CMIP5 Simulaciones de clima: implicaciones para la hidrología

En este artículo se señala a través de las simulaciones climáticas del centro nacional de la investigación atmosférica (NCAR) que, para finales del s. XXI, habrá un incremento de la temperatura del 3.3°C y en algunas áreas hasta de 7-8°C. Este cambio repercutirá sobre la producción agrícola y la mortalidad de los seres humanos. El nuevo enfoque que integra las ciencias sociales con las ciencias físicas busca manejar el problema de adaptación al cambio climático y, asimismo, influir en la toma de decisiones para afrontar la vulnerabilidad y los retos que impone la adaptación al cambio climático

Experiments on the effect of tropical Atlantic heating anomalies upon GCM rain forecasts over the Americas

A series of real data experiments is performed with a general circulation model in order to ascertain the sensitivity of extended range rain forecasts over the Americas to the structure and magnitude of tropical heating anomalies. The emphasis is upon heat inputs over the tropical Atlantic which have shown particularly significant drying influences over North America in our prior simulations. The heating imposed in the prior experiments is compared to the condensation heating rates that naturally occur in the forecast model, and shown to be excessive by approximately a factor of two. Present experiments reduce the imposed anomaly by a factor of three, and also incorporate sea-surface temperature decreases over the eastern tropical Pacific Ocean. The new experimental results are in many ways consistent with our prior results. The dry North American response is statistically more significant than the South American response, and occurs at least as frequently in the different members of the experimental ensembles as in our prior experiments. The drying effect is accentuated by the presence of East Pacific cooling, but this does not appear to be the dominant influence. Over tropical South America, the Pacific and Atlantic modifications produce compensating influences, with the former dominating dominant, and allowing increased rainfall over the Amazon Basin.A series of real data experiments is performed with a general circulation model in order to ascertain the sensitivity of extended range rain forecasts over the Americas to the structure and magnitude of tropical heating anomalies. The emphasis is upon heat inputs over the tropical Atlantic which have shown particularly significant drying influences over North America in our prior simulations. The heating imposed in the prior experiments is compared to the condensation heating rates that naturally occur in the forecast model, and shown to be excessive by approximately a factor of two. Present experiments reduce the imposed anomaly by a factor of three, and also incorporate sea-surface temperature decreases over the eastern tropical Pacific Ocean. The new experimental results are in many ways consistent with our prior results. The dry North American response is statistically more significant than the South American response, and occurs at least as frequently in the different members of the experimental ensembles as in our prior experiments. The drying effect is accentuated by the presence of East Pacific cooling, but this does not appear to be the dominant influence. Over tropical South America, the Pacific and Atlantic modifications produce compensating influences, with the former dominating dominant, and allowing increased rainfall over the Amazon Basin

The Power to See: A New Graphical Test of Normality

Many statistical procedures assume the underlying data generating process involves Gaussian errors. Among the well-known procedures are ANOVA, multiple regression, linear discriminant analysis and many more. There are a few popular procedures that are commonly used to test for normality such as the Kolmogorov-Smirnov test and the ShapiroWilk test. Excluding the Kolmogorov-Smirnov testing procedure, these methods do not have a graphical representation. As such these testing methods offer very little insight as to how the observed process deviates from the normality assumption. In this paper we discuss a simple new graphical procedure which provides confidence bands for a normal quantile-quantile plot. These bands define a test of normality and are much narrower in the tails than those related to the Kolmogorov-Smirnov test. Correspondingly the new procedure has much greater power to detect deviations from normality in the tails

Statistical Inference for Exploratory Data Analysis and Model Diagnostics

We propose to furnish visual statistical methods with an inferential framework and protocol, modelled on confirmatory statistical testing. In this framework, plots take on the role of test statistics, and human cognition the role of statistical tests. Statistical significance of ‘discoveries’ is measured by having the human viewer compare the plot of the real dataset with collections of plots of simulated datasets. A simple but rigorous protocol that provides inferential validity is modelled after the ‘lineup’ popular from criminal legal procedures. Another protocol modelled after the ‘Rorschach’ inkblot test, well known from (pop-)psychology, will help analysts acclimatize to random variability before being exposed to the plot of the real data. The proposed protocols will be useful for exploratory data analysis, with reference datasets simulated by using a null assumption that structure is absent. The framework is also useful for model diagnostics in which case reference datasets are simulated from the model in question. This latter point follows up on previous proposals. Adopting the protocols will mean an adjustment in working procedures for data analysts, adding more rigour, and teachers might find that incorporating these protocols into the curriculum improves their students’ statistical thinking