Potential for small scale added value of RCM’s downscaled climate change signal

In recent decades, the need of future climate information at local scales have pushed the climate modelling community to perform increasingly higher resolution simulations and to develop alternative approaches to obtain fine-scale climatic information. In this article, various nested regional climate model (RCM) simulations have been used to try to identify regions across North America where high-resolution downscaling generates fine-scale details in the climate projection derived using the “delta method”. Two necessary conditions were identified for an RCM to produce added value (AV) over lower resolution atmosphere-ocean general circulation models in the fine-scale component of the climate change (CC) signal. First, the RCM-derived CC signal must contain some non-negligible fine-scale information—independently of the RCM ability to produce AV in the present climate. Second, the uncertainty related with the estimation of this fine-scale information should be relatively small compared with the information itself in order to suggest that RCMs are able to simulate robust fine-scale features in the CC signal. Clearly, considering necessary (but not sufficient) conditions means that we are studying the “potential” of RCMs to add value instead of the AV, which preempts and avoids any discussion of the actual skill and hence the need for hindcast comparisons. The analysis concentrates on the CC signal obtained from the seasonal-averaged temperature and precipitation fields and shows that the fine-scale variability of the CC signal is generally small compared to its large-scale component, suggesting that little AV can be expected for the time-averaged fields. For the temperature variable, the largest potential for fine-scale added value appears in coastal regions mainly related with differential warming in land and oceanic surfaces. Fine-scale features can account for nearly 60 % of the total CC signal in some coastal regions although for most regions the fine scale contributions to the total CC signal are of around ～5 %. For the precipitation variable, fine scales contribute to a change of generally less than 15 % of the seasonal-averaged precipitation in present climate with a continental North American average of ～5 % in both summer and winter seasons. In the case of precipitation, uncertainty due to sampling issues may further dilute the information present in the downscaled fine scales. These results suggest that users of RCM simulations for climate change studies in a delta method framework have little high-resolution information to gain from RCMs at least if they limit themselves to the study of first-order statistical moments. Other possible benefits arising from the use of RCMs—such as in the large scale of the downscaled fields– were not explored in this research.     

Nonparametric analysis of high wind speed data

In this paper, nonparametric curve estimation methods are applied to analyze time series of wind speeds, focusing on the extreme events exceeding a chosen threshold. Classical parametric statistical approaches in this context consist in fitting a generalized Pareto distribution (GPD) to the tail of the empirical cumulative distribution, using maximum likelihood or the method of the moments to estimate the parameters of this distribution. Additionally, confidence intervals are usually computed to assess the uncertainty of the estimates. Nonparametric methods to estimate directly some quantities of interest, such as the probability of exceedance, the quantiles or return levels, or the return periods, are proposed. Moreover, bootstrap techniques are used to develop pointwise and simultaneous confidence intervals for these functions. The proposed models are applied to wind speed data in the Gulf Coast of US, comparing the results with those using the GPD approach, by means of a split-sample test. Results show that nonparametric methods are competitive with respect to the standard GPD approximations. The study is completed generating synthetic data sets and comparing the behavior of the parametric and the nonparametric estimates in this framework.     

The droughts of 1997 and 2005 in Amazonia: floodplain hydrology and its potential ecological and human impacts

It is well known that most of the severe droughts in Amazonia, such as that of 1997, are El Niño-related. However, in 2005, the Amazon was affected by a severe drought that was not El Niño-related, as most of the rainfall anomalies that have happened in southwestern Amazonia are driven by sea surface temperature anomalies in the tropical North Atlantic. Earlier studies have analyzed both droughts in terms of their meteorological causes and impacts in terra firme (non-flooded) forests. This study compares the hydrological effects of both droughts on the Amazonian floodplain and discusses their potential ecological and human impacts based on an extensive literature review. The results revealed that the effects of the 2005 drought were exacerbated because rainfall was lower and evaporation rates were higher at the peak of the dry season compared to the 1997 drought. This induced a more acute depletion of water levels in floodplain lakes and was most likely associated with higher fish mortality rates. Based on the fact that the stem growth of many floodplain species is related to the length of the non-flooded period, it is hypothesized that the 1997 drought had more positive effects on floodplain forest growth than the 2005 drought. The fishing community of Silves in central Amazonia considered both droughts to have been equally severe. However, the 2005 drought was widely broadcasted by the press; therefore, the governmental mitigation efforts were more comprehensive. It is suggested that the availability of new communication technology and greater public awareness regarding environmental issues, combined with the new legal framework for assessing the severity of calamities in Brazil, are among the primary factors that explain the difference in societal response between the two droughts.     

Climate change and infectious diseases: Can we meet the needs for better prediction?

The next generation of climate-driven, disease prediction models will most likely require a mechanistically based, dynamical framework that parameterizes key processes at a variety of locations. Over the next two decades, consensus climate predictions make it possible to produce forecasts for a number of important infectious diseases that are largely independent of the uncertainty of longer-term emissions scenarios. In particular, the role of climate in the modulation of seasonal disease transmission needs to be unravelled from the complex dynamics resulting from the interaction of transmission with herd immunity and intervention measures that depend upon previous burdens of infection. Progress is also needed to solve the mismatch between climate projections and disease projections at the scale of public health interventions. In the time horizon of seasons to years, early warning systems should benefit from current developments on multi-model ensemble climate prediction systems, particularly in areas where high skill levels of climate models coincide with regions where large epidemics take place. A better understanding of the role of climate extremes on infectious diseases is urgently needed.     

Multifractal analysis of the rainfall time distribution on the metropolitan area of Barcelona (Spain)

In most of the studies on scale properties in the rainfall process, multifractal behavior has been investigated without taking into account the different rain generation mechanisms involved. However, it is known that rain processes are related to certain scales, determined by climatological characteristics as well as regional and local meteorological features. One of the implications derived from these correspondences is the possibility that the multifractal parameters of the rainfall could depend on the dominant precipitation generation mechanism. Fractal analysis techniques have been applied in this work to rainfall data recorded in the metropolitan area of Barcelona in the period 1994–2001, as well as to a selection of synoptic rainfall events registered in the same city in the period 1927–1992. The multifractal parameters obtained have been significantly different in each case probably showing the influence of the rain generation mechanisms involved. This influence has been revealed also in the analysis of the effects of seasonality on the multifractal behavior of rainfall in Barcelona.     

A model for binary-binary close encounters and collisions from a dynamical point of view

The goal of this paper is to provide a model for binary-binary interactions in star clusters, which is based on simultaneous binary collision of a special case of the one-dimensional 4-body problem where four masses move symmetrically about the center of mass. From the theoretical point of view, the singularity due to binary collisions between point masses can be handled by means of regularization theory. Our main tool is a change of coordinates due to McGehee by which we blow-up the singular set associated to total collision and replace it with an invariant manifold which includes binary and simultaneous binary collisions, and then gain a complete picture of the local behavior of the solutions near to total collision via the homothetic orbit.     

A Full-Sun Magnetic Index from Helioseismology Inferences

Solar magnetic indices are used to model the solar irradiance and ultimately to forecast it. However, the observation of such indices is generally limited to the Earth-facing hemisphere of the Sun. Seismic maps of the far side of the Sun have proven their capability to locate and track medium–large active regions at the non-visible hemisphere. We present here the possibility of using the average signal from these seismic far-side maps, combined with similarly calculated near-side maps, as a proxy to the full-Sun magnetic activity.