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Climate change is predicted to change forest composition, decrease carbon, and increase disturbance, with some forests at high risk of all three.

Fire is an important climate-driven disturbance in terrestrial ecosystems, also modulated by human ignitions or fire suppression. Changes in fire emissions can feed back on the global carbon cycle, but whether the trajectories of changing fire activity will exacerbate or attenuate climate change is poorly understood. Here, we quantify fire dynamics under historical and future climate and human demography using a coupled global climate–fire–carbon cycle model that emulates 34 individual Earth system models (ESMs). Results are compared with counterfactual worlds, one with a constant preindustrial fire regime and another without fire. Although uncertainty in projected fire effects is large and depends on ESM, socioeconomic trajectory, and emissions scenario, we find that changes in human demography tend to suppress global fire activity, keeping more carbon within terrestrial ecosystems and attenuating warming. Globally, changes in fire have acted to warm climate throughout most of the 20th century. However, recent and predicted future reductions in fire activity may reverse this, enhancing land carbon uptake and corresponding to offsetting ∼5 to 10 y of global CO 2 emissions at today’s levels. This potentially reduces warming by up to 0.11 °C by 2100. We show that climate–carbon cycle feedbacks, as caused by changing fire regimes, aremore »most effective at slowing global warming under lower emission scenarios. Our study highlights that ignitions and active and passive fire suppression can be as important in driving future fire regimes as changes in climate, although with some risk of more extreme fires regionally and with implications for other ecosystem functions in fire-dependent ecosystems.« less

Polymer dielectrics have been widely used in electrical and electronic systems for capacitive energy storage and electrical insulation. However, emerging applications such as electric vehicles and hybrid electric aircraft demand improved polymer dielectrics for operation not only under high electric fields and high temperatures, but also extreme conditions, for example, low pressures at high altitudes, with largely increased likelihood of electrical partial discharges. To meet these stringent requirements of grand electrifications for payload efficiency, polymers with enhanced discharge resistance are highly desired. Here, we present a surface-engineering approach for Kapton® coated with self-assembled two-dimensional montmorillonite nanosheets. By suppressing the magnitude of the high-field partial discharges, this nanocoating endows polymers with improved discharge resistance, with satisfactory discharge endurance life of 200 hours at a high electric field of 46 kV mm −1 while maintaining the surface morphology of the polymer. Moreover, the MMT nanocoating can also improve the thermal stability of Kapton®, with significantly suppressed temperature coefficients for both the dielectric constant and dielectric loss over a wide temperature range from 25 to 205 °C. This work provides a practical method of surface nanocoating to explore high-discharge-resistant polymers for harsh condition electrification.

Identifying anomalies, especially weak anomalies in constantly changing targets, is more difficult than in stable targets. In this article, we borrow the dynamics metrics and propose the concept of dynamics signature (DS) in multi-dimensional feature space to efficiently distinguish the abnormal event from the normal behaviors of a variable star. The corresponding dynamics criterion is proposed to check whether a star's current state is an anomaly. Based on the proposed concept of DS, we develop a highly optimized DS algorithm that can automatically detect anomalies from millions of stars' high cadence sky survey data in real-time. Microlensing, which is a typical anomaly in astronomical observation, is used to evaluate the proposed DS algorithm. Two datasets, parameterized sinusoidal dataset containing 262,440 light curves and real variable stars based dataset containing 462,996 light curves are used to evaluate the practical performance of the proposed DS algorithm. Experimental results show that our DS algorithm is highly accurate, sensitive to detecting weak microlensing events at very early stages, and fast enough to process 176,000 stars in less than 1 s on a commodity computer.