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Despite major improvements in weather and climate modelling and substantial increases in remotely sensed observations, drought prediction remains a major challenge. After a review of the existing methods, we discuss major research gaps and opportunities to improve drought prediction. We argue that current approaches are top-down, assuming that the process(es) and/or driver(s) are known—i.e. starting with a model and then imposing it on the observed events (reality). With the help of an experiment, we show that there are opportunities to develop bottom-up drought prediction models—i.e. starting from the reality (here, observed events) and searching for model(s) and driver(s) that work. Recent advances in artificial intelligence and machine learning provide significant opportunities for developing bottom-up drought forecasting models. Regardless of the type of drought forecasting model (e.g. machine learning, dynamical simulations, analogue based), we need to shift our attention to robustness of theories and outputs rather than event-based verification. A shift in our focus towards quantifying the stability of uncertainty in drought prediction models, rather than the goodness of fit or reproducing the past, could be the first step towards this goal. Finally, we highlight the advantages of hybrid dynamical and statistical models for improving current drought prediction models. This article is partmore »of the Royal Society Science+ meeting issue ‘Drought risk in the Anthropocene’.« less

Abstract We studied the magnetic excitations in the quasi-one-dimensional (q-1D) ladder subsystem of Sr 14−x Ca x Cu 24 O 41 (SCCO) using Cu L 3 -edge resonant inelastic X-ray scattering (RIXS). By comparing momentum-resolved RIXS spectra with high ( x  = 12.2) and without ( x  = 0) Ca content, we track the evolution of the magnetic excitations from collective two-triplon (2 T) excitations ( x  = 0) to weakly-dispersive gapped modes at an energy of 280 meV ( x  = 12.2). Density matrix renormalization group (DMRG) calculations of the RIXS response in the doped ladders suggest that the flat magnetic dispersion and damped excitation profile observed at x  = 12.2 originates from enhanced hole localization. This interpretation is supported by polarization-dependent RIXS measurements, where we disentangle the spin-conserving Δ S  = 0 scattering from the predominant Δ S  = 1 spin-flip signal in the RIXS spectra. The results show that the low-energy weight in the Δ S  = 0 channel is depleted when Sr is replaced by Ca, consistent with a reduced carrier mobility. Our results demonstrate that off-ladder impurities can affect both the low-energy magnetic excitations and superconducting correlations in the CuO 4 plaquettes. Finally, our study characterizes the magnetic and charge fluctuations in the phase frommore »which superconductivity emerges in SCCO at elevated pressures.« less

Algorithmic decisions made by machine learning models in high-stakes domains may have lasting impacts over time. However, naive applications of standard fairness criterion in static settings over temporal domains may lead to delayed and adverse effects. To understand the dynamics of performance disparity, we study a fairness problem in Markov decision processes (MDPs). Specifically, we propose return parity, a fairness notion that requires MDPs from different demographic groups that share the same state and action spaces to achieve approximately the same expected time-discounted rewards. We first provide a decomposition theorem for return disparity, which decomposes the return disparity of any two MDPs sharing the same state and action spaces into the distance between group-wise reward functions, the discrepancy of group policies, and the discrepancy between state visitation distributions induced by the group policies. Motivated by our decomposition theorem, we propose algorithms to mitigate return disparity via learning a shared group policy with state visitation distributional alignment using integral probability metrics. We conduct experiments to corroborate our results, showing that the proposed algorithm can successfully close the disparity gap while maintaining the performance of policies on two real-world recommender system benchmark datasets.

Systems composed of large ensembles of isolated or interacted dynamic units are prevalent in nature and engineered infrastructures. Linear ensemble systems are inarguably the simplest class of ensemble systems and have attracted intensive attention to control theorists and practionars in the past years. Comprehensive understanding of dynamic properties of such systems yet remains far-fetched and requires considerable knowledge and techniques beyond the reach of modern control theory. In this paper, we explore the classes of linear ensemble systems with system matrices that are not globally diagonalizable. In particular, we focus on analyzing their controllability properties under a Sobolev space setting and develop conditions under which uniform controllability of such ensemble systems is equivalent to that of their diagonalizable counterparts. This development significantly facilitates controllability analysis for linear ensemble systems through examining diagonalized linear systems.

Horizontal distribution of the vertically integrated barotropic‐to‐baroclinic energy conversion has been widely studied to examine the generation of internal tides at steep topography. The vertical structure of the energy conversion that provides insights into the associated dynamics, however, is masked by the often used depth‐integrated approach. Here, we reveal the vertical profile of barotropic‐to‐baroclinic energy conversion by employing an idealized ocean model in a slope‐shelf context forced byM₂barotropic tidal flow. The model shows two vertically separated hotspots of energy conversion, one near the sloping bottom and the other at the thermocline, resulting from the stronger vertical velocity and enhancement of the density perturbation, respectively. Isolation of the hotspots demonstrates that baroclinic energy generated in the bottom layer radiates toward onshore and offshore primarily in the form of internal wave beams, whereas that generated at the thermocline propagates away in the form of internal wave modes. Although energy converted at the thermocline contributes to only a small portion of the total energy conversion, it plays an important role in onshore baroclinic energy radiation and can be significantly affected by the internal wave activity at the bottom layer. With a fixed bottom topography, the percentage of baroclinic energy generated at themore »thermocline is linearly related to a body force exerted by the barotropic tidal flow over topography that can be estimated analytically. This provides a convenient way to estimate the overall barotropic‐to‐baroclinic energy conversion over a continental slope in the real ocean by measuring the energy conversion in the thermocline only.

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