Índice de Tendências Climáticas de Longo Prazo em Área Urbana na Amazônia Oriental

Esta pesquisa tem como objetivo analisar a variabilidade e a tendência de variáveis meteorológicas no longo prazo para caracterizar o clima urbano da cidade de Macapá-AP. Compreender a variabilidade dos índices climáticos em ambientes urbanos tende a mostrar possíveis interferências na qualidade de vida dos moradores locais, bem como torna possível comparar a realidade das cidades amazônicas em um contexto regional, nacional e mundial, contribuindo ao debate acadêmico. No presente caso as variáveis-chave são a temperatura do ar e a precipitação pluviométrica. A metodologia consiste nas seguintes etapas: a) coleta e consistência da série de dados por um período contínuo de 52 anos para o Estado do Amapá (1968 – 2020), b) a utilização do aplicativo RClimDex 1.1/IPCC para estimar as variações e as tendências climáticas locais utilizando-se 27 parâmetros climáticos extremos previstos pela equipe de peritos do CCI/CLIVAR e Climate Change Detection Monitoring and Indices (ETCCDMI). Os resultados obtidos acusaram treze indicadores estatísticos significativos (p<0,05), sugerindo tendência generalizada da elevação da temperatura média do ar na zona urbana da cidade. Como consequência, estes indicadores mostraram não somente uma significativa elevação das temperaturas máximas, médias e mínimas, mas também quais são os indicadores mais coerentemente associados com tendências de aquecimento temporal de cidades amazônicas, tanto para períodos diurnos quanto para períodos noturnos. Esse comportamento dos indicadores confirma a hipótese de predisposição a formação de ilha de calor em Macapá. Esta tendência mudou significativamente a partir de 2010. Index of Long Term Climate Trends in Urban Area in the Eastern AmazonA B S T R A C T This research aims to analyze the variability and trend of meteorological variables in the long term to characterize the urban climate of the city of Macapá-AP. Understanding the variability of climate indices in urban environments reveals possible interferences in the quality of life of the inhabitants, especially in urban locations. However, it has been relatively difficult to quantify trends in historical series that reliably represent climate indices relevant to the reality of Amazonian cities, both at a local and regional level. In the present case, the key variables analyzed were air temperature and rainfall. The methodology followed the following steps: a) collection and consistency of the data series over a continuous period of 52 years for the State of Amapá (1968–2020), b) using the data series by the RClimDex 1.1/IPCC application to estimate the local climate variations and trends using 27 extreme weather parameters predicted by the CCI/CLIVAR and Climate Change Detection Monitoring and Indices (ETCCDMI) team of experts. The results showed thirteen significant statistical indicators (p<0.05), suggesting a general trend towards an increase in the average air temperature in the urban area of the city. As a consequence, these indicators showed not only a significant increase in maximum, average and minimum temperatures, but also the indicators most coherently associated with temporal warming trends in Amazonian cities. So many that these effects seem to affect both the day and night periods, confirming the hypothesis of a predisposition to the formation of an urban heat island, with a significant change in this trend from 2010 onwards. Keywords: RClimDex 1.1, climate change indice, Macapá, Amapá

The Effect of Climate Change on Rain Characteristics in the Mepanga Watershed

Climate change has brought changes to the characteristics of the rain, wherein the rainy season duration is short; however, the dry season is getting longer. This study aims to detect the climate change presence or absence, identify the relationship of climate change to the nature of rainfall, and the relationship of climate change to rainy, humid and dry months. This research was conducted in the Mepanga Watershed, Parigi Moutong Regency, Central Sulawesi, Indonesia. The Makesens method is used for climate change detection. The rainfall characteristics use standard deviation statistical methods; furthermore, the Oldman method is used for dry, humid, and wet months. The data used is rainfall data for 30 years (1990-2019) from Kayu Agung Station. The results show that there has been a climate change in the Mepanga watershed. Marked by a Z ≠ zero value, where there is a positive trend (Z> 0) and a negative trend (Z <0). The increase in rainfall and conversely indicates the positive trend; otherwise, a decrease in rainfall indicates a negative trend. There is an effect of climate change on the nature of rain, wet, humid and dry months but not significant.

Visual Analytics for Climate Change Detection in Meteorological Time-Series

The importance of high-resolution meteorological time-series data for detection of transformative changes in the climate system is unparalleled. These data sequences allow for a comprehensive study of natural and forced evolution of warming and cooling tendencies, recognition of distinct structural changes, and periodic behaviors, among other things. Such inquiries call for applications of cutting-edge analytical tools with powerful computational capabilities. In this regard, we documented the application potential of visual analytics (VA) for climate change detection in meteorological time-series data. We focused our study on long- and short-term past-to-current meteorological data of three Central European cities (i.e., Vienna, Munich, and Zürich), delivered in different temporal intervals (i.e., monthly, hourly). Our aim was not only to identify the related transformative changes, but also to assert the degree of climate change signal that can be derived given the varying granularity of the underlying data. As such, coarse data granularity mostly offered insights on general trends and distributions, whereby a finer granularity provided insights on the frequency of occurrence, respective duration, and positioning of certain events in time. However, by harnessing the power of VA, one could easily overcome these limitations and go beyond the basic observations.

Tendências em índices extremos de precipitação em Cabaceiras (PB) para períodos distintos

Este trabalho teve como objetivo aplicar e estudar 11 índices de extremos de precipitação formulados pelo ETCCDI (Expert Team on Climate Change Detection and Indices, www.clivar.org/organization/etccdi), para a cidade de Cabaceiras-PB, utilizando dados diários de precipitação contínuos de 90 anos. Os índices foram calculados para o comprimento total da série, 1928 a 2017, assim como para três segmentos de 30 anos (1928-1957, 1958-1987 e 1988-2017). Os resultados evidenciaram que para muitos índices, tendências opostas e estatisticamente significativas podem ser observadas a depender do subperíodo estudado, assim como haver diferença entre estas tendências e as obtidas ao analisar-se o período total dos dados. Exemplos disso aconteceram para os índices R1mm, R10mm, R20mm, CWD e PRCPTOT. Trends in extreme precipitation indexes in Cabaceiras (PB) for different periods A B S T R A C TThis work aimed to apply and analyze 11 precipitation extremes indexes formulated by ETCCDI (Expert Team on Climate Change Detection and Indices, www.clivar.org/organization/etccdi), for the city of Cabaceiras, located in the Borborema mesoregion and microregion of Paraíba Oriental Cariri. A municipality in the semiarid region, it has the title of municipality where it rains less in Brazil, with an annual average of just over 300mm. Daily 90-year continuous precipitation data were used for the extreme indices, with the time series analyzed for four distinct periods, the total length of the series, 1928 to 2017, as well as three 30-year segments (1928-1957, 1958- 1987 and 1988-2017). The results showed that for many indices, opposite and significant trends can be observed depending on the sub period studied, as well as differences between these trends and those obtained by analyzing the total data period. The R1, R10 and R20mm indices show significant negative trends in the 1928-1957 sub period, but positive in the following two sub periods, reflecting a significant positive trend in the total period from 1928 to 2017. Other interesting examples are CDD indices for consecutive dry days, and PRCPTOT, for total annual rainfall with rainfall greater than 1mm. The CDD showed significant positive trend only in the 1928-1957 sub period, but non-significant negative trends in the subsequent sub periods, reflecting non-significant negative trends in the total length of the series. The PRCPTOT index shows behavior opposite to the CDD index, with a significant negative trend in the 1928-1957 sub period, positive in 1958-1987 and negative again in 1988-2017, but for the total length of the series the trend is positive and significant. These results show that the analysis of extreme trends is noticeably sensitive to the sample of the analyzed period, and may not reflect the reality of the time series the longer the total length of the time series, and need to be used with caution.Keywords: climate variability, dry and wet periods, semiarid.