El Nino-Southern Oscillation Phenomenon Research Articles

Triple dip La-Nina, unorthodox circulation and unusual spin in air quality of India

The recent La-Nina phase of the El Nino Southern Oscillation (ENSO) phenomenon unusually lasted for third consecutive year, has disturbed global weather and linked to Indian monsoon. However, our understanding on the linkages of such changes to regional air quality is poor. We hereby provide a mechanism that beyond just influencing the meteorology, the interactions between the ocean and the atmosphere during the retreating phase of the La-Niña produced secondary results that significantly influenced the normal distribution of air quality over India through disturbed large-scale wind patterns. The winter of 2022–23 that coincided with retreating phase of the unprecedented triple dip La-Niña, was marred by a mysterious trend in air quality in different climatological regions of India, not observed in recent decades. The unusually worst air quality over South-Western India, whereas relatively cleaner air over the highly polluted North India, where levels of most toxic pollutant (PM2.5) deviating up to about ±30 % from earlier years. The dominance of higher northerly wind in the transport level forces influx and relatively slower winds near the surface, trapping pollutants in peninsular India, thereby notably increasing PM2.5 concentration. In contrast, too feeble western disturbances, and unique wind patterns with the absence of rain and clouds and faster ventilation led to a significant improvement in air quality in the North. The observed findings are validated by the chemical-transport model when forced with the climatology of the previous year. The novelty of present research is that it provides an association of air quality with climate change. We demonstrate that the modulated large-scale wind patterns linked to climatic changes may have far-reaching consequences even at a local scale leading to unusual changes in the distribution of air pollutants, suggesting ever-stringent emission control actions.

Analisis Korelasi Statistikal Fenomena Kejadian ENSO dan MJO terhadap Kondisi Klimatologis di Provinsi Riau

El Nino-Southern Oscillation (ENSO) and Madden-Julian Oscillation (MJO) events are significant factors in the global climate system. Riau Province, located on the island of Sumatra, is highly influenced by these phenomena. This study collects climate data from weather stations in Riau Province and reliable sources for ENSO and MJO indices. Statistical analyses, including correlation and regression, are employed to determine the relationship between climate variables in Riau Province and ENSO and MJO. Based on data from 2001-2019, the results reveal a medium category correlation value (r = 0.452) with coefficients a and b of 1.288 and 0.089, respectively. The increased MJO value contrasts with the rainfall during the 2001-2019 timeframe. This information serves as a reference for understanding the ENSO and MJO phenomena in the Riau region.

The Effect of the El Nino Southern Oscillation on Precipitation Extremes in the Hindu Kush Mountains Range

The El Nino Southern Oscillation (ENSO) phenomenon is devastating as it negatively impacts global climatic conditions, which can cause extreme events, including floods and droughts, which are harmful to the region’s economy. Pakistan is also considered one of the climate change hotspot regions in the world. Therefore, the present study investigates the effect of the ENSO on extreme precipitation events across the Upper Indus Basin. We examined the connections between 11 extreme precipitation indices (EPIs) and two ENSO indicators, the Southern Oscillation Index (SOI) and the Oceanic Niño Index (ONI). This analysis covers both annual and seasonal scales and spans the period from 1971 to 2019. Statistical tests (i.e., Mann–Kendall (MK) and Innovative Trend Analysis (ITA)) were used to observe the variations in the EPIs. The results revealed that the number of Consecutive Dry Days (CDDs) is increasing more than Consecutive Wet Days (CWDs); overall, the EPIs exhibited increasing trends, except for the Rx1 (max. 1-day precipitation) and Rx5 (max. 5-day precipitation) indices. The ENSO indicator ONI is a temperature-related ENSO index. The results further showed that the CDD value has a significant positive correlation with the SOI for most of the UIB (Upper Indus Basin) region, whereas for the CWD value, high elevated stations gave a positive relationship. A significant negative relationship was observed for the lower portion of the UIB. The Rx1 and Rx5 indices were observed to have a negative relationship with the SOI, indicating that El Nino causes heavy rainfall. The R95p (very wet days) and R99p (extreme wet days) indices were observed to have significant negative trends in most of the UIB. In contrast, high elevated stations depicted a significant positive relationship that indicates they are affected by La Nina conditions. The PRCPTOT index exhibited a negative relationship with the SOI, revealing that the El Nino phase causes wet conditions in the UIB. The ONI gave a significant positive relationship for the UIB region, reinforcing the idea that both indices exhibit more precipitation during El Nino. The above observations imply that while policies are being developed to cope with climate change impacts, the effects of the ENSO should also be considered.

Validation of Satellite Rainfall Product (GPM-IMERG) an Bali and Nusa Tenggara: A Comparison of Normal Seasons, El Nino and La Nina Events

Bali and Nusa Tenggara are regions where monsoonal wind changes and strange interactions between the ocean and atmosphere influence rainfall. The purpose of this research is to evaluate Integrated Multi-Satellite Retrievals for GPM (IMERG) rainfall data using in-situ observations from Bali and Nusa Tenggara, Indonesia, while considering seasonal variations and the El Nino-Southern Oscillation (ENSO) phenomenon. The study combines rainfall data from synoptic stations with rain gauge measurements over ten years, from January 2012 to December 2021, to obtain more accurate verification results. The study's findings indicate that, apart from the transitional seasons, IMERG data provides substantial estimates of monthly rainfall accumulation with low error values for both light and heavy rainfall. The study also reveals that the islands' complexity and topography can impact each province's validation values. The verification results show excellent accuracy in flat terrain areas and moderate elevations, while performance decreases in regions with high altitudes. These findings are significant because IMERG data can estimate rainfall for regions lacking monitoring stations during specific seasons and active ENSO conditions. Thus, this information can serve as a valuable tool to address the issue of data unavailability in hard-to-access areas and contribute to optimizing water resource management and weather-related disaster mitigation. Keywords: Validation, Rainfall, IMERG, ENSO

Cardamom agro-environmental interrelationships analysis in Indian cardamom hills

The rainfall pattern seen in the Indian Cardamom Hills (ICH) has been extremely variable and complicated, with El Niño-Southern Oscillation (ENSO) playing a crucial role in shaping this pattern. In light of this, more investigation is required through improved statistical analysis. During the study period, there was greater variability in rainfall and the frequency of rainy days. About 2,730 mm of rainfall was reported in 2018, while the lowest amount (1168.3 mm) was registered for 2016. The largest decrease in decadal rainfall (>65 mm) was given by the decade 1960–1969, followed by 1980–1989 (>40 mm) and 2010–2019 (>10 mm). In the last 60 years of study, there has been a reduction of rainy days by 5 days in the last decade (2000–2009), but in the following decade (2010–2019), it registered an increasing trend, which is only slightly <2 days. The highest increase in decadal rainy days was observed for the 1970–1979 period. The smallest decadal increase was reported for the last decade (2010–2019). Total sunshine hours were the highest (1527.47) for the lowest rainfall year of 2016, while the lowest value (1,279) was recorded for the highest rainfall year (2021). The rainfall characteristics of ICH are highly influenced by the global ENSO phenomenon, both positively and negatively, depending on the global El Nino and La Nina conditions. Correspondingly, below and above-average rainfall was recorded consecutively for 1963–1973, 2003–2016, and 1970–2002. Higher bright forenoon sun hours occurred only during SWM months, which also reported maximum disease intensity on cardamom. The year 2016 was regarded as a poorly distributed year, with the lowest rainfall and the highest bright afternoon sun hours during the winter and summer months (January-May). Over the last three decades, the production and productivity of cardamom have shown a steady increase along with the ongoing local climatic change. Many of our statistical tests resulted in important information in support of temporal climatic change and variability. Maintaining shade levels is essential to address the adverse effects of increasing surface air temperature coupled with the downward trend of the number of rainy days and elevated soil temperature levels.

Analysis of the effect of ENSO and IOD on the productivity of yellowfin tuna (Thunnus albacares) in the South Indian Ocean, East Java, Indonesia

Abstract. Sambah AB, Noor’izzah A, Intyas CA, Widhiyanuriyawan D, Affandy DP, Wijaya A. 2023. Analysis of the effect of ENSO and IOD on the productivity of yellowfin tuna (Thunnus albacares) in the South Indian Ocean, East Java, Indonesia. Biodiversitas 24: 2689-2700. Yellowfin tuna (Thunnus albacares) is one of the fish that migrates through the Indian Ocean and is primarily caught in the south Java waters which are directly adjacent to the Indian Ocean. Fish abundance and migration are influenced by oceanographic factors, including climatic factors which affect annual and interannual variations, such as the Indian Ocean Dipole (IOD) and the El Nino Southern Oscillation (ENSO). This study aimed to determine the effect of climate anomalies on the productivity of yellowfin tuna in the Indian Ocean south of East Java, Indonesia. Data collection was carried out at Coastal Fishing Port of Pondokdadap East Java, involving 58 respondents consisting of fishermen with yellowfin tuna catches. The boundaries of the research area are at coordinates 110.9°-114.5° East Longitude and 8°-11° South Latitude. The data used in the analysis consisted of Sea Surface Temperature (SST) data, chlorophyll-a data, Nino 3.4 data, Dipole Mode Index (DMI) data, and yellowfin tuna catch data for the year 2017-2021. The results showed that of the fifteen GAM models, the combination of variables that most affected fish productivity was the distribution of chlorophyll-a and the ENSO phenomenon with values ??of AIC (1503.33) and DE (64.30%). Pearson correlation analysis showed that the IOD phenomenon was influenced by SST and chlorophyll-a, while SST and chlorophyll-a did not significantly influence the ENSO phenomenon. These results indicated that the phenomenon of climate anomalies and the oceanographic conditions in the waters indirectly affect fish productivity through the food chain process.

Spatial-temporal transformer network for multi-year ENSO prediction

The El Niño-Southern Oscillation (ENSO) is a quasi-periodic climate type that occurs near the equatorial Pacific Ocean. Extreme periods of this climate type can cause terrible weather and climate anomalies on a global scale. Therefore, it is critical to accurately, quickly, and effectively predict the occurrence of ENSO events. Most existing research methods rely on the powerful data-fitting capability of deep learning which does not fully consider the spatio-temporal evolution of ENSO and its quasi-periodic character, resulting in neural networks with complex structures but a poor prediction. Moreover, due to the large magnitude of ocean climate variability over long intervals, they also ignored nearby prediction results when predicting the Niño 3.4 index for the next month, which led to large errors. To solve these problem, we propose a spatio-temporal transformer network to model the inherent characteristics of the sea surface temperature anomaly map and heat content anomaly map along with the changes in space and time by designing an effective attention mechanism, and innovatively incorporate temporal index into the feature learning procedure to model the influence of seasonal variation on the prediction of the ENSO phenomenon. More importantly, to better conduct long-term prediction, we propose an effective recurrent prediction strategy using previous prediction as prior knowledge to enhance the reliability of long-term prediction. Extensive experimental results show that our model can provide an 18-month valid ENSO prediction, which validates the effectiveness of our method.

Prediksi Perubahan Iklim Ekstrem di Kota Palembang dan Kaitannya dengan Fenomena El Niño-Southern Oscillation (ENSO) Berbasis Machine Learning

The purpose of this study is to provide predictions of climate conditions and trend of temperature rise in the city of Palembang in the 21st century. The BMKG station data used, namely SK Palembang and SM SMB II from the 2000-2020 period were analyzed based on the agreement of the Expert Team for Climate Change Detection and Indices (ETCCDI). ). Analysis using google colab shows that rainfall in the city of Palembang has a fairly high variability and a decrease in the number of rainy days that occur in the city of Palembang with the trend of air temperature, namely TMAXmean and TMINmean, has increased significantly by 2.40C for 50 years. Based on the correlation analysis between rainfall and El Niño-Southern Oscillation (ENSO) with Google Colab, a negative relationship was found. The El Niño-Southern Oscillation (ENSO) index used is the Niño 3.4 index. The study concluded that although the geographical location of Palembang city is located in the Asian Monsoon area, the ENSO phenomenon does not significantly affect rainfall variability in Palembang city.

The effect of long-term climatic variability on wild mammal populations in a tropical forest hotspot: A business intelligence framework

Anthropogenic climate change has emerged as a new threat to biodiversity over the last decades. It affects climate systems such as El Niño Southern Oscillation (ENSO) with possible impacts on mammal populations due to changes in local climate. Since this issue had not been addressed in any previous work, the main contribution of this research was the development of a Business Intelligence (BI) system which significantly supports the analysis of patterns in the variation of the population of small mammals in the tropical forest and its response to ENSO phenomenon. In this sense, a Data Warehouse (DW) and Online Analytical Processing (OLAP) tool were built to support the analyses of the data amassed by a 22-year monitoring study of small mammal populations in a highly threatened biodiversity hotspot, the Atlantic Forest. The BI system was applied for three purposes: evaluating the population size trends throughout time, analyzing the relationship between ENSO events and local weather, and evaluating the effects of the most intense ENSO events on the population size of a small, endemic marsupial, Marmosops incanus. The data exploration started by the interactive and visual analyses of the BI dashboards, followed by a statistical analysis which confirmed some trends observed in the dashboards. Very strong and strong La Niña episodes reduced the average temperature and increased precipitation in the study area. These changes affected the population of M. incanus with a reduction in population size in short-term (4 and 6 months) as well as long-term (24 months), indicating the negative effects of reduced temperatures on the population dynamics. Since this BI system is available to the scientific community, we believe that it can stimulate further studies involving the relationship between the population dynamics of other Atlantic Forest species and changes in local and global climate.

Relationship of ENSO (El Niño – Southernoscillation ) and monsoon index on variability of rainfall and sea surface height in coastal City Semarang, Central Java

The city of Semarang is one of the coastal areas in Indonesia that is prone to flooding. Flood events that often occur in the Semarang City area can be triggered by high rainfall. Variations in rainfall and sea level are closely related to global atmospheric circulation such as ENSO and regional atmospheric circulation, Monsoon. This study aims to examine the relationship between ENSO and Monsoon with the distribution of rainfall and sea level in coastal city Semarang from 2012 to 2021. Correlation and composite analysis were used to analyze the relationship and impact of ENSO and Monsoon phenomena on rainfall and sea level. The results showed the strong correlation of the ENSO index (Southern Oscillation Index) to rainfall in the JJA and SON periods. Generally, El Niño (La Niña) has an impact on increasing (decreasing) rainfall. Meanwhile, Australian Summer Monsoon Index (AUSMI) only strongly correlates with rainfall in the SON period. The westerly (eastern) wind indicated by a positive (negative) monsoon index in the SON period has the effect of increasing (reducing) rainfall. The ENSO phenomenon with sea level during the JJA period has a strong relationship. In JJA, the highest sea level (maximum tide) rises by 12.6 cm during El Niño and decreases by 0.6 cm during La Niña. Meanwhile, the lowest sea level (minimum low tide) decreased by 16.6 cm during El Niño and increased by 0.7 cm during La Niña. These results can explain the influence of global and regional atmospheric circulation on a local scale on the coast of Semarang City.Keywords:RainfallSea levelEl NiñoLa NiñaMonsoon

Analysis of the relationship between the ENSO phenomenon, Net Primary Productivity and catches of yellowfin tuna in Sibolga Waters, Indonesia

Abstract. Fadhilah A, Leidonald R, Susetya IE, Dewinta AF. 2022. Analysis of the relationship between the ENSO phenomenon, Net Primary Productivity and catches of yellowfin tuna in Sibolga Waters, Indonesia. Biodiversitas 23: 4440-4447. The eastern Indian Ocean includes the western seas off of North Sumatra. They have a high potential for catching fish, which is a measure of their degree of primary production. Numerous factors, including oceanic conditions and the El Niño Southern Oscillation (ENSO) phenomena, frequently have an impact on its potential. Additionally, ENSO has an influence on the availability of nutrients that generate chlorophyll, a component of primary production, as well as water mass. It is also responsible for anomalies of sea surface temperature (SST) that occur in the Pacific Ocean. Therefore, this study aims to determine the effect of the ENSO phenomenon on primary productivity as well as the abundance of yellowfin tuna (Thunnus albacares) in the western waters of North Sumatra. Data were obtained from the Aqua MODIS level 3 satellite for analysis of net primary productivity (NPP) using various parameters, namely Vertically Generalized Production Model (VGPM), Ocean Niño Index (ONI), yellowfin tuna catch, winds from ERDDAP, and currents from AVISO. Its relationship with the abundance of yellowfin tuna, and ENSO was then determined using the Pearson correlation. The VGPM calculation showed that NPP for 2016-2020 ranged from 275.169 mgC/m2 to 994.898 mgC/m2. The study also revealed that the ENSO phenomenon has a weak relationship with primary productivity and the abundance of yellowfin tuna with correlation values of 0.262 and -0.191, respectively.

ENSO Asymmetry in CMIP6 Models

Abstract An interesting aspect of the El Niño–Southern Oscillation (ENSO) phenomenon is the asymmetry between its two phases. This paper evaluates the simulations of this property of ENSO by the Coupled Model Intercomparison Project phase 6 (CMIP6) models. Both the surface and subsurface signals of ENSO are examined for this purpose. The results show that the models still underestimate ENSO asymmetry as shown in the SST field, but do a better job in the subsurface. A much weaker negative feedback from the net surface heat flux during La Niña in the models is identified as a factor causing the degradation of the ENSO asymmetry at the surface. The simulated asymmetry in the subsurface is still weaker than the observations owing to a weaker dynamic coupling between the atmosphere and ocean. Consistent with the finding of a weaker dynamic coupling strength, the precipitation response to the SST changes is also found to be weaker in the models. The results underscore that a more objective assessment of the simulation of ENSO by climate models may have to involve the examination of the subsurface signals. Future improvements in simulating ENSO will likely require a better simulation of the surface heat flux feedback from the atmosphere as well as the dynamical coupling strength between the atmosphere and ocean. Significance Statement The ENSO phenomenon affects weather and climate worldwide. An interesting aspect of this phenomenon is the asymmetry between its two phases. Previous studies have reported a weaker asymmetry in the simulations by climate models. But these studies have focused on the ENSO asymmetry at the surface. Here by examining the ENSO asymmetry at the surface and the subsurface, we have found that ENSO asymmetry is better simulated in the subsurface than at the surface. We have also identified factors that are responsible for the degradation of the ENSO asymmetry at the surface as well as the remaining weakness in the subsurface, pointing out specific pathways to take to further improve ENSO simulations by coupled climate models.

Analysis of ENSO-Driven Variability, and Long-Term Changes, of Extreme Precipitation Indices in Colombia, Using the Satellite Rainfall Estimates CHIRPS

Climate change includes the change of the long-term average values and the change of the tails of probability density functions, where the extreme events are located. However, obtaining average values are more straightforward than the high temporal resolution information necessary to catch the extreme events on those tails. Such information is difficult to get in areas lacking sufficient rain stations. Thanks to the development of Satellite Precipitation Estimates with a daily resolution, this problem has been overcome, so Extreme Precipitation Indices (EPI) can be calculated for the entire Colombian territory. However, Colombia is strongly affected by the ENSO (El Niño—Southern Oscillation) phenomenon. Therefore, it is pertinent to ask if the EPI’s long-term change due to climate change is more critical than the anomalies due to climate variability induced by the warm and cold phases of ENSO (El Niño and La Niña, respectively). In this work, we built EPI annual time series at each grid-point of the selected Satellite Precipitation Estimate (CHIRPSv2) over Colombia to answer the previous question. Then, the Mann-Whitney-Wilcoxon test was used to compare the samples drawn in each case (i.e., change tests due to both long-term and climatic variability). After performing the analyses, we realized that the importance of the change depends on the region analyzed and the considered EPI. However, some general conclusions became evident: during El Niño years (La Niña), EPI’s anomaly follows the general trend of reduction -drier conditions- (increase; -wetter conditions-) observed in Colombian annual precipitation amount, but only on the Pacific, the Caribbean, and the Andean region. In the Eastern plains of Colombia (Orinoquía and Amazonian region), EPI show a certain insensitivity to change due to climatic variability. On the other hand, EPI’s long-term changes in the Pacific, the Caribbean, and the Andean region are spatially scattered. Still, long-term changes in the eastern plains have a moderate spatial consistency with statistical significance.

Eastern‐Pacific and Central‐Pacific Types of ENSO Elicit Diverse Responses of Vegetation in the West Pacific Region

AbstractThe El Niño‐Southern Oscillation (ENSO) is one of the main factors causing extreme climate events and, thus, has a significant influence on global climate systems. However, the long‐term effects of ENSO on vegetation are not well understood due to the complexity of the ENSO phenomenon under global warming. Here, we examined the variations in the response of Leaf Area Index (LAI) to different types of ENSO from 1982 to 2017 in the west Pacific region and explored their relationship with climatic factors. Results show that about 34.5% of the vegetated area in the west Pacific region displayed LAI anomalies correlated with ENSO index from 1982 to 2017, which distributed differently across climatic types. Moreover, LAI anomalies elicited by the Central‐Pacific type and by the Eastern‐Pacific type of ENSO events were significantly different, which were contributed to the controls of the different types of ENSO on precipitation and temperature.

Complex Oscillatory Dynamics in a Three-Timescale El Ni\~No Southern Oscillation Model

We study the three-timescale dynamics of a model that describes the El Ni\~no Southern Oscillation (ENSO) phenomenon, which was proposed in [A. Roberts, J. Guckenheimer, E. Widiasih, A. Timmermann, and C. K. Jones, Mixed-mode oscillations of El Ni\~no--Southern Oscillation, Journal of the Atmospheric Sciences, 73 (2016), pp. 1755--1766]. While ENSO phenomena are inherently characterised by the presence of multiple distinct timescales, the above model has previously been studied in a two-timescale context only. Here, we uncover the geometric mechanisms that are responsible for complex oscillatory dynamics in a three-timescale regime, and we demonstrate that the system exhibits a variety of qualitatively different behaviours, such as mixed-mode oscillation (MMO) with ``plateaus" -- trajectories where epochs of quiescence alternate with dramatic excursions -- and relaxation oscillation. The latter, although emergent also in the two-timescale context in appropriate parameter regimes, had not been documented previously. Moreover, we show that these mechanisms are relevant to models from other fields of ecological and population dynamics, as the underlying geometry is similar to the unfolding of Rosenzweig--MacArthur-type models in three dimensions.

Assessing the distribution and variation characteristics of marine primary productivity in the coastal marine area of Vietnam South Centre

Primary production (PP) of phytoplankton plays an essential role in food web dynamics, biogeochemical cycles and marine fisheries. It is used as one of the basic information for evaluating marine ecosystems. In this paper, monthly composite PP data on a 4 km x 4 km grid for the period 2003-2020 was used to evaluate the distributional characteristics of PP in the coastal marine area of Vietnam South Centre. The statistical results show that the climatological average of PP in 18 years reached 449.2 mgC/m2/day, ranged from 272.1 to 14,205.4 mgC/m2/day. The PP has seasonal and spatial variations. In time, the lowest value of PP was in spring, and the highest was in winter; in space, PP tended to decrease from shore to offshore, PP was higher in coastal areas than in the open sea areas. During the northeast monsoon season, PP increased by more than 1000 mgC/m2/day in the coastal area. Meanwhile, in the southwest monsoon season, due to the ecological influence of the upwelling phenomenon, PP increased with a value greater than 1500 mgC/m2/day, distributed along the coastline of Ninh Thuan - Binh Thuan. Primary productivity positively correlated with chlorophyll content but negatively correlated with sea surface temperature with correlation coefficients of 0.9 and -0.6, respectively. There was a weak correlation between PP and ONI with correlation coefficients of -0.23. The temporal-spatial variation of PP was affected by the ENSO (El Niño-Southern Oscillation) phenomenon, the positive phase of ENSO (El Niño conditions) corresponded to lower PP, and the negative phase of ENSO (La Niña conditions) corresponded to higher PP. The research results from this paper can be used as a reference in marine ecosystem management.

The Effect of ENSO (El Nino Southern Oscillation) phenomenon on Fishing Season of Small Pelagic Fishes in Indonesia Waters

The El Nino Southern Oscillation (ENSO) phenomenon causes changes in environmental conditions such as water temperature, salinity, and rainfall. In fisheries sector, the changing environment has affected the fishing seasons and Catch per Unit Effort (CPUE) of some pelagic species. This research was conducted by calculating CPUE and fishing season index for several small pelagic fishes in Makassar Strait, Bali Strait, and Aceh waters, then comparing the index value with the fishing season pattern in two extreme periods that are 2010-2011 and 2016 - 2017. An ANOVA test was conducted to assess the significant difference between normal and extreme conditions. The results of the analysis showed that there was a significant different in CPUE between average normal condition and ENSO period. Every single species showed different response to ENSO event, mostly decreased in CPUE relate to El Nino event, except for sardine in Bali and Makassar Strait and scad in Makassar Strait. ENSO affects shift in the fishing season of big-eye scad, scad, sardine, and neritic tuna in Makassar Strait, Bali Strait, and around Aceh waters. Indian mackerel in Makassar Strait showed no change in fishing season but the CPUE showed lower than normal condition. This study shows that ENSO was significantly affected fisheries in Indonesia waters.

Improving prediction quality of sea surface temperature (SST) in Niño3.4 region using Bayesian Model Averaging

Prediction of Sea Surface Temperature (SST) in Niño3.4 region (170 W - 120 W; 5S - 5N) is important as a valuable indicator to identify El Niño Southern Oscillation (ENSO), i.e., El Niño, La Niña, and Neutral condition for coming months. More accurate prediction Niño3.4 SST can be used to determine the response of ENSO phenomenon to rainfall over Indonesia region. SST predictions are routinely released by meteorological institutions such as the European Center for Medium-Range Weather Forecasts (ECMWF). However, SST predictions from the direct output (RAW) of global models such as ECMWF seasonal forecast is suffering from bias that affects the poor quality of SST predictions. As a result, it also increases the potential errors in predicting the ENSO events. This study uses SST from the output Ensemble Prediction System (EPS) of ECMWF seasonal forecast, namely SEAS5. SEAS5 SST is downloaded from The Copernicus Climate Change Service (C3S) for period 1993-2020. One value representing SST over Niño3.4 region is calculated for each lead-time (LT), LT0-LT6. Bayesian Model Averaging (BMA) is selected as one of the post-processing methods to improve the prediction quality of SEAS5-RAW. The advantage of BMA over other post-processing methods is its ability to quantify the uncertainty in EPS, which is expressed as probability density function (PDF) predictive. It was found that the BMA calibration process reaches optimal performance using 160 months training window. The result show, prediction quality of Niño3.4 SST of BMA output is superior to SEAS5-RAW, especially for LT0, LT1, and LT2. In term deterministic prediction, BMA shows a lower Root Mean Square Error (RMSE), higher Proportion of Correct (PC). In term probabilistic prediction, the error rate of BMA, which is showed by the Brier Score is lower than RAW. Moreover, BMA shows a good ability to discriminating ENSO events which indicates by AUC ROC close to a perfect score.

Spatiotemporal Variation in Rainfall Erosivity and Correlation with the ENSO on the Tibetan Plateau since 1971.

Soil erosion is a serious ecological problem in the fragile ecological environment of the Tibetan Plateau (TP). Rainfall erosivity is one of the most important factors controlling soil erosion and is associated with the El Niño southern oscillation (ENSO). However, there is a lack of studies related to the spatial distribution and temporal trends of rainfall erosivity on the TP as a whole. Additionally, the understanding of the general influence of ENSO on rainfall erosivity across the TP remains to be developed. In this study, long-term (1971–2020) daily precipitation data from 91 meteorological stations were selected to calculate rainfall erosivity. The analysis combines co-kriging interpolation, Sen’s slope estimator, and the Mann–Kendall trend test to investigate the spatiotemporal patten of rainfall erosivity across the TP. The Oceanic Niño Index (ONI) and multivariate ENSO Index (MEI) were chosen as ENSO phenomenon characterization indices, and the relationship between ENSO and rainfall erosivity was explored by employing a continuous wavelet transform. The results showed that an increasing trend in annual rainfall erosivity was detected on the TP from 1971 to 2020. The seasonal and monthly rainfall erosivity was highly uneven, with the summer erosivity accounting for 60.36%. The heterogeneous spatial distribution of rainfall erosivity was observed with an increasing trend from southeast to northwest. At the regional level, rainfall erosivity in the southeastern TP was mainly featured by a slow increase, while in the northwest was more destabilizing and mostly showed no significant trend. The rainfall erosivity on the whole TP was relatively high during non-ENSO periods and relatively low during El Niño/La Niña periods. It is worth noting that rainfall erosivity in the northwest TP appears to be more serious during the La Niña event. Furthermore, there were obvious resonance cycles between the rainfall erosivity and ENSO in different regions of the plateau, but the cycles had pronounced discrepancies in the occurrence time, direction of action and intensity. These findings contribute to providing references for soil erosion control on the TP and the formulation of future soil conservation strategies.

Study of ENSO impact on agricultural food crops price as basic knowledge to improve community resilience in climate change

Climate change has become an pivotal issue and impacted on the socio-economic community because it contributes not only to prolonged land droughts and fires in the dry season but also to increased rainfall and flooding in the wet season. One of the climate change phenomena in Indonesia is El Nino Southern Oscillation (ENSO) consisting of El Nino and La Nina. The ENSO phenomenon drives rainfall variability that impacts on the agricultural sector which depends on water availability. The objective of this study was to analyse the impact of El Nino and La Nina on the prices of six agricultural food crops using static panel data. The data used consist of 23 provinces affected by El Nino and La Nina in 2010-2017. Rainfall is used as a proxy for El Nino and La Nina, supported by a negative and significant correlation between Oceanic Nino Index (ONI) and rainfall. The results showed that El Nino has greater impacts on food prices than La Nina by increasing the price of rice, sweet potato, and mung bean. While La Nina has a significant impact in increasing the price of cassava. Considering the importance of food crops for Indonesians, efforts can be made to improve community resilience, such as using adaptive varieties of climate, developing agricultural insurance, time and planting patterns adjustment and agroforestry patterns for communities nearby the forests in the framework of climate change mitigation and adaptation. Ultimately, this study can provide important insights to formulate effective mitigation and adaptation strategies to minimize the climate change impact.