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Precipitation is considered one of the most important forcing data in scientific investigations involving agriculture, water management, and climate variability. Knowledge of precipitation variation during rainy seasons is the key to the understanding of the precipitation variability under the effect of climate change. This study evaluated some rainy season features (e.g., onset, retreat, and rainy-season precipitation) over the Yellow River basin (YRB), China, and the influence of the El Niño-Southern Oscillation (ENSO). The multi-scale moving t-test was applied to capture the onset and retreat of rainy season. The possible linkage between ENSO-induced precipitation and monsoon and atmospheric circulation was also explored. From the analysis, four conclusions can be drawn: (1) Rainy season began the earliest (latest) and ended the latest (earliest) in the southern (northern) YRB, with rainy-season precipitation increasing from northwest to southeast. (2) Rainy-season precipitation showed strong correlation to SST in Niño regions. Precipitation can reach up to 20% above the average precipitation for decaying central Pacific warming (CPW) and down to 35% below the average precipitation for developing eastern Pacific warming (EPW) in most areas of the YRB. (3) Developing El Niño showed the strongest dry signals among developing ENSO and ENSO Modoki phases. Decaying El Niño and El Niño Modoki indicated overall increasing precipitation, with La Niña and La Niña Modoki during the corresponding period showing the opposite tendency. (4) Different performances of ENSO-induced precipitation attributed to the combined influence of the monsoon from the India Ocean and the atmospheric circulation in the Western North Pacific (WNP). Stronger anti-cyclone and monsoon are related to increasing rainy-season precipitation. These findings can improve the predictability of rainy season features and ENSO-induced precipitation. [ABSTRACT FROM AUTHOR]

River floods cause extensive losses to economy, ecology, and society throughout the world. They are driven by the space‐time structure of catchment rainfall, which is determined by large‐scale, or even global‐scale, atmospheric processes. The identification of coherent, large‐scale atmospheric circulation structures that determine the moisture transport and convergence associated with rainfall‐induced flooding can help improve its predictability and phenomenology. In this paper, we extend a methodology, used for the analysis of extreme rainfall events, to high streamflow events (HSEs). The approach combines multiple machine learning methods to link HSEs to atmospheric circulation patterns. An application to the German streamflow network using reanalysis data for the period 1960 to 2012 is presented. Daily streamflow from 166 gauges, homogeneously distributed across Germany, are used. Geopotential height fields and integrated vapor transport (IVT) are derived from reanalysis data. An unsupervised neural network, Self Organizing Maps, is applied to geopotential height to identify a finite number of circulation patterns (CPs). Event synchronization between CPs and HSEs is used to establish if they are linked or not. If they are linked, the Event Synchronization method computes the delay between the occurrence of a CP and a HSE. Finally, local logistic regression is used to estimate the probability of occurrence of a HSE, as function of CP and IVT. We demonstrate that our approach is very effective to evaluate HSE probability occurrence across Germany. Key Points: Atmospheric circulation patterns that generate high streamflow events in Germany are identifiedEvent synchronization is used to determine synchronization and delay between atmospheric circulation patterns and high streamflow eventsThe occurrence probability of high streamflow events is conditioned on atmospheric circulation patterns and integrated water vapor transport [ABSTRACT FROM AUTHOR]

Tropical moisture exports (TMEs) may play an important role in extreme precipitation. An analysis of the spatiotemporal structure of precipitation associated with TMEs for the eastern United States at seasonal and daily time scales is presented. TME-based precipitation is characterized based on the change in specific humidity along TME tracks delineated in a Lagrangian analysis of the ERA-Interim dataset. The empirical orthogonal functions (EOFs) of seasonal TME-based precipitation are analyzed separately for each season to identify the dominant modes of interannual variability. Loading patterns for the first EOF show a distinct seasonal cycle in the core region of TME-based precipitation across the eastern United States, while the second EOF describes a northwest-southeast oscillation in the center of TME-induced precipitation occurrence. The EOFs for TMEs are compared against EOFs of gauged flood count data, which exhibit similar spatial structures. Correlations between TME EOFs, geopotential heights, and sea surface temperatures suggest a strong connection between TME-based precipitation, the Pacific-North American (PNA) pattern, Pacific decadal oscillation (PDO), and the Intra-Americas Sea patterns for much of the calendar year. Daily TME-based and total precipitation is projected onto the leading seasonal EOFs to examine the characteristics of upper-quantile daily events. The daily analysis suggests that the PNA can potentially provide information regarding heavy TME-based precipitation at a lead time of 1-10 days or more in most seasons and total precipitation in the winter. The potential for subseasonal, seasonal, and decadal forecasts or conditional simulations of precipitation in the study region is discussed. [ABSTRACT FROM AUTHOR]

Correlation networks identified from financial, genomic, ecological, epidemiological, social, and climatic data are being used to provide useful topological insights into the structure of high-dimensional data. Strong convection over the oceans and the atmospheric moisture transport and flow convergence indicated by atmospheric pressure fields may determine where and when extreme precipitation occurs. Here, the spatiotemporal relationship among sea surface temperature (SST), sea level pressure (SLP), and extreme global precipitation is explored using a graph-based approach that uses the concept of reciprocity to generate cluster pairs of locations with similar spatiotemporal patterns at any time lag. A global time-lagged relationship between pentad SST anomalies and pentad SLP anomalies is investigated to understand the linkages and influence of the slowly changing oceanic boundary conditions on the development of the global atmospheric circulation. This study explores the use of this correlation network to predict extreme precipitation globally over the next 30 days, using a logistic principal component regression on the strong global dipoles found between SST and SLP. Predictive skill under cross validation and blind prediction for the occurrence of 30-day precipitation that is higher than the 90th percentile of days in the wet season is indicated for the selected global regions considered. [ABSTRACT FROM AUTHOR]

Summary: Here we analyze the variability of MAM (March–April–May) and JJAS (June–July–August–September) seasonal Satluj River flow into the Bhakra dam in India through Pearson anomaly correlation and composite analyses with antecedent and concurrent seasonal climatic and atmospheric circulation patterns. The MAM seasonal inflow of Bhakra dam is significantly correlated with winter (DJF/FM) precipitation and temperature of the Satluj basin while the correlation with FM was more prominent for precipitation (snow=+0.72, rainfall=+0.60), and temperature (diurnal temperature range (DTR)=−0.76 and maximum temperature (T max)=−0.57). The JJAS inflow was also positively correlated with DJF/FM as well as JJAS precipitation of the Satluj basin while the correlation with basin average FM was the largest (+0.54). These suggested that both MAM and JJAS inflow anomalies are linked with DJF/FM climate over the Western Himalayas and adjoining north and central Indian plains, which were also found to be linked with the fluctuation of equatorial concurrent Sea Surface Temperature anomalies over the western Indian Ocean (max anomaly correlation was>+0.70) and mean sea level pressure over western pole of the Southern Oscillation sea-saw region (max Pearson anomaly correlation was∼+0.60). Low (high) MAM inflow was found to be associated with negative (positive) precipitation anomalies over the basin and north India in DJF and FM while FM precipitation anomaly is more concentrated over the Western Himalayas. In addition, low (high) JJAS inflow is also associated with negative (positive) precipitation anomalies over the basin and north India in DJF and over the Western Himalaya in FM and JJAS. Negative geopotential height anomaly at 500hPa (Z500) over Siberia and northwestern pacific in DJF, and positive Z500 anomaly over the northwest India in FM were noticed in low MAM inflow years. Whereas high inflow in MAM was linked with a negative Z500 anomaly between two positive Z500 anomaly regions – one over eastern Siberia stretched up to northern Pacific and second over the Eastern Europe in DJF, which gets stronger in FM. We also found southwesterly (northeasterly) wind vectors at 850hPa pressure level (uv850) bringing more (less) moisture to the Western Himalayas in DJF and FM in high (low) MAM/JJAS flow years. [Copyright &y& Elsevier]

An improved Bayesian optimal weighting scheme is developed and used to combine six atmospheric general circulation model (GCM) seasonal hindcast ensembles. The approach is based on the prior belief that the forecast probabilities of tercile-category precipitation and near-surface temperature are equal to the climatological ones. The six GCMs are integrated over the 1950–97 period with observed monthly SST prescribed at the lower boundary, with 9–24 ensemble members. The weights of the individual models are determined by maximizing the log likelihood of the combination by season over the integration period. A key ingredient of the scheme is the climatological equal-odds forecast, which is included as one of the “models” in the multimodel combination. Simulation skill is quantified in terms of the cross-validated ranked probability skill score (RPSS) for the three-category probabilistic hindcasts. The individual GCM ensembles, simple poolings of three and six models, and the optimally combined multimodel ensemble are compared. The Bayesian optimal weighting scheme outperforms the pooled ensemble, which in turn outperforms the individual models. In the extratropics, its main benefit is to bring much of the large area of negative-precipitation RPSS values up to near-zero values. The skill of the optimal combination is almost always increased (in the large spatial averages considered) when the number of models in the combination is increased from three to six, regardless of which models are included in the three-model combination. Improvements are made to the original Bayesian scheme of Rajagopalan et al. by reducing the dimensionality of the numerical optimization, averaging across data subsamples, and including spatial smoothing of the likelihood function. These modifications are shown to yield increases in cross-validated RPSS skills. The revised scheme appears to be better suited to combining larger sets of models, and, in the future, it should be possible to include statistical models into the weighted ensemble without fundamental difficulty. [ABSTRACT FROM AUTHOR]

This paper identifies connections between the time variability of the volume of the Great Salt Lake (GSL), Utah, and selected atmospheric circulation indices. The indices considered are the Southern Oscillation Index (SOI), the Pacific/North America (PNA) climatic pattern, and the Central North Pacific (CNP) index. We focus on interannual time scales. Low-frequency (interannual or interdecadal) relationships between the Great Salt Lake (GSL) volume change and atmospheric circulation indices are of importance because of their significance for the understanding and prediction of the GSL volume. We use Singular Spectral Analysis (SSA), Multichannel Singular Spectral Analysis (MSSA), and Multitaper Spectral Analysis [or Multitaper Method (MTM)], to identify persistent or nearly periodic patterns in time in each series. MSSA examines the joint modes of variability across the time series, while SSA decomposes each series into its component time patterns. MTM is used for identification of peaks and frequency band structure. [ABSTRACT FROM AUTHOR]