Three-dimensional computer model for the atmospheric general circulation experiment

An efficient, flexible, three-dimensional, hydrodynamic, computer code has been developed for a spherical cap geometry. The code will be used to simulate NASA's Atmospheric General Circulation Experiment (AGCE). The AGCE is a spherical, baroclinic experiment which will model the large-scale dynamics of our atmosphere; it has been proposed to NASA for future Spacelab flights. In the AGCE a radial dielectric body force will simulate gravity, with hot fluid tending to move outwards. In order that this force be dominant, the AGCE must be operated in a low gravity environment such as Spacelab. The full potential of the AGCE will only be realized by working in conjunction with an accurate computer model. Proposed experimental parameter settings will be checked first using model runs. Then actual experimental results will be compared with the model predictions. This interaction between experiment and theory will be very valuable in determining the nature of the AGCE flows and hence their relationship to analytical theories and actual atmospheric dynamics

Relationships between solar activity and climate change

The relationship between recurrent droughts in the High Plains of the United States and the double sunspot cycle is discussed in detail. It is suggested that high solar activity is generally related to an increase in meridional circulation and blocking patterns at high and intermediate latitudes, especially in winter, and the effect is related to the sudden formation of cirrus clouds during strong geomagnetic activity that originates in the solar corpuscular emission

Tests of physical mechanisms linking solar activity and/or geomagnetic disturbances with the large-scale circulation of the lower stratosphere and upper troposphere

Empirical facts are presented to help in selecting the most probable physical mechanisms that are responsible for the sun-weather connections

Subgeometric ergodicity of strong Markov processes

We derive sufficient conditions for subgeometric f-ergodicity of strongly Markovian processes. We first propose a criterion based on modulated moment of some delayed return-time to a petite set. We then formulate a criterion for polynomial f-ergodicity in terms of a drift condition on the generator. Applications to specific processes are considered, including Langevin tempered diffusions on R^n and storage models.Comment: Published at http://dx.doi.org/10.1214/105051605000000115 in the Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute of Mathematical Statistics (http://www.imstat.org

Physician Assisted Dying: A Turning Point?

Physician Assisted Dying (PAD) has been lawful in some countries since the 1940s and in the United States since 1997. There is a body of social and scientific research that has focused on whether the practice has been misused and whether gaps exist in legislative safeguards. There are multiple concerns with physicians assisting patients to die: incompatibility with the physician’s role as a healer, devaluation of human life, coercion of vulnerable individuals (e.g., the poor and disabled), and the risk that PAD will be used beyond a narrow group of terminally ill individuals. Statutes in the United States have been drafted with these concerns in mind in an effort to mitigate the possible risks of PAD while still providing individuals with access. There seems to be a shift in attitudes towards PAD. Currently four states statutorily permit PAD and it is being discussed by multiple legislatures across the country. There also seems to be a shift in medical practice as demonstrated by a 2015 survey that showed for the first time that more than half of physicians surveyed favored medical assistance in dying. PAD is a deeply personal choice. The question is whether more states will authorize the practice and, if so, what safeguards will be put in place to ensure the practice is not misused and remains consistent with prevailing social and ethical thought

Harris recurrence of Metropolis-within-Gibbs and trans-dimensional Markov chains

A $\phi$-irreducible and aperiodic Markov chain with stationary probability distribution will converge to its stationary distribution from almost all starting points. The property of Harris recurrence allows us to replace ``almost all'' by ``all,'' which is potentially important when running Markov chain Monte Carlo algorithms. Full-dimensional Metropolis--Hastings algorithms are known to be Harris recurrent. In this paper, we consider conditions under which Metropolis-within-Gibbs and trans-dimensional Markov chains are or are not Harris recurrent. We present a simple but natural two-dimensional counter-example showing how Harris recurrence can fail, and also a variety of positive results which guarantee Harris recurrence. We also present some open problems. We close with a discussion of the practical implications for MCMC algorithms.Comment: Published at http://dx.doi.org/10.1214/105051606000000510 in the Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute of Mathematical Statistics (http://www.imstat.org

Accelerating Parallel Tempering: Quantile Tempering Algorithm (QuanTA)

Using MCMC to sample from a target distribution, $\pi(x)$ on a $d$-dimensional state space can be a difficult and computationally expensive problem. Particularly when the target exhibits multimodality, then the traditional methods can fail to explore the entire state space and this results in a bias sample output. Methods to overcome this issue include the parallel tempering algorithm which utilises an augmented state space approach to help the Markov chain traverse regions of low probability density and reach other modes. This method suffers from the curse of dimensionality which dramatically slows the transfer of mixing information from the auxiliary targets to the target of interest as $d \rightarrow \infty$. This paper introduces a novel prototype algorithm, QuanTA, that uses a Gaussian motivated transformation in an attempt to accelerate the mixing through the temperature schedule of a parallel tempering algorithm. This new algorithm is accompanied by a comprehensive theoretical analysis quantifying the improved efficiency and scalability of the approach; concluding that under weak regularity conditions the new approach gives accelerated mixing through the temperature schedule. Empirical evidence of the effectiveness of this new algorithm is illustrated on canonical examples