Record tests to detect non-stationarity in the tails with an application to climate change

The analysis of trends and other non-stationary behaviours at the extremes of a series is an important problem in global warming. This work proposes and compares several statistical tools to analyse that behaviour, using the properties of the occurrence of records in i.i.d. series. The main difficulty of this problem is the scarcity of information in the tails, so it is important to obtain all the possible evidence from the available data. First, different statistics based on upper records are proposed, and the most powerful is selected. Then, using that statistic, several approaches to join the information of four types of records, upper and lower records of forward and backward series, are suggested. It is found that these joint tests are clearly more powerful. The suggested tests are specifically useful in analysing the effect of global warming in the extremes, for example, of daily temperature. They have a high power to detect weak trends and can be widely applied since they are non-parametric. The proposed statistics join the information of M independent series, which is useful given the necessary split of the series to arrange the data. This arrangement solves the usual problems of climate series (seasonality and serial correlation) and provides more series to find evidence. These tools are used to analyse the effect of global warming on the extremes of daily temperature in Madrid.

Next Generation Gravity Mission Elements of the Mass Change and Geoscience International Constellation: From Orbit Selection to Instrument and Mission Design

ESA’s Next Generation Gravity Mission (NGGM) is a candidate Mission of Opportunity for ESA–NASA cooperation in the frame of the Mass Change and Geosciences International Constellation (MAGIC). The mission aims at enabling long-term monitoring of the temporal variations of Earth’s gravity field at relatively high temporal (down to 3 days) and increased spatial resolutions (up to 100 km) at longer time intervals. This implies also that time series of GRACE and GRACE-FO can be extended towards a climate series. Such variations carry information about mass change induced by the water cycle and the related mass exchange among atmosphere, oceans, cryosphere, land and solid Earth and will complete our picture of global and climate change. The main observable is the variation of the distance between two satellites measured by a ranging instrument. This is complemented by accelerometers that measure the nongravitational accelerations, which need to be reduced from ranging measurements to obtain the gravity signal. The preferred satellite constellation comprises one satellite pair in a near-polar and another in an inclined circular orbit. The paper focuses on the orbit selection methods for optimizing the spatial sampling for multiple temporal resolutions and then on the methodology for deriving the engineering requirements for the space segment, together with a discussion on the main mission parameters.

Extracting Modulated Annual Cycle in Climate and Ocean Time Series Using an Enhanced Harmonic Analysis

Accurate extraction of the modulated annual cycle (MAC) is important for climatic and oceanic research. A variety of methods are available to extract the annual cycle with inconsistent results. Since actual annual cycles are unknown in the observation series, the reliability and applicability of the results extracted by these methods are difficult to estimate. In this study, three widely used decomposition methods, ensemble empirical mode decomposition (EEMD), nonlinear mode decomposition (NMD), and enhanced harmonic analysis (EHA), are evaluated by idealized numerical experiments for extracting modulated annual cycles from climate series. Idealized numerical experiments are carried out and show that the recently proposed EHA had the most accuracy in extracting the MAC from the constructed data. The optimal independent point (IP) number, which makes the most accurate result for EHA, can be found in each ideal experiment. In the actual experiment, two IP selection criteria are proposed for EHA to extract MAC from observations.

Record Tests to Detect Non Stationarity in the Tails With an Application to Climate Change

The analysis of non stationary behaviours and trends in the extremes of a series is an important problem in global warming. This work develops statistical tools to analyse that behaviour, using the properties of the occurrence of records in i.i.d. series. The main difficulty of this problem is the scarcity of information in the tails, so that it is important to obtain all the possible evidences from the data available. To that end, first, different statistics based on upper records are proposed and the most powerful is selected. Then, using that statistic, several approaches to join the information of four types of records, upper and lower records of forward and backward series, are suggested. It is found than these joint tests are clearly more powerful. The suggested tests are specifically useful in the analysis of the effect of global warming in the extremes, for example of daily temperature. They have a high power to detect weak trends and they can be widely applied, since they are non parametric. The proposed statistics join the information of M independent series, what is useful given the necessary split of the series to arrange the data. This arrangement solves usual problems of climate series (seasonality and serial correlation) and provides more series to find evidences. These tools are used to analyse the effect of global warming in the extremes of daily temperature in Madrid.

Processing of high-resolution temporal climate data for daily simulations of a complex agro-ecosystem.

Ecosystem services, such as natural pest control, are essential tools to be incorporated in future agricultural methodologies. In this paper we focus on the processing of climate data series that feed to a system of computer models simulating daily interactions of a pest and its predator, in a dynamic landscape, the olive grove. We filled hourly climate data series and converted them to daily climate series using R language. The methodology used produces acceptable climate data series for the system to run and allows to segregate specific periods of the day while maintaining daily temporal resolution. We expect this paper can be helpful when dealing with similar data and purpose.

STATISTICAL MODELS FOR FORECASTING AVERAGE MONTHLY TEMPERATURE AND MONTHLY PRECIPITATION AMOUNT IN PERM

The article proposes an approach to forecasting mean temperature and total precipitation for the upcoming month, based on the study of the regularities of the influence of statistical characteristics of temperature and precipitation of previous periods on them. Among the predictors, along with the basic statistical characteristics, we use the fractality index which is an indicator of the randomness/ determinism of the climate series. Within the framework of this approach, we have developed models of different levels to predict the temperature and total precipitation amount in the upcoming month. The main parameters of these models are described and the possibilities of their variation are indicated. Examples are given to illustrate the forecasting methodology using various types of models and include the results of quality control of the models, calculation of forecast accuracy and dependence of forecast accuracy of average temperature and precipitation on the month (climate season). When tested in 2020, models for forecasting temperature and precipitation for the upcoming month give good results: 9 correct forecasts of temperature anomalies out of 10 (90%) and 7 correct forecasts of precipitation anomalies out of 9 (77,7%).

Análise da Variação Sazonal e de Tendências na Precipitação Pluviométrica no Município de Juazeiro-BA

A cidade de Juazeiro-BA destaca-se por ser um importante polo de desenvolvimento agrícola em bases irrigadas no Nordeste, no qual a distribuição sazonal da precipitação pluviométrica tem influência decisiva no contexto da relação saúde e saneamento em áreas urbanizadas, na agricultura familiar, no abastecimento de água de famílias isoladas e na economia dessa região. Assim, essa pesquisa tem como objetivo avaliar a distribuição das chuvas no município de Juazeiro-BA, analisando uma série diária de 32 anos de dados pluviométricos, calculando os valores anuais e sazonais do Índice de Concentração da Precipitação (ICP) e realizando um estudo de tendência da precipitação pluviométrica, a partir do método de Mann-Kendall. Os resultados revelaram que, em Juazeiro, o período chuvoso é bem definido, com os maiores índices de precipitação entre os meses de dezembro e março. O ICP anual tem o valor de 28,48 %, indicando alta irregularidade na distribuição das chuvas anuais, possibilitando a ocorrência de eventos extremos, tanto de inundações quanto de escassez hídrica. O teste de Mann-Kendall, para o período em estudo, apresentou tendência de diminuição da média pluviométrica anual em 7,51 mm, o que pode aumentar o déficit hídrico na região.Palavras-chave: Chuvas; Séries Climáticas; Concentração Pluviométrica; Mann-Kendall. Seasonal Variation and Trend in Rainfall in the Municipality of Juazeiro-BA A B S T R A C TThe city of Juazeiro-BA stands out for being an important agricultural development hub on irrigated bases in the Northeast, in which the seasonal distribution of pluviometric precipitation has a decisive influence in the context of the health and sanitation relationship in urbanized areas, in family agriculture, in water supplying of isolated families and the economy of that region. Thus, this research aims to assess the distribution of rainfall in the municipality of Juazeiro-BA, analyzing a daily series of 32 years of rainfall data, calculating the annual and seasonal values of the Precipitation Concentration Index (PCI) and conducting a rainfall trend study, using the Mann-Kendall method. The results revealed that, in Juazeiro, the rainy period is well defined, with the highest levels of precipitation between the months of December and March. The annual PCI has a value of 28.48 %, indicating a high irregularity in the distribution of annual rainfall, enabling the occurrence of extreme events, both floods and water scarcity. The Mann-Kendall test, for the period under study, presented tendency decrease of the annual rainfall average by 7.51 mm, which may increase the water deficit in the region.Keywords: Rainfall; Climate Series; Rain Concentration; Mann-Kendall.

Climate records in ancient Chinese diaries and their application in historical climate reconstruction – a case study of <i>Yunshan Diary</i>

Private diaries are important sources of historical data for research on climate change. Their advantages include a high veracity and reliability, accurate time and location information, a high temporal resolution, seasonal integrity, and rich content. In particular, these data are suitable for reconstructing short-term, high-resolution climate series and extreme climatic events. Through a case study of Yunshan Diary, authored by Bi Guo of the Yuan dynasty of China, this article demonstrates how to delve into climate information in diaries, including species distribution records, phenological records, daily weather descriptions and personal experiences of meteorological conditions. In addition, this article considers how to use these records, supplemented by other data, to reconstruct climate change and extreme climatic events on various timescales, from multidecadal to annual or daily. The study of Yunshan Diary finds that there was a relatively low amount of precipitation in central and southern Jiangsu Province in the summer of 1309; the winter of 1308–1309 was abnormally cold in the Taihu Lake basin. In the early 14th century at the latest, the climate in eastern China had begun to turn cold, which reflects the transition from the Medieval Warm Period to the Little Ice Age.