, our work prioritizes equity in decision-making, recognizing multiple knowledge systems as equally valuable. Through intentional team-building, trust, and reciprocity, we examine successes, challenges, and opportunities in co-stewardship. Key successes include fostering meaningful relationships, integrating Indigenous perspectives into scientific and policy discussions, and uplifting innovative knowledge-sharing tools such as oral histories and visual storytelling. However, structural challenges persist, including colonial policy frameworks, inadequate funding models, and a lack of institutional mechanisms to support Indigenous leadership in co-stewardship initiatives. We propose policy shifts, long-term funding commitments, and greater Indigenous representation in decision-making as steps toward meaningful change. This work underscores the importance of Indigenous-led stewardship in addressing Arctic environmental and social challenges, offering a model for collaborative governance rooted in respect and reciprocity. 

Abstract Antarctic sea ice exhibits considerable regional variability that is influenced by ocean and atmospheric conditions. Previous studies have suggested that this variability may be predictable on seasonal-to-interannual time scales. Here, we use initial-value predictability experiments of the Community Earth System Model, version 2 (CESM2), paired with analysis of the CESM2 large ensemble, to further assess the inherent predictability in regional Antarctic sea ice conditions. As in previous studies, we find that Antarctic sea ice area predictability is high for several months after initialization. It is then lost when ice retreats, and predictability is regained in the following ice advance period. In our simulations, this process acts on multiyear time scales with little sensitivity to the seasonal initialization timing but has a strong regional dependence. Long-lived ocean temperature anomalies in the vicinity of the winter ice edge are the primary source of sea ice predictability. Different predictability characteristics occur across regions, depending on how these ocean temperature anomalies are advected relative to regional sea zones. Our results show that sea ice predictability can impart predictability to primary productivity in the Southern Ocean due to its impact on light availability. This has implications for the understanding and management of Southern Ocean marine ecosystems. 

Abstract. In the high-latitude Arctic, wintertime sea ice and snowinsulate the relatively warmer ocean from the colder atmosphere. While theclimate warms, wintertime Arctic surface heat fluxes remain dominated by theinsulating effects of snow and sea ice covering the ocean until the sea icethins enough or sea ice concentrations decrease enough to allow for directocean–atmosphere heat fluxes. The Community Earth System Model version 1 LargeEnsemble (CESM1-LE) simulates increases in wintertime conductive heat fluxesin the ice-covered Arctic Ocean by ∼ 7–11 W m−2 bythe mid-21st century, thereby driving an increased warming of theatmosphere. These increased fluxes are due to both thinning sea ice anddecreasing snow on sea ice. The simulations analyzed here use a sub-grid-scaleice thickness distribution. Surface heat flux estimates calculated usinggrid-cell mean values of sea ice thicknesses underestimate mean heat fluxesby ∼16 %–35 % and overestimate changes in conductive heatfluxes by up to ∼36 % in the wintertime Arctic basin evenwhen sea ice concentrations remain above 95 %. These results highlight howwintertime conductive heat fluxes will increase in a warming world evenduring times when sea ice concentrations remain high and that snow and thedistribution of snow significantly impact large-scale calculations ofwintertime surface heat budgets in the Arctic. 

Under rising atmospheric greenhouse gas concentrations, the Arctic exhibits amplified warming relative to the globe. This Arctic amplification is a defining feature of global warming. However, the Arctic is also home to large internal variability, which can make the detection of a forced climate response difficult. Here we use results from seven model large ensembles, which have different rates of Arctic warming and sea ice loss, to assess the time of emergence of anthropogenically-forced Arctic amplification. We find that this time of emergence occurs at the turn of the century in all models, ranging across the models by a decade from 1994–2005. We also assess transient changes in this amplified signal across the 21st century and beyond. Over the 21st century, the projections indicate that the maximum Arctic warming will transition from fall to winter due to sea ice reductions that extend further into the fall. Additionally, the magnitude of the annual amplification signal declines over the 21st century associated in part with a weakening albedo feedback strength. In a simulation that extends to the 23rd century, we find that as sea ice cover is completely lost, there is little further reduction in the surface albedo and Arctic amplification saturates at a level that is reduced from its 21st century value. 

Abstract Population ecology and biogeography applications often necessitate the transfer of models across spatial and/or temporal dimensions to make predictions outside the bounds of the data used for model fitting. However, ecological data are often spatiotemporally unbalanced such that the spatial or the temporal dimension tends to contain more data than the other. This unbalance frequently leads model transfers to become substitutions, which are predictions to a different dimension than the predictive model was built on. Despite the prevalence of substitutions in ecology, studies validating their performance and their underlying assumptions are scarce.Here, we present a case study demonstrating both space‐for‐time and time‐for‐space substitutions (TFSS) using emperor penguins (Aptenodytes forsteri) as the focal species. Using an abundance‐based species distribution model (aSDM) of adult emperor penguins in attendance during spring across 50 colonies, we predict long‐term annual fluctuations in fledgling abundance and breeding success at a single colony, Pointe Géologie. Subsequently, we construct statistical models from time series of extended counts on Pointe Géologie to predict average colony abundance distribution across 50 colonies.Our analysis reveals that the distance to nearest open water (NOW) exhibits the strongest association with both temporal and spatial data. Space‐for‐time substitution performance of the aSDM, as measured by the Pearson correlation coefficient, was 0.63 and 0.56 when predicting breeding success and fledgling abundance time series, respectively. Linear regression of fledgling abundance on NOW yields similar TFSS performance when predicting the abundance distribution of emperor penguin colonies with a correlation coefficient of 0.58.We posit that such space–time equivalence arises because: (1) emperor penguin colonies conform to their existing fundamental niche; (2) there is not yet any environmental novelty when comparing the spatial versus temporal variation of distance to the nearest open water; and (3) models of more specific components of life histories, such as fledgling abundance, rather than total population abundance, are more transferable. Identifying these conditions empirically can enhance the qualitative validation of substitutions in cases where direct validation data are lacking.