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Abstract Winter Arctic sea-ice concentration (SIC) decline plays an important role in Arctic amplification which, in turn, influences Arctic ecosystems, midlatitude weather and climate. SIC over the Barents-Kara Seas (BKS) shows large interannual variations, whose origin is still unclear. Here we find that interannual variations in winter BKS SIC have significantly strengthened in recent decades likely due to increased amplitudes of the El Niño-Southern Oscillation (ENSO) in a warming climate. La Niña leads to enhanced Atlantic Hadley cell and a positive phase North Atlantic Oscillation-like anomaly pattern, together with concurring Ural blocking, that transports Atlantic ocean heat and atmospheric moisture toward the BKS and promotes sea-ice melting via intensified surface warming. The reverse is seen during El Niño which leads to weakened Atlantic poleward transport and an increase in the BKS SIC. Thus, interannual variability of the BKS SIC partly originates from ENSO via the Atlantic pathway.

We give sharp conditions for the large time asymptotic simplification of aggregation-diffusion equations with linear diffusion. As soon as the interaction potential is bounded and its first and second derivatives decay fast enough at infinity, then the linear diffusion overcomes its effect, either attractive or repulsive, for large times independently of the initial data, and solutions behave like the fundamental solution of the heat equation with some rate. The potential$$W(x) \sim \log |x|$$$W\left(x\right)\sim log\left|x\right|$for$$|x| \gg 1$$$\left|x\right|\gg 1$appears as the natural limiting case when the intermediate asymptotics change. In order to obtain such a result, we produce uniform-in-time estimates in a suitable rescaled change of variables for the entropy, the second moment, Sobolev norms and the$$C^\alpha $$$C^{\alpha }$regularity with a novel approach for this family of equations using modulus of continuity techniques.

Iterative computing, where the output accuracy gradually improves over multiple iterations, enables dynamic reconfiguration of energy-quality trade-offs by adjusting the latency (i.e., number of iterations). In order to take full advantage of the dynamic reconfigurability of iterative computing hardware, an efficient method for determining the optimal latency is crucial. In this paper, we introduce an integer linear programming (ILP)-based scheduling method to determine the optimal latency of iterative computing hardware. We consider the input-dependence of output accuracy of approximate hardware using data-driven error modeling for accurate quality estimation. The proposed method finds optimal or near-optimal latency with a significant speedup compared to exhaustive search and decision tree-based optimization.