Nino3 Region Research Articles

The Impact of ENSO on the Precipitation in Iraq Regions

Abstract Precipitation is considered one of the important weather variables in determining the climate of any region in the world, and perhaps the precipitation in Iraq is witnessing an incomprehensible seasonal fluctuation. The work tries to identify the reasons for this fluctuation, from one season to another. The monthly average precipitation data in Iraq was obtained by Global Precipitation Climatology Project GPCP. Also, data for sea surface temperature anomalies (SST) for the four El Nino regions, as well as relative humidity and zonal wind speed were obtained from the National Centres for Environmental Prediction NCEP and National Centre for Atmospheric Research NCAR. The study period extended from 1980 to 2022, where the correlation of monthly precipitation average in Iraq with El Nino and La Niña events was tested, and it was found that there was a positive proportional effect of the El Nino event on the increase in rainfall rates in Iraq, the correlation coefficient R about 0.8 in North and Northern-East of Iraq. To explain this, the correlation between El Niño events, relative humidity, and zonal wind speed was tested. It was found that there is a proportional effect of El Nino on the relative humidity and an inverse effect on the zonal wind speed. Central and southern Iraq is less affected by El Niño and La Niña events, as the correlation coefficient of monthly precipitation average with El Niño phenomenon is approximately 0.5 to 0.6.

HiGRN: A Hierarchical Graph Recurrent Network for Global Sea Surface Temperature Prediction

Sea surface temperature (SST) is one critical parameter of global climate change, and accurate SST prediction is important to various applications, e.g., weather forecasting, fishing directions, and disaster warnings. The global ocean system is unified and complex, and the SST patterns in different oceanic regions are highly diverse and correlated. However, existing data-driven SST prediction methods mainly consider the local patterns within a certain oceanic region, e.g., El Nino region and the Black sea. It is challenging but necessary to model the global SST correlations rather than that in a specific region to enhance the prediction accuracy of SST. In this work, we proposed a new method called Hierarchical Graph Recurrent Network (HiGRN) to address the issue. First, to learn the dynamic and diverse local SST patterns of specific locations, we design an adaptive node embedding with self-learned parameters to learn various SST patterns. Then we develop a hierarchical cluster generator to aggregate the locations with similar patterns into regional clusters and utilize a graph convolution network to learn the spatial correlations among these clusters. Finally, we introduce a multi-level attention mechanism to fuse the local patterns and regional correlations, and the output is fed into a recurrent network to achieve SST predictions. Extensive experiments on two real-world datasets show that our method largely outperforms the state-of-the-art SST prediction methods. The source code is available at https://github.com/Neoyanghc/HiGRN .

El Niño‐Southern Oscillation Modulation of Springtime Diurnal Rainfall Over a Tropical Indian Ocean Island

AbstractThis study analyzes the interannual variability of boreal springtime (March and April) diurnal rainfall (hereafter SDR) over the second biggest Island of the Indian Ocean, Sri Lanka (SL), and expresses the possible effects of the El Niño Southern Oscillation (ENSO) events in modulating this variability. The analysis is primarily based on high spatiotemporal resolution satellite precipitation estimates and reanalysis data from 2001 to 2019. Results indicate that the SDR in SL exhibited a consistent afternoon peak throughout the study period. In contrast, the SDR's amplitude consists of a notable 2 to 4‐year oscillation period, similar to the oscillation period of ENSO events. Further analysis revealed that the interannual variation of the SDR's amplitude in SL has a negative relationship with the springtime and previous winter sea surface temperature changes in the central and eastern tropical Pacific Ocean covering NINO3.4 and NINO3 regions (referred to as ENSO indices). When there is a winter La Niña (El Niño), the following SDR in SL is more (less) active. A possible explanation is that during La Niña (El Niño) years, the region around SL experienced an enhanced (suppressed) cyclonic circulation, enhanced (suppressed) large‐scale moisture flux convergence, and enhanced (suppressed) local‐scale diurnal varying moisture flux convergence, leading to an increase (decrease) in the potential for rainfall and more (less) active SDR over SL. These findings highlight the potential to use ENSO indices to predict the interannual variation of SDR activities over SL.

Satellite Oceanographic Data Processing and Analysis: Correlation Between Nino 3.4 Sea Surface Temperature & Sea Surface High and Wind

El Nino Southern Oscillation (ENSO) is an irregular climate oscillation induced by sea surface temperature anomalies (SSTA) in the Equatorial Pacific Ocean. An anomalous warming in this area is known as El Nino, while an anomalous cooling bears the name of La Nina. The objectives of this study are to: reproduce Oceanic Nino Index (ONI) based on MW OISST; produce Nino Region 3.4 wind and Sea Surface High (SSH); analyze the correlation between SST and Wind & SSH; discuss Typhoon Soudelor based on SST, Wind, and SSH; and analyze the correlation of El Nino and Precipitation in specific area. MWOI-SST was used to produce monthly mean SST over Nino 3.4 region from January 1998 – May 2020. Monthly wind data was obtained from QuickScat. Daily Sea Surface High (SSH) was obtained from Copernicus Marine Environment Monitoring Service (CMEMS). Daily precipitation from TRMM 3B42 over Bandung City (Indonesia) was used to assess the correlation between El Nino and precipitation in specific area. The results show that in Nino 3.4 region, 2015 is the hottest year during 1998-2020 period with average SST of 28.5oC, and 1999 is the coldest year with average SST of 25.7oC. The result shows that the MWOI-SST Ocean Nino Index has very strong correlation with ERSST.v5 with coefficient of correlation is 0.92 and RMSE is 0.36oC. the wind speed of Nino 3.4 region is range from 5.23 m/s to 7.97 m/s. Unlike Sea Surface Temperature (SST), annual average wind speed is more stable with monthly variation. The wind speed is observed high in the beginning and the end of years. Sea Surface Height (SSH) over Nino 3.4 region varied from 65.8 cm to 106.8 cm. 2015 is the highest SSH with annual average of 96 cm, whereas 1999 is the lowest SSH with annual average of 71.4 cm. It is observed that Sea Surface Temperature (SST) has negative correlation with wind speed with coefficient of correlation of 0.28. Conversely, Sea Surface Temperature (SST) over Nino 3.4 region has positive correlation with Sea Surface High (SSH) with coefficient of correlation of 0.30. which mean the higher temperature, the higher Sea Surface Height. During the passage of Typhoon Soudelor, there is evident cool trail along its track with rightward bias. We can assume that the decreasing precipitation in Bandung City might be affected by strong El Nino occurrence in 2015.

Probabilistic Evaluation of the Multicategory Seasonal Precipitation Re-Forecast

The Meteo-France seasonal forecasting system 7 provides a 7-month forecast range with 25 ensembles. The seasonal precipitation re-forecast (from May to November 1993–2015) was evaluated by the Brier score in terms of accuracy and reliability based on tercile probabilities. Multiple analyses were performed to assess the robustness of the score. These results show that the spatial distribution of the Brier score depends significantly on tercile thresholds, reference data, sampling methods, and ensemble types. Large probabilistic errors over the dry regions on land and the Nino regions in the Pacific can be reduced by adjusting the tercile thresholds. The forecast errors were identified when they were insensitive to different analysis methods. All the analyses detected that the errors increase/decrease with the lead time over the tropical Indian/Pacific Ocean. The intra-seasonal analysis reveals that some of these errors are inherited from monthly forecasts, which may be related to large-scale, short-term variability modes. A new confidence interval calculation was formulated for the “uncertain” case in the reference data. The confidence interval at a 95% level for the mean Brier score over the entire tropical region was quantified. The best estimations are ~6% the mean Brier score for both the above and below-normal terciles.

Development of a new ENSO index to assess the effects of ENSO on temperature over southern Vietnam

The aim of this study was to assess the effects of El Nino and the Southern Oscillation (ENSO) on the temperature in southern Vietnam (TSV) and to establish a new ENSO index for the study area (named southern Vietnam Enso Index, VEI). Data used in this study included TSV, sea surface temperature (SST) in Nino regions, sea level pressure (SLP) at Tahiti and Darwin, and ENSO indices. The results showed that, among all ENSO indices, the oceanic Nino Index (ONI) and Japan Meteorological Agency ENSO index (JEI) gave a better correlation with TSV, but their correlation coefficients (R) were relatively low. That may suggest that these indices could be used with low efficiency for climate monitoring and prediction. The VEI was built based on the pair of SST in Nino 3.4 and Nino.West, which gained a higher correlation coefficient with the temperature in southern Vietnam in comparison with other ENSO indices. The lag time between VEI and TSV was about 5 months, which was one month longer than the one between ONI and TSV. Moreover, analyzing the temperature difference between the warm and cold phases also indicated that using VEI led to a higher contrast than using ONI. In brief, the newly established VEI could be more suitable for the study area.

Accounting for the surface temperature persistence by using signal energy

The autocorrelation function (ACF) and its finite Fourier transform, referred to as signal energy, have been investigated using the ECMWF daily surface temperature data. ACF itself provides a measure of the influence of leading fluctuation between two different time points. Considering the decay of ACF, it is found that the scaling power-rule of ACF is only valid in a very short period, as the decay of ACF exists before it reaches a random noise state. Therefore, the method of the critical exponent of ACF is limited in the short length of the temporal interval. On the other hand, the distributions of the signal energy always show nice patterns, indicating the degree of persistence change. It is found, for a short period, that the distributions of the signal energy and the critical exponent are very similar, with a correlation coefficient over 0.97. For a longer period, though the critical exponent of ACF becomes invalid, the signal energy can always provide an effective method to investigate climate persistence in different lengths of time. In a 5-day period of boreal winter, the southern part of North America has a larger value of signal energy compared to the northern part; thus, the surface temperature is more stable in the north part. The result becomes opposite in the boreal summer. The method of signal energy can also be applied to a particular interval of time. In different temporal intervals, the signal energy presents very different results, especially over the El Nino regions

Role of thermocline feedback in the increasing occurrence of Central Pacific ENSO

Monthly mean sea level variations computed using a linear, reduced-gravity, multi-mode model are combined with satellite measurements to explore why Central Pacific (CP) ENSO events occur more frequently since 2000s. The pivot point for sea level (and hence thermocline) variations has shifted westward in response to an increase in zonal wind stress variance in the western equatorial Pacific. As a result, the Nino4 region is increasingly to the east of the pivot point enabling the thermocline feedback to operate there, strengthening the Bjerknes feedback mechanism in the Nino4 region and leading to an increase in the occurrence of CP events. The increased variance of wind stress in the western Pacific is, in turn, caused by the resulting increase in the frequency of CP events. These arguments imply a positive feedback in which CP events are self-maintaining and suggest that they may be part of the natural variability of the climate system and could occur without the need for changes in external forcing.

Boreal-winter teleconnections with tropical Indo-Pacific rainfall in HighResMIP historical simulations from the PRIMAVERA project

This study investigates how teleconnections linking tropical rainfall anomalies and wintertime circulation in the northern extra-tropics are represented in historical simulations for the period 1950–2010 run by partners of the EU-funded PRIMAVERA project, following the HighResMIP protocol of CMIP6. The analysis focusses on teleconnections from the western/central Indian Ocean in mid-winter and from the NINO4 region in both the early and the late part of winter; this choice is justified by a substantial change in the relationship between ENSO and the North Atlantic Oscillation (NAO) in the two parts of the season. Model results for both coupled integrations and runs with prescribed sea-surface temperature (SST) are validated against data from the latest ECMWF 20th-century re-analysis, CERA20C. Simulations from six modelling groups are considered, comparing the impact of increasing atmospheric resolution in runs with prescribed SST, and of moving from uncoupled to coupled simulations in the high-resolution version of each model. Single runs were available for each model configurations at the time of writing, with one centre (ECMWF) also providing a 6-member ensemble. Results from this ensemble are compared with those of a 6-member multi-model ensemble (MME) formed by including one simulation from each model. Using only a single historical simulation from each model configuration, it is difficult to detect a consistent change in the fidelity of model-generated teleconnections when either atmospheric resolution is increased or ocean coupling is introduced. However, when simulations from six different models are pooled together in the MME, some improvements in teleconnection patterns can be seen when moving from uncoupled to coupled simulations. For the ECMWF ensemble, improvements in the coupled simulations are only apparent for the late-winter NINO4 teleconnection. While the Indian Ocean teleconnection and the late-winter NINO4 teleconnection appear equally robust in the re-analysis record, the latter is well simulated in the majority of both uncoupled and coupled runs, while the former is reproduced with (generally) much larger errors, and a high degree of variability between individual models and ensemble members. Most of the simulations with prescribed SST fail to produce a realistic estimate of multi-decadal changes between the first and the second part of the 60-year record. This is (at least partially) due to their inability to simulate an Indian Ocean rainfall change which, in observations, has a zonal gradient out of phase with SST changes. In coupled runs, at least one model run with both realistic teleconnections and a good simulation of the inter-decadal pattern of Indian Ocean rainfall also shows a realistic NAO signal in extratropical multi-decadal variability.

Estimation of Monsoon Seasonal Precipitation Teleconnection with El Niño-Southern Oscillation Sea Surface Temperature Indices over the Western Ghats of Karnataka

The Western Ghats (WG) of India are basically north-to-south oriented mountains with three distinct meteorological divisions. These mountains exhibit the characteristic features of precipitation and distribution during the summer monsoon season and possess latitudinal variations. It is a well-known fact that sea surface temperature (SST) combined with the El Nino-Southern Oscillation (ENSO) enacts a predominant role in the precipitation over the entire Western Ghats during the summer monsoon season. Whereas the Nino regions affect the variability of the Western Ghats’ precipitation in an asymmetric relationship. Nevertheless, the simulation of precipitation has been evidenced to be difficult. The current study attempts to predict the seasonal precipitation over the coastal region and the Western Ghats of Karnataka. The relationship between summer monsoon precipitation (SMP) and SST is examined up to eight seasons by conducting the correlation analysis with three seasons that lag before the onset of the monsoon season. The significant and positively correlated lagged Nino indices with the SMP index are identified as the predictors. The selected predictors are used for predicting the SMP by using statistical models, the multiple linear regression model and the artificial neural network (ANN) model. The statistical models are based on the combined lagged indices and the principle component as the predictor. The results of the statistical models on comparison suggest that neural network models have a better predictive skill than the linear regression models. Neural network models with combined lagged indices being used as predictors are slightly better, but a few more climatic parameters must be verified and the usage of this method on other meteorological divisions of the West Coast of India needs to be further investigated.

Influence of Intraseasonal Oscillation on the Asymmetric Decays of El Niño and La Niña

Warm and cold phases of El Nino–Southern Oscillation (ENSO) exhibit a significant asymmetry in their decay speed. To explore the physical mechanism responsible for this asymmetric decay speed, the asymmetric features of anomalous sea surface temperature (SST) and atmospheric circulation over the tropical Western Pacific (WP) in El Nino and La Nina mature-to-decay phases are analyzed. It is found that the interannual standard deviations of outgoing longwave radiation and 850 hPa zonal wind anomalies over the equatorial WP during El Nino (La Nina) mature-to-decay phases are much stronger (weaker) than the intraseasonal standard deviations. It seems that the weakened (enhanced) intraseasonal oscillation during El Nino (La Nina) tends to favor a stronger (weaker) interannual variation of the atmospheric wind, resulting in asymmetric equatorial WP zonal wind anomalies in El Nino and La Nina decay phases. Numerical experiments demonstrate that such asymmetric zonal wind stress anomalies during El Nino and La Nina decay phases can lead to an asymmetric decay speed of SST anomalies in the central-eastern equatorial Pacific through stimulating different equatorial Kelvin waves. The largest negative anomaly over the Nino3 region caused by the zonal wind stress anomalies during El Nino can be threefold greater than the positive Nino3 SSTA anomalies during La Nina, indicating that the stronger zonal wind stress anomalies over the equatorial WP play an important role in the faster decay speed during El Nino.

Identification Standard for ENSO Events and Its Application to Climate Monitoring and Prediction in China

The El Nino–Southern Oscillation (ENSO) reflects anomalous variations in the sea surface temperature (SST) and atmospheric circulation over the tropical central–eastern Pacific. It remarkably impacts on weather and climate worldwide, so monitoring and prediction of ENSO draw intensive research. However, there is not yet a unique standard internationally for identifying the timing, intensity, and type of ENSO events. The National Climate Center of China Meteorological Administration (NCC/CMA) has led the effort to establish a national identification standard of ENSO events, which was officially endorsed by the National Standardization Administration of China and implemented operationally in NCC/CMA in 2017. In this paper, two key aspects of this standard are introduced. First, the Nino3.4 SST anomaly index, which is well-recognized in the international ENSO research community and used operationally in the US, has replaced the previous Nino Z index and been used to identify the start, end, and peak times, and intensity of ENSO events. Second, two new indices—the eastern Pacific ENSO (EP) index and the central Pacific ENSO (CP) index, based on the SST conditions in Nino3 and Nino4 region respectively, are calculated to first determine the ENSO type before monitoring and assessing the impacts of ENSO on China’s climate. With this standard, all historical ENSO events since 1950 are consistently re-identified; their distinct properties are diagnosed and presented; and the impacts of ENSO events under different types on China’s climate are re-assessed. This standard is also employed to validate the intensity, grade, and type of the ENSO events predicted by the NCC/CMA operational ENSO prediction system. The new standard and the thus derived unified set of re-analyzed historical ENSO events and associated information provide a good reference for better monitoring and prediction of future ENSO events.

The dominant role of the atmospheric component of coupled models in ENSO amplitude simulations

To explore the relative contributions of the atmospheric and oceanic components of coupled models to ENSO amplitude simulations, we innovatively “assembled” four coupled models and performed analyses on their ENSO simulations. Specifically, the atmospheric and oceanic components of two commonly used coupled models are cross-coupled to construct four parent models. Based on the simulated ENSO amplitude, the four parent models are classified into two groups: Grid-point Atmospheric Model of IAP LASG Version 2 (GAMIL2)-based models whose ENSO amplitudes are comparable to (although slightly weaker than) observations, and Community Atmosphere Model Version 4.0 (CAM4)-based models whose ENSO amplitudes reach up to twice those of observations. The BJ-index analysis reveals that the atmospheric components modulate ENSO amplitude by affecting the atmospheric thermodynamic (TD) feedback and the oceanic thermocline (TH) feedback. The TD feedback biases in the CAM4-based models are attributable to an overly negative low-cloud fraction feedback and low-cloud liquid water feedback in the Nino-3 region. The underestimated TH feedback in the GAMIL2-based models is due to an underestimated mean upwelling ( $$\bar {w}$$ ), while the seemingly accurate TH term in the CAM4-based models is the result of compensation by an overestimated regression of zonal tilt of the thermocline on the equatorial zonal wind stress ( $${\beta _h}$$ ) and an underestimated $$\bar {w}$$ . Furthermore, $${\beta _h}$$ dominates TH differences in the two atmospheric groups, and is mainly associated with the normalized wind stress anomaly over the Nino-4 region and the vertical ocean subsurface temperature structure.

Subseasonal Reversal of East Asian Surface Temperature Variability in Winter 2014/15

Although there has been a considerable amount of research conducted on the East Asian winter-mean climate, subseasonal surface air temperature (SAT) variability reversals in the early and late winter remain poorly understood. In this study, we focused on the recent winter of 2014/15, in which warmer anomalies dominated in January and February but colder conditions prevailed in December. Moreover, Arctic sea-ice cover (ASIC) in September–October 2014 was lower than normal, and warmer sea surface temperature (SST) anomalies occurred in the Nino4 region in winter, together with a positive Pacific Decadal Oscillation (PDO|+) phase. Using observational data and CMIP5 historical simulations, we investigated the PDO|+ phase modulation upon the winter warm Nino4 phase (autumn ASIC reduction) influence on the subseasonal SAT variability of East Asian winter. The results show that, under a PDO|+ phase modulation, warm Nino4 SST anomalies are associated with a subseasonal delay of tropical surface heating and subsequent Hadley cell and Ferrel cell intensification in January–February, linking the tropical and midlatitude regions. Consistently, the East Asian jet stream (EAJS) is significantly decelerated in January–February and hence promotes the warm anomalies over East Asia. Under the PDO|+ phase, the decrease in ASIC is related to cold SST anomalies in the western North Pacific, which increase the meridional temperature gradient and generate an accelerated and westward-shifted EAJS in December. The westward extension of the EAJS is responsible for the eastward-propagating Rossby waves triggered by declining ASIC and thereby favors the connection between ASIC and cold conditions over East Asia.

On the Surface Independent Signals Within the ENSO Events

By analyzing the two types of El Nino Southern-Oscillation (ENSO) indices, i.e., the Central Pacific (CP) type index and the Eastern Pacific (EP) type index by Ren and Jin (2011), this study finds that the low correlation between the two types of indices by some previous studies should be reconsidered. Then based on previous ideas of the unified Nino index systems, the new ocean surface regions for the CP and EP El Nino indices’ calculation are defined. The features of the new CP and EP El Nino indices are consistent with sea surface temperature anomaly (SSTA) evolution along the Pacific equator. This study suggests that, concerning the El Nino characteristics, the CP and EP El Nino indices are not necessarily independent of each other; but their differences are almost absolutely independent of the unified Nino region SSTA. The results quantitatively confirm the relationship between the Trans-Nino Index (TNI) and Nino 3.4 indices (which are nearly independent of each other and provide different flavors for each El Nino event). Results presented here contribute to a better understanding of the nature of the El Nino events.

Latitudinal variation in summer monsoon rainfall over Western Ghat of India and its association with global sea surface temperatures

The Western Ghats (WG) of India are basically north-south oriented mountains having narrow zonal width with a steep rising western face. The summer monsoon winds during June to September passing over the Arabian Sea are obstructed by the WG and thus orographically uplift to produce moderate-to-heavy precipitation over the region. However, it is seen that characteristic features of rainfall distribution during the season vary from north to south. Also its correlation with all-India summer monsoon rainfall increases from south to north. In the present study, an attempt is also made to examine long-term as well as short-term trends and variability in summer monsoon rainfall over different subdivisions of WG using monthly rainfall data for the period 1871–2014. Konkan & Goa and Coastal Karnataka show increase in rainfall from 1871 to 2014 in all individual summer monsoon months. Short-term trend analysis based on 31-year sliding window indicates that the trends are not monotonous, but has epochal behavior. In recent epoch, magnitudes of negative trends are consistently decreasing and have changed its sign to positive during 1985–2014.It has been observed that Indian Ocean Dipole (IOD) plays a dominant positive role in rainfall over entire WG in all summer monsoon months, whereas role of Nino regions are asymmetric over WG rainfall. Indian summer monsoon is known for its negative relationship with Nino SST. Negative correlations are also seen for WG rainfall with Nino regions but only during onset and withdrawal phase. During peak monsoon months July and August subdivisions of WG mostly show positive correlation with Nino SST.

Mean-state dependence of ENSO atmospheric feedbacks in climate models

We investigate the dependence of ENSO atmospheric feedbacks on the mean-state in a perturbed atmospheric physics ensemble with the Kiel Climate Model (KCM) and in CMIP5 models. Additionally, uncoupled simulations are conducted with the atmospheric component of the KCM to obtain further insight into the mean-state dependence. It is found that the positive zonal wind feedback and the negative heat flux feedback, with the short-wave flux as dominant component, are strongly linearly related through sea surface temperature (SST) and differences in model physics are less important. In observations, strong zonal wind and heat flux feedbacks are caused by a convective response in the western central equatorial Pacific (Nino4 region), resulting from an eastward (westward) shift of the rising branch of the Walker Circulation (WC) during El Nino (La Nina). Many state-of-the-art climate models exhibit an equatorial cold SST bias in the Nino4 region, i.e. are in a La Nina-like mean-state. Therefore they simulate a too westward located rising branch of the WC (by up to 30°) and only a weak convective response. Thus, the position of the WC determines the strength of both the amplifying wind and usually damping heat flux feedback, which also explains why biases in these two feedbacks partly compensate in many climate models. Furthermore, too weak atmospheric feedbacks can cause quite different ENSO dynamics than observed, while enhanced atmospheric feedbacks lead to a substantial improvement of important ENSO properties such as seasonal ENSO phase locking and asymmetry between El Nino and La Nina. Differences in the mean-state SST are suggested to be a major source of ENSO diversity in current climate models.

Effects of ocean initial perturbation on developing phase of ENSO in a coupled seasonal prediction model

Coupled prediction systems for seasonal and inter-annual variability in the tropical Pacific are initialized from ocean analyses. In ocean initial states, small scale perturbations are inevitably smoothed or distorted by the observational limits and data assimilation procedures, which tends to induce potential ocean initial errors for the El Nino-Southern Oscillation (ENSO) prediction. Here, the evolution and effects of ocean initial errors from the small scale perturbation on the developing phase of ENSO are investigated by an ensemble of coupled model predictions. Results show that the ocean initial errors at the thermocline in the western tropical Pacific grow rapidly to project on the first mode of equatorial Kelvin wave and propagate to the east along the thermocline. In boreal spring when the surface buoyancy flux weakens in the eastern tropical Pacific, the subsurface errors influence sea surface temperature variability and would account for the seasonal dependence of prediction skill in the NINO3 region. It is concluded that the ENSO prediction in the eastern tropical Pacific after boreal spring can be improved by increasing the observational accuracy of subsurface ocean initial states in the western tropical Pacific.

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

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

Accounting for centennial-scale variability when detecting changes in ENSO: A study of the Pliocene

The El Nino Southern Oscillation (ENSO) is the dominant mode of interannual climate variability. However, climate models are inconsistent in future predictions of ENSO, and long term variations in ENSO cannot be quantified from the short instrumental records available. Here we analyse ENSO behaviour in millennial-scale climate simulations of a warm climate of the past, the mid-Pliocene Warm Period (mPWP; ∼3.3 − 3.0Ma). We consider centennial-scale variability in ENSO for both the mPWP and the preindustrial, and consider which changes between the two climates are detectable above this variability. We find that El Nino typically occurred 12% less frequently in the mPWP but with a 20% longer duration, and with stronger amplitude in precipitation and temperature. However low frequency variability in ENSO meant that Pliocene-preindustrial changes in El Nino temperature amplitude in the NINO3.4 region (5° N-5° S, 170° W-120° W) were not always detectable. The Pliocene-preindustrial El Nino temperature signal in the NINO4 region (5° N-5° S, 160° E-150° W) and the El Nino precipitation signal are usually larger than centennial scale variations of El Nino amplitude, and provide consistent indications of ENSO amplitude change. The enhanced mPWP temperature signal in the NINO4 region is associated with an increase in Central Pacific El Nino events similar to those observed in recent decades and predicted for the future. This study highlights the importance of considering centennial scale variability when comparing ENSO changes between two climate states. If centennial scale variability in ENSO has not first been established, results suggesting changes in ENSO behaviour may not be robust.