El Nino Southern Oscillation Index Research Articles

The Relationship between River Flow Regimes and Climate Indices of ENSO and IOD on Code River, Southern Indonesia

Predicting the streamflow regimes using climate dynamics is important in water resource management. However, in Indonesia, there are few studies targeting climate indices and streamflow. A previous study found difficulty in developing a statistical prediction model for this relationship due to its non-linear nature. This study attempted to address that gap by applying multiple regression (MR) models using second- and third-order polynomial functions to show the non-linear relationship between climate and flow regime indices. First, a correlation analysis was performed to check the variable relationships. There was a good and significant correlation of El Niño Southern Oscillation (ENSO) with the flow regime indices. Secondly, MR models were developed with the same-time variables. The developed model showed that the Indian Ocean Dipole (IOD) had the effect of strongly increasing the high flow in La Niña phases. Finally, time-lagged MRs were developed aiming at forecasting. Lagged MR models with six-month leading climate indices demonstrated a relatively good correlation with the observed data (mostly R > 0.700) with moderate accuracy (root mean square error = 44–51%). It suggests that the forecasting of flow regime may be possible using ENSO and IOD indices.

Comparison of MMCFS and SINTEX‐F2 for seasonal prediction of Indian summer monsoon rainfall

AbstractA comparison of the Indian summer Monsoon Rainfall (ISMR) in two different coupled models (viz., Scale Interaction Experiment‐Frontier‐F2; SINTEX‐F2, and Monsoon Mission Climate Forecast System; MMCFS) is carried out to ascertain the predictability sources in these models and their strengths and weaknesses. SINTEX‐F2 has a stronger cold sea surface temperature (SST) bias in the central equatorial Pacific, and it simulates mean ISMR better while underestimating the interannual variability of ISMR. On the other hand, MMCFS has warmer SST bias in the tropical Pacific off the equator, simulates a drier mean monsoon, but has a more realistic ISMR interannual variability. Further, the cold SST bias in the central tropical Pacific adversely affects the ability of the SINTEX‐F2 to capture the El Niño–Southern Oscillation (ENSO) related interannual variability and teleconnection patterns with monsoon by shifting it further westward in the tropical Pacific Ocean. The models' skill in simulating various climate indices are compared by considering criteria such as anomaly correlation coefficient (ACC), spread to root mean square error (RMSE) ratio, and signal to noise ratio (SNR). The prediction skill for ISMR in terms of ACC in MMCFS (.53) and SINTEX‐F2 (.45) are comparable for hindcasts initialized in February month. However, RMSE for ISMR from February initial conditions in SINTEX‐F2 (1.43 mm/day) is small compared to MMCFS (2.34 mm/day). A simple multi‐model ensemble prediction system based on MMCFS and SINTEX‐F2 results in better prediction skill in terms of ACC for tropical SST and ISMR. The spread/RMSE ratio for ISMR is similar in both the models but is better for ENSO indices in MMCFS at a longer lead time than in SINTEX‐F2. Considering the SNR as performance criteria, MMCFS has an advantage due to better predictability of SST and vertical wind shear in several parts of the Indo‐Pacific domain.

Predicting the Interannual Variability of California's Total Annual Precipitation

AbstractUnderstanding and predicting precipitation characteristics on seasonal and longer timescales can help California prepare for long‐term droughts and precipitation extremes. We find that interannual variations in total precipitation across California are primarily determined by precipitation frequency. As was shown previously for total precipitation, the precipitation frequency is linked to the North Pacific jet stream location. This indicates that California precipitation frequency is primarily controlled by where the jet guides precipitate weather systems rather than how moist or energetic the systems are. The jet's position, in turn, depends on the states of the El Niño‐Southern Oscillation (ENSO) and of the Pacific Decadal Oscillation (PDO). We use this to construct a regression model that predicts variations in California's annual total precipitation and precipitation frequency. Up to 20% of the wintertime precipitation variance in Southern California is predictable using decorrelated ENSO and PDO indices in the previous summer.

Sources of Subseasonal‐To‐Seasonal Predictability of Atmospheric Rivers and Precipitation in the Western United States

AbstractAtmospheric rivers (ARs) account for a large portion of winter precipitation in the western US. To evaluate the sources of AR and precipitation predictability at subseasonal‐to‐seasonal timescales, we examine the relationships between two climate modes, the El Niño/Southern Oscillation (ENSO) and Madden‐Julian oscillation (MJO), and winter hydroclimate in the western US. Our analysis uses a large ensemble of the Weather Research and Forecast (WRF) model simulations from 1981 to 2017, facilitating the assessment of uncertainty in climate mode‐AR relationship due to climate variability and short length of observations. Over the North Pacific basin, we find ENSO‐related latitudinal shifts of ARs, but ENSO has little effect on basin‐wide averaged AR frequency. Over the western US, there is some uncertainty in the ENSO‐AR connection due to the impacts of ENSO indices and datasets. However, extreme El Niño events defined by the ENSO longitude index are consistently linked to increased landfalling AR activity. The MJO can lead to significantly enhanced or suppressed landfalling AR activity in the large‐ensembles, depending on the phase of MJO and time lag, while observations are too short in length to robustly show this signal. ENSO substantially modulates the MJO‐AR relationship, triggering variegated responses of landfalling ARs and AR precipitation in La Niña and El Niño years. Our findings highlight the need to evaluate concurrent effects of different climate modes on ARs and precipitation, and may shed light on a path toward more accurate subseasonal‐to‐seasonal prediction of ARs and precipitation over the western US.

Unified deep learning model for El Niño/Southern Oscillation forecasts by incorporating seasonality in climate data

Although deep learning has achieved a milestone in forecasting the El Niño-Southern Oscillation (ENSO), the current models are insufficient to simulate diverse characteristics of the ENSO, which depends on the calendar season. Consequently, a model was generated for specific seasons which indicates these models did not consider physical constraints between different target seasons and forecast lead times, thereby leading to arbitrary fluctuations in the predicted time series. To overcome this problem and account for ENSO seasonality, we developed an all-season convolutional neural network (A_CNN) model. The correlation skill of the ENSO index was particularly improved for forecasts of the boreal spring, which is the most challenging season to predict. Moreover, activation map values indicated a clear time evolution with increasing forecast lead time. The study findings reveal the comprehensive role of various climate precursors of ENSO events that act differently over time, thus indicating the potential of the A_CNN model as a diagnostic tool.

Statistical predictability of wintertime PM2.5 concentrations over East Asia using simple linear regression

Abstract The interannual meteorological variability plays an important role in wintertime air quality in East Asia. In particular, monsoons and the El Nino Southern Oscillation (ENSO) are known as important mechanisms for determining wintertime PM2.5 concentrations. In addition, Arctic Oscillation, North Atlantic Oscillation, and Pacific Decadal Oscillation are also known to affect meteorological conditions and thus PM2.5 concentrations in East Asia. Here, we used a global 3-D chemical transport model (GEOS-Chem) with assimilated meteorological fields to investigate the long-term (1980–2014) relationship between 16 different climate indices and wintertime PM2.5 concentrations in this region. We show that wintertime PM2.5 concentrations in Northeast Asia (33–41°N, 118–141°E) are highly correlated with ENSO indices and the Siberian high-pressure system. Furthermore, we develop a simple linear regression (SLR) model for the prediction of wintertime PM2.5 concentrations. Despite the use of a single predictor, the SLR model shows good performance with r > 0.72 in reproducing targeted PM2.5 concentrations. The hit and false alarm rates are 77% and 11%, respectively, indicating the high predictive accuracy of the model. In particular, the model shows excellent performance for capturing the abnormal variability of wintertime PM2.5 concentrations in Northeast Asia.

Entropy analysis and pattern recognition in rainfall data, north Algeria

Algerian climate is characterized by the transition between the subtropical climate in the north and the hot Saharan climate in the south. Understanding the spatiotemporal variability of rainfall patterns in such areas has significant implications for water resources management. To account for the spatial variation in the rainfall pattern of north Algeria, Tsallis entropy analysis, pattern recognition, and Precipitation Concentration Index (PCI) have been analyzed over a period of 33 years (1980–2013). The rainfall trend is identified by analyzing the results of the structural break test and Mann-Kendall (MK) trend tests. The Tsallis entropy produced spatial patterns for a better understanding of rainfall characteristics and the results show that entropy values were higher for higher rainfall values. The magnitude of rainfall change indicates that a large amount of rainfall occurs over the northern country boundary compared with the central part of Algeria. The rainfall record shows a structural break in the year 1992 during the selected time period. The MK trend analysis revealed a significant decreasing trend over the central and no trend in north and south of the study area. The PCI indicates a moderate rainfall concentration across the northern border region compared with a significant irregular rainfall distribution over the central region. The Wavelet Coherence Analysis (WCA) between El Nino Modoki (EMI) and Southern Oscillation Index (SOI) events on monthly rainfall data were also investigated to find a possible influence of global climatic indicators on the rainfall events. The results show a significant correlation of EMI and SOI with the rainfall pattern of north Algeria.

The Impact Study of El Niño-Southern Oscillation to the Wind and Solar Data in Malaysia Using the Wavelet Analysis

Malaysia has launched initiatives for utilizing renewable energy (RE) as a source of electricity since 2011 by establishing renewable energy-related laws and policies. Malaysia's geographical location and climate have led to a limited amount of intermittent RE resources. Thus, a more thorough study of the various factors affecting the RE-based electricity generation is needed for energy output optimization. This article aims to understand the impact of El Niño-Southern Oscillation (ENSO) events on wind and solar reanalysis datasets using the Wavelet Transform. The thirty-year ERA5 solar and wind datasets were used in the study, together with the multivariate ENSO Index (MEI). As a result, the selected sites experienced an increase in solar irradiation during moderate to very strong El Niño and a decrease during the La Niña period. The wind speed increases during La Niña and decreases during El Niño, with the exception of the high wind speed during the Northeast monsoon season. Also, there was a significant coherence relationship between the wind and solar datasets with the ENSO index at a specified period. Therefore, the ENSO is essential as an input factor for future development plans for wind and solar power, energy predictions, and risk assessment.

Effects of El Nino southern oscillation events on rainfall variability over northeast Ethiopia

This paper analyzed the effects of various stages of El Nino southern oscillation (ENSO) events on the variability of temporal and spatial summer rainfall over northeast Ethiopia, where multiple hazards occurred. For this paper, precipitation and ENSO index data were used from 1983 to 2015. To investigate the relationship between summer rainfall and ENSO stages over the region, correlation and composite analysis was used. The composite analysis aimed to determine the link between sea surface temperature (SST) and rainfall in the study area. The findings show that there is a strong and positive connection between southern India, the Atlantic, and most of the western Pacific Ocean and rainfall in northeastern Ethiopia. The results also showed that La Nina’s development stage favors increased rainfall with extended spatial distribution over the region and that El Nino’s development stage favors less rainfall with the restricted spatial distribution. Future progress in the ENSO forecast is therefore important for future increases in summer rainfall in northeast Ethiopia.

Rainfall Variability over Tanzania during October to December and Its Association with Sea Surface Temperature (SST)

The current study examines the interannual rainfall variability and its associated atmospheric circulation in Tanzania during October-December (OND) rainfall season based on 1974 to 2010 climatology. The Empirical Orthogonal Function (EOF), composite and correlation analysis were used in this study. Years with enhanced precipitation are found to be associated with the low level moist and unstable wind from Congo basin which organizes and forms a confluent zone, an inter tropical convergence zone (ITCZ) extending from Congo to northern sector of the country. It however, characterizes low-level westerly moisture flux transport sourced from Congo basin, ascending limb of the local Indian Ocean Walker circulation over East Africa which enhances convection for wetness condition. Wet years are also coupled with the positive Indian Ocean Dipole (IOD) and the warm phase of the El Nino Southern Oscillation (ENSO) condition. On the spatial scale, both the IOD and ENSO indices are well correlated with OND rains over the bimodal areas (Lake Victoria basin, North Eastern Highlands (NEH), and northern coast) with strong correlation being to the NEH. Strong temporal correlation is revealed between the OND rains and IOD (r = 0.6304) compared to ENSO (r = 0.5538) indicating that anomalous warming over the western Indian Ocean has a faster response to OND rains in Tanzania than the remote influence induced by anomalous warming from the central Pacific Ocean. The patterns associated with dry years are found to be linked with the low-level divergence accompanied by convergence in the upper level. This condition enhances continuous descending motion accompanied with suppression in rainfall activities. Dry years are also associated with negative IOD, cold phase of ENSO condition, descending limb of the Walker Circulation and significant reduction in the westerly moisture flux transport sourced from Congo basin towards the western sector and Lake Victoria basin.

Analysis of sand and dust storm events over Saudi Arabia in relation with meteorological parameters and ENSO

Sand and dust storm (SDS) events are closely associated with meteorological parameters. This study aims to analyze SDS events in relation to temperature, precipitation, and wind speed. In addition, since El Nino–Southern Oscillation Index (ENSO) cycle has a great impact on precipitation and temperature, the relationship between SDS events and ENSO cycles are also investigated. For this purpose, data for 30 years (1985–2014) from relevant organizations are retrieved and analyzed. Results of the study show that SDS, precipitation, and wind speed have a decreasing trend while temperature has an increasing trend in the KSA during the study period. The results of the study also reveal that SDS events have the highest correlation with temperature and lowest correlation with precipitation. Furthermore, the relationship between SDS events and ENSO shows that El Nino was dominant during the first two decades of the study period while La Nina prevailed in the last decade of the study period. It is envisaged that the outcome of the current endeavor will be very useful for aerosol-related research both at regional and at a global scale.

The Sea-Level Changes in Hong Kong From Tide-Gauge Records and Remote Sensing Observations Over the Last Seven Decades

The importance of studying the sea-level change (SLC) in Hong Kong (HK) is emphasized by factors related to high population density, intensive urban, and industrial development, particularly along the coast that involves a considerable landreclamation. To address this issue, we investigate the spatiotemporal characteristics of SLC in the HK territories by analyzing tide-gauge (TG) records collected from 1954 to 2019 and satellite-altimetry (SA) data from 1993 to 2019. The application of the ocean-tide and inverted barometer corrections to TG data substantially decreased uncertaintie by achieving a submillimeter accuracy. The SLC rates detected at six TG stations vary significantly even within this relatively small coastline. According to our estimates, the annual rates vary between 0.32 ± 0.51 and 4.19 ± 0.46 mm/yr from 1997 to 2019. These large differences are related to different patterns of ocean currents and the freshwater discharge in the Pearl River Estuary. The empirical orthogonal function analysis confirms the expected increasing rising in SLC with two anomalous periods during 2002-2003 and 2015-2016 that are likely attributed to the El Niño-Southern Oscillation (ENSO). SLCs are positively correlated with the ENSO index and ENSO precedes several months (normally 5 months) the interannual SLC. The analysis of the GPS trends reveals a prevailing subsidence in the HK territories, which aggravates the relative SLC estimates. Between 1997 and 2019, the absolute sea level raised at the annual rate of 3.17 ± 1.56 mm/yr (when using the GPS+TG) and 2.88 ± 0.59 mm/yr (when using SA observations).

The impacts of climate variability on coffee yield in five indonesian coffee production centers

Coffee is an annual crop sensitive to climate variability. Most Indonesian coffee is cultivated on marginal lands that are vulnerable to environmental changes, including climate. Indonesia's climate variability is influenced by several factors, including the monsoon, local aspects, and global climate oscillations such as the El Nino-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD). It is crucial to identify the impacts of climate variability on both the production and the economy to develop adaptative measures. This study aims to determine the effects of global climate variability, namely ENSO and IOD, on coffee production in several Indonesian production centers. It uses annual coffee production data in the five major production centers in Indonesia. The ENSO indicators used in this study were the Oceanic Nino Index (ONI) in the Nino 3.4 region and the IOD indicator in the Dipole Mode Index (DMI). The production anomaly analysis approach in neutral years and the extreme ENSO and IOD phases were applied in this study. The results showed that the effects of ENSO and IOD were different in each region. The highest decline in production occurred in the La-Nina year in almost all production centers. The decline in output in the La-Nina year ranged from 6 to 22%. Meanwhile, the IOD that had a decreasing effect on production was positive IOD with a decrease ranging from 1 to 15%. Key words: ENSO; IOD; Economy; Climate change.

Caracterización climática y variabilidad de temperatura superficial de la llanura costera de Nayarit y su teleconexión con ENSO y PDO

Para entender el cambio climático se debe estudiar la variabilidad climática, esta se centra en denotar aquellos comportamientos del estado del tiempo considerados fuera de lo normal y su relación con la variación natural del planeta, como el fenómeno El Niño-Oscilación del Sur (ENSO) y la Oscilación Decadal del Pacífico (PDO). El objetivo de esta investigación fue conocer la variabilidad climática de un fragmento de la llanura costera del estado de Nayarit, México, área predominante de cobertura manglar, l agunas y uso agropecuario. La investigación se expone en tres partes: caracterización climática, análisis de variabilidad (anual, mensual e interdecadal) y su teleconexión con índices ENSO y PDO. La variación interdecadal demostró un aumento de la temperatura mínima, disminución de la temperatura máxima y disminución de precipitación en las últimas dos décadas. Se encontró 30% de correlación con ENSO (las correlaciones más altas en los meses de verano) y 25% con PDO.

El Niño‐Southern Oscillation Impacts on Global Wave Climate and Potential Coastal Hazards

AbstractWave direction and wave power are the main climate‐related drivers of large‐scale coastal morphological hazards on open sandy coastlines. El Niño‐Southern Oscillation (ENSO) is the main climate driver affecting global wave climates on interannual timescales. Hence, a quantification of wave climate variability with ENSO would aid the characterization of coastal risk as a first‐pass assessment tool. However, no such data are available on the global scale at present. To address this, this work (1) quantifies ENSO impacts on global mean wave direction and wave power, and (2) classifies the global coast according to the ENSO wave climate hazard. The correlation coefficient (R) of the Multivariate ENSO Index between wave power and mean wave direction was calculated. Then, employing a multivariate technique, the resulting R values were used to classify the global coastlines. The results show that ENSO has distinct latitudinal impacts around the world. El Niño increases the wave power of swells generated in the extra‐tropics which propagate toward the tropics, a phenomenon which is particularly notable in the Pacific Basin, while the most significant rotations in wave direction with ENSO occur at tropical and subtropical latitudes. Based on this new information, the global coastline was classified into five distinct types, depending on the directional wave climate response to ENSO.

El Niño events and chlorophyll levels affect the reproductive frequency but not the seasonal reproductive output of East Pacific green turtles

The El Niño-Southern Oscillation (ENSO), which includes El Niño and La Niña phases, affects oceanographic conditions and primary productivity of marine ecosystems at a global scale. During El Niño events, warm temperatures in the central and eastern tropical Pacific decrease primary productivity, affecting the biology of many species including sea turtles. We aimed to determine the effect of ENSO (as a global index) and primary productivity in the foraging areas (as a local index) on remigration intervals (number of years between nesting seasons) and seasonal reproductive output (clutch size and number of clutches in a season) of green turtles Chelonia mydas in the North Pacific off Costa Rica, an area highly influenced by ENSO. We used the Multivariate ENSO Index (MEI) and mean values of chlorophyll a from previously identified feeding areas for this population over different time periods (up to 3 yr before the nesting season). Chlorophyll a levels in foraging areas and MEI strongly influenced the reproductive frequency of green turtles. Variability in the remigration intervals was better explained by models that included MEI conditions 2 and 3 yr before the onset of the nesting season and chlorophyll levels in the previous 3 yr. Models using global and local indexes were equally good at predicting changes in the remigration intervals. Positive values of MEI (El Niño phase) and lower levels of chlorophyll a corresponded to longer remigration intervals. No statistically significant relationships were found between seasonal reproductive output and MEI or chlorophyll a levels. As El Niño events may become more frequent and last longer due to climate change, the lifetime reproductive output of green turtles in this area may be compromised in the future.

Variations of mesoscale eddy SST fronts based on an automatic detection method in the northern South China Sea

SST fronts at the mesoscale eddy edge (ME fronts) were investigated from 2007–2017 in the northern South China Sea (NSCS) based on an automatic method using satellite sea level anomaly (SLA) and SST data. The relative probabilities between the number of anticyclonic/cyclonic ME fronts (AEF/CEF) and the number of anticyclones/cyclones reached 20%. The northeastern and southwestern parts of these anticyclones had more fronts than the northwestern and southeastern parts, although CEFs were nearly equally distributed in all directions. The number of ME fronts had remarkable seasonal variations, while the eddy kinetic energy (EKE) showed no seasonal variations. The total EKE at the ME fronts was three times of that within the MEs, and it was much stronger in AEFs than in CEFs. The interannual variability in the number of ME fronts and EKE had no significant correlation with the El Nino-Southern Oscillation (ENSO) index. Possible mechanisms of ME fronts were discussed, but the contributions of mesoscale eddies to SST fronts need to be quantified in future studies.

Are trends in Olive Ridley sea turtle (Lepidochelys olivacea) nesting abundance affected by El Niño Southern Oscillation (ENSO) variability? Sixteen years of monitoring on the Pacific coast of northern Central America

Long-term monitoring is essential for the identification of population trends, and to understand how these trends are affected by climate variability. The El Niño Southern Oscillation (ENSO) is the strongest global interannual pattern of climate variability, resulting in the disruption of the annual phenological cycles of sea turtles. Among sea turtles, the Olive Ridley (Lepidochelys olivacea) is the most abundant, and on many beaches their nests are relocated to hatcheries as part of conservation management, especially in northern Central America. However, Olive Ridley nesting abundance trends in northern Central America and the effects of ENSO variability on these trends are still not fully understood. Here, we present the first long-term study of this subject. We predicted an upward trend in Olive Ridley nesting abundance on the Pacific coast of Guatemala, and a negative effect of increasing ENSO variability on nesting abundance. As proxies for nesting abundance, we analysed two different data sets; a 16-year period of Olive Ridley nesting data, using nesting tracks from one index beach (Hawaii in Guatemala), and the yearly number of eggs buried in the 25–35 hatcheries that operate along the Pacific coast of Guatemala. Revised Multivariate ENSO Index values were applied to estimate annual ENSO variability. During this 16-year study period, ENSO variability was distributed in eight neutral years, two normal El Niño years, four normal La Niña years and two extreme ENSO events; an extreme La Niña in 2010 and an extreme El Niño in 2015. We found a clear overall upward trend in Olive Ridley numbers of nesting tracks and eggs buried in hatcheries but no clear effect of ENSO variability on these nesting abundance proxies. However, a decrease in the net change of eggs buried in hatcheries occurred the respective years after the two extreme ENSO events during the study period. In the second year after those events, the net change of eggs buried in hatcheries bounced back to resume the overall positive trend. Our results suggest a clear upward trend, resilient to ENSO variability, of the nesting abundance of the Pacific coast Olive Ridley population in Guatemala. Community-based hatchery management efforts seem to be effective for Olive Ridley conservation on the Pacific coast of Guatemala. However, longer term monitoring including additional nesting beaches in northern Central America are necessary to further elucidate the effects of ENSO variability on the nesting abundance of Olive Ridley.

Spatio-temporal variability of temperature and chlorophyll-a concentration of sea surface in Bali Strait, Indonesia

Abstract. Wijaya A, Zakiyah U, Sambah AB, Setyohadi D. 2020. Spatio-temporal variability of temperature and chlorophyll-a concentration of sea surface in Bali Strait, Indonesia. Biodiversitas 21: 5283-5290. The Bali Strait is influenced by seasonal and inter-annual systems. El Nino-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) are climate variabilities that affect water conditions. The knowledge about influence of ENSO and IOD variations on the fertility of waters in the Bali Strait is still lacking. The purpose of this study was to determine the effect of seasonal and inter-annual variability on the variability of Sea Surface Temperature (SST) and Sea Surface Chlorophyll-a (SSC) in the Bali Strait. This study applied SST and SSC data collected from the Aqua/Terra MODIS satellite, as well as the ENSO and IOD indices during March 2000-December 2019. The results described that the effect of ENSO on SST and SSC was low and IOD on SST and SSC was quite high. The effect was quite high between IOD and SST anomaly of-0.401. Seasonal variations indicate the abundance of high SSC and low SST in the southeast monsoon (JJA) which characterizes upwelling. Meanwhile, in the northwest monsoon (DJF), SSC was low and SST was high which characterizes downwelling. This condition cannot separate from the monsoonal process that occurred in the Bali Strait. The inter-annual variation showed that in the strong El Nino period and IOD (+) triggers a negative SST anomaly and a positive SSC results in strong upwelling, while in the strong La Nina period and strong IOD (-) triggers a positive SST anomaly and a negative SSC results in downwelling. The inter-annual variability of SSC influenced by IOD rather than ENSO. This condition indicates that the ENSO/IOD event changes the period of SSC concentration.

El-Niño Southern Oscillation (ENSO) effects on the variability of Patos Lagoon Suspended Particulate Matter

South America is particularly subject to El-Niño Southern Oscillation (ENSO)-triggered phenomena, with above-average precipitation during ENSO warm events (El Niño) and below-average precipitation during cold events (La Niña). Seventeen years (2003–2019) of reflectance data from the Moderate Resolution Imaging Spectroradiometer (MODIS) - Aqua sensor were used to quantify the Suspended Particulate Matter (SPM) distribution and variability in Patos Lagoon, southern Brazil, based on three semi-analytical algorithms. An atmospheric correction suitable for extremely turbid coastal waters was applied. Occasional negative retrievals of remote sensing reflectance (Rrs) products were filtered and removed. SPM concentrations were modeled from filtered Rrs values by applying the chosen semi-analytical algorithms: a single band algorithm, a weighted algorithm, and a multi-band algorithm (referred here as NECHAD10, HAN16, and NOVOA17, respectively). After those steps, the daily modeled SPM data were averaged into eight-day, monthly, and yearly composites. SPM time series were then compared to the ENSO index and environmental parameters. As a result of substantial interannual variability, the highest SPM concentrations were found to match El-Niño events (in 2009 and 2012) while the lowest corresponded to a strong La Niña (2010) and a neutral year (2014). The modeled SPM was compared to in-situ SPM data to ensure the applicability to/in Patos Lagoon. Our main results suggest that while the three tested algorithms allow the analysis of temporal and spatial SPM variability, HAN16 and NOVOA17 seem to provide the best results for the Patos Lagoon region. On the other hand, NECHAD10 yields more satisfactory results considering the yearly and eight-day composites. Therefore, we call attention to the methods of handling data as each approach might lead to a different interpretation of the same phenomena.