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  1. Abstract

    The influence of El Niño–Southern Oscillation (ENSO) in the Asian monsoon region can persist through the post-ENSO summer, after the sea surface temperature (SST) anomalies in the tropical Pacific have dissipated. The long persistence of coherent post-ENSO anomalies is caused by a positive feedback due to interbasin ocean–atmospheric coupling, known as the Indo-western Pacific Ocean capacitor (IPOC) effect, although the feedback mechanism itself does not necessarily rely on the antecedence of ENSO events, suggesting the potential for substantial internal variability independent of ENSO. To investigate the respective role of ENSO forcing and non-ENSO internal variability, we conduct ensemble “forecast” experiments with a full-physics, globally coupled atmosphere–ocean model initialized from a multidecadal tropical Pacific pacemaker simulation. The leading mode of internal variability as represented by the forecast-ensemble spread resembles the post-ENSO IPOC, despite the absence of antecedent ENSO forcing by design. The persistent atmospheric and oceanic anomalies in the leading mode highlight the positive feedback mechanism in the internal variability. The large sample size afforded by the ensemble spread allows us to identify robust non-ENSO precursors of summer IPOC variability, including a cool SST patch over the tropical northwestern Pacific, a warming patch in the tropical North Atlantic, and downwelling oceanic Rossby waves in the tropical Indian Ocean south of the equator. The pathways by which the precursors develop into the summer IPOC mode and the implications for improved predictability are discussed.

     
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  2. Abstract

    The northeastern Pacific climate system features an extensive low-cloud deck off California on the southeastern flank of the subtropical high that accompanies intense northeasterly trades and relatively low sea surface temperatures (SSTs). This study assesses climatological impacts of the low-cloud deck and their seasonal differences by regionally turning on and off the low-cloud radiative effect in a fully coupled atmosphere–ocean model. The simulations demonstrate that the cloud radiative effect causes a local SST decrease of up to 3°C on an annual average with the response extending southwestward with intensified trade winds, indicative of the wind–evaporation–SST (WES) feedback. This nonlocal wind response is strong in summer, when the SST decrease peaks due to increased shortwave cooling, and persists into autumn. In these seasons when the background SST is high, the lowered SST suppresses deep-convective precipitation that would otherwise occur in the absence of the low-cloud deck. The resultant anomalous diabatic cooling induces a surface anticyclonic response with the intensified trades that promote the WES feedback. Such seasonal enhancement of the atmospheric response does not occur without air–sea couplings. The enhanced trades accompany intensified upper-tropospheric westerlies, strengthening the vertical wind shear that, together with the lowered SST, acts to shield Hawaii from powerful hurricanes. On the basin scale, the anticyclonic surface wind response accelerates the North Pacific subtropical ocean gyre to speed up the Kuroshio by as much as 30%. SST thereby increases along the Kuroshio and its extension, intensifying upward turbulent heat fluxes from the ocean to increase precipitation.

     
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  3. Abstract We study single electron tunnelling from the barrier in the binary InAs/GaAs quantum structure including quantum well (QW) and quantum dot (QD). The tunneling is described in the terms of localized/delocalized states and their spectral distribution. The modeling is performed by using the phenomenological efective potential approach for InAs/GaAs heterostructures. The results for the two and three-dimensional models are presented. We focus on the efect of QD-QW geometry variations. The relation to the PL experiments is shown. 
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  4. Reliable probability estimation is of crucial importance in many real-world applications where there is inherent (aleatoric) uncertainty. Probability-estimation models are trained on observed outcomes (e.g. whether it has rained or not, or whether a patient has died or not), because the ground-truth probabilities of the events of interest are typically unknown. The problem is therefore analogous to binary classification, with the difference that the objective is to estimate probabilities rather than predicting the specific outcome. This work investigates probability estimation from high-dimensional data using deep neural networks. There exist several methods to improve the probabilities generated by these models but they mostly focus on model (epistemic) uncertainty. For problems with inherent uncertainty, it is challenging to evaluate performance without access to ground-truth probabilities. To address this, we build a synthetic dataset to study and compare different computable metrics. We evaluate existing methods on the synthetic data as well as on three real-world probability estimation tasks, all of which involve inherent uncertainty: precipitation forecasting from radar images, predicting cancer patient survival from histopathology images, and predicting car crashes from dashcam videos. We also give a theoretical analysis of a model for high-dimensional probability estimation which reproduces several of the phenomena evinced in our experiments. Finally, we propose a new method for probability estimation using neural networks, which modifies the training process to promote output probabilities that are consistent with empirical probabilities computed from the data. The method outperforms existing approaches on most metrics on the simulated as well as real-world data. 
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  6. Abstract

    The fast decline of Arctic sea ice necessitates a stronger focus on understanding the Arctic sea ice predictability and developing advanced forecast methods for all seasons and for pan-Arctic and regional scales. In this study, the operational forecasting system combining an advanced eddy-permitting ocean–sea ice ensemble reanalysis ORAS5 and state-of-the-art seasonal model-based forecasting system SEAS5 is used to investigate effects of sea ice dynamics and thermodynamics on seasonal (growth-to-melt) Arctic sea ice predictability in 1993–2020. We demonstrate that thermodynamics (growth/melt) dominates the seasonal evolution of mean sea ice thickness at pan-Arctic and regional scales. The thermodynamics also dominates the seasonal predictability of sea ice thickness at pan-Arctic scale; however, at regional scales, the predictability is dominated by dynamics (advection), although the contribution from ice growth/melt remains perceptible. We show competing influences of sea ice dynamics and thermodynamics on the temporal change of ice thickness predictability from 1993–2006 to 2007–20. Over these decades, there was increasing predictability due to growth/melt, attributed to increased winter ocean heat flux in both Eurasian and Amerasian basins, and decreasing predictability due to advection. Our results demonstrate an increasing impact of advection on seasonal sea ice predictability as the region of interest becomes smaller, implying that correct modeling of sea ice drift is crucial for developing reliable regional sea ice predictions. This study delivers important information about sea ice predictability in the “new Arctic” conditions. It increases awareness regarding sea ice state and implementation of sea ice forecasts for various scientific and practical needs that depend on accurate seasonal sea ice forecasts.

     
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  7. null (Ed.)
    In this work we study numerically liquid metal flow in a square duct under the influence of a transverse magnetic field applied in a spanwise direction (coplanar). The key interest of the present study is an attempt of passive control of flow regimes developed under magnetic field and thermal loads by applying specially shaped conditions, such as swirling, at the duct inlet. In this paper, we report results of numerical simulations of the interaction of swirling flow and transverse magnetic field in a square duct flow. Analysis of the obtained regimes might be important for the development of an experimental setup, in order to design corresponding inlet sections. 
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  8. Abstract

    Large meridional excursions of a jet stream are conducive to blocking and related midlatitude weather extremes, yet the physical mechanism of jet meandering is not well understood. This paper examines the mechanisms of jet meandering in boreal winter through the lens of a potential vorticity (PV)-like tracer advected by reanalysis winds in an advection–diffusion model. As the geometric structure of the tracer displays a compact relationship with PV in observations and permits a linear mapping from tracer to PV at each latitude, jet meandering can be understood by the geometric structure of tracer field that is only a function of prescribed advecting velocities. This one-way dependence of tracer field on advecting velocities provides a new modeling framework to quantify the effects of time mean flow versus transient eddies on the spatiotemporal variability of jet meandering. It is shown that the mapped tracer wave activity resembles the observed spatial pattern and magnitude of PV wave activity for the winter climatology, interannual variability, and blocking-like wave events. The anomalous increase in tracer wave activity for the composite over interannual variability or blocking-like wave events is attributed to weakened composite mean winds, indicating that the low-frequency winds are the leading factor for the overall distributions of wave activity. It is also found that the tracer model underestimates extreme wave activity, likely due to the lack of feedback mechanisms. The implications for the mechanisms of jet meandering in a changing climate are also discussed.

     
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