Understanding ENSO diversity

El Niño–Southern Oscillation (ENSO) is a naturally occurring mode of tropical Pacific variability, with global impacts on society and natural ecosystems. While it has long been known that El Niño events display a diverse range of amplitudes, triggers, spatial patterns, and life cycles, the realization that ENSO’s impacts can be highly sensitive to this event-to-event diversity is driving a renewed interest in the subject. This paper surveys our current state of knowledge of ENSO diversity, identifies key gaps in understanding, and outlines some promising future research directions.

Ocean heat content variability and change in an ensemble of ocean reanalyses

Accurate knowledge of the location and magnitude of ocean heat content (OHC) variability and change is essential for understanding the processes that govern decadal variations in surface temperature, quantifying changes in the planetary energy budget, and developing constraints on the transient climate response to external forcings. We present an overview of the temporal and spatial characteristics of OHC variability and change as represented by an ensemble of dynamical and statistical ocean reanalyses (ORAs). Spatial maps of the 0–300 m layer show large regions of the Pacific and Indian Oceans where the interannual variability of the ensemble mean exceeds ensemble spread, indicating that OHC variations are well-constrained by the available observations over the period 1993–2009. At deeper levels, the ORAs are less well-constrained by observations with the largest differences across the ensemble mostly associated with areas of high eddy kinetic energy, such as the Southern Ocean and boundary current regions. Spatial patterns of OHC change for the period 1997–2009 show good agreement in the upper 300 m and are characterized by a strong dipole pattern in the Pacific Ocean. There is less agreement in the patterns of change at deeper levels, potentially linked to differences in the representation of ocean dynamics, such as water mass formation processes. However, the Atlantic and Southern Oceans are regions in which many ORAs show widespread warming below 700 m over the period 1997–2009. Annual time series of global and hemispheric OHC change for 0–700 m show the largest spread for the data sparse Southern Hemisphere and a number of ORAs seem to be subject to large initialization ‘shock’ over the first few years. In agreement with previous studies, a number of ORAs exhibit enhanced ocean heat uptake below 300 and 700 m during the mid-1990s or early 2000s. The ORA ensemble mean (±1 standard deviation) of rolling 5-year trends in full-depth OHC shows a relatively steady heat uptake of approximately 0.9 ± 0.8 W m−2 (expressed relative to Earth’s surface area) between 1995 and 2002, which reduces to about 0.2 ± 0.6 W m−2 between 2004 and 2006, in qualitative agreement with recent analysis of Earth’s energy imbalance. There is a marked reduction in the ensemble spread of OHC trends below 300 m as the Argo profiling float observations become available in the early 2000s. In general, we suggest that ORAs should be treated with caution when employed to understand past ocean warming trends—especially when considering the deeper ocean where there is little in the way of observational constraints. The current work emphasizes the need to better observe the deep ocean, both for providing observational constraints for future ocean state estimation efforts and also to develop improved models and data assimilation methods.

Cloud streets and land-water interactions in the Amazon

Cloud streets are common feature in the Amazon Basin. They form from the combination of the vertical trade wind stress and moist convection. Here, satellite imagery, data collected during the COBRA-PARA (Caxiuan Observations in the Biosphere, River and Atmosphere of Para) field campaign, and high resolution modeling are used to understand the streets` formation and behavior. The observations show that the streets have an aspect ratio of about 3.5 and they reach their maximum activity around 15:00 UTC when the wind shear is weaker, and the convective boundary layer reaches its maximum height. The simulations reveal that the cloud streets onset is caused by the local circulations and convection produced at the interfaces between forest and rivers of the Amazon. The satellite data and modeling show that the large rivers anchor the cloud streets producing a quasi-stationary horizontal pattern. The streets are associated with horizontal roll vortices parallel to the mean flow that organizes the turbulence causing advection of latent heat flux towards the upward branches. The streets have multiple warm plumes that promote a connection between the rolls. These spatial patterns allow fundamental insights on the interpretation of the Amazon exchanges between surface and atmosphere with important consequences for the climate change understanding.

An assessment of the latent and sensible heat flux on the simulated regional climate over Southwestern South Atlantic Ocean

A Regional Climate Model (RegCM3) 10-year (1990-1999) simulation over southwestern South Atlantic Ocean (SAO) is evaluated to assess the mean climatology and the simulation errors of turbulent fluxes over the sea. Moreover, the relationship between these fluxes and the rainfall over some cyclogenetic areas is also analyzed. The RegCM3 results are validated using some reanalyses datasets (ERA40, R2, GPCP and WHOI). The summer and winter spatial patterns of latent and sensible heat fluxes simulated by the RegCM3 are in agreement with the reanalyses (WHOI, R2 and ERA40). They show large latent heat fluxes exchange in the subtropical SAO and at higher latitudes in the warm waters of Brazil Current. In particular, the magnitude of RegCM3 latent heat fluxes is similar to the WHOI, which is probably related to two factors: (a) small specific humidity bias, and (b) the RegCM3 flux algorithm. In contrast, the RegCM3 presents large overestimation of sensible heat flux, though it simulates well their spatial pattern. This simulation error is associated with the RegCM3 underestimation of the 2-m air temperature. In southwestern SAO, in three known cyclogenetic areas, the reanalyses and the RegCM3 show the existence of different physical mechanisms that control the annual cycles of latent/sensible heating and rainfall. It is shown that over the eastern coast of Uruguay (35A degrees-43A degrees S) and the southeastern coast of Argentina (44A degrees-52A degrees S) the sea-air moisture and heat exchange play an important role to control the annual cycle of precipitation. This does not happen on the south/southeastern coast of Brazil.

Are Reanalysis Data Useful for Calculating Climate Indices over South America?

Precipitation and temperature climate indices are calculated using the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis and validated against observational data from some stations over Brazil and other data sources. The spatial patterns of the climate indices trends are analyzed for the period 1961-1990 over South America. In addition, the correlation and linear regression coefficients for some specific stations were also obtained in order to compare with the reanalysis data. In general, the results suggest that NCEP/NCAR reanalysis can provide useful information about minimum temperature and consecutive dry days indices at individual grid cells in Brazil. However, some regional differences in the climate indices trends are observed when different data sets are compared. For instance, the NCEP/NCAR reanalysis shows a reversal signal for all rainfall annual indices and the cold night index over Argentina. Despite these differences, maps of the trends for most of the annual climate indices obtained from the NCEP/NCAR reanalysis and BRANT analysis are generally in good agreement with other available data sources and previous findings in the literature for large areas of southern South America. The pattern of trends for the precipitation annual indices over the 30 years analyzed indicates a change to wetter conditions over southern and southeastern parts of Brazil, Paraguay, Uruguay, central and northern Argentina, and parts of Chile and a decrease over southwestern South America. All over South America, the climate indices related to the minimum temperature (warm or cold nights) have clearly shown a warming tendency; however, no consistent changes in maximum temperature extremes (warm and cold days) have been observed. Therefore, one must be careful before suggesting an), trends for warm or cold days.

EVOLUTIONARY BIOLOGY IN BIODIVERSITY SCIENCE, CONSERVATION, AND POLICY: A CALL TO ACTION

Evolutionary biologists have long endeavored to document how many species exist on Earth, to understand the processes by which biodiversity waxes and wanes, to document and interpret spatial patterns of biodiversity, and to infer evolutionary relationships. Despite the great potential of this knowledge to improve biodiversity science, conservation, and policy, evolutionary biologists have generally devoted limited attention to these broader implications. Likewise, many workers in biodiversity science have underappreciated the fundamental relevance of evolutionary biology. The aim of this article is to summarize and illustrate some ways in which evolutionary biology is directly relevant We do so in the context of four broad areas: (1) discovering and documenting biodiversity, (2) understanding the causes of diversification, (3) evaluating evolutionary responses to human disturbances, and (4) implications for ecological communities, ecosystems, and humans We also introduce bioGENESIS, a new project within DIVERSITAS launched to explore the potential practical contributions of evolutionary biology In addition to fostering the integration of evolutionary thinking into biodiversity science, bioGENESIS provides practical recommendations to policy makers for incorporating evolutionary perspectives into biodiversity agendas and conservation. We solicit your involvement in developing innovative ways of using evolutionary biology to better comprehend and stem the loss of biodiversity.

Assessing the impact of deforestation and climate change on the range size and environmental niche of bird species in the Atlantic forests, Brazil

Aim Habitat loss and climate change are two major drivers of biological diversity. Here we quantify how deforestation has already changed, and how future climate scenarios may change, environmental conditions within the highly disturbed Atlantic forests of Brazil. We also examine how environmental conditions have been altered within the range of selected bird species. Location Atlantic forests of south-eastern Brazil. Methods The historical distribution of 21 bird species was estimated using Maxent. After superimposing the present-day forest cover, we examined the environmental niches hypothesized to be occupied by these birds pre- and post-deforestation using environmental niche factor analysis (ENFA). ENFA was also used to compare conditions in the entire Atlantic forest ecosystem pre- and post-deforestation. The relative influence of land use and climate change on environmental conditions was examined using analysis of similarity and principal components analysis. Results Deforestation in the region has resulted in a decrease in suitable habitat of between 78% and 93% for the Atlantic forest birds included here. Further, Atlantic forest birds today experience generally wetter and less seasonal forest environments than they did historically. Models of future environmental conditions within forest remnants suggest generally warmer conditions and lower annual variation in rainfall due to greater precipitation in the driest quarter of the year. We found that deforestation resulted in a greater divergence of environmental conditions within Atlantic forests than that predicted by climate change. Main conclusions The changes in environmental conditions that have occurred with large-scale deforestation suggest that selective regimes may have shifted and, as a consequence, spatial patterns of intra-specific variation in morphology, behaviour and genes have probably been altered. Although the observed shifts in available environmental conditions resulting from deforestation are greater than those predicted by climate change, the latter will result in novel environments that exceed temperatures in any present-day climates and may lead to biotic attrition unless organisms can adapt to these warmer conditions. Conserving intra-specific diversity over the long term will require considering both how changes in the recent past have influenced contemporary populations and the impact of future environmental change.

Trips and their CO2 emissions induced by a shopping center

Most previous studies have focused on entire trips in a geographic region, while a few of them addressed trips induced by a city landmark. Therefore paper explores trips and their CO2 emissions induced by a shopping center from a time-space perspective and their usage in relocation planning. This is conducted by the means of a case study in the city of Borlänge in mid-Sweden where trips to the city’s largest shopping mall in its center are examined. We use GPS tracking data of car trips that end and start at the shopping center. Thereafter, (1) we analyze the traffic emission patterns from a time-space perspective where temporal patterns reveal an hourly-based traffic emission dynamics and where spatial patterns uncover a heterogeneous distribution of traffic emissions in spatial areas and individual street segments. Further, (2) this study reports that most of the observed trips follow an optimal route in terms of CO2 emissions. In this respect, (3) we evaluate how well placed the current shopping center is through a comparison with two competing locations. We conclude that the two suggested locations, which are close to the current shopping center, do not show a significant improvement in term of CO2 emissions.

Geoprocessing journey-to-work data : delineating commuting regions in Dalarna, Sweden

Delineation of commuting regions has always been based on statistical units, often municipalities or wards. However, using these units has certain disadvantages as their land areas differ considerably. Much information is lost in the larger spatial base units and distortions in self-containment values, the main criterion in rule-based delineation procedures, occur. Alternatively, one can start from relatively small standard size units such as hexagons. In this way, much greater detail in spatial patterns is obtained. In this paper, regions are built by means of intrazonal maximization (Intramax) on the basis of hexagons. The use of geoprocessing tools, specifically developed for the processing ofcommuting data, speeds up processing time considerably. The results of the Intramax analysis are evaluated with travel-to-work area constraints, and comparisons are made with commuting fields, accessibility to employment, commuting flow density and network commuting flow size. From selected steps in the regionalization process, a hierarchy of nested commuting regions emerges, revealing the complexity of commuting patterns.

Effect Of Different Hydrological Model Structures On The Assimilation Of Distributed Uncertain Observations

The reliable evaluation of the flood forecasting is a crucial problem for assessing flood risk and consequent damages. Different hydrological models (distributed, semi-distributed or lumped) have been proposed in order to deal with this issue. The choice of the proper model structure has been investigated by many authors and it is one of the main sources of uncertainty for a correct evaluation of the outflow hydrograph. In addition, the recent increasing of data availability makes possible to update hydrological models as response of real-time observations. For these reasons, the aim of this work it is to evaluate the effect of different structure of a semi-distributed hydrological model in the assimilation of distributed uncertain discharge observations. The study was applied to the Bacchiglione catchment, located in Italy. The first methodological step was to divide the basin in different sub-basins according to topographic characteristics. Secondly, two different structures of the semi-distributed hydrological model were implemented in order to estimate the outflow hydrograph. Then, synthetic observations of uncertain value of discharge were generated, as a function of the observed and simulated value of flow at the basin outlet, and assimilated in the semi-distributed models using a Kalman Filter. Finally, different spatial patterns of sensors location were assumed to update the model state as response of the uncertain discharge observations. The results of this work pointed out that, overall, the assimilation of uncertain observations can improve the hydrologic model performance. In particular, it was found that the model structure is an important factor, of difficult characterization, since can induce different forecasts in terms of outflow discharge. This study is partly supported by the FP7 EU Project WeSenseIt.

Exclusão social e projetos habitacionais. Um estudo sobre conjuntos habitacionais, segregação e exclusão social em Natal/RN

In Natal s urban growth process it is given that the performance period of the National Housing Bank (BNH, 1964-1986) was marked by the intense expansion of the urban grid and configuration of outskirts, through the construction of social housing developments. Implanted in segregated areas of the existing formal city, the population installed in these complexes was also excluded from their rights, considering that the housing defines itself not only by the physical dwelling, but also by its access to urban infrastructure, facilities, services, and others. From this reality and the verification of the city s exclusion and sociospatial segregation processes, we aimed to quantitatively demonstrate levels of social exclusion in Natal, based on the methodology developed by Sposati (2000) and adapted by Genovez (2002), which relates IBGE s (Brazilian Institute of Geography and Statistics) database underlying variables such as income, schooling and dwelling s quality. The research unveiled some spatial patterns promoted by the social housings: in these areas islands were developed with higher indicators than surrounding areas, revealing internal hierarchies in the city s outskirts

Efeito da salinidade da água do mar no rendimento, composição e atividades biológicas de frações polissacarídicas da Chlorophyta Caulerpa cupressoides var. flabellata

Seaweeds sulfated polysaccharides have been described as having various pharmacological activities. However, nothing is known about the influence of salinity on the structure of sulfated polysaccharides from green seaweed and pharmacological activities they perform. Therefore, the main aim of this study was to evaluate the effect of salinity of seawater on yield and composition of polysaccharides-rich fractions from green seaweed Caulerpa cupressoides var. flabellata, collected in two different salinities beaches of the coast of Rio Grande do Norte, and to verify the influence of salinity on their biological activities. We extracted four sulfated polysaccharides-rich fractions from C. cupressoides collected in Camapum beach (denominated CCM F0.3; F0.5; F1.0; F2.0), which the seawater has higher salinity, and Buzios beach (denominated CCB F0.3; F0.5; F1.0; F2.0). Different from that observed for other seaweeds, the proximate composition of C. cupressoides did not change with increased salinity. Moreover, interestingly, the C. cupresoides have high amounts of protein, greater even than other edible seaweeds. There was no significant difference (p>0.05) between the yield of polysaccharide fractions of CCM and its CCB counterparts, which indicates that salinity does not interfere with the yield of polysaccharide fractions. However, there was a significant difference in the sulfate/sugar ratio of F0.3 (p<0.05) and F0.5 (p<0.01) (CCM F0.3 and CCB F0.5 was higher than those determined for their counterparts), while the sulfate/sugar ratio the F1.0 and F2.0 did not change significantly (p>0.05) with salinity. This result suggested that the observed difference in the sulfate/sugar ratio between the fractions from CCM and CCB, is not merely a function of salinity, but probably also is related to the biological function of these biopolymers in seaweed. In addition, the salinity variation between collection sites did not influence algal monosaccharide composition, eletrophoretic mobility or the infrared spectrum of polysaccharides, demonstrating that the salinity does not change the composition of sulfated polysaccharides of C. cupressoides. There were differences in antioxidant and anticoagulant fractions between CCM and CCB. CCB F0.3 (more sulfated) had higher total antioxidant capacity that CCM F0.3, since the chelating ability the CCM F0.5 was more potent than CCB F0.5 (more sulfated). These data indicate that the activities of sulfated polysaccharides from CCM and CCB depend on the spatial patterns of sulfate groups and that it is unlikely to be merely a charge density effect. C. cupressoides polysaccharides also exhibited anticoagulant activity in the intrinsic (aPTT test) and extrinsic pathway (PT test). CCB F1.0 and CCM F1.0 showed different (p<0,001) aPTT activity, although F0.3 and F0.5 showed no difference (p>0,05) between CCM and CCB, corroborating the fact that the sulfate/sugar ratio is not a determining factor for biological activity, but rather for sulfate distribution along the sugar chain. Moreover, F0.3 and F0.5 activity in aPTT test was similar to that of clexane®, anticoagulant drug. In addition, F0.5 showed PT activity. These results suggest that salinity may have created subtle differences in the structure of sulfated polysaccharides, such as the distribution of sulfate groups, which would cause differences in biological activities between the fractions of the CCM and the CCB

Análise de agrupamentos dos dados de DFA oriundos de perfis elétricos de indução de poços de petróleo

The main objective of this study is to apply recently developed methods of physical-statistic to time series analysis, particularly in electrical induction s profiles of oil wells data, to study the petrophysical similarity of those wells in a spatial distribution. For this, we used the DFA method in order to know if we can or not use this technique to characterize spatially the fields. After obtain the DFA values for all wells, we applied clustering analysis. To do these tests we used the non-hierarchical method called K-means. Usually based on the Euclidean distance, the K-means consists in dividing the elements of a data matrix N in k groups, so that the similarities among elements belonging to different groups are the smallest possible. In order to test if a dataset generated by the K-means method or randomly generated datasets form spatial patterns, we created the parameter Ω (index of neighborhood). High values of Ω reveals more aggregated data and low values of Ω show scattered data or data without spatial correlation. Thus we concluded that data from the DFA of 54 wells are grouped and can be used to characterize spatial fields. Applying contour level technique we confirm the results obtained by the K-means, confirming that DFA is effective to perform spatial analysis

Análise de DFA e de agrupamento do perfil de densidade de poços de petróleo

In recent years, the DFA introduced by Peng, was established as an important tool capable of detecting long-range autocorrelation in time series with non-stationary. This technique has been successfully applied to various areas such as: Econophysics, Biophysics, Medicine, Physics and Climatology. In this study, we used the DFA technique to obtain the Hurst exponent (H) of the profile of electric density profile (RHOB) of 53 wells resulting from the Field School of Namorados. In this work we want to know if we can or not use H to spatially characterize the spatial data field. Two cases arise: In the first a set of H reflects the local geology, with wells that are geographically closer showing similar H, and then one can use H in geostatistical procedures. In the second case each well has its proper H and the information of the well are uncorrelated, the profiles show only random fluctuations in H that do not show any spatial structure. Cluster analysis is a method widely used in carrying out statistical analysis. In this work we use the non-hierarchy method of k-means. In order to verify whether a set of data generated by the k-means method shows spatial patterns, we create the parameter Ω (index of neighborhood). High Ω shows more aggregated data, low Ω indicates dispersed or data without spatial correlation. With help of this index and the method of Monte Carlo. Using Ω index we verify that random cluster data shows a distribution of Ω that is lower than actual cluster Ω. Thus we conclude that the data of H obtained in 53 wells are grouped and can be used to characterize space patterns. The analysis of curves level confirmed the results of the k-means

Comparação da análise de diferentes perfis de poços pelo método DFA

The study of complex systems has become a prestigious area of science, although relatively young . Its importance was demonstrated by the diversity of applications that several studies have already provided to various fields such as biology , economics and Climatology . In physics , the approach of complex systems is creating paradigms that influence markedly the new methods , bringing to Statistical Physics problems macroscopic level no longer restricted to classical studies such as those of thermodynamics . The present work aims to make a comparison and verification of statistical data on clusters of profiles Sonic ( DT ) , Gamma Ray ( GR ) , induction ( ILD ) , neutron ( NPHI ) and density ( RHOB ) to be physical measured quantities during exploratory drilling of fundamental importance to locate , identify and characterize oil reservoirs . Software were used : Statistica , Matlab R2006a , Origin 6.1 and Fortran for comparison and verification of the data profiles of oil wells ceded the field Namorado School by ANP ( National Petroleum Agency ) . It was possible to demonstrate the importance of the DFA method and that it proved quite satisfactory in that work, coming to the conclusion that the data H ( Hurst exponent ) produce spatial data with greater congestion . Therefore , we find that it is possible to find spatial pattern using the Hurst coefficient . The profiles of 56 wells have confirmed the existence of spatial patterns of Hurst exponents , ie parameter B. The profile does not directly assessed catalogs verification of geological lithology , but reveals a non-random spatial distribution