El Nino And The Southern Oscillation Research Articles

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.

Representation of Tropical Cyclones by the Modern-Era Retrospective Analysis for Research and Applications Version 2

This study examines the veracity of the tropical cyclone (TC) statistics estimated from the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) global atmospheric reanalysis, focusing on the climatological-mean genesis regions, tracks and their lifetime maximum intensity, as well as the interannual and intraseasonal variations in TC activity. The results are validated against the International Best Track Archive for Climate Stewardship (IBTrACS) data for 1980–2016. MERRA-2 represents the spatial distribution of the TC genesis location and the tracks realistically over all main development regions (MDRs), but the simulated TCs are initiated at lower latitudes closer to the equator compared with the observations. Over the western North Pacific and the North Atlantic, MERRA-2 underestimates recurving TCs steered by background westerlies in the mid-latitudes and thereby exaggerates northwestward moving TCs, resulting in an overestimate of the landfall probability in East Asia and North America. Excessive development of TCs over the Bay of Bengal also tends to exaggerate the landfall probability in India. In spite of the discrepancies in the annual TC number, the seasonal variation of TC genesis is realistic in MERRA-2. MERRA-2 also captures the TC intensity relationship between the minimum pressure and the maximum surface wind speed at the mature stage, although the maximum intensity is weaker than in the observations. While MERRA-2 tends to reproduce the interannual variations of the observed TC number and the power dissipation index (PDI), the level of accuracy varies by each ocean basin. MERRA-2 describes the changes in the TC genesis region and tracks realistically according to the different phases of El Nino and the Southern Oscillation (ENSO) and the Madden-Julian Oscillation (MJO), although it is less realistic over the North Indian Ocean.

Hybrid Causality Analysis of ENSO’s Global Impacts on Climate Variables Based on Data-Driven Analytics and Climate Model Simulation

Numerous studies have indicated that El Nino and the Southern Oscillation (ENSO) could have determinant impacts on remote weather and climate using the conventional correlation-based methods, which however cannot identify the cause-and-effect of such linkage and ultimately determine a direction of causality. This study employs the Vector Auto-Regressive (VAR) model estimation method with the long-term observational sea surface temperature (SST) data and the NCEP/NCAR reanalysis data to demonstrate the Granger causality between ENSO and other climate attributes. Results showed that ENSO as the modulating factor can result in abnormal surface temperature, pressure, precipitation and wind circulation remotely, not vice versa. We also carry out the global climate model sensitivity simulations using the parallel computing techniques to double confirm the causality relations between ENSO and abnormal events in remote regions. Our statistical and climate model-based analyses may enrich our current understanding on the occurrences of extreme events worldwide caused by different ENSO strengths through teleconnections.

Improved seasonal predictive skill and enhanced predictability of the Asian summer monsoon rainfall following ENSO events in NCEP CFSv2 hindcasts

The dominant modes of the Asian summer monsoon (ASM) rainfall variability, as well as their seasonal predictive skill and predictability, are investigated using two sets of seasonal hindcasts made with the NCEP Climate Forecast System (CFSv2): one from the NCEP CFS Reanalysis and Reforecast Project (CFS_RR) and the other using a Multi-ocean Analyses Ensemble initialization scheme (CFS_MAE). The 1st and 2nd empirical orthogonal function (EOF) modes of the observed ASM rainfall anomalies correspond respectively to the contemporaneous and delayed responses to El Nino and the Southern Oscillation (ENSO) in its developing and decaying years. In general, CFSv2 is capable of skillfully predicting these two dominant ASM modes on the seasonal time scale up to 5 months in advance. Moreover, the predictive skill of the ASM rainfall in CFS_MAE is much higher with respect to the delayed ENSO mode than the contemporaneous one. The predicted principal component of the former maintains high correlation skill and small ensemble spread about two seasons ahead while the latter is significantly degraded in both measures after one season. A maximized signal-to-noise EOF analysis further shows that the delayed ASM response to ENSO is also the most predictable pattern at long leads in CFS_RR. The improved predictive skill of the ASM rainfall following ENSO events originates from the enhanced predictability associated with the active air-sea feedback in the Indo-northwestern Pacific domain from the ENSO peak to the ENSO demise phase, which are well captured in the CFSv2 hindcasts.

Inter-decadal variations in the linkages between ENSO, the IOD and south-eastern Australian springtime rainfall in the past 30 years

The 30 year period 1985–2014 experienced a swing of the Inter-decadal Pacific Oscillation (IPO) from the warm phase to the cold phase. Here we investigate variation of the relation between El Nino and the Southern Oscillation (ENSO) and the Indian Ocean Dipole mode (IOD) and resultant changes in the predictability of the IOD and south-eastern Australian (SEA) springtime rainfall associated with this swing in the IPO. Using observational analyses, we show that during the warm phase of the IPO in the 1980s–1990s, the amplitudes of ENSO and the IOD were large, and the correlation between them was high; thus predictability of the IOD was high. Nevertheless, during these decades SEA spring rainfall was only weakly related to ENSO and the IOD, and therefore predictability of SEA rainfall was low. In contrast, during the cold phase of the IPO in the 2000s, the opposite was found: the IOD occurred more independently from ENSO, so the IOD was less predictable. Nonetheless, SEA spring rainfall was more strongly related to ENSO and the IOD, and therefore, SEA rainfall was more predictable in the 2000s than in the 1980s–1990s. The cause of this decadal variation in the relationship of SEA rainfall with ENSO and the IOD between the recent warm and cold states of the IPO appears to be a systematic zonal variation of the rainfall anomalies in the tropical Indo-Pacific associated with the IOD and ENSO and related changes in the Rossby wave train path over Australia.

Heat waves, temperature extremes and their impacts on monsoon rainfall and meteorological drought in Gujarat, India

In the Indian Subcontinent, droughts occur generally due to delayed arrival or early retreat of the south-west monsoon associated with poor precipitation. However, initiation and intensification of droughts may also result from heat waves. With an arid to semi-arid climate, the Gujarat State of India experiences seasonal temperature variations and frequent heat waves during summer and inconsistent rainfall during the rainy (monsoon) season, resulting in recurrent droughts. In the present study, ground data of rainfall and temperature were used to assess meteorological drought in Gujarat during 1981–2010. Geographical information systems (GIS)-based analysis revealed an association between rainfall deficit and heat waves, and an indirect influence of heat waves and temperature extremes on drought development and intensification. The study indicates a relationship between climate extremes and climate change. It also points to the fact that, in certain years, local factors are more influential than global factors such as El Nino and the Southern Oscillation (ENSO) for drought occurrence and non-occurrence in Gujarat.

Mean state and interannual variability of the Indian summer monsoon simulation by NCEP CFSv2

The capability of the National Centers for Environmental Prediction climate forecast system version 2 (CFSv2) in simulating the Indian summer monsoon (ISM) is evaluated in the context of the global monsoon in the Indo-Pacific domain and its variability. Although the CFSv2 captures the ISM spatial structure qualitatively, it demonstrates a severe dry bias over the Indian subcontinent. The weaker model monsoon may be related to an excessive surface convergence over the equatorial Indian Ocean, which reduces the moisture transport toward the Indian subcontinent. The excessively low equatorial pressure is in turn a part of a tropical-wise bias with the largest errors in the central and eastern equatorial Pacific associated with the cold sea surface temperature bias and an overly strong inter-tropical convergence zone. In this sense, the model bias in the tropical Pacific influences those in the Indian Ocean-ISM region substantially. The leading mode of the June–September averaged CFSv2 rainfall anomalies covering the ISM and its adjacent oceanic regions is qualitatively similar to that of the observations, characterized by a spatial pattern of strong anomalies over either side of the Indian peninsula as well as center of opposite sign over Myanmar. However, the model fails to reproduce the northward expansion of rainfall anomalies from Myanmar, leading to opposite anomalies over northeast India and Himalayas region. A substantial amount of the anomalous fluctuation is attributed to the El Nino and the Southern Oscillation (ENSO), although the model variability depends more strongly on ENSO. The active regional influences in the observations may contribute to its baroclinic vertical structure of the geopotential height anomalies in the ISM region, compared with the predominantly barotropic one in CFSv2. Model ENSO deficiencies also affects its ISM simulation significantly.

Improved reliability of ENSO hindcasts with multi-ocean analyses ensemble initialization

Currently, ensemble seasonal forecasts using a single model with multiple perturbed initial conditions generally suffer from an “overconfidence” problem, i.e., the ensemble evolves such that the spread among members is small, compared to the magnitude of the mean error. This has motivated the use of a multi-model ensemble (MME), a technique that aims at sampling the structural uncertainty in the forecasting system. Here we investigate how the structural uncertainty in the ocean initial conditions impacts the reliability in seasonal forecasts, by using a new ensemble generation method to be referred to as the multiple-ocean analysis ensemble (MAE) initialization. In the MAE method, multiple ocean analyses are used to build an ensemble of ocean initial states, thus sampling structural uncertainties in oceanic initial conditions (OIC) originating from errors in the ocean model, the forcing flux, and the measurements, especially in areas and times of insufficient observations, as well as from the dependence on data assimilation methods. The merit of MAE initialization is demonstrated by the improved El Nino and the Southern Oscillation (ENSO) forecasting reliability. In particular, compared with the atmospheric perturbation or lagged ensemble approaches, the MAE initialization more effectively enhances ensemble dispersion in ENSO forecasting. A quantitative probabilistic measure of reliability also indicates that the MAE method performs better in forecasting all three (warm, neutral and cold) categories of ENSO events. In addition to improving seasonal forecasts, the MAE strategy may be used to identify the characteristics of the current structural uncertainty and as guidance for improving the observational network and assimilation strategy. Moreover, although the MAE method is not expected to totally correct the overconfidence of seasonal forecasts, our results demonstrate that OIC uncertainty is one of the major sources of forecast overconfidence, and suggest that the MAE is an essential component of an MME system.

Indian Ocean dipole interpreted in terms of recharge oscillator theory

In this paper we use sea surface height (SSH) derived from satellite altimetry and an analytical linear equatorial wave model to interpret the evolution of the Indian Ocean Dipole (IOD) in the framework of recharge oscillator theory. The specific question we address is whether heat content in the equatorial band, for which SSH is a proxy, is a predictor of IOD development as it is for El Nino and the Southern Oscillation (ENSO) in the Pacific. We find that, as in the Pacific, there are zonally coherent changes in heat content along the equator prior to the onset of IOD events. These changes in heat content are modulated by wind-forced westward propagating Rossby waves in the latitude band 5°–10°S, which at the western boundary reflect into Kelvin waves trapped to the equator. The biennial character of the IOD is affected by this cycling of wave energy between 5° and 10°S and the equator. Heat content changes are a weaker leading indicator of IOD sea surface temperature anomaly development than is the case for ENSO in the Pacific though because other factors are at work in generating IOD variability, one of which is ENSO forcing itself through changes in the Walker Circulation.

The El Niño and Southern Oscillation in the historical centennial integrations of the new generation of climate models

In this study, we compare the simulation of El Nino and the Southern Oscillation (ENSO) in the historical integrations of 17 Coupled Model Intercomparison Project 5 (CMIP5) models with corresponding observations. The mean state and ENSO variations are analyzed in both the atmosphere and ocean and it is found that most of the CMIP5 models exhibit cold (warm) biases in the equatorial (subtropical eastern) Pacific Ocean sea surface temperature that are reminiscent of the split intertropical convergence zone phenomenon found in previous studies. There is, however, a major improvement in the representation of the power spectrum of the Nino3.4 sea surface temperature variations, which shows that, as in the observations, a majority of the models display a spectral peak in the 2–7 year range, have a near-linear relationship with the displacement of the equatorial thermocline and exhibit a robust atmospheric response to ENSO variations. Several issues remain such as erroneous amplitudes in the Nino3.4 sea surface temperature spectrum’s peak and a width of the spectral peak that is either too broad or too narrow. It is also seen that most CMIP5 models unlike the observations extend the ENSO variations in the equatorial Pacific too far westward beyond the dateline and there is very little asymmetry in event duration between the warm and cold phases. ENSO variability forces a dominant mode of rainfall variability in the southeastern United States, especially in the boreal winter season. The CMIP5 exhibited a wide range of response in this metric with several displaying weak to nonexistent, some showing relatively strong, and one indicating excessively zonally symmetric teleconnection over the southeastern United States.

Indigenous Climate Information and Modern Meteorological Records in Sinazongwe District, Southern Province, Zambia

Abstract In 2007, we conducted field research in Sinazongwe District in Southern Province, Zambia, focusing on collecting indigenous information concerning the local climate, which was often embodied in proverbs involving weather forecasts. The indigenous information was compared with recently col-lected meteorological records and a relationship between popular folk knowledge, local climate, and global climate factors such as ENSO (El Nino and the Southern Oscillation) was suggested. Proverbs related to agriculture and climate were categorized into four types of indicators used to forecast rain: the emergence of butterflies; tree characteristics (producing shoots, flowering, and dropping water); wind direction, wind speed, and temperature; and wind sound. The first two types are based on sea-sonal changes in life forms, and the latter two use wind variations produced by synoptic pattern changes. Discipline: Agro-meteorology Additional key words: agriculture in Zambia, indigenous proverbs, meteorological observation, re -silience, weather forecastingThis paper reports the results obtained in the research project ‘Vulnerability and Resilience of Social-Ecological Systems’ from 2006 to 2011, sponsored by the Research Institute for Humanity and Nature (RIHN). *Corresponding author: e-mail kanno@affrc.go.jpReceived 3 March 2012; accepted 15 August 2012.

Understanding the wet season variations over Florida

The wet season of Florida is well defined and is invariably centered in the boreal summer season of June–July–August. In this observational study we objectively define the Length of the Wet Season (LOWS) for Florida and examine its variations with respect to El Nino and the Southern Oscillation (ENSO) and the Atlantic Warm Pool (AWP). Our study reveals that ENSO variability has a profound influence on the LOWS especially over south Florida and parts of panhandle Florida prior to 1976. In the post-1976 era the influence of ENSO has significantly diminished. Our results show that in this pre-1976 era, warm (cold) ENSO events in the boreal winter are followed by long (short) LOWS over the region. This variation is consistent with warm (cold) ENSO events influencing early (late) onset of the wet season in the region. There is significant relationship of the LOWS in south and northeast Florida with the variation of the AWP. Unlike the teleconnection with ENSO the relationship of the demise of the wet season with AWP is stronger in the post-1976 period compared to the pre-1976 period. Furthermore the variability of the LOWS has increased in the post-1976 period.

Interannual variability of latent and sensible heat fluxes in the South China Sea

The South China Sea (SCS) is significantly influenced by El Nino and the Southern Oscillation (ENSO) through ENSO-driven atmospheric and oceanic changes. We analyzed measurements made from 1960 to 2004 to investigate the interannual variability of the latent and sensible heat fluxes over the SCS. Both the interannual variations of latent and sensible heat fluxes are closely related to ENSO events. The low-pass mean heat flux anomalies vary in a coherent manner with the low-pass mean Southern Oscillation Index (SOI). Time lags between the heat flux anomalies and the SST anomalies were also studied. We found that latent heat flux anomalies have a minimum value around January of the year following El Nino events. During and after the mature phase of El Nino, a change of atmospheric circulation alters the local SCS near-surface humidity and the monsoon winds. During the mature phase of El Nino, the wind speed decreases over the entire sea, and the air-sea specific humidity difference anomalies decreases in the northern SCS and increases in the southern SCS. Thus, a combined effect of wind speed anomalies and air-sea specific humidity difference anomalies results in the latent heat flux anomalies attaining minimum levels around January of the year following an El Nino year.

Characteristics of tropical Pacific SST predictability in coupled GCM forecasts using the NCEP CFS

The limits of predictability of El Nino and the Southern Oscillation (ENSO) in coupled models are investigated based on retrospective forecasts of sea surface temperature (SST) made with the National Centers for Environmental Prediction (NCEP) coupled forecast system (CFS). The influence of initial uncertainties and model errors associated with coupled ENSO dynamics on forecast error growth are discussed. The total forecast error has maximum values in the equatorial Pacific and its growth is a strong function of season irrespective of lead time. The largest growth of systematic error of SST occurs mainly over the equatorial central and eastern Pacific and near the southeastern coast of the Americas associated with ENSO events. After subtracting the systematic error, the root-mean-square error of the retrospective forecast SST anomaly also shows a clear seasonal dependency associated with what is called spring barrier. The predictability with respect to ENSO phase shows that the phase locking of ENSO to the mean annual cycle has an influence on the seasonal dependence of skill, since the growth phase of ENSO events is more predictable than the decay phase. The overall characteristics of predictability in the coupled system are assessed by comparing the forecast error growth and the error growth between two model forecasts whose initial conditions are 1 month apart. For the ensemble mean, there is fast growth of error associated with initial uncertainties, becoming saturated within 2 months. The subsequent error growth follows the slow coupled mode related the model’s incorrect ENSO dynamics. As a result, the Lorenz curve of the ensemble mean NINO3 index does not grow, because the systematic error is identical to the same target month. In contrast, the errors of individual members grow as fast as forecast error due to the large instability of the coupled system. Because the model errors are so systematic, their influence on the forecast skill is investigated by analyzing the erroneous features in a long simulation. For the ENSO forecasts in CFS, a constant phase shift with respect to lead month is clear, using monthly forecast composite data. This feature is related to the typical ENSO behavior produced by the model that, unlike the observations, has a long life cycle with a JJA peak. Therefore, the systematic errors in the long run are reflected in the forecast skill as a major factor limiting predictability after the impact of initial uncertainties fades out.

The impact of atmospheric nonlinearities on the fastest growth of ENSO prediction error

In this paper we explore the impact of atmospheric nonlinearities on the optimal growth of initial condition error of El Nino and the Southern Oscillation (ENSO) prediction using singular vector (SV) analysis. This is performed by comparing and analyzing SVs of two hybrid coupled models (HCMs), one composed of an intermediate complexity dynamical ocean model coupled with a linear statistical atmospheric model, and the other one with the same ocean model coupled with a nonlinear statistical atmosphere. Tangent linear and adjoint models for both HCMs are developed. SVs are computed under the initial conditions of seasonal background and actual ENSO cycle simulated by the ocean model forced with the real wind data of 1980–1999. The optimization periods of 3, 6 and 9 months are individually considered. The results show that the first SVs in both HCMs are very similar to each other, characterized by a central east-west dipole pattern spanning over the entire tropical Pacific. The spatial patterns of the leading SV in both HCMs are not sensitive to optimization periods and initial time. However, the first singular value, indicating the optimal growth rate of prediction error, displays considerable differences between the two HCMs, indicating a significant impact of atmospheric nonlinearities on the optimal growth of ENSO prediction error. These differences are greater with increasing optimization time, suggesting that the impact of atmospheric nonlinearities on the optimal growth of prediction error becomes larger for a longer period of prediction.

Validating and understanding the ENSO simulation in two coupled climate models

A newly developed Atmospheric General Circulation Model (AGCM) at T62 spectral truncation with 28 terrainfollowing (σ=pps) levels coupled to the Modular Ocean Model version 3.0 (MOM3.0) is evaluated for its simulation of El Ni˜no and the Southern Oscillation (ENSO). It is also compared with an older version of the AGCM coupled to the same ocean model. A dozen features of ENSO are validated. These characteristics of ENSO highlight its influence on global climate at seasonal to interannual scales.The major improvements of the ENSO simulation from this new coupled climate model are the seasonal phase locking of the ENSO variability to a realistic annual cycle of the eastern equatorial Pacific Ocean, the duration of the ENSO events and its evolution that is comparable to the ocean data assimilation. The two apparent drawbacks of this new model are its relatively weak ENSO variability and the presence of erroneous split ITCZ.The improvement of the ENSO simulation in the new coupled model is attributed to realistic thermocline variability and wind stress simulation.

Precise measurement of travel time in 1999 OAT experiment

The 1999 OAT experiment is performed in the Central Equatorial Pacific to monitor the ocean phenomenon associated with El Niño and the Southern Oscillation (ENSO). In order to analyze the travel-time perturbations due to ocean current, amplitude information of the received signal is used in the conventional method. However, it is difficult to measure the travel-time difference precisely, mainly because of the attenuation of long-range transmission. In this report, we propose a new technique which is the complex vector method by using the amplitude and phase information. From the ray identification results, we estimated the travel-time difference between corresponding rays of the reciprocal transmissions. Since the phase of a ray signal is very stable in 130-s signal duration time, the precision of the measurement of the travel-time difference using phase difference is higher than the conventional estimation. The travel-time differences estimated by using this method have reasonable magnitudes.

Atmospheric Seasonal Predictability Experiments by the JMA AGCM.

Atmospheric seasonal predictability is investigated using the Japan Meteorological Agency (JMA) Atmospheric General Circulation Model (AGCM) which is a global spectral model of T63 resolution used for former operational one-month farecasts. Four-month ensemble integrations were performed from nine consecutive days of initial condition preceding the target season. All four seasons in the 15-year period from 1979 to 1993 are chosen as the target seasons. The model was forced with observed sea surface temperature (SST) during the time integrations. Verification by the anomaly correlation of seasonal averaged 500 hPa height are summarized as follows. (1) In terms of a 15-year mean, ensmble average forecasts tend to have higher skill than means of individual forecasts, persistence forecasts and climatological forecasts over the whole globe. (2) In the Northern Hemisphere, skill is high in spring and winter, and low in summer and autumn. (3) Skill is higher over East Asia and North America, than over Europe and Atlantic regions. In winter, the relatively higher skill over East Asia and North America can be attributed to reasonable reproducibility of Western Pacific (WP) and Pacific/North American (PNA) teleconnection patterns by the model. (4) A larger ensemble size improves the skill of the model both for extratropical regions and the tropics, and for all four seasons. (5) The skill of one-month lead time forecasts is lower than that of no lead time forecasts for all regions and all seasons. Focusing on the Northern Hemisphere, the relatively large skill degradation with respect to lead time is striking in spring, which suggests that the model is more sensitive to initial conditions in spring than in summer and autumn. (6) Skill enhancement for strong El Nino and the Southern Oscillation (ENSO) years is generally evident over the whole globe, but is not so striking in the Northern Hemisphere. Skill over North America increases for ENSO years, but there is no clear skill enhancement over the Europe and Atlantic regions. The model’s ability to reproduce the interannual variability of precipitation was also investigated. Geographycal distributions of interannual temporal correlation coefficients between observed precipitation, and model ensemble average forecast precipitation show that correlations are generally higher in the tropics than in the extratropics for all seasons. In the tropics, skill is relatively higher in the equatorial eastern Pacific ocean and lower over India. The model has difficulty in simulating the interannual variability of precipitation for the Indian summer monsoon season and for the East Asian rainy season in summer, which may originate from the model’s poor climatology over these regions in summer.

Reducing Vulnerability to Climate Variability in Southern Africa: The Growing Role of Climate Information

Since the devastating southern Africa drought of 1991/92 awareness has grown of the potential to better manage climate variability in the region through seasonal climate forecasting and monitoring of El Nino and the Southern Oscillation (ENSO). While other factors besides ENSO affect southern Africa’s climate, and climate forecasting for the region is not based exclusively on ENSO, a major El Nino beginning in 1997 captured the attention of policy-makers and the public. Awareness of drought risks associated with the 1997/98 event was greater than during previous El Nitos in 1991/92 and 1994/95. Mitigation and planning efforts also began earlier, with drought early warnings widely available and being taken seriously prior to the 1997/98 agricultural season. Actions taken include issuance of guidance to the public, on-going monitoring and preparedness efforts including the development of national preparedness plans in some countries, pre-positioning of food stocks, donor coordination, and greater reliance on the private-sector for meeting regional food needs. Although 1998 regional crop production was slightly below average, a major drought did not materialize. Nonetheless the experience is likely to ultimately strengthen capacity within the region to manage climate variability over the long term.

Can El Nińo amplify the solar forcing of climate?

ENSO (El Nińo and the Southern Oscillation) is considered as a stochastic driver that excites the atmospheric anomaly states, such as Pacific North American pattern. This can make 11‐year solar activity forcing of climate feasible through stochastic resonance‐a mechanism that amplifies a weak input to a nonlinear bistable system by the assistance of noise.