Southern Africa is characterized by a high degree of rainfall variability, affecting agriculture and hydrology, among other sectors. This paper aims to investigate such variability and to identify stable relationships with its potential drivers in the climate system; such relationships may be used as the basis for the statistical downscaling of climate model outputs, for example. The analysis uses generalized linear models (GLMs). The GLMs are fitted to twentieth-century observational data for the period 1957-2006 to characterize the dependence of monthly precipitation occurrences and amounts upon the climate indicators of interest. In contrast with many of the analyses that have previously been used to investigate controls on precipitation in the region, GLMs allow for the investigation of the relationships between different components of the climate system (geographical and climatic drivers) simultaneously. Six climate factors were found to drive part of the rainfall variability in the region, and their modeled effect upon rainfall occurrences and amounts resulted in general agreement with previous studies. Among the retained indices, relative humidity and El Niñ o accounted for the highest degree of explained variability. The location and intensity of the jet stream were also found to have a statistically significant and physically meaningful effect upon rainfall variability.
Climate data often suffer from artificial inhomogeneities, resulting from documented or undocumented events. For a time series to be used with confidence in climate analysis, it should only be characterized by variations intrinsic to the climate system. Many methods (e.g., direct or indirect) have been proposed according to the data characteristics (e.g., location, variable, or data completeness). This paper is focused on the abruptchanges problem (when the properties of a time series change abruptly), when their timing is known, and suggests that a nonparametric regression framework provides an appealing way to correct for discontinuities in such a way as to recognize and allow for the existence of other structures such as seasonality and long-term smooth trends. The approach is illustrated by using reanalysis data for southern Africa, for which discontinuities are present because of the introduction of satellite technology in 1979.
Shoreline erosion, flood surges, river sediments, and water pollution are only a few of the common threats to many coastal areas, with extreme climate-related events exacerbating the intensity and urgency of the resulting negative impacts. In addition, some coastal areas are excessively mined for sand, protective mangroves are destroyed, and coastal waters are overfished, affecting the well-being, safety, and livelihoods of local communities. These threats disproportionally affect the poorest and most marginalized groups, including women and children, leading to their increased vulnerability to climate change and adoption of negative coping mechanisms.This chapter proposes an integrated people-centered approach, with a particular focus on women, to address the triple crisis – poverty, climate change, and nature – at the local level. Findings will be shared from a 2-year project implemented in the southernmost coastal region of Kwale County in Kenya, which aimed to achieve beneficial and interconnected social, environmental, and climate outcomes. The chapter discusses findings, successes, and lessons learned from the action and the requirement to position vulnerable groups at the center of initiatives designed to address the triple crisis. Limitations of the study and main recommendations for future programming in similar contexts are also shared.
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