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The evolution of El Niño can be separated into two phases-namely, growth and mature-depending on whether the regional sea surface temperature has adjusted to the tropospheric warming in the remote tropics (tropical regions away from the central and eastern tropical Pacific Ocean). The western Sahel's main rainy season (July-September) is shown to be affected by the growth phase of El Niño through (i) a lack of neighboring North Atlantic sea surface warming, (ii) an absence of an atmospheric column water vapor anomaly over the North Atlantic and western Sahel, and (iii) higher atmospheric vertical stability over the western Sahel, resulting in the suppression of mean seasonal rainfall as well as number of wet days. In contrast, the short rainy season (October-December) of tropical eastern Africa is impacted by the mature phase of El Niño through (i) neighboring Indian Ocean sea surface warming, (ii) positive column water vapor anomalies over the Indian Ocean and tropical eastern Africa, and (iii) higher atmospheric vertical instability over tropical eastern Africa, leading to an increase in the mean seasonal rainfall as well as in the number of wet days. While the modulation of the frequency of wet days and seasonal mean accumulation is statistically significant, daily rainfall intensity (for days with rainfall > 1 mm day−1), whether mean, median, or extreme, does not show a significant response in either region. Hence, the variability in seasonal mean rainfall that can be attributed to the El Niño-Southern Oscillation phenomenon in both regions is likely due to changes in the frequency of rainfall. [ABSTRACT FROM AUTHOR]

ABSTRACT We investigate the space-time structure of extreme precipitation in Europe over the last century, using daily rainfall data from the European Climate Assessment & Dataset ( ECA&D) archive. The database includes 267 stations with records longer than 100 years. In the winter season (October to March), for each station, two classes of daily rainfall amount values are selected that, respectively, exceed the 90th and 95th percentile of daily rainfall amount over all the 100 years. For each class, and at each location, an annual time series of the frequency of exceedance and of the total precipitation, defined respectively as the number of days the rainfall threshold (90th and 95th percentiles) is exceeded and total precipitation on days when the percentile is exceeded, are developed. Space-time structure of the frequency and total precipitation time series at the different locations are then pursued using multivariate time and frequency domain methods. The identified key trends and organized spectral modes are linked to well-known climate indices, as North Atlantic Oscillation ( NAO) and El Nino Southern Oscillation ( ENSO). The spectra of the leading principal component of frequency of exceedance and of total precipitation have a peak with a 5-year period that is significant at the 5% level. These are also significantly correlated with ENSO series with this period. The spectrum of total rainfall is significant at the 10% level with a period of ∼8 years. This appears to be significantly correlated to the NAO index at this period. Thus, a decomposition of both secular trends and quasi-periodic behaviour in extreme daily rainfall is provided. [ABSTRACT FROM AUTHOR]

Atmospheric Rivers (ARs) are being increasingly identified as associated with some extreme floods. More generally, such floods may be associated with tropical moisture exports (TMEs) that exhibit relatively robust teleconnections between moisture source regions and flood regions. A large-scale flood event that persisted over Western Europe in January 1995 is studied here. During the last 10 days of the month, two rare flooding events, associated with heaviest rainfall in 150 years, occurred in two places, one over Brittany (West of France), and the second in the France-Germany border region and parts of neighboring countries. In this paper, we explore the month-long evolution of TMEs and their connection to the precipitation events that led to the Brittany event. The persistent large-scale atmospheric circulation patterns that led to the birth, death, and evolution of these TMEs as ARs with landfalls in Western Europe are identified, and the relationship of daily extreme precipitation to these patterns is examined. Singular value decomposition analysis and a generalized linear model are used to assess whether knowledge of the atmospheric circulation patterns from the prior record is useful for explaining the occurrence of their rare events. The analysis establishes the importance of both global and regional atmospheric circulation modes for the occurrence of such persistent events and the hydrologic importance of diagnosing global atmospheric moisture pathways. [ABSTRACT FROM AUTHOR]

Concerns about the potential effects of anthropogenic climate change have led to a closer examination of how climate varies in the long run, and how such variations may impact rainfall variations at daily to seasonal time scales. For South Florida in particular, the influences of the low-frequency climate phenomena, such as the El Nino Southern Oscillation (ENSO) and the Atlantic Multi-decadal Oscillation (AMO), have been identified with aggregate annual or seasonal rainfall variations. Since the combined effect of these variations is manifest as persistent multi-year variations in rainfall, the question of modeling these variations at the time and space scales relevant for use with the daily time step-driven hydrologic models in use by the South Florida Water Management District (SFWMD) has arisen. To address this problem, a general methodology for the hierarchical modeling of low- and high-frequency phenomenon at multiple rain gauge locations is developed and illustrated. The essential strategy is to use long-term proxies for regional climate to first develop stochastic scenarios for regional climate that include the low-frequency variations driving the regional rainfall process, and then to use these indicators to condition the concurrent simulation of daily rainfall at all rain gauges under consideration. A newly developed methodology, called Wavelet Autoregressive Modeling (WARM), is used in the first step after suitable climate proxies for regional rainfall are identified. These proxies typically have data available for a century to four centuries so that long-term quasi-periodic climate modes of interest can be identified more reliably. Correlation analyses with seasonal rainfall in the region are used to identify the specific proxies considered as candidates for subsequent conditioning of daily rainfall attributes using a Non-homogeneous hidden Markov model (NHMM). The combined strategy is illustrated for the May–June–July (MJJ) season. The details of the modeling methods and results for the MJJ season are presented in this study. [ABSTRACT FROM AUTHOR]