scholarly journals Trends in Total and Extreme South American Rainfall in 1960–2000 and Links with Sea Surface Temperature

2006 ◽  
Vol 19 (8) ◽  
pp. 1490-1512 ◽  
Author(s):  
M. R. Haylock ◽  
T. C. Peterson ◽  
L. M. Alves ◽  
T. Ambrizzi ◽  
Y. M. T. Anunciação ◽  
...  
Keyword(s):  
Large Scale ◽  
Southern Oscillation ◽  
Daily Rainfall ◽  
Extreme Rainfall ◽  
Sea Surface ◽  
Annual Rainfall ◽  
South American ◽  
Southern Chile ◽  
Eastern Brazil ◽  
Historical Climatology

Abstract A weeklong workshop in Brazil in August 2004 provided the opportunity for 28 scientists from southern South America to examine daily rainfall observations to determine changes in both total and extreme rainfall. Twelve annual indices of daily rainfall were calculated over the period 1960 to 2000, examining changes to both the entire distribution as well as the extremes. Maps of trends in the 12 rainfall indices showed large regions of coherent change, with many stations showing statistically significant changes in some of the indices. The pattern of trends for the extremes was generally the same as that for total annual rainfall, with a change to wetter conditions in Ecuador and northern Peru and the region of southern Brazil, Paraguay, Uruguay, and northern and central Argentina. A decrease was observed in southern Peru and southern Chile, with the latter showing significant decreases in many indices. A canonical correlation analysis between each of the indices and sea surface temperatures (SSTs) revealed two large-scale patterns that have contributed to the observed trends in the rainfall indices. A coupled pattern with ENSO-like SST loadings and rainfall loadings showing similarities with the pattern of the observed trend reveals that the change to a generally more negative Southern Oscillation index (SOI) has had an important effect on regional rainfall trends. A significant decrease in many of the rainfall indices at several stations in southern Chile and Argentina can be explained by a canonical pattern reflecting a weakening of the continental trough leading to a southward shift in storm tracks. This latter signal is a change that has been seen at similar latitudes in other parts of the Southern Hemisphere. A similar analysis was carried out for eastern Brazil using gridded indices calculated from 354 stations from the Global Historical Climatology Network (GHCN) database. The observed trend toward wetter conditions in the southwest and drier conditions in the northeast could again be explained by changes in ENSO.

scholarly journals Monitoring Water Resources over the Kotmale Reservoir in Sri Lanka Using ENSO Phases

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Sewwandhi Chandrasekara ◽  
Venkatraman Prasanna ◽  
Hyun-Han Kwon
Keyword(s):  
Sri Lanka ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Southern Oscillation ◽  
Daily Rainfall ◽  
Sea Surface ◽  
Seasonal Rainfall ◽  
Annual Rainfall ◽  
Average Rainfall ◽  
Enso Phases

In this study, the El Nino Southern Oscillation (ENSO) phase index is used for water management over the Kotmale reservoir in Sri Lanka. Daily rainfall data of 9 stations over the Kotmale catchment during 1960–2005 June-September (JJAS) season is investigated over the Kotmale catchment. The ENSO phases are identified based on the 0.5°C sea surface temperature (SST) anomaly over Nino 3 region. The study has brought out few stations showing increasing and a few decreasing seasonal rainfall trends for JJAS season, while there is no change in the annual rainfall for the catchment. Monthly and seasonal rainfall of all the selected stations showed negative correlation with the sea surface temperature (SST) over the Nino-3 region index during JJAS season with varying magnitudes. During the warm phase of ENSO, below average rainfall is prominent for JJAS season over many stations. The rainfall especially during early September showed a significant below average rainfall during the warm ENSO phase. The seasonal rainfall during neutral and cold ENSO phases does not experience similar significant changes as seen during warm ENSO phase. Inflow of the Kotmale reservoir shows decreasing trend for the period of 1960–2005 in the observation from all stations collectively.

Ocean Salinity Aspects of the Ningaloo Niño

2021 ◽  
pp. 1
Author(s):  
Yaru Guo ◽  
Yuanlong Li ◽  
Fan Wang ◽  
Yuntao Wei
Keyword(s):  
Fresh Water ◽  
Large Scale ◽  
Southern Oscillation ◽  
Regional Climate ◽  
Ocean Model ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Near Surface ◽  
Ocean Salinity

AbstractNingaloo Niño – the interannually occurring warming episode in the southeast Indian Ocean (SEIO) – has strong signatures in ocean temperature and circulation and exerts profound impacts on regional climate and marine biosystems. Analysis of observational data and eddy-resolving regional ocean model simulations reveals that the Ningaloo Niño/Niña can also induce pronounced variability in ocean salinity, causing large-scale sea surface salinity (SSS) freshening of 0.15–0.20 psu in the SEIO during its warm phase. Model experiments are performed to understand the underlying processes. This SSS freshening is mutually caused by the increased local precipitation (~68%) and enhanced fresh-water transport of the Indonesian Throughflow (ITF; ~28%) during Ningaloo Niño events. The effects of other processes, such as local winds and evaporation, are secondary (~18%). The ITF enhances the southward fresh-water advection near the eastern boundary, which is critical in causing the strong freshening (> 0.20 psu) near the Western Australian coast. Owing to the strong modulation effect of the ITF, SSS near the coast bears a higher correlation with the El Niño-Southern Oscillation (0.57, 0.77, and 0.70 with Niño-3, Niño-4, and Niño-3.4 indices, respectively) than sea surface temperature (-0.27, -0.42, and -0.35) during 1993-2016. Yet, an idealized model experiment with artificial damping for salinity anomaly indicates that ocean salinity has limited impact on ocean near-surface stratification and thus minimal feedback effect on the warming of Ningaloo Niño.

scholarly journals Analysis of Daily Rainfall and Spatiotemporal Trends of Extreme Rainfall at Paraná Slope of the Itararé Watershed, Brazil

2020 ◽  
Vol 35 (2) ◽  
pp. 357-374
Author(s):  
Paulo Miguel de Bodas Terassi ◽  
José Francisco de Oliveira Júnior ◽  
Givanildo de Gois ◽  
Bruno Serafini Sobral ◽  
Emerson Galvani ◽  
...  
Keyword(s):  
Daily Rainfall ◽  
Extreme Rainfall ◽  
High Volume ◽  
Annual Rainfall ◽  
Annual Number ◽  
Subtropical Climate ◽  
Homogeneous Groups ◽  
The North ◽  
Spatiotemporal Trends ◽  
Mk Test

Abstract The knowledge of intensity and frequency of rainfall allows establishing predictive measures to minimize impacts caused by high volume of rainfall totals in a region. Therefore, the objective is to evaluate daily rainfall for Paraná slope of the Itararé watershed (PSIW) and to verify the spatiotemporal trend of intense and extreme daily rainfall. Rainfall data from 14 stations collected from 1976 to 2012 were used with less than 4% of data faults. Multivariate analysis based on cluster analysis technique (CA) was used applying the Euclidean distance for the identification of homogeneous groups, and the quantiles technique to classify daily rainfall. The Mann-Kendall (MK) test was used to identify trends for annual rainfall totals, annual number of rainy days (ANRD) and for the occurrence of intense (R95p) and extreme (R99p) rainfall. The CA technique identified three rainfall groups (HG I, II and III). Given the latitudinal position of the area, rainfall at the southern sector is characterized by its greater similarities with the subtropical climate, whereas in the North sector there is a consistent reduction of rainfall totals in autumn and, especially, during winter months, which are characteristic of the tropical climate. The MK test identified the downward trend of ANRD, with greater significance for the south-centered sectors of the basin. The observed trends for the intense (R95p) and extreme (R99p) daily rainfall show the predominance of reduction for the Southwest and central sector, followed by a significant increase in the Southeast and North sectors of the PSIW.

scholarly journals Centennial-scale precipitation anomalies in the southern Altiplano (18° S) suggest an extratropical driver for the South American summer monsoon during the late Holocene

2019 ◽  
Vol 15 (5) ◽  
pp. 1845-1859 ◽  
Author(s):  
Ignacio A. Jara ◽  
Antonio Maldonado ◽  
Leticia González ◽  
Armand Hernández ◽  
Alberto Sáez ◽  
...  
Keyword(s):  
Summer Monsoon ◽  
Large Scale ◽  
Southern Oscillation ◽  
Atlantic Coast ◽  
Austral Summer ◽  
High Elevation ◽  
South American ◽  
Tropical Andes ◽  
The South ◽  
Precipitation Anomalies

Abstract. Modern precipitation anomalies in the Altiplano, South America, are closely linked to the strength of the South American summer monsoon (SASM), which is influenced by large-scale climate features sourced in the tropics such as the Intertropical Convergence Zone (ITCZ) and El Niño–Southern Oscillation (ENSO). However, the timing, direction, and spatial extent of precipitation changes prior to the instrumental period are still largely unknown, preventing a better understanding of the long-term drivers of the SASM and their effects over the Altiplano. Here we present a detailed pollen reconstruction from a sedimentary sequence covering the period between 4500 and 1000 cal yr BP in Lago Chungará (18∘ S; 4570 m a.s.l.), a high-elevation lake on the southwestern margin of the Altiplano where precipitation is delivered almost exclusively during the mature phase of the SASM over the austral summer. We distinguish three well-defined centennial-scale anomalies, with dry conditions between 4100–3300 and 1600–1000 cal yr BP and a conspicuous humid interval between 2400 and 1600 cal yr BP, which resulted from the weakening and strengthening of the SASM, respectively. Comparisons with other climate reconstructions from the Altiplano, the Atacama Desert, the tropical Andes, and the southwestern Atlantic coast reveal that – unlike modern climatological controls – past precipitation anomalies at Lago Chungará were largely decoupled from north–south shifts in the ITCZ and ENSO. A regionally coherent pattern of centennial-scale SASM variations and a significant latitudinal gradient in precipitation responses suggest the contribution of an extratropical moisture source for the SASM, with significant effects on precipitation variability in the southern Altiplano.

Including climatic variability in stochastic rainfall for flood catastrophe modelling – The effect of ENSO and SOI in China

2020 ◽  
Author(s):  
Jose Luis Salinas ◽  
Rebecca Smith ◽  
Shuangcai Li ◽  
Ludovico Nicotina ◽  
Arno Hilberts
Keyword(s):  
Tropical Cyclone ◽  
Large Scale ◽  
Southern Oscillation ◽  
Rainfall Variability ◽  
Climatic Variability ◽  
Extreme Rainfall ◽  
Extreme Rainfall Events ◽  
Natural Catastrophes ◽  
Socio Economic Impact ◽  
Catastrophe Modelling

<p>Damages from flooding in China account on average for 60-70% of the total Annual Losses derived from natural catastrophes. The extreme rainfall events responsible for these inundations can be broadly categorised in two well differentiated mechanisms: Tropical Cyclone (TC) induced, and non Tropical Cyclone induced (nonTC) precipitation. Between 2001 and 2015, inland nonTC rainfall flood events occurred roughly with double the frequency as TC events. While TC events can be highly destructive for coastal locations, over the entire China territory nonTC flooding accounted for more than half of the total economic flood loss for events with significant socio-economic impact, highlighting the importance of the nonTC flooding mechanism on the regional and national scale.</p><p>Large-scale modes of climate variability modulate in different ways TC and nonTC induced precipitation, both in the frequency and the magnitude of the events. In a stochastic rainfall generation framework, it becomes therefore useful to model these two mechanisms separately and include their differentiated long-term climatic influences in order to fully reproduce the overall observed rainfall variability. This work presents results on the effect of ENSO and Southern Oscillation Index (SOI) values on seasonal rainfall in China, and how to include this climatic variability in stochastic rainfall for flood catastrophe modelling.</p>

Multidimensional context for extreme analysis of daily streamflow, rainfall and accumulated rainfall across USA

2020 ◽  
Author(s):  
Maria Nezi ◽  
Ioannis Tsoukalas ◽  
Charalampos Ntigkakis ◽  
Andreas Efstratiadis
Keyword(s):  
Soil Moisture ◽  
Daily Rainfall ◽  
Extreme Rainfall ◽  
Annual Rainfall ◽  
Event Analysis ◽  
Antecedent Soil Moisture ◽  
Daily Streamflow ◽  
Accumulated Rainfall ◽  
Moisture Conditions ◽  
Parameter Values

<p>Statistical analysis of rainfall and runoff extremes plays a crucial role in hydrological design and flood risk management. Usually this analysis is performed separately for the two processes of interest, thus ignoring their dependencies, which appear at multiple temporal scales. Actually, the generation of a flood strongly depends on soil moisture conditions, which in turn depends on past rainfall. Using daily rainfall and runoff data from about 400 catchments in USA, retrieved from the MOPEX repository, we investigate the statistical behavior of the corresponding annual rainfall and streamflow maxima, also accounting for the influence of antecedent soil moisture conditions. The latter are quantified by means of accumulated daily rainfall at various aggregation scales (i.e., from 5 up to 30 days) before each extreme rainfall and streamflow event. Analysis of maxima is employed by fitting the Generalized Extreme Value (GEV) distribution, using the L-moments method for extracting the associated parameters (shape, scale, location). Significant attention is paid for ensuring statistically consistent estimations of the shape parameter, which is empirically adjusted in order to minimize the influence of sample uncertainty. Finally, we seek for the possible correlations among the derived parameter values and hydroclimatic characteristics of the studied basins, and also depict their spatial distribution across USA.</p>

scholarly journals Decadal Variability in the Large-Scale Sea Surface Height Field of the South Pacific Ocean: Observations and Causes

2006 ◽  
Vol 36 (9) ◽  
pp. 1751-1762 ◽  
Author(s):  
Bo Qiu ◽  
Shuiming Chen
Keyword(s):  
Pacific Ocean ◽  
Rossby Wave ◽  
Large Scale ◽  
South Pacific ◽  
Sea Surface Height ◽  
Sea Surface ◽  
South American ◽  
The South ◽  
South Pacific Ocean ◽  
Spatially Varying

Abstract Large-scale sea surface height (SSH) changes in the extraequatorial South Pacific Ocean are investigated using satellite altimetry data of the past 12 yr. The decadal SSH signals in the 1990s were dominated by an increasing trend in the 30°–50°S band and a decreasing trend in the central South Pacific Ocean poleward of 50°S. In recent years since 2002 there has been a reversal in both of these trends. Spatially varying low-frequency SSH signals are also found in the tropical region of 10°–25°S where the decadal SSH trend is negative in the eastern basin, but positive in the western basin. To clarify the causes for these observed spatially varying SSH signals, a 1½-layer reduced-gravity model that includes the wind-driven baroclinic Rossby wave dynamics and the responses forced by SSH changes along the South American coast was adopted. The model hindcasts the spatially varying decadal trends in the midlatitude and the eastern tropical regions well. Accumulation of the wind-forced SSH anomalies along Rossby wave characteristics is found to be important for both previously reported long-term trends and their reversals in recent years. The boundary forcing associated with the time-varying SSH signals along the South American coast is crucial for understanding the observed SSH signals of all time scales in the eastern tropical South Pacific basin, but it has little impact upon the midlatitude interior SSH signals.

scholarly journals Simulation of Daily Rainfall Data using Articulated Weather Generator Model for Seasonal Prediction of ENSO-Affected Zones in Indonesia

2020 ◽  
Vol 52 (2) ◽  
pp. 143
Author(s):  
Andung Bayu Sekaranom ◽  
Emilya Nurjani ◽  
Rika Harini ◽  
Andi Syahid Muttaqin
Keyword(s):  
Large Scale ◽  
Southern Oscillation ◽  
Daily Rainfall ◽  
Regression Function ◽  
Seasonal Prediction ◽  
Weather Generator ◽  
Rainfall Data ◽  
Rainfall Simulation ◽  
Rainfall Prediction ◽  
Student’S T

Synthetic rainfall simulation using weather generator models is commonly used as a substitute at locations with incomplete or short rainfall data. It incorporates a method that can be developed into forecasts of future rainfall. This study was designed to modify a rainfall prediction system based on the principles of weather generator models and to test the validity of the modelling results. It processed the data collected from eight rain stations in zones affected by El-Nino Southern Oscillation (ENSO). A large-scale predictor, that is, SST prediction data in the Nino 3.4 region over the Pacific Ocean was used as the influencing variable in projecting rainfall for the following six months after the predefined dates. Rainfall data from weather stations and SST in 1960-2000 were analyzed to identify the effects of ENSO and build a statistical model based on the regression function. Meanwhile, the model was validated using the data from 2001 to 2007 by backtesting six months in a row. The analysis results showed that the model could simulate both low rainfall in the dry season and high one in the rainy season. Validation by the student's t-test confirmed that the six-month synthetic rain data at nearly all observed stations was homogenous. For this reason, the developed model can be potentially used as one of the season prediction systems.  

scholarly journals Seasonal Extreme Rainfall Over Indian Monsoon Region: A Moisture Budget Analysis to Distinguish the Role of ENSO and non-ENSO Forcing

2021 ◽  
Author(s):  
Ashish R Dhakate ◽  
Prasanth A. Pillai
Keyword(s):  
Large Scale ◽  
Southern Oscillation ◽  
Extreme Rainfall ◽  
Summer Monsoon Rainfall ◽  
Central Pacific ◽  
Budget Analysis ◽  
Moisture Advection ◽  
The Difference ◽  
Moist Static Energy Budget ◽  
Sst Anomalies

Abstract Indian summer monsoon rainfall (ISMR) variability of ±10% of its long-term mean leads to flood and drought, affecting the life and economic situation of the country. It is already established that the interannual variability of ISMR is influenced by large scale boundary forcing such as SST anomalies of tropical Pacific, Indian and Atlantic Oceans. The ISMR association between Pacific SST anomalies in the form of El Nino Southern Oscillation (ENSO) is only studied in detail. Meanwhile, the present and previous studies show that the ENSO accounts for around 50% of the extreme years, while the other half is associated with other processes. A differentiation between extremes induced by ENSO and non-ENSO processes are attempted here with the help of moisture and moist static energy budget. The significant contribution to the rainfall extremes comes from moisture advection induced by anomalous winds generated by the boundary forcing and the secondary contribution from moisture convergence. For the non-ENSO cases, there is a contribution from local fluxes, which are not prominent in the cases of ENSO induced cases. In the ENSO cases, anomalous winds are from the equatorial central Pacific, while EQWIN/IOD cases influence extremes through the local evaporation and moisture advection from the Indian Ocean. Extreme years independent of ENSO/IOD/ EQWIN have moisture advection from the anomalous winds across Africa and the Atlantic and are associated with moisture advection toward the northern parts of India. These differences in moisture processes are responsible for the difference in rainfall distribution over India also.

scholarly journals Stochastic generation of annual, monthly and daily climate data: A review

2001 ◽  
Vol 5 (4) ◽  
pp. 653-670 ◽  
Author(s):  
R. Srikanthan ◽  
T. A. McMahon
Keyword(s):  
Climate Change ◽  
Historical Data ◽  
Southern Oscillation ◽  
Daily Rainfall ◽  
Annual Rainfall ◽  
Model Parameters ◽  
Annual Data ◽  
Climate Data ◽  
Time Step ◽  
Method Of Fragments

Abstract. The generation of rainfall and other climate data needs a range of models depending on the time and spatial scales involved. Most of the models used previously do not take into account year to year variations in the model parameters. Long periods of wet and dry years were observed in the past but were not taken into account. Recently, Thyer and Kuczera (1999) developed a hidden state Markov model to account for the wet and dry spells explicitly in annual rainfall. This review looks firstly at traditional time series models and then at the more complex models which take account of the pseudo-cycles in the data. Monthly rainfall data have been generated successfully by using the method of fragments. The main criticism of this approach is the repetitions of the same yearly pattern when only a limited number of years of historical data are available. This deficiency has been overcome by using synthetic fragments but this brings an additional problem of generating the right number of months with zero rainfall. Disaggregation schemes are effective in obtaining monthly data but the main problem is the large number of parameters to be estimated when dealing with many sites. Several simplifications have been proposed to overcome this problem. Models for generating daily rainfall are well developed. The transition probability matrix method preserves most of the characteristics of daily, monthly and annual characteristics and is shown to be the best performing model. The two-part model has been shown by many researchers to perform well across a range of climates at the daily level but has not been tested adequately at monthly or annual levels. A shortcoming of the existing models is the consistent underestimation of the variances of the simulated monthly and annual totals. As an alternative, conditioning model parameters on monthly amounts or perturbing the model parameters with the Southern Oscillation Index (SOI) result in better agreement between the variance of the simulated and observed annual rainfall and these approaches should be investigated further. As climate data are less variable than rainfall, but are correlated among themselves and with rainfall, multisite-type models have been used successfully to generate annual data. The monthly climate data can be obtained by disaggregating these annual data. On a daily time step at a site, climate data have been generated using a multisite type model conditional on the state of the present and previous days. The generation of daily climate data at a number of sites remains a challenging problem. If daily rainfall can be modelled successfully by a censored power of normal distribution then the model can be extended easily to generate daily climate data at several sites simultaneously. Most of the early work on the impacts of climate change used historical data adjusted for the climate change. In recent studies, stochastic daily weather generation models are used to compute climate data by adjusting the parameters appropriately for the future climates assumed.

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