Four Reasons Why There Is No “Poverty Trap” In Rural China

En 2020, China eliminó la pobreza regional general y terminó la ardua tarea de resolver el problema de la pobreza absoluta. Aunque existe un problema real persistente de pobreza relativa, el riesgo objetivo de caer en una trampa de pobreza en las regiones subdesarrolladas de China razonablemente no existe. Este artículo analiza cuatro perspectivas que sustentan tal afirmación: la institucional, la histórica, la individual y la espiritual.In 2020 China eliminated the overall regional poverty and finished the arduous task of solving the problem of absolute poverty. Even though there is an actual lingering problem of relative poverty, the objective risk of falling into a poverty trap in China’s underdeveloped regions does not reasonably exist. This article analyzes four perspectives to underpin such statement: the institutional, the historical, the individual, and the spiritual power

Large-eddy simulation of subtropical cloud-topped boundary layers: 2. Cloud response to climate change

How subtropical marine boundary layer (MBL) clouds respond to warming is investigated using large‐eddy simulations (LES) of a wide range of warmer climates, with CO_2 concentrations elevated by factors 2–16. In LES coupled to a slab ocean with interactive sea surface temperatures (SST), the surface latent heat flux (LHF) is constrained by the surface energy balance and only strengthens modestly under warming. Consequently, the MBL in warmer climates is shallower than in corresponding fixed‐SST LES, in which LHF strengthens excessively and the MBL typically deepens. The inferred shortwave (SW) cloud feedback with a closed energy balance is weakly positive for cumulus clouds. It is more strongly positive for stratocumulus clouds, with a magnitude that increases with warming. Stratocumulus clouds generally break up above 6 K to 9 K warming, or above a four to eightfold increase in CO_2 concentrations. This occurs because the MBL mixing driven by cloud‐top longwave (LW) cooling weakens as the LW opacity of the free troposphere increases. The stratocumulus breakup triggers an abrupt and large SST increase and MBL deepening, which cannot occur in fixed‐SST experiments. SW cloud radiative effects generally weaken while the lower‐tropospheric stability increases under warming—the reverse of their empirical relation in the present climate. The MBL is deeper and stratocumulus persists into warmer climates if large‐scale subsidence decreases as the climate warms. The contrasts between experiments with interactive SST and fixed SST highlight the importance of a closed surface energy balance for obtaining realizable responses of MBL clouds to warming

Large-eddy simulation of subtropical cloud‐topped boundary layers: 1. A forcing framework with closed surface energy balance

Large‐eddy simulation (LES) of clouds has the potential to resolve a central question in climate dynamics, namely, how subtropical marine boundary layer (MBL) clouds respond to global warming. However, large‐scale processes need to be prescribed or represented parameterically in the limited‐area LES domains. It is important that the representation of large‐scale processes satisfies constraints such as a closed energy balance in a manner that is realizable under climate change. For example, LES with fixed sea surface temperatures usually do not close the surface energy balance, potentially leading to spurious surface fluxes and cloud responses to climate change. Here a framework of forcing LES of subtropical MBL clouds is presented that enforces a closed surface energy balance by coupling atmospheric LES to an ocean mixed layer with a sea surface temperature (SST) that depends on radiative fluxes and sensible and latent heat fluxes at the surface. A variety of subtropical MBL cloud regimes (stratocumulus, cumulus, and stratocumulus over cumulus) are simulated successfully within this framework. However, unlike in conventional frameworks with fixed SST, feedbacks between cloud cover and SST arise, which can lead to sudden transitions between cloud regimes (e.g., stratocumulus to cumulus) as forcing parameters are varied. The simulations validate this framework for studies of MBL clouds and establish its usefulness for studies of how the clouds respond to climate change

New results in disturbance decoupled fault reconstruction in linear uncertain systems using two sliding mode observers in cascade

This paper presents a disturbance decoupled fault reconstruction (DDFR) scheme using two sliding mode observers in cascade. Measurable signals from the first observer are found to be the output of a fictitious system that is driven by the fault and disturbances. Then the signals are fed into a second observer which will reconstruct the fault. Sufficient conditions which guarantee DDFR are investigated and presented in terms of the original system matrices, and they are found to be less conservative than if only one single observer is used; therefore DDFR can be achieved for a wider class of systems using two sliding mode observers. A simulation example validates the claims made in this paper

Statistically Steady State Large‐Eddy Simulations Forced by an Idealized GCM: 1. Forcing Framework and Simulation Characteristics

Using large‐eddy simulations (LES) systematically has the potential to inform parameterizations of subgrid‐scale processes in general circulation models (GCMs), such as turbulence, convection, and clouds. Here we show how LES can be run to simulate grid columns of GCMs to generate LES across a cross section of dynamical regimes. The LES setup approximately replicates the thermodynamic and water budgets in GCM grid columns. Resolved horizontal and vertical transports of heat and water and large‐scale pressure gradients from the GCM are prescribed as forcing in the LES. The LES are forced with prescribed surface temperatures, but atmospheric temperature and moisture are free to adjust, reducing the imprinting of GCM fields on the LES. In both the GCM and LES, radiative transfer is treated in a unified but idealized manner (semigray atmosphere without water vapor feedback or cloud radiative effects). We show that the LES in this setup reaches statistically steady states without nudging to thermodynamic GCM profiles. The steady states provide training data for developing GCM parameterizations. The same LES setup also provides a good basis for studying the cloud response to global warming

Numerics and subgrid-scale modeling in large eddy simulations of stratocumulus clouds

Stratocumulus clouds are the most common type of boundary layer cloud; their radiative effects strongly modulate climate. Large eddy simulations (LES) of stratocumulus clouds often struggle to maintain fidelity to observations because of the sharp gradients occurring at the entrainment interfacial layer at the cloud top. The challenge posed to LES by stratocumulus clouds is evident in the wide range of solutions found in the LES intercomparison based on the DYCOMS-II field campaign, where simulated liquid water paths for identical initial and boundary conditions varied by a factor of nearly 12. Here we revisit the DYCOMS-II RF01 case and show that the wide range of previous LES results can be realized in a single LES code by varying only the numerical treatment of the equations of motion and the nature of subgrid-scale (SGS) closures. The simulations that maintain the greatest fidelity to DYCOMS-II observations are identified. The results show that using weighted essentially non-oscillatory (WENO) numerics for all resolved advective terms and no explicit SGS closure consistently produces the highest-fidelity simulations. This suggests that the numerical dissipation inherent in WENO schemes functions as a high-quality, implicit SGS closure for this stratocumulus case. Conversely, using oscillatory centered difference numerical schemes for momentum advection, WENO numerics for scalars, and explicitly modeled SGS fluxes consistently produces the lowest-fidelity simulations. We attribute this to the production of anomalously large SGS fluxes near the cloud tops through the interaction of numerical error in the momentum field with the scalar SGS model

An Extended Eddy-Diffusivity Mass-Flux Scheme for Unified Representation of Subgrid-Scale Turbulence and Convection

Large-scale weather forecasting and climate models are beginning to reach horizontal resolutions of kilometers, at which common assumptions made in existing parameterization schemes of subgrid-scale turbulence and convection—such as that they adjust instantaneously to changes in resolved-scale dynamics—cease to be justifiable. Additionally, the common practice of representing boundary-layer turbulence, shallow convection, and deep convection by discontinuously different parameterizations schemes, each with its own set of parameters, has contributed to the proliferation of adjustable parameters in large-scale models. Here we lay the theoretical foundations for an extended eddy-diffusivity mass flux (EDMF) scheme that has explicit time-dependence and memory of subgrid-scale variables and is designed to represent all subgrid-scale turbulence and convection, from boundary layer dynamics to deep convection, in a unified manner. Coherent up- and downdrafts in the scheme are represented as prognostic plumes that interact with their environment and potentially with each other through entrainment and detrainment. The more isotropic turbulence in their environment is represented through diffusive fluxes, with diffusivities obtained from a turbulence kinetic energy budget that consistently partitions turbulence kinetic energy between plumes and environment. The cross-sectional area of up- and downdrafts satisfies a prognostic continuity equation, which allows the plumes to cover variable and arbitrarily large fractions of a large-scale grid box and to have life cycles governed by their own internal dynamics. Relatively simple preliminary proposals for closure parameters are presented and are shown to lead to a successful simulation of shallow convection, including a time-dependent life cycle

Experimental demonstration of an integrated on-chip p-bit core utilizing stochastic Magnetic Tunnel Junctions and 2D-MoS2_{2} FETs

Probabilistic computing is a novel computing scheme that offers a more efficient approach than conventional CMOS-based logic in a variety of applications ranging from optimization to Bayesian inference, and invertible Boolean logic. The probabilistic-bit (or p-bit, the base unit of probabilistic computing) is a naturally fluctuating entity that requires tunable stochasticity; by coupling low-barrier stochastic Magnetic Tunnel Junctions (MTJs) with a transistor circuit, a compact implementation is achieved. In this work, through integrating stochastic MTJs with 2D-MoS$_{2}$ FETs, the first on-chip realization of a key p-bit building block displaying voltage-controllable stochasticity is demonstrated. In addition, supported by circuit simulations, this work provides a careful analysis of the three transistor-one magnetic tunnel junction (3T-1MTJ) p-bit design, evaluating how the characteristics of each component influence the overall p-bit output. This understanding of the interplay between the characteristics of the transistors and the MTJ is vital for the construction of a fully functioning p-bit, making the design rules presented in this article key for future experimental implementations of scaled on-chip p-bit networks