Mechanisms for the land/sea warming contrast exhibited by simulations of climate change

The land/sea warming contrast is a phenomenon of both equilibrium and transient simulations of climate change: large areas of the land surface at most latitudes undergo temperature changes whose amplitude is more than those of the surrounding oceans. Using idealised GCM experiments with perturbed SSTs, we show that the land/sea contrast in equilibrium simulations is associated with local feedbacks and the hydrological cycle over land, rather than with externally imposed radiative forcing. This mechanism also explains a large component of the land/sea contrast in transient simulations as well. We propose a conceptual model with three elements: (1) there is a spatially variable level in the lower troposphere at which temperature change is the same over land and sea; (2) the dependence of lapse rate on moisture and temperature causes different changes in lapse rate upon warming over land and sea, and hence a surface land/sea temperature contrast; (3) moisture convergence over land predominantly takes place at levels significantly colder than the surface; wherever moisture supply over land is limited, the increase of evaporation over land upon warming is limited, reducing the relative humidity in the boundary layer over land, and hence also enhancing the land/sea contrast. The non-linearity of the Clausius–Clapeyron relationship of saturation specific humidity to temperature is critical in (2) and (3). We examine the sensitivity of the land/sea contrast to model representations of different physical processes using a large ensemble of climate model integrations with perturbed parameters, and find that it is most sensitive to representation of large-scale cloud and stomatal closure. We discuss our results in the context of high-resolution and Earth-system modelling of climate change.

A neural network enhanced generalized minimum variance self-tuning proportional, integral and derivative control algorithm for complex dynamic systems

A neural network enhanced proportional, integral and derivative (PID) controller is presented that combines the attributes of neural network learning with a generalized minimum-variance self-tuning control (STC) strategy. The neuro PID controller is structured with plant model identification and PID parameter tuning. The plants to be controlled are approximated by an equivalent model composed of a simple linear submodel to approximate plant dynamics around operating points, plus an error agent to accommodate the errors induced by linear submodel inaccuracy due to non-linearities and other complexities. A generalized recursive least-squares algorithm is used to identify the linear submodel, and a layered neural network is used to detect the error agent in which the weights are updated on the basis of the error between the plant output and the output from the linear submodel. The procedure for controller design is based on the equivalent model, and therefore the error agent is naturally functioned within the control law. In this way the controller can deal not only with a wide range of linear dynamic plants but also with those complex plants characterized by severe non-linearity, uncertainties and non-minimum phase behaviours. Two simulation studies are provided to demonstrate the effectiveness of the controller design procedure.

Commercial real estate return distributions: a review of literature and empirical evidence

This paper review the literature on the distribution of commercial real estate returns. There is growing evidence that the assumption of normality in returns is not safe. Distributions are found to be peaked, fat-tailed and, tentatively, skewed. There is some evidence of compound distributions and non-linearity. Public traded real estate assets (such as property company or REIT shares) behave in a fashion more similar to other common stocks. However, as in equity markets, it would be unwise to assume normality uncritically. Empirical evidence for UK real estate markets is obtained by applying distribution fitting routines to IPD Monthly Index data for the aggregate index and selected sub-sectors. It is clear that normality is rejected in most cases. It is often argued that observed differences in real estate returns are a measurement issue resulting from appraiser behaviour. However, unsmoothing the series does not assist in modelling returns. A large proportion of returns are close to zero. This would be characteristic of a thinly-traded market where new information arrives infrequently. Analysis of quarterly data suggests that, over longer trading periods, return distributions may conform more closely to those found in other asset markets. These results have implications for the formulation and implementation of a multi-asset portfolio allocation strategy.

The role of northern Arabian Sea surface temperature biases in CMIP5 model simulations and future projections of Indian summer monsoon rainfall

Many climate models have problems simulating Indian summer monsoon rainfall and its variability, resulting in considerable uncertainty in future projections. Problems may relate to many factors, such as local effects of the formulation of physical parametrisation schemes, while common model biases that develop elsewhere within the climate system may also be important. Here we examine the extent and impact of cold sea surface temperature (SST) biases developing in the northern Arabian Sea in the CMIP5 multi-model ensemble, where such SST biases are shown to be common. Such biases have previously been shown to reduce monsoon rainfall in the Met Office Unified Model (MetUM) by weakening moisture fluxes incident upon India. The Arabian Sea SST biases in CMIP5 models consistently develop in winter, via strengthening of the winter monsoon circulation, and persist into spring and summer. A clear relationship exists between Arabian Sea cold SST bias and weak monsoon rainfall in CMIP5 models, similar to effects in the MetUM. Part of this effect may also relate to other factors, such as forcing of the early monsoon by spring-time excessive equatorial precipitation. Atmosphere-only future time-slice experiments show that Arabian Sea cold SST biases have potential to weaken future monsoon rainfall increases by limiting moisture flux acceleration through non-linearity of the Clausius-Clapeyron relationship. Analysis of CMIP5 model future scenario simulations suggests that, while such effects are likely small compared to other sources of uncertainty, models with large Arabian Sea cold SST biases suppress the range of potential outcomes for changes to future early monsoon rainfall.

A method for incorporating climate variability in climate change impact assessments: sensitivity of river flows in the Eden catchment to precipitation scenarios

Interest in the impacts of climate change is ever increasing. This is particularly true of the water sector where understanding potential changes in the occurrence of both floods and droughts is important for strategic planning. Climate variability has been shown to have a significant impact on UK climate and accounting for this in future climate cahgne projections is essential to fully anticipate potential future impacts. In this paper a new resampling methodology is developed which includes the variability of both baseline and future precipitation. The resampling methodology is applied to 13 CMIP3 climate models for the 2080s, resulting in an ensemble of monthly precipitation change factors. The change factors are applied to the Eden catchment in eastern Scotland with analysis undertaken for the sensitivity of future river flows to the changes in precipitation. Climate variability is shown to influence the magnitude and direction of change of both precipitation and in turn river flow, which are not apparent without the use of the resampling methodology. The transformation of precipitation changes to river flow changes display a degree of non-linearity due to the catchment's role in buffering the response. The resampling methodology developed in this paper provides a new technique for creating climate change scenarios which incorporate the important issue of climate variability.

Probabilistic wind power forecasts with an inverse power curve transformation and censored regression

Forecasting wind power is an important part of a successful integration of wind power into the power grid. Forecasts with lead times longer than 6 h are generally made by using statistical methods to post-process forecasts from numerical weather prediction systems. Two major problems that complicate this approach are the non-linear relationship between wind speed and power production and the limited range of power production between zero and nominal power of the turbine. In practice, these problems are often tackled by using non-linear non-parametric regression models. However, such an approach ignores valuable and readily available information: the power curve of the turbine's manufacturer. Much of the non-linearity can be directly accounted for by transforming the observed power production into wind speed via the inverse power curve so that simpler linear regression models can be used. Furthermore, the fact that the transformed power production has a limited range can be taken care of by employing censored regression models. In this study, we evaluate quantile forecasts from a range of methods: (i) using parametric and non-parametric models, (ii) with and without the proposed inverse power curve transformation and (iii) with and without censoring. The results show that with our inverse (power-to-wind) transformation, simpler linear regression models with censoring perform equally or better than non-linear models with or without the frequently used wind-to-power transformation.

Precipitation and floodiness

There are a number of factors that lead to non-linearity between precipitation anomalies and flood hazard; this non-linearity is a pertinent issue for applications that use a precipitation forecast as a proxy for imminent flood hazard. We assessed the degree of this non-linearity for the first time using a recently developed global-scale hydrological model driven by the ERA-Interim Land precipitation reanalysis (1980–2010). We introduced new indices to assess large-scale flood hazard, or floodiness, and quantified the link between monthly precipitation, river discharge and floodiness anomalies at the global and regional scales. The results show that monthly floodiness is not well correlated with precipitation, therefore demonstrating the value of hydrometeorological systems for providing floodiness forecasts for decision-makers. A method is described for forecasting floodiness using the Global Flood Awareness System, building a climatology of regional floodiness from which to forecast floodiness anomalies out to two weeks.

An idealized study of coupled atmosphere-ocean 4D-Var in the presence of model error

Atmosphere only and ocean only variational data assimilation (DA) schemes are able to use window lengths that are optimal for the error growth rate, non-linearity and observation density of the respective systems. Typical window lengths are 6-12 hours for the atmosphere and 2-10 days for the ocean. However, in the implementation of coupled DA schemes it has been necessary to match the window length of the ocean to that of the atmosphere, which may potentially sacrifice the accuracy of the ocean analysis in order to provide a more balanced coupled state. This paper investigates how extending the window length in the presence of model error affects both the analysis of the coupled state and the initialized forecast when using coupled DA with differing degrees of coupling. Results are illustrated using an idealized single column model of the coupled atmosphere-ocean system. It is found that the analysis error from an uncoupled DA scheme can be smaller than that from a coupled analysis at the initial time, due to faster error growth in the coupled system. However, this does not necessarily lead to a more accurate forecast due to imbalances in the coupled state. Instead coupled DA is more able to update the initial state to reduce the impact of the model error on the accuracy of the forecast. The effect of model error is potentially most detrimental in the weakly coupled formulation due to the inconsistency between the coupled model used in the outer loop and uncoupled models used in the inner loop.

Positive solutions for the p-Laplacian involving critical and supercritical nonlinearities with zeros

In this paper we show the existence of multiple solutions to a class of quasilinear elliptic equations when the continuous non-linearity has a positive zero and it satisfies a p-linear condition only at zero. In particular, our approach allows us to consider superlinear, critical and supercritical nonlinearities. (C) 2009 Elsevier Masson SAS. All rights reserved.

Análise dinâmica de solos submetidos à ação simultânea das três componentes da excitação na base rochosa

Durante a análise sísmica de estruturas complexas, o modelo matemático empregado deveria incluir não só as distribuicões irregulares de massas e de rigidezes senão também à natureza tridimensional da ecitação sísmica. Na prática, o elevado número de graus de liberdade involucrado limita este tipo de análise à disponibilidade de grandes computadoras. Este trabalho apresenta um procedimento simplificado, para avaliar a amplificação do movimento sísmico em camadas de solos. Sua aplicação permitiria estabelecer critérios a partir dos quais avalia-se a necessidade de utilizar modelos de interação solo-estrutura mais complexos que os utilizados habitualmente. O procedimento proposto possui as seguientes características : A- Movimento rígido da rocha definido em termos de três componentes ortagonais. Direção de propagação vertical. B- A ecuação constitutiva do solo inclui as características de não linearidade, plasticidade, dependência da história da carga, dissipação de energia e variação de volume. C- O perfil de solos é dicretizado mediante um sistema de massas concentradas. Utiliza-se uma formulação incremental das equações de movimento com integração directa no domínio do tempo. As propriedades pseudo-elásticas do solo são avaliadas em cada intervalo de integração, em função do estado de tensões resultante da acção simultânea das três componentes da excitação. O correcto funcionamento do procedimento proposto é verificado mediante análises unidimensionais (excitação horizontal) incluindo estudos comparativos com as soluções apresentadas por diversos autores. Similarmente apresentam-se análises tridimensionais (acção simultânea das três componentes da excitação considerando registros sísmicos reais. Analisa-se a influência que possui a dimensão da análise (uma análise tridimensional frente a três análises unidimensionais) na resposta de camadas de solos submetidos a diferentes níveis de exçitação; isto é, a limitação do Princípio de Superposisão de Efeitos.

Detectando não-linearidades nos retornos dos fundos multimercados

Estudos recentes apontam que diversas estratégias implementadas em hedge funds geram retornos com características não lineares. Seguindo as sugestões encontradas no paper de Agarwal e Naik (2004), este trabalho mostra que uma série de hedge funds dentro da indústria de fundos de investimentos no Brasil apresenta retornos que se assemelham ao de uma estratégia em opções de compra e venda no índice de mercado Bovespa. Partindo de um modelo de fatores, introduzimos um índice referenciado no retorno sobre opções de modo que tal fator possa explicar melhor que os tradicionais fatores de risco a característica não linear dos retornos dos fundos de investimento.

Análise da função de reação do Banco Central do Brasil entre 2000 e 2012: inclusão de preços de ativos, não linearidade e queda da taxa de juros neutra

Neste trabalho são abordados empiricamente dois temas bastante atuais no âmbito da política monetária: a estimativa de uma Regra de Taylor aumentada com a inclusão de um vetor de preços de ativos financeiros e a hipótese de não-linearidade da Regra de Taylor. Os principais resultados encontrados sugerem que o Banco Central do Brasil não segue uma Regra de Taylor aumentada na condução da política monetária e que há evidências de não-linearidade de sua função de reação. Além disso, encontramos evidência de recuo da taxa de juros real de equilíbrio (ou neutra) da economia brasileira.

Modelagem do produto interno bruto brasileiro utilizando modelos não lineares

O trabalho tem como objetivo aplicar uma modelagem não linear ao Produto Interno Bruto brasileiro. Para tanto foi testada a existência de não linearidade do processo gerador dos dados com a metodologia sugerida por Castle e Henry (2010). O teste consiste em verificar a persistência dos regressores não lineares no modelo linear irrestrito. A seguir a série é modelada a partir do modelo autoregressivo com limiar utilizando a abordagem geral para específico na seleção do modelo. O algoritmo Autometrics é utilizado para escolha do modelo não linear. Os resultados encontrados indicam que o Produto Interno Bruto do Brasil é melhor explicado por um modelo não linear com três mudanças de regime, que ocorrem no inicio dos anos 90, que, de fato, foi um período bastante volátil. Através da modelagem não linear existe o potencial para datação de ciclos, no entanto os resultados encontrados não foram suficientes para tal análise.

Identificação em tempo real de modelo dinâmico de robô móvel com acionamento diferencial e zona morta

Several mobile robots show non-linear behavior, mainly due friction phenomena between the mechanical parts of the robot or between the robot and the ground. Linear models are efficient in some cases, but it is necessary take the robot non-linearity in consideration when precise displacement and positioning are desired. In this work a parametric model identification procedure for a mobile robot with differential drive that considers the dead-zone in the robot actuators is proposed. The method consists in dividing the system into Hammerstein systems and then uses the key-term separation principle to present the input-output relations which shows the parameters from both linear and non-linear blocks. The parameters are then simultaneously estimated through a recursive least squares algorithm. The results shows that is possible to identify the dead-zone thresholds together with the linear parameters

Controle por modos deslizantes com compensação difusa aplicado a sistemas com descontinuidade

The development of non-linear controllers gained space in the theoretical ambit and of practical applications on the moment that the arising of digital computers enabled the implementation of these methodologies. In comparison with the linear controllers more utilized, the non -linear controllers present the advantage of not requiring the linearity of the system to determine the parameters of control, which permits a more efficient control especially when the system presents a high level of non-linearity. Another additional advantage is the reduction of costs, since to obtain the efficient control through linear controllers it is necessary the utilization of sensors and more refined actuators than when it is utilized a non-linear controller. Among the non-linear theories of control, the method of control by gliding ways is detached for being a method that presents more robustness, before uncertainties. It is already confirmed that the adoption of compensation on the region of residual error permits to improve better the performance of these controllers. So, in this work it is described the development of a non-linear controller that looks for an association of strategy of control by gliding ways, with the fuzzy compensation technique. Through the implementation of some strategies of fuzzy compensation, it was searched the one which provided the biggest efficiency before a system with high level of nonlinearities and uncertainties. The electrohydraulic actuator was utilized as an example of research, and the results appoint to two configurations of compensation that permit a bigger reduction of the residual error