An evaluation of decadal probability forecasts from state-of-the-art climate models

While state-of-the-art models of Earth's climate system have improved tremendously over the last 20 years, nontrivial structural flaws still hinder their ability to forecast the decadal dynamics of the Earth system realistically. Contrasting the skill of these models not only with each other but also with empirical models can reveal the space and time scales on which simulation models exploit their physical basis effectively and quantify their ability to add information to operational forecasts. The skill of decadal probabilistic hindcasts for annual global-mean and regional-mean temperatures from the EU Ensemble-Based Predictions of Climate Changes and Their Impacts (ENSEMBLES) project is contrasted with several empirical models. Both the ENSEMBLES models and a “dynamic climatology” empirical model show probabilistic skill above that of a static climatology for global-mean temperature. The dynamic climatology model, however, often outperforms the ENSEMBLES models. The fact that empirical models display skill similar to that of today's state-of-the-art simulation models suggests that empirical forecasts can improve decadal forecasts for climate services, just as in weather, medium-range, and seasonal forecasting. It is suggested that the direct comparison of simulation models with empirical models becomes a regular component of large model forecast evaluations. Doing so would clarify the extent to which state-of-the-art simulation models provide information beyond that available from simpler empirical models and clarify current limitations in using simulation forecasting for decision support. Ultimately, the skill of simulation models based on physical principles is expected to surpass that of empirical models in a changing climate; their direct comparison provides information on progress toward that goal, which is not available in model–model intercomparisons.

A constrained recursive least squares algorithm for adaptive combination of multiple models

In this paper, we develop a novel constrained recursive least squares algorithm for adaptively combining a set of given multiple models. With data available in an online fashion, the linear combination coefficients of submodels are adapted via the proposed algorithm.We propose to minimize the mean square error with a forgetting factor, and apply the sum to one constraint to the combination parameters. Moreover an l1-norm constraint to the combination parameters is also applied with the aim to achieve sparsity of multiple models so that only a subset of models may be selected into the final model. Then a weighted l2-norm is applied as an approximation to the l1-norm term. As such at each time step, a closed solution of the model combination parameters is available. The contribution of this paper is to derive the proposed constrained recursive least squares algorithm that is computational efficient by exploiting matrix theory. The effectiveness of the approach has been demonstrated using both simulated and real time series examples.

Chagas disease-related mortality trends, state of Sao Paulo, Brazil, 1985 to 2006: a study using multiple causes of death

Objectives. To study mortality trends related to Chagas disease taking into account all mentions of this cause listed on any line or part of the death certificate. Methods. Mortality data for 1985-2006 were obtained from the multiple cause-of-death database maintained by the Sao Paulo State Data Analysis System (SEADE). Chagas disease was classified as the underlying cause-of-death or as an associated cause-of-death (non-underlying). The total number of times Chagas disease was mentioned on the death certificates was also considered. Results. During this 22-year period, there were 40 002 deaths related to Chagas disease: 34 917 (87.29%) classified as the underlying cause-of-death and 5 085 (12.71%) as an associated cause-of-death. The results show a 56.07% decline in the death rate due to Chagas disease as the underlying cause and a stabilized rate as associated cause. The number of deaths was 44.5% higher among men. The fact that 83.5% of the deaths occurred after 45 years of age reflects a cohort effect. The main causes associated with Chagas disease as the underlying cause-of-death were direct complications due to cardiac involvement, such as conduction disorders, arrhythmias and heart failure. Ischemic heart disease, cerebrovascular disorders and neoplasms were the main underlying causes when Chagas was an associated cause-of-death. Conclusions. For the total mentions to Chagas disease, a 51.34% decline in the death rate was observed, whereas the decline in the number of deaths was only 5.91%, being lower among women and showing a shift of deaths to older age brackets. Using the multiple cause-of-death method contributed to the understanding of the natural history of Chagas disease.

Paracoccidioidomycosis-related mortality trend, state of Sao Paulo, Brazil: a study using multiple causes of death

Objective. To investigate mortality in which paracoccidioidomycosis appears on any line or part of the death certificate. Method. Mortality data for 1985-2005 were obtained from the multiple cause-of-death database maintained by the Sao Paulo State Data Analysis System (SEADE). Standardized mortality coefficients were calculated for paracoccidioidomycosis as the underlying cause-of-death and as an associated cause-of-death, as well as for the total number of times paracoccidioidomycosis was mentioned on the death certificates. Results. During this 21-year period, there were 1950 deaths related to paracoccidioidomycosis; the disease was the underlying cause-of-death in 1 164 cases (59.69%) and an associated cause-of-death in 786 (40.31%). Between 1985 and 2005 records show a 59.8% decline in the mortality coefficient due to paracoccidioidomycosis as the underlying cause and a 53.0% decline in the mortality as associated cause. The largest number of deaths occurred among men, in the older age groups, and among rural workers, with an upward trend in winter months. The main causes associated with paracoccidioidomycosis as the underlying cause-of-death were pulmonary fibrosis, chronic lower respiratory tract diseases, and pneumonias. Malignant neoplasms and AIDS were the main underlying causes when paracoccidioidomycosis was an associated cause-of-death. The decision tables had to be adapted for the automated processing of causes of death in death certificates where paracoccidioidomycosis was mentioned. Conclusions. Using the multiple cause-of-death method together with the traditional underlying cause-of-death approach provides a new angle on research aimed at broadening our understanding of the natural history of paracoccidioidomycosis.

Using non-homogeneous Poisson models with multiple change-points to estimate the number of ozone exceedances in Mexico City

In this paper, we consider some non-homogeneous Poisson models to estimate the probability that an air quality standard is exceeded a given number of times in a time interval of interest. We assume that the number of exceedances occurs according to a non-homogeneous Poisson process (NHPP). This Poisson process has rate function lambda(t), t >= 0, which depends on some parameters that must be estimated. We take into account two cases of rate functions: the Weibull and the Goel-Okumoto. We consider models with and without change-points. When the presence of change-points is assumed, we may have the presence of either one, two or three change-points, depending of the data set. The parameters of the rate functions are estimated using a Gibbs sampling algorithm. Results are applied to ozone data provided by the Mexico City monitoring network. In a first instance, we assume that there are no change-points present. Depending on the adjustment of the model, we assume the presence of either one, two or three change-points. Copyright (C) 2009 John Wiley & Sons, Ltd.

A generalization of judds method of out-steady-state comparisons in perfect foresight models

We show that Judd (1982)’s method can be applied to any finite system, contrary to what he claimed in 1987. An example shows how to employ the technic to study monetary models in presence of capital accumulation.

Multiple structural change models:a simulation analysis

In a reccnt paper. Bai and Perron (1998) considcrccl theoretical issues relatec\ lo lhe limiting distriblltion of estimators and test. statist.ics in the linear model \\'ith multiplc struct ural changes. \Ve assess. via simulations, the adequacy of the \'arious I1Iethods suggested. These CO\'er the size and power of tests for structural changes. the cO\'erage rates of the confidence Íntervals for the break dates and the relat.Í\'e merits of methods to select the I1umber of breaks. The \'arious data generating processes considered alIo,,' for general conditions OIl the data and the errors including differellces across segmcll(s. Yarious practical recommendations are made.

Random regressions models to describe the genetic variation of milk yield over multiple parities in Buffaloes

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Low-Resolution Molecular Models Reveal the Oligomeric State of the PPAR and the Conformational Organization of Its Domains in Solution

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Integrable aspects of the scaling q-state Potts models I: bound states and bootstrap closure

We discuss the q-state Potts models for q less than or equal to 4, in the scaling regimes close to their critical or tricritical points. Starting from the kink S-matrix elements proposed by Chim and Zamolodchikov, the bootstrap is closed for the scaling regions of all critical points, and for the tricritical points when 4 > q greater than or equal to 2. We also note a curious appearance of the extended last line of Freudenthal's magic square in connection with the Potts models. (C) 2003 Elsevier B.V. B.V. All rights reserved.

Combinatorial identities and quantum state densities of supersymmetric sigma models on N-folds

There is a remarkable connection between the number of quantum states of conformal theories and the sequence of dimensions of Lie algebras. In this paper, we explore this connection by computing the asymptotic expansion of the elliptic genus and the microscopic entropy of black holes associated with (supersymmetric) sigma models. The new features of these results are the appearance of correct prefactors in the state density expansion and in the coefficient of the logarithmic correction to the entropy.

Integrable aspects of the scaling q-state Potts models II: finite-size effects

We continue our discussion of the q-state Potts models for q less than or equal to 4, in the scaling regimes close to their critical and tricritical points. In a previous paper, the spectrum and full S-matrix of the models on an infinite line were elucidated; here, we consider finite-size behaviour. TBA equations are proposed for all cases related to phi(21) and phi(12) perturbations of unitary minimal models. These are subjected to a variety of checks in the ultraviolet and infrared limits, and compared with results from a recently-proposed non-linear integral equation. A non-linear integral equation is also used to study the flows from tricritical to critical models, over the full range of q. Our results should also be of relevance to the study of the off-critical dilute A models in regimes 1 and 2. (C) 2003 Elsevier B.V. B.V. All rights reserved.

Solutions of the bound-state Faddeev-Yakubovsky equations in three dimensions by using NN and 3N potential models

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)