ENSO Evolution Research Articles

The Influence of Anomalous Biomass Emissions on ENSO in CESM2

Abstract The influence of biomass burning (BB) aerosols arising from wildfires and agricultural fires on the transient coupled evolution of ENSO is explored in CESM2. For both El Niño and La Niña, two 20-member ensembles are generated from initial states that are predisposed to evolve into ENSO events. For each ENSO phase, one ensemble is forced with the observed BB emissions during satellite-era ENSO events while the other is forced with a climatological annual cycle, with the responses to anomalous BB emissions estimated from inter-ensemble differences. It is found that the regional responses to anomalous BB emissions occur mainly during boreal fall, which is also the time of the climatological seasonal maximum in emissions. Transient responses are identified in precipitation, clouds, and radiation in both the tropics and extratropics. At the onset of El Niño, these include an increase precipitation in the northern branch of the ITCZ and an enhancement of cloud albedo and amount across the Maritime Continent and eastern subtropical Pacific Ocean. Additional responses are identified through the course of El Niño and successive La Niña events, the net effect of which is to strengthen SST anomalies in the eastern Pacific Ocean during El Niño and warm the tropical Pacific Ocean during La Niña. These responses improve simulation of ENSO power, diversity, and asymmetry in CESM2.

Unprecedented cross-equatorial southerly wind anomalies during the 2020–2023 triple-dip La Niña: Impacts and mechanisms

The 2020–2023 La Niña event, the third triple-dip occurrence since 1950, stands out for its unique characteristics when compared to the preceding events in 1973–1976 and 1998–2001. Unlike its predecessors, the latest event emerged without a preceding strong El Niño episode, starting from a state of weak discharge. Moreover, the resurgence of cold sea surface temperature (SST) anomalies during 2020–2023 showed a loose link to ocean-atmosphere variability in the northern subtropical Pacific. The spatial pattern of anomalous conditions in this recent event featured persistent SST cooling in the southeast tropical Pacific, accompanied by pronounced meridional southerly wind anomalies. Our comprehensive investigation indicates a minimal role of subsurface ocean dynamics, underscoring the exceptional strength of cross-equatorial meridional southerly wind anomalies as a primary driver of sustained negative sea surface temperature anomalies. By considering the influences of intra-basin and inter-basin factors, we propose that the observed patterns may be the combined outcome of interannual variability within the tropical Pacific and decadal variations beyond the tropics. Noteworthy contributions from the low-frequency components of the South Pacific Meridional Mode and the Atlantic Warm Pool further shape the dynamics of this event. Our findings underline the increasing importance of meridional winds in shaping the evolution of ENSO in recent decades.

Distinct Impacts of Two Types of Summer ENSO with Different Temporal Evolutions on the Asian Summer Monsoon: Role of the Tropical Indo-Western Pacific

Abstract ENSO shows great diversity in temporal evolution, exerting different climatic impacts on the global scale. Previous studies suggest that summer ENSOs can be classified into two types with different evolutions: “continuing ENSOs” persist from the previous winter and “emerging ENSOs” newly develop from late spring. In this study, we define two indices for continuing and emerging ENSOs via the linear combination of the two leading modes of the evolutionary anomalies of tropical sea surface temperature (SST) from the prior September to August, which reflects the distinct characteristics of the two types of ENSO well. The two types of ENSO dominate the two leading modes of the SST variability in the tropical Indo–western Pacific (TIWP) and the related Asian summer monsoon anomalies. During continuing ENSOs, the related TIWP SST anomalies show positive anomalies in the tropical Indian Ocean and northwest Pacific and negative ones in the New Guinea region. The warm Kelvin wave forced by the tropical Indian Ocean warming propagates into the tropical western Pacific, exciting the meridional East Asia–Pacific/Pacific–Japan pattern and causing a dipole precipitation pattern in East Asia. During emerging ENSOs, the related TIWP SST anomalies show negative anomalies in the warm pool and positive ones in the western Indian Ocean. The negative SST anomalies in the warm pool induce a westward equatorial Rossby wave conducive to the suppression of the Indian summer monsoon rainfall, whereas the Matsuno–Gill response to the warm pool cooling leads to negative anomalies in East Asian summer rainfall. This mechanism can be simulated well in the CMIP6 and Pacific Ocean–Global Atmosphere experiments. Significance Statement ENSO displays diversity in its amplitude, spatial pattern, temporal evolution, and impact. Although previous studies have systematically investigated the diversity of ENSO spatial locations, there are few studies on the diversity of ENSO evolution and the impacts on the Asian summer monsoon. Here, we define two indices for so-called continuing and emerging ENSOs via the linear combination of the two leading modes of the evolutionary anomalies of tropical sea surface temperature (SST) from the prior September to August. Then, we reveal that the two leading modes of the tropical Indo–western Pacific SST variability are dominated by the two types of ENSO, respectively, and act as a bridge via which the two types of ENSO can exert different impacts on the Asian summer monsoon.

Error Evolutions and Analyses on Joint Effects of SST and SL via Intermediate Coupled Models and Conditional Nonlinear Optimal Perturbation Method

A seasonal predictability barrier has long been noticed in ENSO forecasting with numerical models. Previous studies explored the impact of seasonal optimal initial perturbation evolutions in sea surface temperature anomalies (SSTA) on ENSO forecasting using the intermediate coupled model (ICM) via the conditional nonlinear optimal perturbation (CNOP) method. In this paper, we investigate the joint effects of SSTA and sea level anomalies (SLA) from the perspective of the optimal growth initial error (OGE). After determining the four seasonal OGEs about SSTA and SLA (i.e., SSTA-OGE, SLA-OGE and Joint-OGE), we first demonstrate the patterns, evolutions and the resulting spring predictability barrier (SPB) of the above OGEs. Then, we analyze the mechanism of OGE evolutions and SPB. Finally, we conduct observing system simulation experiments to determine the best (economic) observation network. Our experimental results indicate that the ENSO evolution error induced by SSTA-OGE and Joint-OGE presents season dependency, but SLA-OGE has no impact on ENSO evolution. Moreover, Joint-OGEs induce error evolutions and the SPB with more significant intensity than SSTA-OGEs and SLA-OGEs. From mechanism analyses, the evolutions of SSTA-OGEs are mainly dominated by Bjerknes feedback. Further, the evolution dynamics of Joint-OGEs primarily contain the continuous heating between the upper ocean combined with Bjerknes feedback and thermal diffusion in response to the discharge process. In addition, comprehensive and economical sensitive areas are identified through Joint-OGE, including the central-eastern equatorial Pacific and the western and north-eastern tropical Pacific boundary, which contribute to the ENSO prediction benefits reaching 58.31% on average.

The extremely wet spring of 2022 in Southwest China was driven by La Niña and Tibetan Plateau warming

In the spring of 2022, an excessive amount of rainfall fell in Southwest China (SWC) under the background of frequent droughts in history. This extreme event occurred in the decaying phase of a La Niña event, and thus, presumably La Niña played a role in this extreme event. Based on observational diagnoses and model forecasts, the atmospheric circulation anomalies, contributions of remote forcing, and the predictability of this event were examined in this work. It is suggested that La Niña and the Tibetan Plateau upper-tropospheric warming are two major factors leading to the extreme event. In addition to the recognized impact of La Niña, the upper-tropospheric warming over the Tibetan Plateau modulates the Asian atmospheric circulation by inducing a northwest-southeast wave pattern extending from the Ural Mountains to the Indochina Peninsula via the western Tibetan Plateau. The meridional heat contrast associated with the Tibetan Plateau warming favors upward motion and excessive rainfall in SWC. The statistical connection between the SWC spring rainfall anomaly and the northwest-southeast wave pattern is confirmed by a climate model forecast. The model captured the wet pattern in SWC in spring 2022 in short (1–3 months) lead real-time predictions though there are biases in the area and severity. That may be due to that the model did not well capture the atmospheric circulation anomalies at the middle and high latitudes associated with the Tibetan Plateau upper-tropospheric warming. These results indicate that such an event is predictable to some extent if both the ENSO evolution and heat condition over the Tibetan Plateau can be well predicted.

A deep learning–based U-Net model for ENSO-related precipitation responses to sea surface temperature anomalies over the tropical Pacific

SST–precipitation feedback plays an important role in ENSO evolution over the tropical Pacific and thus it is critically important to realistically represent precipitation-induced feedback for accurate simulations and predictions of ENSO. Typically, in hybrid coupled modeling for ENSO predictions, statistical atmospheric models are adopted to determine linear precipitation responses to interannual SST anomalies. However, in current coupled climate models, the observed precipitation–SST relationship is not well represented. In this study, a data-driven deep learning-based U-Net model was used to construct a nonlinear response model of interannual precipitation variability to SST anomalies. It was found that the U-Net model outperformed the traditional EOF-based method in calculating the precipitation variability. Particularly over the western-central tropical Pacific, the mean-square error (MSE) of the precipitation estimates in the U-Net model was smaller than that in the EOF model. The performance of the U-Net model was further improved when additional tendency information on SST and precipitation variability was also introduced as input variables, leading to a pronounced MSE reduction over the ITCZ.摘要SST–降水反馈过程在热带太平洋ENSO演变过程中起着重要作用, 能否真实地在数值模式中表征SST–降水年际异常之间的关系及相关反馈过程, 对于准确模拟和预测ENSO至关重要. 例如, 在一些模拟ENSO的混合型耦合模式中, 通常采用大气统计模型 (如经验正交函数; EOF) 来表征降水 (海气界面淡水通量的一个重要分量) 对SST年际异常的线性响应. 然而在当前的耦合模式中, 真实观测到的降水–SST统计关系还不能被很好地再现出来, 从而引起 ENSO模拟误差和不确定性. 在本研究中, 使用基于深度学习的U-Net模型来构建热带太平洋降水异常场对SST年际异常的非线性响应模型. 研究发现: U-Net模型的性能优于传统的基于EOF方法的模型. 特别是在热带西太平洋海区, U-Net模型估算的降水误差远小于EOF模型的模拟. 此外, 当SST和降水异常的趋势信息作为输入变量也被同时引入以进一步约束模式训练时, U-Net模型的性能可以进一步提高, 如能使热带辐合带区域的误差显著降低.

Decadal variation of prediction skill for Indian Ocean dipole over the past century

Indian Ocean dipole (IOD) is one of the dominant modes of interannual variability in the Indian Ocean, which has global climate impacts and thus is one of the key targets of seasonal predictions. In this study, based on a century-long seasonal hindcast experiment from the Coupled Seasonal Forecasts of the 20th century (CSF-20C), we show that the prediction skill for IOD exhibits remarkable decadal variations, with low skill in the early-to-mid 20th century but high skill in the second half of the 20th century. The decadal variations of prediction skills for IOD are caused by two factors. The first is associated with the decadal variation of the ENSO-IOD relationship. Although individual members of the predictions can simulate the variation of the ENSO-IOD relationship, with amplitude close to that in the observation, the feature is greatly suppressed in the ensemble mean due to the asynchrony of variation phases among individual members. In the ensemble mean, the IOD evolution shows an unrealistic stable and high correlation with ENSO evolution. This causes the prediction to have much higher skill for those periods during which IOD is accompanied by ENSO in the observation. The second factor is associated with the decadal variation of IOD predictability in the prediction system. In the prediction system, the decadal variation of IOD signal strength closely follows that of ENSO signal strength. Meanwhile, the IOD noise strength shows variations opposite to the IOD signal strength. As a result, the signal-to-noise ratio greatly increases in the second half of the 20th century due to the enhancement of the ENSO signal strength, which represents the increase of IOD predictability in the prediction system.

Recent ENSO evolution and its real-time prediction challenges.

The frequent occurrences of the second-year surface cooling condition in the eastern equatorial Pacific, as observed in late 2021, are attributed to decadal changes in the thermocline depth, which determine the relative dominances of local cooling effect in the east and subsurface warming effect remotely from the west. Coupled models need to adequately represent these processes in a balanced way, thus being able to successfully predict the observed sea surface temperature evolution in late 2021.

Rectified Effects of Interannual Chlorophyll Variability on the Tropical Pacific Climate Revealed by a Hybrid Coupled Physics‐Biology Model

AbstractEl Niño‐Southern Oscillation (ENSO) induces large interannual chlorophyll variability (ICV) in the tropical Pacific, which modulates shortwave penetration in the upper ocean. However, the extent to which ICV can have rectified effect (higher‐frequency process impacts low‐frequency climate variability (i.e., ENSO) and mean state) in the tropical Pacific remains unclear. Here, a hybrid coupled physics‐biogeochemistry model with tunable coupling ocean‐atmosphere intensity (α) is used to examine this rectified effect. The simulations consist of a control run in which the ICV effect is represented in the model and the other with the ICV effect being purposely excluded by only reserving the climatological chlorophyll field. Results show that the ICV effect depends on α; ENSO amplitude is reduced by 12%–40% with decreased annual mean sea surface temperature by 0.05–0.4°C in the eastern equatorial Pacific when taking α = 0.8–1.15. A stability analysis reveals that the ICV effect on ENSO exhibits two feedback loops. (a) Positive (negative) chlorophyll anomalies during La Niña (El Niño) act to increase (decrease) shortwave absorption within the mixed layer, which enhances thermodynamic damping (slightly weakening ENSO amplitude). (b) Chlorophyll anomalies modulate vertical redistribution of penetrative shortwave radiation between the mixed layer and subsurface layers; this differential heating affects ocean stratification and vertical mixing, which weakens the thermocline and Ekman feedback (predominantly weakening ENSO amplitude). Additionally, ICV exhibits asymmetric effects on ENSO evolution, with the El Niño amplitude being damped stronger than La Nina; this residual effect leads to a net cooling condition in the eastern tropical Pacific.

Restored relationship between ENSO and Indian summer monsoon rainfall around 1999/2000

SummaryEl Niño–Southern Oscillation (ENSO) was identified as the dominant predictor for the Indian summer monsoon rainfall (ISMR) in the early 1900s. An apparent weakening of the ENSO–ISMR relationship has been observed since the 1970s. Here, we found a clear restoration of the ENSO–ISMR relationship since 1999/2000. This restoring relationship is closely linked to the interdecadal transition of ENSO evolution and the associated sea surface temperature anomalies (SSTAs) over the tropical Atlantic. During 1979–1997, summer ENSO events mainly continued from the previous winter, which can drive apparent Atlantic Niña SSTAs to offset ENSO's impact on ISMR and weaken the ENSO–ISMR relationship. In contrast, when ENSO events newly emerge from late spring, as they have done more recently during 2000–2018, the associated tropical Atlantic SSTAs are weak and shift to the tropical North Atlantic, which can offset the contribution of Atlantic Niña and reinforce the ENSO–ISMR relationship. We identified that the diversity of ENSO's evolution, continuing from the previous winter or emerging from late spring, is the dominant factor perturbing the ENSO–ISMR relationship in recent epochs, with tropical Atlantic SSTAs as the crucial bridge. This finding should be considered in our efforts to improve ISMR prediction.

Forecasts of ENSO evolution using spatial–temporal projection model

AbstractEl Niño–Southern Oscillation (ENSO) prediction is one of the most debated and challenging tasks, whilst its real‐time operational prediction skill still has room for improvement. In this study, spatial–temporal projection model is applied to predict Niño3.4 index at lead time of one to six months. By regressing variable fields onto Niño3.4 index month by month, physical‐based predictability sources, that is, the mixed‐layer oceanic temperature, sea surface temperature, thermocline depth, and accumulated westerly‐wind‐events index over the specific regions are detected as the predictors. Based on the temporal evolution of coupled modes of predictors and Niño3.4 index, the Niño3.4 index from JAS (July, August, and September) to DJF (December, January, and February) can be predicted once a year. The model could nicely reproduce the evolution of Niño3.4 index from JAS to DJF. It also achieved high prediction skills for the year‐to‐year DJF Niño3.4 index, with a root‐mean‐square error of 0.46 in the training period (1950–2000) and 0.52 in the independent‐forecast period (2001–2016). Further investigation shows that the forecast is more reliable when the forecasted ENSO amplitude is much larger.

Asymmetry in the dominant co-variation mode of boreal summer monsoon rainfall regulated by the ENSO evolution

Interannual variability of boreal summer monsoon (BSM) rainfall exhibits a prominent co-variation mode, which affects the regions with the highest population density in the world. The mode intensity depends on the rainfall variation over the western North Pacific (WNP) and the North America (NAM) monsoon regions. This study suggests that relative importance of the WNP and NAM rainfall anomalies is asymmetric between the strong positive (SP) and strong negative (SN) phases of this mode, which can be attributed to the distinct seasonal evolution of ENSO events. In the SP-phase, the suppressed summer rainfall over the WNP determines the mode following a decaying El Nino event. When the tropical Indian Ocean (TIO) warms and the eastern Equatorial Pacific (EEP) cools, the emanating Kelvin wave- and zonal SST difference-induced low-level WNP anticyclone thus gets enhanced to damp the local rainfall, which further affects the other members of the BSM. However, the SN-phase of this mode, showing closer association with anomalous NAM rainfall, appears with a developing El Nino event. The tropical North Atlantic (TNA) becomes colder from spring to summer, when the EEP warms due to the zonal circulation anomalies. A stronger zonal SST difference between the warm EEP and cold TNA suppresses the NAM rainfall by strengthening the low-level anticyclone in situ. Such asymmetry in different phases of the BSM co-variation mode is validated by the pre-industrial control runs in a state-of-the-art CGCM, indicating the roles of ENSO evolution in regulating the interaction between the BSM members.

Idealized Experiments for Optimizing Model Parameters Using a 4D-Variational Method in an Intermediate Coupled Model of ENSO

Large biases exist in real-time ENSO prediction, which can be attributed to uncertainties in initial conditions and model parameters. Previously, a 4D variational (4D-Var) data assimilation system was developed for an intermediate coupled model (ICM) and used to improve ENSO modeling through optimized initial conditions. In this paper, this system is further applied to optimize model parameters. In the ICM used, one important process for ENSO is related to the anomalous temperature of subsurface water entrained into the mixed layer (Te), which is empirically and explicitly related to sea level (SL) variation. The strength of the thermocline effect on SST (referred to simply as “the thermocline effect”) is represented by an introduced parameter, αTe. A numerical procedure is developed to optimize this model parameter through the 4D-Var assimilation of SST data in a twin experiment context with an idealized setting. Experiments having their initial condition optimized only, and having their initial condition plus this additional model parameter optimized, are compared. It is shown that ENSO evolution can be more effectively recovered by including the additional optimization of this parameter in ENSO modeling. The demonstrated feasibility of optimizing model parameters and initial conditions together through the 4D-Var method provides a modeling platform for ENSO studies. Further applications of the 4D-Var data assimilation system implemented in the ICM are also discussed.

Sea Surface Temperature in the Subtropical Pacific Boosted the 2015 El Niño and Hindered the 2016 La Niña

After the quick decaying of the 2015 super El Niño, the predicted La Niña unexpectedly failed to materialize to the anticipated standard in 2016. Diagnostic analyses, as well as numerical experiments, showed that this ENSO evolution of the 2015 super El Niño and the hindered 2016 La Niña may be essentially caused by sea surface temperature anomalies (SSTAs) in the subtropical Pacific. The self-sustaining SSTAs in the subtropical Pacific tend to weaken the trade winds during boreal spring–summer, leading to anomalous westerlies along the equatorial region over a period of more than one season. Such long-lasting wind anomalies provide an essential requirement for ENSO formation, particularly before a positive Bjerknes feedback is thoroughly built up between the oceanic and atmospheric states. Besides the 2015 super El Niño and the hindered La Niña in 2016, there were several other El Niño and La Niña events that cannot be explained only by the oceanic heat content in the equatorial Pacific. However, the questions related to those eccentric El Niño and La Niña events can be well explained by suitable SSTAs in the subtropical Pacific. Thus, the leading SSTAs in the subtropical Pacific can be treated as an independent indicator for ENSO prediction, on the basis of the oceanic heat content inherent in the equatorial region. Because ENSO events have become more uncertain under the background of global warming and the Pacific decadal oscillation during recent decades, thorough investigation of the role of the subtropical Pacific in ENSO formation is urgently needed.

Influence of the preceding austral summer Southern Hemisphere annular mode on the amplitude of ENSO decay

There is increasing evidence of the possible role of extratropical forcing in the evolution of ENSO. The Southern Hemisphere Annular Mode (SAM) is the dominant mode of atmospheric circulation in the Southern Hemisphere extratropics. This study shows that the austral summer (December–January–February; DJF) SAM may also influence the amplitude of ENSO decay during austral autumn (March–April–May; MAM). The mechanisms associated with this SAM–ENSO relationship can be briefly summarized as follows: The SAM is positively (negatively) correlated with SST in the Southern Hemisphere middle (high) latitudes. This dipole-like SST anomaly pattern is referred to as the Southern Ocean Dipole (SOD). The DJF SOD, caused by the DJF SAM, could persist until MAM and then influence atmospheric circulation, including trade winds, over the Nino3.4 area. Anomalous trade winds and SST anomalies over the Nino3.4 area related to the DJF SAM are further developed through the Bjerkness feedback, which eventually results in a cooling (warming) over the Nino3.4 area followed by the positive (negative) DJF SAM.

The ENSO Transition Probabilities

ENSO is investigated here by considering it as a transition from different states. Transition probability matrices can be defined to describe the evolution of ENSO in this way. Sea surface temperature anomalies are classified into four categories, or states, and the probability to move from one state to another has been calculated for both observations and a simulation from a GCM. This could be useful for understanding and diagnosing general circulation models elucidating the mechanisms that govern ENSO in models. Furthermore, these matrices have been used to define a predictability index of ENSO based on the entropy concept introduced by Shannon. The index correctly identifies the emergence of the spring predictability barrier and the seasonal variations of the transition probabilities. The transition probability matrices could also be used to formulate a basic prediction model for ENSO that was tested here on a case study.

Change of ENSO characteristics in response to global warming

By using datasets of HadISST monthly SST from 1895 to 2014 and 600-year simulations of two CESM model experiments with/without doubling of CO2 concentration, ENSO characteristics (such as intensity, maximum signal location, lifetime and frequency) are compared pre- and post- global warming. The analysis periods are defined by the eras of 1895–1954 and 1955–2014 in the observations and the two model experiments, without and with doubling of CO2 concentration, in the model, respectively. The main conclusions are as follows. Due to global warming, the maximum climatological SST warming occurs in the tropical western Pacific (La Nina-like background warming) and the tropical eastern Pacific (El Nino-like background warming) for observations and model, respectively, resulting in opposite zonal SST gradient anomalies in the tropical Pacific. The La Nina-like background warming induces intense surface divergence in the tropical central Pacific, which enhances the easterly trade winds in the tropical central-western Pacific and shifts the strongest ocean-atmosphere coupling westward, correspondingly. On the contrary, the El Nino-like background warming causes westerly winds in the whole tropical Pacific and moves the strongest ocean-atmosphere coupling eastward. Different background warmings determine the different evolution characteristics of ENSO events. Under the background of La Nina-like warming, ENSO tends to develop and mature in the tropical central Pacific, because the background easterly wind anomaly weakens the ENSO-induced westerly wind anomaly in the tropical western Pacific, leading to the so-called “Central Pacific ENSO (CP ENSO)”. However, the so-called “Eastern Pacific ENSO (EP ENSO)” is likely formed due to increased westerly wind anomaly by the El Nino-like background warming. ENSO lifetime is significantly extended under both the El Nino-like and the La Nina-like background warmings, and especially, it can be prolonged by up to 3 months in the situation of El Nino-like background warming. The prolonged El Nino lifetime mainly applies to extreme El Nino events, which is caused by two processes in response to global warming. On the one hand, the earlier outbreak of the westerly wind bursts (WWB) in the tropical western Pacific makes El Nino occur earlier. On the other hand, the climatological thermocline depth becomes shallower in the tropical Pacific, which can slow down the eastward propagation speed of the negative-feedback Kelvin wave in the subsurface ocean. In addition, the climatological upper-ocean temperature gradient between the equator and off-equator reduces in the tropical central-eastern Pacific, which can weaken the “discharge” rate of the ENSO warm signal. Both of the above processes make El Nino decay slowly and persist longer. Results from both observations and the model also show that the frequency of ENSO events greatly increases due to global warming, and many more extreme El Nino and La Nina events appear under the El Nino-like and the La Nina-like background warmings, respectively. Based on the theories of ENSO evolution, this study shows the possible changes of ENSO characteristics in response to global warming, and reconciles the phenomena and mechanisms of different characteristics of ENSO changes in observations and models, which are essential for the understanding and prediction of the change of ENSO characteristics and its impact under the global warming background in the future.

Influence of surface nudging on climatological mean and ENSO feedbacks in a coupled model

Studies have suggested that surface nudging could be an efficient way to reconstruct the subsurface ocean variability, and thus a useful method for initializing climate predictions (e.g., seasonal and decadal predictions). Surface nudging is also the basis for climate models with flux adjustments. In this study, however, some negative aspects of surface nudging on climate simulations in a coupled model are identified. Specifically, a low-resolution version of the NCEP Climate Forecast System, version 2 (CFSv2L) is used to examine the influence of nudging on simulations of climatological mean and on the coupled feedbacks during ENSO. The effect on ENSO feedbacks is diagnosed following a heat budget analysis of mixed layer temperature anomalies. Diagnostics of the climatological mean state indicates that, even though SST biases in all ocean basins, as expected, are eliminated, the fidelity of climatological precipitation, surface winds and subsurface temperature (or the thermocline depth) could be highly ocean basin dependent. This is exemplified by improvements in the climatology of these variables in the tropical Atlantic, but degradations in the tropical Pacific. Furthermore, surface nudging also distorts the dynamical feedbacks during ENSO. For example, while the thermocline feedback played a critical role during the evolution of ENSO in a free simulation, it only played a minor role in the nudged simulation. These results imply that, even though the simulation of surface temperature could be improved in a climate model with surface nudging, the physics behind might be unrealistic.

Modulation of equatorial Pacific sea surface temperature response to westerly wind events by the oceanic background state

Equatorial Pacific Westerly Wind Events (WWEs) impact ENSO evolution through their local and remote oceanic response. This response depends upon the WWE properties (duration, intensity, fetch…) but also on the underlying oceanic state. Oceanic simulations with an identical idealised western Pacific WWE applied every 3 months on seasonally and interannually varying oceanic conditions over the 1980–2012 period allow characterizing and understanding the modulation of the WWE response by the oceanic background state. These simulations reveal that the amplitude of the Sea Surface Temperature (SST) response, which can vary by one order of magnitude, is far more sensitive to the oceanic background conditions than the dynamical response to WWEs. The amplitude of the surface-flux driven cooling in the western Pacific is strongly modulated by zonal advection, through interannual variations in the background SST zonal gradient. The amplitude of the warming at the warm pool eastern edge is controlled by horizontal advection, and varies as a function of the zonal SST gradient and distance between the WWE and warm pool eastern edge. The amplitude of the eastern Pacific warming varies as a function of the background thermocline depth and local winds. Overall, only the amplitude of the WWE-driven western Pacific cooling can be clearly related to the phase of ENSO, while the WWE driven SST response in the central and eastern Pacific is more diverse and less easily related to large-scale properties. The implications of these findings for ENSO predictability are discussed.

Two interannual dominant modes of the South Asian High in May and their linkage to the tropical SST anomalies

The variations of the intensity and position of the South Asian High (SAH) in May are critical for the interannual change in the Asian summer monsoon onset process. The present work has defined two dominant modes of the SAH in May via the principal component analysis of SAH’s strength, zonal extension and ridgeline position indices during 1979–2015. The first mode (SAH intensity mode) features a coherent enhancement of the SAH strength and zonal extension, while the second mode (SAH position mode) exhibits a meridional swing of the SAH ridgeline. Both diagnosis analysis and numerical experiments validate that the distinct evolution of ENSO in boreal spring determines its different influences on the two modes. When an El Nino decays quickly during March–May, its resultant warm anomalies of sea surface temperature (SST) in the tropical Indian Ocean can affect the SAH intensity mode by modifying the tropical convection over the southwestern Bay of Bengal, acting as a local SST forcing to the circulation. However, the SAH position mode depends on the persistence of the ENSO event during boreal spring. The remote El Nino-related SST forcing can enhance the large-scale descent over the Indo–Pacific Ocean, which suppresses the tropical convection to modify the mid–upper-level thermal structure over South Asia, inducing the interannual variation of the SAH ridgeline in May.