Impact of strong El Niño events on river discharge in South America

2021 ◽  
Author(s):  
Markus Deppner ◽  
Bedartha Goswami
Keyword(s):  
Machine Learning ◽  
Missing Data ◽  
South America ◽  
River Discharge ◽  
Amazon Basin ◽  
Missing Values ◽  
Southern Oscillation ◽  
Enso Events ◽  
Streamflow Data ◽  
The Impact

<p>The impact of the El Niño Southern Oscillation (ENSO) on rivers are well known, but most existing studies involving streamflow data are severely limited by data coverage. Time series of gauging stations fade in and out over time, which makes hydrological large scale and long time analysis or studies of rarely occurring extreme events challenging. Here, we use a machine learning approach to infer missing streamflow data based on temporal correlations of stations with missing values to others with data. By using 346 stations, from the “Global Streamflow Indices and Metadata archive” (GSIM), that initially cover the 40 year timespan in conjunction with Gaussian processes we were able to extend our data by estimating missing data for an additional 646 stations, allowing us to include a total of 992 stations. We then investigate the impact of the 6 strongest El Niño (EN) events on rivers in South America between 1960 and 2000. Our analysis shows a strong correlation between ENSO events and extreme river dynamics in the southeast of Brazil, Carribean South America and parts of the Amazon basin. Furthermore we see a peak in the number of stations showing maximum river discharge all over Brazil during the EN of 1982/83 which has been linked to severe floods in the east of Brazil, parts of Uruguay and Paraguay. However EN events in other years with similar intensity did not evoke floods with such magnitude and therefore the additional drivers of the 1982/83  floods need further investigation. By using machine learning methods to infer data for gauging stations with missing data we were able to extend our data by almost three-fold, revealing a possible heavier and spatially larger impact of the 1982/83 EN on South America's hydrology than indicated in literature.</p>

scholarly journals The Impact of El Niño - Southern Oscillation Events on South America

2006 ◽  
Vol 6 ◽  
pp. 221-225 ◽  
Author(s):  
J. L. Santos
Keyword(s):  
South America ◽  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
Ultra Violet ◽  
Risk Groups ◽  
Natural Ecosystems ◽  
Enso Events ◽  
Ultra Violet Radiation ◽  
The Impact

Abstract. The presence of ENSO Events in South America is felt in two ways: a) through its effects on both the atmosphere and ocean systems, and b) through its impacts on natural ecosystems (both marine and terrestrial) and on societal and economical sectors (like fisheries, health, and agriculture). The main effects of El Niño/La Niña are: Increment/Decrement of sea surface temperature and salinity, Increment/Decrement of sea level and wave activity, Increment/Decrement of air temperature and amount of ultra violet radiation reaching the surface of the earth, and Changes in the rainfall and evaporation patterns. It is not easy to make an "average" pattern of ENSO impacts for a variety of reasons: the impacts depend greatly of factors like geographical extent and position of the oceanic anomalies, and intensity and timing of the anomalies; also the influence of social, economic and political structures determines whether climate anomalies caused by ENSO in a particular region will lead to severe societal and economical impacts. The scientific community also plays a potential role in the extent of the impacts that ENSO can produce, if scientists can provide information on the impact of the presence of ENSO by identifying and focusing on its precursors, intervention could be taken early enough. There is however, something to be said against that: information can be misleading, target inappropriate at-risk groups, or generate a false sense of security.

scholarly journals The impact of the subtropical South Atlantic SST on South American precipitation

2008 ◽  
Vol 26 (11) ◽  
pp. 3457-3476 ◽  
Author(s):  
A. S. Taschetto ◽  
I. Wainer
Keyword(s):  
South America ◽  
Southern Oscillation ◽  
South Atlantic ◽  
Empirical Orthogonal Functions ◽  
Convergence Zone ◽  
South American ◽  
The South ◽  
Inter Tropical Convergence Zone ◽  
The Impact ◽  
Sst Anomalies

Abstract. The Community Climate Model (CCM3) from the National Center for Atmospheric Research (NCAR) is used to investigate the effect of the South Atlantic sea surface temperature (SST) anomalies on interannual to decadal variability of South American precipitation. Two ensembles composed of multidecadal simulations forced with monthly SST data from the Hadley Centre for the period 1949 to 2001 are analysed. A statistical treatment based on signal-to-noise ratio and Empirical Orthogonal Functions (EOF) is applied to the ensembles in order to reduce the internal variability among the integrations. The ensemble treatment shows a spatial and temporal dependence of reproducibility. High degree of reproducibility is found in the tropics while the extratropics is apparently less reproducible. Austral autumn (MAM) and spring (SON) precipitation appears to be more reproducible over the South America-South Atlantic region than the summer (DJF) and winter (JJA) rainfall. While the Inter-tropical Convergence Zone (ITCZ) region is dominated by external variance, the South Atlantic Convergence Zone (SACZ) over South America is predominantly determined by internal variance, which makes it a difficult phenomenon to predict. Alternatively, the SACZ over western South Atlantic appears to be more sensitive to the subtropical SST anomalies than over the continent. An attempt is made to separate the atmospheric response forced by the South Atlantic SST anomalies from that associated with the El Niño – Southern Oscillation (ENSO). Results show that both the South Atlantic and Pacific SSTs modulate the intensity and position of the SACZ during DJF. Particularly, the subtropical South Atlantic SSTs are more important than ENSO in determining the position of the SACZ over the southeast Brazilian coast during DJF. On the other hand, the ENSO signal seems to influence the intensity of the SACZ not only in DJF but especially its oceanic branch during MAM. Both local and remote influences, however, are confounded by the large internal variance in the region. During MAM and JJA, the South Atlantic SST anomalies affect the magnitude and the meridional displacement of the ITCZ. In JJA, the ENSO has relatively little influence on the interannual variability of the simulated rainfall. During SON, however, the ENSO seems to counteract the effect of the subtropical South Atlantic SST variations on convection over South America.

Impact of Indo-Pacific Feedback Interactions on ENSO Dynamics Diagnosed Using Ensemble Climate Simulations

2012 ◽  
Vol 25 (21) ◽  
pp. 7743-7763 ◽  
Author(s):  
A. Santoso ◽  
M. H. England ◽  
W. Cai
Keyword(s):  
Indian Ocean ◽  
Climate Model ◽  
Southern Oscillation ◽  
Ocean Basin ◽  
Climate Feedback ◽  
Enso Events ◽  
Thermocline Feedback ◽  
Enso Dynamics ◽  
The Indian Ocean ◽  
The Impact

The impact of Indo-Pacific climate feedback on the dynamics of El Niño–Southern Oscillation (ENSO) is investigated using an ensemble set of Indian Ocean decoupling experiments (DCPL), utilizing a millennial integration of a coupled climate model. It is found that eliminating air–sea interactions over the Indian Ocean results in various degrees of ENSO amplification across DCPL simulations, with a shift in the underlying dynamics toward a more prominent thermocline mode. The DCPL experiments reveal that the net effect of the Indian Ocean in the control runs (CTRL) is a damping of ENSO. The extent of this damping appears to be negatively correlated to the coherence between ENSO and the Indian Ocean dipole (IOD). This type of relationship can arise from the long-lasting ENSO events that the model simulates, such that developing ENSO often coincides with Indian Ocean basin-wide mode (IOBM) anomalies during non-IOD years. As demonstrated via AGCM experiments, the IOBM enhances western Pacific wind anomalies that counteract the ENSO-enhancing winds farther east. In the recharge oscillator framework, this weakens the equatorial Pacific air–sea coupling that governs the ENSO thermocline feedback. Relative to the IOBM, the IOD is more conducive for ENSO growth. The net damping by the Indian Ocean in CTRL is thus dominated by the IOBM effect which is weaker with stronger ENSO–IOD coherence. The stronger ENSO thermocline mode in DCPL is consistent with the absence of any IOBM anomalies. This study supports the notion that the Indian Ocean should be viewed as an integral part of ENSO dynamics.

scholarly journals On the ability of a global atmospheric inversion to constrain variations of CO<sub>2</sub> fluxes over Amazonia

2015 ◽  
Vol 15 (2) ◽  
pp. 1915-1952
Author(s):  
L. Molina ◽  
G. Broquet ◽  
P. Imbach ◽  
F. Chevallier ◽  
B. Poulter ◽  
...  
Keyword(s):  
South America ◽  
Amazon Basin ◽  
Transport Model ◽  
Net Ecosystem Exchange ◽  
Extreme Rainfall ◽  
Atmospheric Composition ◽  
Interannual Variations ◽  
The Impact ◽  
Seasonal And Interannual Variations ◽  
Atmospheric Inversion

Abstract. The exchanges of carbon, water, and energy between the atmosphere and the Amazon Basin have global implications for current and future climate. Here, the global atmospheric inversion system of the Monitoring of Atmospheric Composition and Climate service (MACC) was used to further study the seasonal and interannual variations of biogenic CO2 fluxes in Amazonia. The system assimilated surface measurements of atmospheric CO2 mole fractions made over more than 100 sites over the globe into an atmospheric transport model. This study added four surface stations located in tropical South America, a region poorly covered by CO2 observations. The estimates of net ecosystem exchange (NEE) optimized by the inversion were compared to independent estimates of NEE upscaled from eddy-covariance flux measurements in Amazonia, and against reports on the seasonal and interannual variations of the land sink in South America from the scientific literature. We focused on the impact of the interannual variation of the strong droughts in 2005 and 2010 (due to severe and longer-than-usual dry seasons), and of the extreme rainfall conditions registered in 2009. The spatial variations of the seasonal and interannual variability of optimized NEE were also investigated. While the inversion supported the assumption of strong spatial heterogeneity of these variations, the results revealed critical limitations that prevent global inversion frameworks from capturing the data-driven seasonal patterns of fluxes across Amazonia. In particular, it highlighted issues due to the configuration of the observation network in South America and the lack of continuity of the measurements. However, some robust patterns from the inversion seemed consistent with the abnormal moisture conditions in 2009.

scholarly journals Machine learning based imputation techniques for estimating phylogenetic trees from incomplete distance matrices

2019 ◽  
Author(s):  
Ananya Bhattacharjee ◽  
Md. Shamsuzzoha Bayzid
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Missing Data ◽  
Phylogenetic Trees ◽  
Large Scale ◽  
Missing Values ◽  
Gene Tree ◽  
Estimation Methods ◽  
Learning Technique ◽  
Distance Matrices

AbstractBackgroundDue to the recent advances in sequencing technologies and species tree estimation methods capable of taking gene tree discordance into account, notable progress has been achieved in constructing large scale phylogenetic trees from genome wide data. However, substantial challenges remain in leveraging this huge amount of molecular data. One of the foremost among these challenges is the need for efficient tools that can handle missing data. Popular distance-based methods such as neighbor joining and UPGMA require that the input distance matrix does not contain any missing values.ResultsWe introduce two highly accurate machine learning based distance imputation techniques. One of our approaches is based on matrix factorization, and the other one is an autoencoder based deep learning technique. We evaluate these two techniques on a collection of simulated and biological datasets, and show that our techniques match or improve upon the best alternate techniques for distance imputation. Moreover, our proposed techniques can handle substantial amount of missing data, to the extent where the best alternate methods fail.ConclusionsThis study shows for the first time the power and feasibility of applying deep learning techniques for imputing distance matrices. The autoencoder based deep learning technique is highly accurate and scalable to large dataset. We have made these techniques freely available as a cross-platform software (available at https://github.com/Ananya-Bhattacharjee/ImputeDistances).

scholarly journals Multi-variate infilling of missing daily discharge data on the Niger basin

2021 ◽  
Author(s):  
Ganiyu Titilope Oyerinde ◽  
Agnide E. Lawin ◽  
Oluwafemi E. Adeyeri
Keyword(s):  
Missing Data ◽  
River Discharge ◽  
Missing Values ◽  
Spatial Scales ◽  
Gap Filling ◽  
Discharge Data ◽  
Flow Duration ◽  
Daily Discharge ◽  
Flow Duration Curves ◽  
Discharge Patterns

Abstract The Niger basin have experienced historical drought episodes and floods in recent times. Reliable hydrological modelling has been hampered by missing values in daily river discharge data. We assessed the potential of using the Multivariate Imputation by Chained Equations (MICE) to estimate both continuous and discontinuous daily missing data across different spatial scales in the Niger basin. The study was conducted on 22 discharge stations that have missing data ranging from 2% to 70%. Four efficiency metrics were used to determine the effectiveness of MICE. The Flow Duration Curves (FDC) of observed and filled data were compared to determine how MICE captured the discharge patterns. Mann-Kendall, Modified Mann-Kendall, Pettit and Sen's Slope were used to assess the complete discharge trends using the gap-filled data. Results shows that MICE near perfectly filled the missing discharge data with Nash-Sutcliffe Efficiency (NSE) range of 0.94–0.99 for the calibration (1992–1994) period. Good fits were obtained between FDC of observed and gap-filled data in all considered stations. All the catchments showed significantly increasing discharge trend since 1990s after gap filling. Consequently, the use of MICE in handling missing data challenges across spatial scales in the Niger basin was proposed.

Atmospheric Circulation and Climatic Variability

2007 ◽  
Author(s):  
René D. Garreaud ◽  
Patricio Aceituno
Keyword(s):  
South America ◽  
Atmospheric Circulation ◽  
Amazon Basin ◽  
Southern Oscillation ◽  
Climatic Variability ◽  
Climatic Conditions ◽  
The West ◽  
The Andes ◽  
Weather And Climate ◽  
Atmospheric Phenomena

Regional variations in South America’s weather and climate reflect the atmospheric circulation over the continent and adjacent oceans, involving mean climatic conditions and regular cycles, as well as their variability on timescales ranging from less than a few months to longer than a year. Rather than surveying mean climatic conditions and variability over different parts of South America, as provided by Schwerdtfeger and Landsberg (1976) and Hobbs et al. (1998), this chapter presents a physical understanding of the atmospheric phenomena and precipitation patterns that explain the continent’s weather and climate. These atmospheric phenomena are strongly affected by the topographic features and vegetation patterns over the continent, as well as by the slowly varying boundary conditions provided by the adjacent oceans. The diverse patterns of weather, climate, and climatic variability over South America, including tropical, subtropical, and midlatitude features, arise from the long meridional span of the continent, from north of the equator south to 55°S. The Andes cordillera, running continuously along the west coast of the continent, reaches elevations in excess of 4 km from the equator to about 40°S and, therefore, represents a formidable obstacle for tropospheric flow. As shown later, the Andes not only acts as a “climatic wall” with dry conditions to the west and moist conditions to the east in the subtropics (the pattern is reversed in midlatitudes), but it also fosters tropical-extratropical interactions, especially along its eastern side. The Brazilian plateau also tends to block the low-level circulation over subtropical South America. Another important feature is the large area of continental landmass at low latitudes (10°N–20°S), conducive to the development of intense convective activity that supports the world’s largest rain forest in the Amazon basin. The El Niño–Southern Oscillation phenomenon, rooted in the ocean-atmosphere system of the tropical Pacific, has a direct strong influence over most of tropical and subtropical South America. Similarly, sea surface temperature anomalies over the Atlantic Ocean have a profound impact on the climate and weather along the eastern coast of the continent. In this section we describe the long-term annual and monthly mean fields of several meteorological variables.

scholarly journals Interactions of ENSO, the IOD, and the SAM in CMIP3 Models

2011 ◽  
Vol 24 (6) ◽  
pp. 1688-1704 ◽  
Author(s):  
Wenju Cai ◽  
Arnold Sullivan ◽  
Tim Cowan
Keyword(s):  
Southern Oscillation ◽  
Global Climate ◽  
Coupled Model ◽  
Southern Annular Mode ◽  
Enso Events ◽  
Intercomparison Project ◽  
Wave Trains ◽  
Induced Signal ◽  
The Impact ◽  
The Relationship

Abstract Simulations of individual global climate drivers using models from the Coupled Model Intercomparison Project phase 3(CMIP3) have been examined; however, the relationship among them has not been assessed. This is carried out to address several important issues, including the likelihood of the southern annular mode (SAM) forcing Indian Ocean dipole (IOD) events and the possible impact of the IOD on El Niño–Southern Oscillation (ENSO) events. Several conclusions emerge from statistics based on multimodel outputs. First, ENSO signals project strongly onto the SAM, although ENSO-forced signals tend to peak before ENSO. This feature is similar to the situation associated with the IOD. The IOD-induced signal over southern Australia, through stationary equivalent Rossby barotropic wave trains, peak before the IOD itself. Second, there is no control by the SAM on the IOD, in contrast to what has been suggested previously. Indeed, no model produces a SAM–IOD relationship that supports a positive (negative) SAM driving a positive (negative) IOD event. This is the case even in models that do not simulate a statistically significant relationship between ENSO and the IOD. Third, the IOD does have an impact on ENSO. The relationship between ENSO and the IOD in the majority of models is far weaker than the observed. However, the ENSO’s influence on the IOD is boosted by a spurious oceanic teleconnection, whereby ENSO discharge–recharge signals transmit to the Sumatra–Java coast, generating thermocline anomalies and changing IOD properties. Without the spurious oceanic teleconnection, the influence of the IOD on ENSO is comparable to the impact of ENSO on the IOD. Other model deficiencies are discussed.

Simulating ENSO SSTAs from TAO/TRITON Winds: The Impacts of 20 Years of Buoy Observations in the Pacific Waveguide and Comparison with Reanalysis Products

2017 ◽  
Vol 30 (3) ◽  
pp. 1041-1059 ◽  
Author(s):  
Andrew M. Chiodi ◽  
D. E. Harrison
Keyword(s):  
Wind Stress ◽  
Southern Oscillation ◽  
Surface Wind ◽  
Major Elements ◽  
Ocean Model ◽  
Tropical Pacific ◽  
Wind Fields ◽  
Enso Events ◽  
The Pacific ◽  
The Impact

Abstract The fundamental importance of near-equatorial zonal wind stress in the evolution of the tropical Pacific Ocean’s seasonal cycle and El Niño–Southern Oscillation (ENSO) events is well known. It has been two decades since the TAO/TRITON buoy array was deployed, in part to provide accurate surface wind observations across the Pacific waveguide. It is timely to revisit the impact of TAO/TRITON winds on our ability to simulate and thereby understand the evolution of sea surface temperature (SST) in this region. This work shows that forced ocean model simulations of SST anomalies (SSTAs) during the periods with a reasonably high buoy data return rate can reproduce the major elements of SSTA variability during ENSO events using a wind stress field computed from TAO/TRITON observations only. This demonstrates that the buoy array usefully fulfills its waveguide-wind-measurement purpose. Comparison of several reanalysis wind fields commonly used in recent ENSO studies with the TAO/TRITON observations reveals substantial biases in the reanalyses that cause substantial errors in the variability and trends of the reanalysis-forced SST simulations. In particular, the negative trend in ERA-Interim is much larger and the NCEP–NCAR Reanalysis-1 and NCEP–DOE Reanalysis-2 variability much less than seen in the TAO/TRITON wind observations. There are also mean biases. Thus, even with the TAO/TRITON observations available for assimilation into these wind products, there remain oceanically important differences. The reanalyses would be much more useful for ENSO and tropical Pacific climate change study if they would more effectively assimilate the TAO/TRITON observations.

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