• Energy Research
• 2021-2025close
• natural sciences close
• Max Planck Society close

AbstractIndirect climate effects on tree fecundity that come through variation in size and growth (climate-condition interactions) are not currently part of models used to predict future forests. Trends in species abundances predicted from meta-analyses and species distribution models will be misleading if they depend on the conditions of individuals. Here we find from a synthesis of tree species in North America that climate-condition interactions dominate responses through two pathways, i) effects of growth that depend on climate, and ii) effects of climate that depend on tree size. Because tree fecundity first increases and then declines with size, climate change that stimulates growth promotes a shift of small trees to more fecund sizes, but the opposite can be true for large sizes. Change the depresses growth also affects fecundity. We find a biogeographic divide, with these interactions reducing fecundity in the West and increasing it in the East. Continental-scale responses of these forests are thus driven largely by indirect effects, recommending management for climate change that considers multiple demographic rates.

Abstract Pellet edge localized mode (ELM) triggering is a well-established scheme for decreasing the time between two successive ELM crashes below its natural value. Reliable ELM pacing has been demonstrated experimentally in several devices, increasing the ELM frequency considerably. However, it was also shown that the frequency cannot be increased arbitrarily due to a so-called lag-time. During this time, after a preceding natural or triggered ELM crash, neither a natural ELM crash occurs nor is it possible to trigger an ELM crash by pellet injection. For this article, pellet ELM triggering simulations are advanced beyond previous studies in two ways. Firstly, realistic E × B and diamagnetic background flows are included. And secondly, the pellet is injected at different stages of the pedestal build-up. This allows us to recover the lag time for the first time in simulations and investigate it in detail. A series of nonlinear extended MHD simulations is performed to investigate the plasma dynamics resulting from an injection at different time points during the pedestal build-up. The experimentally observed lag-time is qualitatively reproduced. In particular, a sharp transition is observed between the regime where no ELMs can be triggered and the regime where pellet injection causes an ELM crash. Via variations of pellet parameters and injection time, the two regimes are studied and compared in detail, revealing pronounced differences in the nonlinear dynamics. The toroidal mode spectrum is significantly broader when an ELM crash is triggered, enhancing the stochasticity and therefore also the losses of thermal energy along magnetic field lines. In the heat fluxes to the divertor targets, pronounced toroidal asymmetries are observed. In the case of high injection velocities leading to deep penetration, the excitation of core modes like the 2/1 neoclassical tearing mode is also observed.

AbstractWe study the use of deep learning techniques to reconstruct the kinematics of the neutral current deep inelastic scattering (DIS) process in electron–proton collisions. In particular, we use simulated data from the ZEUS experiment at the HERA accelerator facility, and train deep neural networks to reconstruct the kinematic variables $$Q^2$$ Q 2 and x. Our approach is based on the information used in the classical construction methods, the measurements of the scattered lepton, and the hadronic final state in the detector, but is enhanced through correlations and patterns revealed with the simulated data sets. We show that, with the appropriate selection of a training set, the neural networks sufficiently surpass all classical reconstruction methods on most of the kinematic range considered. Rapid access to large samples of simulated data and the ability of neural networks to effectively extract information from large data sets, both suggest that deep learning techniques to reconstruct DIS kinematics can serve as a rigorous method to combine and outperform the classical reconstruction methods.

DIII-D L-mode experiments with local boron powder injection for real-time wall conditioning have been interpreted for the first time with the 3D plasma edge transport Monte Carlo code EMC3-EIRENE. Local B sourcing in plasma scenarios with upstream densities 1.5⋅1019m−3 and 2.2 MW heating results in a non-axisymmetric B distribution in the scrape-off layer (SOL) and on the divertor. The SOL frictional flows at high plasma density cause a strong inboard drag of injected impurities (≈90%), while lower background plasma densities tend to result in a more uniform distribution. The thermal forces prevent B deposition in the near SOL while the frictional force causes B fluxes to cover the divertor plasma-facing components in a region 7–10 cm beyond the strike line. Radiative dissipation occurs for B influxes above 1⋅1020s−1 and causes a moderate, non-axisymmetric reduction of the far SOL divertor heat fluxes. A comparison of top and midplane B injection shows no substantial difference in inboard vs. outboard asymmetries of the B distribution. On the other hand, erosion or recycling at the strike line may distribute the boron more uniformly in the SOL.

Energy dissipation in the plasma edge is key for future tokamaks. The potential of neon as radiating seeding species in disconnected double null (DDN) configuration is assessed in EAST discharges in high confinement mode (H-mode). As the separation between the two separatrices in the studied DDN discharges is minimum 1.5 cm, the configuration is effectively a single null configuration, and the benefits of the double null topology are minimal. Neon seeding, on the other hand, has a favourable effect: both the target heat flux and the divertor temperature decrease more than five-fold with increased seeding rate in high-recycling conditions. Interpretive edge plasma simulations with SOLPS-ITER in support of ongoing transport analysis are presented. For the unseeded case the numerical results agree with the experimental data within a factor two for the target temperature conditions and measured neutral pressures in the active divertor. The key for achieving good agreement is a suitable selection of coefficients for anomalous transport and neutral conductances between the upper cryopump and the main chamber.

AbstractUnprecedented and dramatic transformations are occurring in the Arctic in response to climate change, but academic, public, and political discourse has disproportionately focussed on the most visible and direct aspects of change, including sea ice melt, permafrost thaw, the fate of charismatic megafauna, and the expansion of fisheries. Such narratives disregard the importance of less visible and indirect processes and, in particular, miss the substantive contribution of the shelf seafloor in regulating nutrients and sequestering carbon. Here, we summarise the biogeochemical functioning of the Arctic shelf seafloor before considering how climate change and regional adjustments to human activities may alter its biogeochemical and ecological dynamics, including ecosystem function, carbon burial, or nutrient recycling. We highlight the importance of the Arctic benthic system in mitigating climatic and anthropogenic change and, with a focus on the Barents Sea, offer some observations and our perspectives on future management and policy.