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Investigations into the climatic forcings that affect the long-term variability of the Indian summer monsoon are constrained by a lack of reliable rainfall data prior to the late nineteenth century. Extensive qualitative and quantitative meteorological information for the pre-instrumental period exists within historical documents, although these materials have been largely unexplored. This paper presents the first reconstruction of monsoon variability using documentary sources, focussing on western India for the period 1781-1860. Three separate reconstructions are generated, for (1) Mumbai, (2) Pune and (3) the area of Gujarat bordering the Gulf of Khambat. A composite chronology is then produced from the three reconstructions, termed the Western India Monsoon Rainfall reconstruction (WIMR). The WIMR exhibits four periods of generally deficient monsoon rainfall (1780-1785, 1799-1806, 1830-1838 and 1845-1857) and three of above-normal rainfall (1788-1794, 1813-1828 and 1839-1844). The WIMR shows good correspondence with a dendroclimatic drought reconstruction for Kerala, although agreement with the western Indian portion of the tree-ring derived Monsoon Asia Drought Atlas is less strong. The reconstruction is used to examine the long-term relationship between the El Nino-Southern Oscillation (ENSO) and monsoon rainfall over western India. This exhibits peaks and troughs in correlation over time, suggesting a regular long-term fluctuation. This may be an internal oscillation in the ENSO-monsoon system or may be related to volcanic aerosol forcings. Further reconstructions of monsoon rainfall are necessary to validate this. The study highlights uncertainties in existing published rainfall records for 1817-1846 for western India. [ABSTRACT FROM AUTHOR]

Abstract: Climate change is expected to alter rainfall regimes across most parts of the world. The implications of this could be more severe in arid environments where rainfall is limited and highly variable in space and time. However, lack of good quality data, of sufficient record length and spatial coverage usually restricts model development and performance geared towards assessing the effects of climate change in these areas. This paper presents an analysis of rainfall and climate data in order to determine the time of change in rainfall series and identify possible correlations between rainfall and temperature. In addition, the paper aims to make predictions of future rainfall patterns in Botswana. This is achieved by using historical rainfall and climate data from rainfall stations spread across Botswana from 1965 to 2008. In addition, large scale reanalysis data from NCAR/NCEP and El Nino Southern Oscillation (ENSO) data were used to augment the limited observed spatial climate data series when developing a rainfall model. Temperature and ENSO indices have been used to predict rainfall regimes for the present climate. Based on these, the effects of climate change were quantified using a stochastic generalised linear rainfall model (GLM) driven by outputs of global climate models (GCMs). The results indicate that temperature is a significant rainfall predictor in Botswana compared to ENSO indices. [Copyright &y& Elsevier]

The variations of global atmospheric oscillations over the last millennium, including the North Atlantic Oscillation (NAO), the North Pacific Oscillation (NPO) highly associated with the Pacific Decadal Oscillation (PDO), the Southern Oscillation (SO) and the Antarctic Oscillation (AAO), are studied and compared in this paper based on observations and reconstructed data. The cross correlation analysis of AAO, NAO and NPO shows that there is no significant relationship on interannual variation among them. However, the consistency on decadal variability is prominent. During A.D.1920--1940 and A.D.1980--2000, the positive (strong) phase was dominant and the negative (weak) one noticeable during A.D.1940--1980. In addition, the reconstructed atmospheric oscillations series demonstrate that the positive phase existed in the early of the last millennium for NAO and in the late of the last millennium for AAO, respectively; while it occurred in the mid-late of the last millennium for PDO and ENSO. [ABSTRACT FROM AUTHOR]

Abstract: Over the past decade, soil moisture products derived from microwave remote sensing data have gained increasing attention for the monitoring of agricultural, hydrological and climate processes. ERS-1 and ERS-2 scatterometer-derived time series of data available since 1992 enable the user to analyze the change of soil moisture patterns over time and to monitor events deviating from the normal. In this paper, ERS scatterometer-derived soil moisture products are compared with known occurrences of heavy rain and drought related to the strong El Niño (EN) event of 1997/1998. We investigated globally if the typical EN-related short term climate changes can be depicted in soil moisture data. EN consequences and their manifestation in the data are presented for the Americas, Africa and Asia. Furthermore, the weather situation during the EN event in China is compared to the scatterometer-derived soil moisture data set in more detail. It could be observed that most EN-related climate variations on the different continents are clearly reflected by remote sensing soil moisture data. Heavy rain and drought conditions reported in daily media, science papers, and forecasts by the can be observed in anomaly data sets showing the deviation of soil moisture from the average. This also applies for the strong EN-related flood, which hit China in 1998 and following La Niña-related droughts. These analyses thus serve to demonstrate that information relevant to climatology is contained in the satellite scatterometer-derived soil moisture product, as well as in anomaly time series, derived from the existing soil moisture time series. Our procedure thus consisted of soil moisture time series calculation, soil moisture anomaly calculation, creation of time series and anomaly maps and the comparison of anomalies with meteorological data on three continents for the time period of outstanding El Nino years. The soil moisture data of TU Wien is available via the internet free of charge. [Copyright &y& Elsevier]

Abstract: Palaeoenvironmental and palaeoclimatic data indicate that during the mid-to-late Holocene eastern Australia became significantly drier and experienced more intense and more frequent droughts. These changes, driven by the re-emergence and intensification of the ENSO climate phenomena, have been argued to have had considerable impact on Aboriginal societies, although there is uncertainty as to the exact nature, timing and magnitude of this impact. This paper analyses changes in the utilisation of toxic Macrozamia (cycad) seeds at seven archaeological sites in eastern Australia, identifying an extremely close correlation between the intensity of seed use and two proxy ENSO datasets, and a weaker correlation with a third ENSO dataset. Given the ecological attributes and resource potential of these plants, it is argued that these correlations are best explained as an intensified exploitation of a lower-ranked resource in direct response to the increased subsistence risks and lower productivity created by ENSO-driven climatic conditions. It also suggests that by 3000 BP the intensification of the ENSO system was driving changes in human subsistence behaviour on a sufficient scale to have considerable impact on other aspects of the wider cultural systems. [Copyright &y& Elsevier]

In this study, a new approach for extracting flow-dependent empirical singular vectors (FESVs) for seasonal prediction using ensemble perturbations obtained from an ensemble Kalman filter (EnKF) assimilation is presented. Due to the short interval between analyses, EnKF perturbations primarily contain instabilities related to fast weather variability. To isolate slower, coupled instabilities that would be more suitable for seasonal prediction, an empirical linear operator for seasonal time-scales (i.e. several months) is formulated using a causality hypothesis; then, the most unstable mode from the linear operator is extracted for seasonal time-scales. It is shown that the flow-dependent operator represents nonlinear integration results better than a conventional empirical linear operator static in time. Through 20 years of retrospective seasonal predictions, it is shown that the skill of forecasting equatorial SST anomalies using the FESV is systematically improved over that using Conventional ESV (CESV). For example, the correlation skill of the NINO3 SST index using FESV is higher, by about 0.1, than that of CESV at 8-month leads. In addition, the forecast skill improvement is significant over the locations where the correlation skill of conventional methods is relatively low, indicating that the FESV is effective where the initial uncertainty is large. [ABSTRACT FROM AUTHOR]

As climate changes, regional responses may become more apparent; impacts often can become natural hazards, adversely affecting millions of people, on all continents and most nations. The coupling of hazards to climate change scenarios is a great challenge of climate change science. Nevertheless, it is extremely important to observe, simulate and ultimately understand this coupling, for the benefit of society and sustainability of the Earth's environment. Different applied mathematical techniques have been used to discern real effects of these changes and study long term trends. Moreover, those techniques can be applied in addressing the intensity and frequency of extreme events associated with climate change at regional scales and would be an important step in facing future extreme events associated with climate change. Computational methods include applying statistical data analysis, mesoscale and climate simulations while assisting modeling efforts with satellite based observations. Predictive analytics platforms would be very useful for assessing impacts of climate variability and change on the frequency and intensity of extreme events and how these extreme events can affect water and air quality issues globally. These tools are an innovative technology that applies data mining methods, predictive models, analysis and reporting to data, without the inherent limitations of current On Line Analytical Processing tools that suffer from cube rigidity, database explosion and dimensional constriction. Climate-induced changes are complex and vary across a wide range of dimensions. An important part of predictive analytics platforms are their unlimited dimensionality and the segmentation of data by 'physical' or 'performance' characteristics. For example, a physical dimension might be the climate divisions in the state of California. A performance dimension could be the arithmetical, mathematical, or statistical segmentation of data based on its performance with regard to time. WIREs Comput Stat 2012 doi: 10.1002/wics.1213 [ABSTRACT FROM AUTHOR]