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Academic literature on the topic 'ENSO-index'
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Journal articles on the topic "ENSO-index"
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Lu, Bo, Fei-Fei Jin, and Hong-Li Ren. "A Coupled Dynamic Index for ENSO Periodicity." Journal of Climate 31, no. 6 (2018): 2361–76. http://dx.doi.org/10.1175/jcli-d-17-0466.1.
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El Niño–Southern Oscillation (ENSO) is the most active interannual climatic mode, with great global impacts. The state-of-the-art climate models can simulate this dominant mode variability to a large extent. Nevertheless, some of ENSO’s fundamental time–space characteristics still have a large spread in the simulations across the array of recent climate models. For example, the large biases of ENSO periodicity still exist among model simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5). Based on the recharge oscillator framework, a coupled dynamic index for ENSO periodicity is proposed in this study, referred to as the Wyrtki index, in parallel to the Bjerknes index for ENSO instability. The Wyrtki index provides an approximate dynamic measure for ENSO linear periodicity. It has two main contribution terms: the thermocline and zonal advective feedbacks (or F factor) multiplied by the efficiency factor B of discharging–recharging of the equatorial heat content driven by ENSO wind stress anomalies. It is demonstrated that the diversity of simulated ENSO periodicity in CMIP5 models results from the biases in mean state and several key parameters that control ENSO dynamics. A larger F factor would result in a shorter ENSO period [e.g., BCC_CSM1.1(m)], whereas a smaller B factor would lead to a longer ENSO period (e.g., HadGEM2-ES). The Wyrtki index serves as a useful tool for a quantitative assessment of the sources for ENSO periodicity in reanalysis data and its biases in CMIP5 model simulations.
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Lv, Aifeng, Lei Fan, and Wenxiang Zhang. "Impact of ENSO Events on Droughts in China." Atmosphere 13, no. 11 (2022): 1764. http://dx.doi.org/10.3390/atmos13111764.
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The El Niño Southe58rn Oscillation (ENSO) is a typical oscillation affecting climate change, and its stable periodicity, long-lasting effect, and predictable characteristics have become important indicators for regional climate prediction. In this study, we analyze the Standardized Precipitation Evapotranspiration Index (SPEI), the Niño3.4 index, the Southern Oscillation Index (SOI), and the Multivariate ENSO Index (MEI). Additionally, we explore the spatial and temporal distribution of the correlation coefficients between ENSO and SPEI and the time lag between ENSO events of varying intensities and droughts. The results reveal that the use of Nino3.4, MEI, and SOI produces differences in the occurrence time, end time, and intensity of ENSO events. Nino3.4 and MEI produce similar results for identifying ENSO events, and the Nino3.4 index accurately identifies and describes ENSO events with higher reliability. In China, the drought-sensitive areas vulnerable to ENSO events include southern China, the Jiangnan region, the middle and lower reaches of the Yangtze River, and the arid and semi-arid areas of northwestern China. Droughts in these areas correlate significantly with meteorological drought, and time-series correlations between ENSO events and droughts are significantly stronger in regions close to the ocean. Drought occurrence lags ENSO events: when using the Niño3.4 index to identify ENSO, droughts lag the strongest and weakest El Niño events by 0–12 months. However, when using the MEI as a criterion for ENSO, droughts lag the strongest and weakest El Niño events by 0–7 months. The time lag between the strongest ENSO event and drought is shorter than that for the weakest ENSO event, and droughts have a wider impact. The results of this study can provide a climate-change-compatible basis for drought monitoring and prediction.
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Evans, M. N., R. G. Fairbanks, and J. L. Rubenstone. "A proxy index of ENSO teleconnections." Nature 394, no. 6695 (1998): 732–33. http://dx.doi.org/10.1038/29424.
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Aprilia, Bunga, Marzuki Marzuki, and Imam Taufiq. "Prediksi El Nino Southern Oscillation (ENSO) Menggunakan Jaringan Saraf Tiruan (JST)-Backpropagation." Jurnal Fisika Unand 9, no. 4 (2021): 421–27. http://dx.doi.org/10.25077/jfu.9.4.421-427.2020.
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Penelitian ini bertujuan untuk memprediksi nilai indeks ENSO yaitu Sea Surface Temperature (Nino 1.2, Nino 3, Nino 3.4 dan Nino 4), Southern Oscillation Index (SOI) dan Multivariate ENSO Index versi 2 (MEI.v2) yang diambil dari tahun 1979-2018. Prediksi dilakukan dengan menggunakan metode JST-backpropagation dengan memvariasikan learning rate dan momentum. Semua indeks menghasilkan nilai akurasi prediksi ENSO yang tinggi, namun indeks Nino 4 merupakan indeks yang memiliki akurasi tertinggi karena nilai Mean Square Error (MSE) pelatihan dan pengujiannya yang relatif lebih kecil dibandingkan dengan indeks lainnya. Indeks Nino 4 memiliki MSE pelatihan 0,0072739 yang berhenti pada epoch ke-69 dan MSE pengujian 0,0085917 dengan akurasi prediksi 99,9989%. Hasil ini diperoleh dari arsitektur JST-backpropagation 12-10-1 dengan nilai learning rate 0,10 dan momentum 0,40. Prediksi ENSO berdasarkan indeks Nino 4 untuk tahun 2021 menunjukkan keadaan iklim dunia dalam kondisi normal. This study aims to predict ENSO index using Sea Surface Temperature (Nino 1.2, Nino 3, Nino 3.4 and Nino 4 indexes), Southern Oscillation Index (SOI), and Multivariate ENSO Index version 2 (MEI.v2) during 1979 - 2018. The prediction was carried out using the ANN-backpropagation method by varying the learning rate and momentum. All indices produce high ENSO prediction accuracy values, but the Nino 4 index is the best one because the Mean Square Error (MSE) for training and testing steps are relatively smaller than other indexes. The Nino 4 index has a training MSE of 0.0072739 which stops at the 69th epoch and a testing MSE of 0.0085917 with a predictive accuracy of 99.9989%. These results were obtained from the back-propagation architecture ANN 12-10-1 with a learning rate of 0.10 and a momentum of 0.40. The prediction of ENSO in 2021 based on the Nino 4 index shows that the world climate condition is under normal conditions.
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Westra, Seth, Benjamin Renard, and Mark Thyer. "The ENSO–Precipitation Teleconnection and Its Modulation by the Interdecadal Pacific Oscillation." Journal of Climate 28, no. 12 (2015): 4753–73. http://dx.doi.org/10.1175/jcli-d-14-00722.1.
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Abstract This study evaluates the role of the interdecadal Pacific oscillation (IPO) in modulating the El Niño–Southern Oscillation (ENSO)–precipitation relationship. The standard IPO index is described together with several alternatives that were derived using a low-frequency ENSO filter, demonstrating that an equivalent IPO index can be obtained as a low-frequency version of ENSO. Several statistical artifacts that arise from using a combination of raw and smoothed ENSO indices in modeling the ENSO–precipitation teleconnection are then described. These artifacts include the potentially spurious identification of low-frequency variability in a response variable resulting from the use of smoothed predictors and the potentially spurious modulation of a predictor–response relationship by the low-frequency version of the predictor under model misspecification. The role of the IPO index in modulating the ENSO–precipitation relationship is evaluated using a global gridded precipitation dataset, based on three alternative statistical models: stratified, linear, and piecewise linear. In general, the information brought by the IPO index, beyond that already contained in the Niño-3.4 index, is limited and not statistically significant. An exception is in northeastern Australia using annual precipitation data, and only for the linear model. Stratification by the IPO index induces a nonlinear ENSO–precipitation relationship, suggesting that the apparent modulation by the IPO is likely to be spurious and attributable to the combination of sample stratification and model misspecification. Caution is therefore required when using smoothed climate indices to model or explain low-frequency variability in precipitation.
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Lin, Chen-Chih, Yi-Jiun Liou, and Shih-Jen Huang. "Impacts of Two-Type ENSO on Rainfall over Taiwan." Advances in Meteorology 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/658347.
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Impacts of two-type ENSO (El Niño/Southern Oscillation), canonical ENSO and ENSO Modoki, on rainfall over Taiwan are investigated by the monthly mean rainfall data accessed from Taiwan Central Weather Bureau. The periods of the two-type ENSO are distinguished by Niño 3.4 index and ENSO Modoki index (EMI). The rainfall data in variously geographical regions are analyzed with the values of Niño 3.4 and EMI by correlation method. Results show that the seasonal rainfalls over Taiwan are different depending on the effects of two-type ENSO. In canonical El Niño episode, the rainfall increases in winter and spring while it reduces in summer and autumn. On the contrary, the rainfall increases in summer and autumn but reduces in winter and spring in El Niño Modoki episode. Nevertheless, two types of La Niña cause similar effects on the rainfall over Taiwan. It increases in autumn only. The rainfall variations in different types of ENSO are mainly caused by the monsoon and topography.
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Koem, S., R. J. Lahay, and S. K. Nasib. "The sensitivity of meteorological drought index towards El Nino-Southern Oscillation." IOP Conference Series: Earth and Environmental Science 1089, no. 1 (2022): 012005. http://dx.doi.org/10.1088/1755-1315/1089/1/012005.
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Abstract El Nino-Southern Oscillation (ENSO) contributes to the regional climates, such as precipitation and droughts. The objectives of the present work were to: (1) identify the severity index; (2) analyze the correlation of SPI and RDI, and; (3) identify the response of SPI and RDI towards ENSO. SPI and RDI were calculated for time scales (3, 6, and 12 months), and these represented the seasonal and annual drought. The identification of the responses of the drought severity index, based on ENSO, consisted of several thresholds, namely weak, moderate, and strong. The correlational value and RMSE only represented the performance of SPI and RDI on different time scales. The drought severity index would decline along with an increase in the time scales. The strong El Nino phase could be significant to the seasonal and annual drought. In other words, ENSO was impactful on the precipitation and dynamics of drought. Drought periods were due to the moderate and strong El Nino phase, while the weak phase led to a normal condition. For this reason, ENSO could be functioned as an indicator to predict drought.
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Ziemke, J. R., S. Chandra, L. D. Oman, and P. K. Bhartia. "A new ENSO index derived from satellite measurements of column ozone." Atmospheric Chemistry and Physics Discussions 10, no. 2 (2010): 2859–87. http://dx.doi.org/10.5194/acpd-10-2859-2010.
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Abstract. Column Ozone measured in tropical latitudes from Nimbus 7 TOMS, Earth Probe TOMS, NOAA SBUV, and Aura OMI satellite instruments are used to derive an El Niño-Southern Oscillation (ENSO) index. This index, which covers a time period from 1979 to the present, is defined as the Ozone ENSO Index (OEI) and is the first developed from atmospheric trace gas measurements. Using a data mining technique with existing ENSO indices of surface pressure and sea-surface temperature, the OEI is constructed by first averaging monthly mean column ozone over two broad regions in the western and eastern Pacific and taking their difference. This differencing yields a self-calibrating ENSO index which is independent of individual instrument calibration offsets and drifts in measurements over the long record. The combined Aura OMI and MLS ozone data confirm that zonal variability in total column ozone in the tropics caused by ENSO events lies almost entirely in the troposphere. As a result, the OEI can be derived directly from total column ozone instead of tropospheric column ozone. For clear-sky ozone measurements a +1 K change in Nino 3.4 index corresponds to +2.9 DU (Dobson Unit) change in the OEI, while a +1 hPa change in SOI coincides with a −1.7 DU change in the OEI. For ozone measurements under all cloud conditions these numbers are +2.4 DU and −1.4 DU, respectively. As an ENSO index based upon ozone, it is potentially useful in evaluating climate models predicting long term changes in ozone and other trace gases.
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Xu, Zhen, and G. Cornelis van Kooten. "The El Niño Southern Oscillation index and wildfire prediction in British Columbia." Forestry Chronicle 90, no. 05 (2014): 592–98. http://dx.doi.org/10.5558/tfc2014-122.
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This study investigates the potential to predict monthly wildfires and area burned in British Columbia's interior using El Niño Southern Oscillation (ENSO). The zero-inflated negative binomial (ZINB) and the generalized Pareto (GP) distributions are used, respectively, to account for uncertainty in wildfire frequency and area burned. Results indicate that a four-month lag of the ENSO index has a strong positive influence on monthly wildfire occurrence. Upon fitting the GP distribution with a logit model regressed on the ENSO index, we predict the probabilities that monthly area burned exceeds 1700 ha and find that risks of large fires are significantly higher in northwestern BC. However, the ENSO is likely unable to provide consistent predictions of the total area burned in any month. Sensitivity analysis indicates that increases in the mean value of the monthly ENSO index result in a small increase in the predicted number of fires and an increase in the probability of large burns. This study has several implications for decision-making regarding firefighting budget planning and insurance for firefighting expenditures.
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Beckers, Joost V. L., Albrecht H. Weerts, Erik Tijdeman, and Edwin Welles. "ENSO-conditioned weather resampling method for seasonal ensemble streamflow prediction." Hydrology and Earth System Sciences 20, no. 8 (2016): 3277–87. http://dx.doi.org/10.5194/hess-20-3277-2016.
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Abstract. Oceanic–atmospheric climate modes, such as El Niño–Southern Oscillation (ENSO), are known to affect the local streamflow regime in many rivers around the world. A new method is proposed to incorporate climate mode information into the well-known ensemble streamflow prediction (ESP) method for seasonal forecasting. The ESP is conditioned on an ENSO index in two steps. First, a number of original historical ESP traces are selected based on similarity between the index value in the historical year and the index value at the time of forecast. In the second step, additional ensemble traces are generated by a stochastic ENSO-conditioned weather resampler. These resampled traces compensate for the reduction of ensemble size in the first step and prevent degradation of skill at forecasting stations that are less affected by ENSO. The skill of the ENSO-conditioned ESP is evaluated over 50 years of seasonal hindcasts of streamflows at three test stations in the Columbia River basin in the US Pacific Northwest. An improvement in forecast skill of 5 to 10 % is found for two test stations. The streamflows at the third station are less affected by ENSO and no change in forecast skill is found here.