Do the recent severe droughts in the Amazonia have the same period of length?

We propose a new measure based on drought period length to assess the temporal difference between the recent two severe droughts of 2005 and 2010 in the Amazonia. The sensitivity of the measure is demonstrated by disclosing the distinct spatial responding mechanisms of the Northeastern and Southwestern Amazon (NA, SA) to the surrounding sea surface temperature (SST) variabilities. The Pacific and Atlantic oceans have different roles on the precipitation patterns in Amazonia. More specifically, the very dry periods in the NA are influenced by El Ni\~no events, while the very dry periods in the SA are affected by the anomalously warming of the SST in the North Atlantic. We show convincingly that the drought 2005 hit SA, which is caused by the North Atlantic only. There are two phases in the drought 2010: (i) it was started in the NA in August 2009 affected by the El Ni\~no event, and (ii) later shifted the center of action to SA resulted from anomalously high SST in North Atlantic, which further intensifies the impacts on the spatial coverage.Comment: 5 figure

Probabilistic methods for seasonal forecasting in a changing climate: Cox-type regression models

For climate risk management, cumulative distribution functions (CDFs) are an important source of information. They are ideally suited to compare probabilistic forecasts of primary (e.g. rainfall) or secondary data (e.g. crop yields). Summarised as CDFs, such forecasts allow an easy quantitative assessment of possible, alternative actions. Although the degree of uncertainty associated with CDF estimation could influence decisions, such information is rarely provided. Hence, we propose Cox-type regression models (CRMs) as a statistical framework for making inferences on CDFs in climate science. CRMs were designed for modelling probability distributions rather than just mean or median values. This makes the approach appealing for risk assessments where probabilities of extremes are often more informative than central tendency measures. CRMs are semi-parametric approaches originally designed for modelling risks arising from time-to-event data. Here we extend this original concept to other positive variables of interest beyond the time domain. We also provide tools for estimating CDFs and surrounding uncertainty envelopes from empirical data. These statistical techniques intrinsically account for non-stationarities in time series that might be the result of climate change. This feature makes CRMs attractive candidates to investigate the feasibility of developing rigorous global circulation model (GCM)-CRM interfaces for provision of user-relevant forecasts. To demonstrate the applicability of CRMs, we present two examples for El Niño/Southern Oscillation (ENSO)-based forecasts: the onset date of the wet season (Cairns, Australia) and total wet season rainfall (Quixeramobim, Brazil). This study emphasises the methodological aspects of CRMs rather than discussing merits or limitations of the ENSO-based predictor

AVALIAÇÃO DE DADOS DE PRECIPITAÇÃO PARA O MONITORAMENTO DO PADRÃO ESPAÇO-TEMPORAL DA SECA NO NORDESTE DO BRASIL

Um importante fator intrínseco da região Nordeste do Brasil (NEB) é sua variabilidade climática natural. A caracterização da variabilidade espaço-temporal da chuva nesta região torna-se importante no contexto da gestão do risco de seca, seja para subsidiar a tomada de decisões em relação às tendências climáticas, como para o monitoramento de eventos extremos de precipitação em curto e médio prazo. No entanto, a falta de conjuntos de dados observacionais de precipitação de longo prazo e concomitantemente consistentes, são os grandes obstáculos para o estudo e o monitoramento do padrão espaço-temporal da chuva. Posto isto, o objetivo do presente estudo foi avaliar o desempenho espaço-temporal de duas diferentes fontes de dados de precipitação (dados observacionais interpolados e obtidos por sensoriamento remoto) com a finalidade de criar um banco de dados conciso para a caracterização e o monitoramento da seca no NEB. De modo geral, o padrão sazonal de precipitação no NEB foi bem representado tanto por meio dos dados observacionais interpolados (dados oriundos do CPTEC/INPE) como pelos dados obtidos por sensoriamento remoto (CHIRPS). As análises quantitativas evidenciaram também que o CHIRPS apresenta erros sistemáticos de subestimativa e superestimava, porém, os valores de RMSE não ultrapassam 5 mm por estação do ano. Dessa maneira, foi possível a criação de uma nova série temporal de precipitação que compreende o período 1988 a 2017. O SPI calculado considerando o novo conjunto de dados de precipitação (CHIRPS-CPTEC/INPE) representou de maneira acurada os eventos secos e chuvosos no NEB quando comparados com aqueles identificados apenas com o uso do CHIRPS. Tal resultado corrobora a utilização da nova série climatológica no monitoramento de eventos extremos na região, principalmente os eventos de seca, uma vez que, são recorrentes e persistentes, permitindo assim uma melhor avaliação dos seus impactos na região.

Coalescing disparate data sources for the geospatial prediction of mosquito abundance, using Brazil as a motivating case study

One of the barriers to performing geospatial surveillance of mosquito occupancy or infestation anywhere in the world is the paucity of primary entomologic survey data geolocated at a residential property level and matched to important risk factor information (e.g., anthropogenic, environmental, and climate) that enables the spatial risk prediction of mosquito occupancy or infestation. Such data are invaluable pieces of information for academics, policy makers, and public health program managers operating in low-resource settings in Africa, Latin America, and Southeast Asia, where mosquitoes are typically endemic. The reality is that such data remain elusive in these low-resource settings and, where available, high-quality data that include both individual and spatial characteristics to inform the geospatial description and risk patterning of infestation remain rare. There are many online sources of open-source spatial data that are reliable and can be used to address such data paucity in this context. Therefore, the aims of this article are threefold: (1) to highlight where these reliable open-source data can be acquired and how they can be used as risk factors for making spatial predictions for mosquito occupancy in general; (2) to use Brazil as a case study to demonstrate how these datasets can be combined to predict the presence of arboviruses through the use of ecological niche modeling using the maximum entropy algorithm; and (3) to discuss the benefits of using bespoke applications beyond these open-source online data sources, demonstrating for how they can be the new “gold-standard” approach for gathering primary entomologic survey data. The scope of this article was mainly limited to a Brazilian context because it builds on an existing partnership with academics and stakeholders from environmental surveillance agencies in the states of Pernambuco and Paraiba. The analysis presented in this article was also limited to a specific mosquito species, i.e., Aedes aegypti, due to its endemic status in Brazil

Influence of decadal sea surface temperature variability on northern Brazil rainfall in CMIP5 simulations

The Amazonia and Northeast regions of northern Brazil are characterized by very different rainfall regimes but have certain similarities in terms of their variability. The precipitation variability in both regions is strongly linked to the tropical Atlantic sea surface temperature (SST) gradient and the tropical Pacific SST anomalies, which at decadal timescales are modulated by the Atlantic Multidecadal Variability (AMV) and the Interdecadal Pacific Oscillation (IPO) modes of SST, respectively. On the other hand, it has been found that state-of-the-art models from the fifth phase of the Coupled Model Intercomparison Project (CMIP5) are able to reproduce some of the characteristics of the low-frequency SST variability modes. In this work we analyze how CMIP5 models simulate the observed response of precipitation in the Amazonia and Northeast regions to the AMV and the IPO and the atmospheric mechanisms involved. Results show that, in both CMIP5 simulations and observations, Amazonia and Northeast rainfall response to the AMV is the opposite, due to the modulation of the intertropical convergence zone (ITCZ) position. Conversely, the IPO affects equally both regions as a consequence of anomalous subsidence over the entire northern Brazil triggered by warm SST anomalies in the tropical Pacific. Such results suggest that an improvement of the predictability of decadal SST modes will directly revert into a better prediction of changes in the Amazonia and Northeast rainfall at longer timescales

Probabilistic methods for seasonal forecasting in a changing climate: Cox-type regression models.

For climate risk management, cumulative distribution functions (CDFs) are an important source of information. They are ideally suited to compare probabilistic forecasts of primary (e.g. rainfall) or secondary data (e.g. crop yields). Summarised as CDFs, such forecasts allow an easy quantitative assessment of possible, alternative actions. Although the degree of uncertainty associated with CDF estimation could influence decisions, such information is rarely provided. Hence, we propose Cox-type regression models (CRMs) as a statistical framework for making inferences on CDFs in climate science. CRMs were designed for modelling probability distributions rather than just mean or median values. This makes the approach appealing for risk assessments where probabilities of extremes are often more informative than central tendency measures. CRMs are semi-parametric approaches originally designed for modelling risks arising from time-to-event data. Here we extend this original concept beyond time-dependent measures to other variables of interest. We also provide tools for estimating CDFs and surrounding uncertainty envelopes from empirical data. These statistical techniques intrinsically account for non-stationarities in time series that might be the result of climate change. This feature makes CRMs attractive candidates to investigate the feasibility of developing rigorous global circulation model (GCM)-CRM interfaces for provision of user-relevant forecasts. To demonstrate the applicability of CRMs, we present two examples for El Ni ? no/Southern Oscillation (ENSO)-based forecasts: the onset date of the wet season (Cairns, Australia) and total wet season rainfall (Quixeramobim, Brazil). This study emphasises the methodological aspects of CRMs rather than discussing merits or limitations of the ENSO-based predictors

Expectations Of Orthodontic Treatment In Adults: The Conduct In Orthodontist/patient Relationship.

The high demand for orthodontic treatment, evidenced over the last few decades, has been justified mainly by the greater importance given to facial esthetics, influencing individual's self esteem. However, the professional frequently does not meet all the patient's expectations, for not establishing good communication and not knowing about the critical points during orthodontic treatment. The aim of this study was to elucidate patients' desires and doubts regarding orthodontic treatment, by means of a survey applied to 60 adult patients. The analysis of results revealed that most individuals (38.3%) noticed treatment success after its conclusion. Occlusion deviation was pointed out by 66.7% as the main reason for seeking treatment, and esthetics ranked as second (with 48.3%). Treatment time was considered within the prediction by 46.7% of the interviewees and the results were judged as very good by 43.3%. The social relations of most participants were not affected by treatment (73.3%). Also, 58.3% of the interviewees reported pain as the main complaint and 53.3% found it difficult to use dental floss. Most participants saw the orthodontist as a professional who was concerned about their health (76.7%), and believed that he/she was more able to treat them (96.6%) when compared with the general practitioner. The orthodontist/patient relationship enables an understanding of the expectations regarding orthodontic treatment, resulting in greater motivation and cooperation, leading to a successful outcome.1888-9

Phlebotominae (Diptera: psycodidae) fauna in the Chaco region and Cutaneous Leishmaniasis transmission patterns in Argentina

In Argentina, the incidence of American Cutaneous Leishmaniasis (ACL) has shown a steady increase over the last few decades. In the Chaco biogeographical region, specifically, several outbreaks of ACL were recently reported in addition to the usual time-space scattering of ACL cases. However, little is known about the sandfly composition in the eastern, humid Chaco (HC) region or the western, dry Chaco (DC) region. Therefore, phlebotomine captures were performed throughout this region and an analysis of the distribution of reported ACL cases was conducted in order to assess the vector diversity in ACL endemic and epidemic scenarios in the Chaco region. The results support the hypothesis of two distinct patterns: (1) the DC, where Lutzomyia migonei was the most prevalent species, had isolated ACL cases and a zoonotic cycle; (2) the HC, where Lutzomyia neivai was the most prevalent species, had an increase in ACL incidence and outbreaks and an anthropozoonotic cycle. The epidemic risk in the Chaco region may be associated with the current climate trends, landscape modification, connection with other ACL foci, and Lu. neivai predominance and abundance. Therefore, changes in sandfly population diversity and density in the Chaco region are an indicator of emergent epidemic risk in sentinel capture sites.Facultad de Ciencias Naturales y Muse

Rainfall Variability and Change in Central and Southern Peruvian Andes

Understanding the spatiotemporal variability of rainfall over mountainous regions such as the Andes is crucial for the maintenance of water resources and ecosystems. Rainfall variability and change in the Central and Southern Peruvian Andes (CSPA) and their relationship with large-scale atmospheric dynamics is not fully understood. This study examines observed intraseasonal-to-interannual variability and long-term rainfall changes in CSPA. Chapter 1 describes the motivation, research questions, hypotheses and objectives of this study. Chapter 2 explains observed regional rainfall features over CSPA during 1965-2010. A regionalization of stations was performed using principal component and clustering analyses of forty-seven daily gauged time-series. Four major homogeneous regions are identified: Amazon, Central Pacific, Southern Pacific and Titicaca. The total and extreme rainfall indices proposed by the Expert Team on Climate Change Detection and Indices (ETCCDI) were calculated for each station. Rainfall indices and geographic features are similar among stations of the same region. Furthermore, this chapter investigates the effect of El Niño Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO) on rainfall indices. ENSO and PDO influences on rainfall are regionally dependent. Most stations in CSPA exhibit positive (negative) anomaly in total and extreme rainfall indices (consecutive dry days) during La Niña (El Niño) years of 1965-2010. Nevertheless, these rainfall patterns are decade-dependent. Positive (negative) PDO is associated with positive (negative) anomalies of annual total rainfall in Titicaca basin, and with positive (negative) anomalies of consecutive dry days in Amazon, Southern Pacific and Titicaca basins.Chapter 3 provides a comprehensive analysis of the signal, statistical significance and spatial pattern of rainfall trends from 1965 to 2010 in CSPA. Trends were examined with Mann-Kendall test and Sen’s slope estimator applied to yearly rainfall indices following the hydrologic calendar. Significant regional patterns of changes in rainfall extremes were investigated and compared with previous studies. The drying signal within the southern Peruvian Andes is more complex than pointed out in previous studies. Here, statistically significant trends observed in about 30% of stations in each CSPA region is described. The annual total rainfall has decreased in the Amazon basin, despite the increase in the number of rainy days and some extreme rainfall indices. Decrease in one-day and five-day yearly maximum rainfall is observed in Central Pacific, along with an increase in the number of wet days. Positive trends in indices related to the intensity of very strong daily rainfall are evident in Southern Pacific. Titicaca basin shows an increase in the intensity of rainfall extremes. The ENSO-PDO low-frequency conditions seem to influence the complex and mostly non-statistically significant long-term trends in CSPA. Chapter 4 examines the Madden-Julian Oscillation (MJO) and ENSO combined influence on rainfall during November-March 1979-2010 in CSPA. Positive standardized rainfall anomalies and a higher frequency of extreme rainfall events occurs during active MJO than during inactive MJO phases. This pattern is enhanced (suppressed) during La Niña (El Niño). MJO-ENSO modulation of rainfall varies according to the location of the stations. During ENSO-neutral conditions, enhanced rainfall in the upper Amazon and Central Pacific basins is evident during MJO phases 4 and 5, whereas MJO phase 2 (phases 6, 7 and 8) enhances rainfall in Southern Pacific (Titicaca) basin. During La Niña, enhanced rainfall occurs during MJO phases 5, 6 and 8 in the Amazon and Central Pacific basins, whereas MJO phases 1, 3 and 6 (phases 2, 4, 7 and 8) enhance rainfall in Southern Pacific (Titicaca) basin. During El Niño, suppressed rainfall occurs during MJO phases 1, 2, 5 and 8 in the Amazon basin, during MJO phase 4 in the Central Pacific basin, during MJO phase 8 in the Southern Pacific basin, and during MJO phases 1, 3 and 5 in the Titicaca basin. Certain MJO-ENSO conditions related with positive and negative anomalies of rainfall show different spatially-coherent statistically significant moisture flux anomalies in each CSPA region. Chapter 5 presents the final conclusions of this study and proposes some recommendations for future research