Cirrus parameterisation and the role of ice nuclei.

A parametrization of cirrus clouds formed by homogeneous nucleation is improved so that it can be used more easily in general-circulation models (GCMs) and climate models. The improved parametrization is completely analytical and requires no fitting of parameters to models or measurements; it compares well with full microphysical model results even when monodisperse aerosol particles are used in the parametrization to determine cirrus ice-crystal number densities. However, the presence of ice nuclei in the atmosphere can modify the formation of cirrus clouds. If sufficient ice particles have been generated by heterogeneous nucleation, the saturation ratio of the air parcel will never reach that required for homogeneous nucleation. We calculate the critical number density of ice nuclei, above which homogeneous nucleation will be suppressed. The critical number density depends on the temperature, the updraught velocity, and the supersaturation at which ice nuclei activate. The theory points to key uncertainties in our observations of ice nuclei in the upper troposphere; for ice nuclei that activate at relatively low supersaturations, number density is more important than a precise knowledge of the activation supersaturation. Overall, the theory provides a general framework within which to interpret observations and the results of full microphysical cloud models. The theory can provide analytical test cases as benchmarks for the testing of models in development, and can be implemented itself into larger-scale atmospheric models, such as GCMs. Copyright © 2005 Royal Meteorological Societ

INTERPRETATIONS AND TRANSFORMATIONS OF SCALE FOR THE PRATT-ARROW ABSOLUTE RISK AVERSION COEFFICIENT: IMPLICATIONS FOR GENERALIZED STOCHASTIC DOMINANCE: REPLY

Research Methods/ Statistical Methods, Risk and Uncertainty,

A Bayesian Approach to Graphical Record Linkage and De-duplication

We propose an unsupervised approach for linking records across arbitrarily many files, while simultaneously detecting duplicate records within files. Our key innovation involves the representation of the pattern of links between records as a bipartite graph, in which records are directly linked to latent true individuals, and only indirectly linked to other records. This flexible representation of the linkage structure naturally allows us to estimate the attributes of the unique observable people in the population, calculate transitive linkage probabilities across records (and represent this visually), and propagate the uncertainty of record linkage into later analyses. Our method makes it particularly easy to integrate record linkage with post-processing procedures such as logistic regression, capture-recapture, etc. Our linkage structure lends itself to an efficient, linear-time, hybrid Markov chain Monte Carlo algorithm, which overcomes many obstacles encountered by previously record linkage approaches, despite the high-dimensional parameter space. We illustrate our method using longitudinal data from the National Long Term Care Survey and with data from the Italian Survey on Household and Wealth, where we assess the accuracy of our method and show it to be better in terms of error rates and empirical scalability than other approaches in the literature.Comment: 39 pages, 8 figures, 8 tables. Longer version of arXiv:1403.0211, In press, Journal of the American Statistical Association: Theory and Methods (2015

Stellar Motions in the Polar Ring Galaxy NGC 4650A

We present the first measurement of the stellar kinematics in the polar ring of NGC 4650A. There is well defined rotation, with the stars and gas rotating in the same direction, and with similar amplitude. The gaseous and stellar kinematics suggest an approximately flat rotation curve, providing further support for the hypothesis that the polar material resides in a disk rather than in a ring. The kinematics of the emission line gas at and near the center of the S0 suggests that the polar disk lacks a central hole. We have not detected evidence for two, equal mass, counterrotating stellar polar streams, as is predicted in the resonance levitation model proposed by Tremaine & Yu. A merger seems the most likely explanation for the structure and kinematics of NGC 4650A.Comment: 4 pages, accepted for publication in ApJ Letter

Optimisation in ‘Self-modelling’ Complex Adaptive Systems

When a dynamical system with multiple point attractors is released from an arbitrary initial condition it will relax into a configuration that locally resolves the constraints or opposing forces between interdependent state variables. However, when there are many conflicting interdependencies between variables, finding a configuration that globally optimises these constraints by this method is unlikely, or may take many attempts. Here we show that a simple distributed mechanism can incrementally alter a dynamical system such that it finds lower energy configurations, more reliably and more quickly. Specifically, when Hebbian learning is applied to the connections of a simple dynamical system undergoing repeated relaxation, the system will develop an associative memory that amplifies a subset of its own attractor states. This modifies the dynamics of the system such that its ability to find configurations that minimise total system energy, and globally resolve conflicts between interdependent variables, is enhanced. Moreover, we show that the system is not merely ‘recalling’ low energy states that have been previously visited but ‘predicting’ their location by generalising over local attractor states that have already been visited. This ‘self-modelling’ framework, i.e. a system that augments its behaviour with an associative memory of its own attractors, helps us better-understand the conditions under which a simple locally-mediated mechanism of self-organisation can promote significantly enhanced global resolution of conflicts between the components of a complex adaptive system. We illustrate this process in random and modular network constraint problems equivalent to graph colouring and distributed task allocation problems

Living on the margin: Assessing the economic impacts of Landcare in the Philippine uplands

In the Philippines, about 38 per cent of the population resides in rural areas where poverty remains a significant problem. In 2006, 47 per cent of all households in Bohol Province fell below the national poverty line, with the percentage even higher in upland communities. These households often exist in marginal landscapes that are under significant pressure from ongoing resource degradation and rising input costs. This paper first explores whether the adoption of Landcare practices in a highly degraded landscape has resulted in improved livelihood outcomes for upland farming families in Bohol. Second, it analyses the potential for the piecemeal adoption of these measures to deliver tangible benefits at the watershed scale. Finally, using a BCA approach, these outcomes are compared to the costs of the research and extension projects that have helped achieve them.Landcare, Philippines, livelihoods, poverty, watershed, ACIAR,

Associative memory in gene regulation networks

The pattern of gene expression in the phenotype of an organism is determined in part by the dynamical attractors of the organism’s gene regulation network. Changes to the connections in this network over evolutionary time alter the adult gene expression pattern and hence the fitness of the organism. However, the evolution of structure in gene expression networks (potentially reflecting past selective environments) and its affordances and limitations with respect to enhancing evolvability is poorly understood in general. In this paper we model the evolution of a gene regulation network in a controlled scenario. We show that selected changes to connections in the regulation network make the currently selected gene expression pattern more robust to environmental variation. Moreover, such changes to connections are necessarily ‘Hebbian’ – ‘genes that fire together wire together’ – i.e. genes whose expression is selected for in the same selective environments become co-regulated. Accordingly, in a manner formally equivalent to well-understood learning behaviour in artificial neural networks, a gene expression network will therefore develop a generalised associative memory of past selected phenotypes. This theoretical framework helps us to better understand the relationship between homeostasis and evolvability (i.e. selection to reduce variability facilitates structured variability), and shows that, in principle, a gene regulation network has the potential to develop ‘recall’ capabilities normally reserved for cognitive systems

The Effect of an Employer Health Insurance Mandate on Health Insurance Coverage and the Demand for Labor: Evidence from Hawaii

Over the past few decades, policy makers have considered employer mandates as a strategy for stemming the tide of declining health insurance coverage. In this paper we examine the long term effects of the only employer health insurance mandate that has ever been enforced in the United States, Hawaii's Prepaid Health Care Act, using a standard supply-demand framework and Current Population Survey data covering the years 1979 to 2005. During this period, the coverage gap between Hawaii and other states increased, as did real health insurance costs, implying a rising burden of the mandate on Hawaii's employers. We use a variant of the traditional permutation (placebo) test across all states to examine the magnitude and statistical properties of these growing coverage differences and their impacts on labor market outcomes, conditional on an extensive set of covariates. As expected, the coverage gap is larger for workers who tend to have low rates of coverage in the voluntary market (primarily those with lower skills). We also find that relative wages fell in Hawaii over time, but the estimates are statistically insignificant. By contrast, a parallel analysis of workers employed fewer than 20 hours per week indicates that the law significantly increased employers' reliance on such workers in order to reduce the burden of the mandate. We find no evidence suggesting that the law reduced employment probabilities.health insurance, employment, hours, wages

How Harmful are Adaptation Restrictions

The dominant assumption in economic models of climate policy remains that adaptation will be implemented in an optimal manner. There are, however, several reasons why optimal levels of adaptation may not be attainable. This paper investigates the effects of suboptimal levels of adaptation, i.e. adaptation restrictions, on the composition and level of climate change costs and on welfare. Several adaptation restrictions are identified and then simulated in a revised DICE model, extended with adaptation (AD-DICE). We find that especially substantial over-investment in adaptation can be very harmful due to sharply increasing marginal adaptation costs. Furthermore the potential of mitigation to offset suboptimal adaptation is investigated. When adaptation is not possible at extreme levels of climate change, it is cost-effective to use more stringent mitigation policies in order to keep climate change limited, thereby making adaptation possible. Furthermore not adjusting the optimal level of mitigation to these adaptation restrictions may double the costs of adaptation restrictions, and thus in general it is very harmful to ignore existing restrictions on adaptation when devising (efficient) climate policies.Integrated Assessment Modelling, Adaptation, Climate Change

Transformations in the Scale of Behaviour and the Global Optimisation of Constraints in Adaptive Networks

The natural energy minimisation behaviour of a dynamical system can be interpreted as a simple optimisation process, finding a locally optimal resolution of problem constraints. In human problem solving, high-dimensional problems are often made much easier by inferring a low-dimensional model of the system in which search is more effective. But this is an approach that seems to require top-down domain knowledge; not one amenable to the spontaneous energy minimisation behaviour of a natural dynamical system. However, in this paper we investigate the ability of distributed dynamical systems to improve their constraint resolution ability over time by self-organisation. We use a ‘self-modelling’ Hopfield network with a novel type of associative connection to illustrate how slowly changing relationships between system components can result in a transformation into a new system which is a low-dimensional caricature of the original system. The energy minimisation behaviour of this new system is significantly more effective at globally resolving the original system constraints. This model uses only very simple, and fully-distributed positive feedback mechanisms that are relevant to other ‘active linking’ and adaptive networks. We discuss how this neural network model helps us to understand transformations and emergent collective behaviour in various non-neural adaptive networks such as social, genetic and ecological networks