El Nino Southern Oscillation Dynamics Research Articles

El Niño‐La Niña Asymmetries in the Changes of ENSO Complexities and Dynamics Since 1990

AbstractIn around 1990, significant shifts occurred in the spatial pattern and temporal evolution of the El Niño‐Southern Oscillation (ENSO), with these shifts showing asymmetry between El Niño and La Niña phases. El Niño transitioned from the Eastern Pacific (EP) to the Central Pacific (CP) type, while La Niña's multi‐year (MY) events increased. These changes correlated with shifts in ENSO dynamics. Before 1990, El Niño was influenced by the Tropical Pacific (TP) ENSO dynamic, shifting to the Subtropical Pacific (SP) ENSO dynamic afterward, altering its spatial pattern. La Niña was influenced by the SP ENSO dynamic both before and after 1990 and has maintained the CP type. The strengthened SP ENSO dynamic since 1990, accompanied by enhanced precipitation efficiency during La Niña, make it easier for La Niña to transition into MY events. In contrast, there is no observed increase in precipitation efficiency during El Niño.

ENSO phase locking, asymmetry and predictability in the INMCM Earth system model

Abstract Advanced numerical climate models are known to exhibit biases in simulating some features of El Niño–Southern Oscillation (ENSO), which is a key mode of interannual climate variability. In this study we analyze how two fundamental features of observed ENSO – asymmetry between hot and cold states and phase-locking to the annual cycle – are reflected in two different versions of the INMCM Earth system model (state-of-the-art Earth system model participating in the Coupled Model Intercomparison Project). We identify the above ENSO features using the conventional empirical orthogonal functions (EOF) analysis, which is applied to both observed and simulated upper ocean heat content (OHC) data in the tropical Pacific. We obtain that the observed tropical Pacific OHC variability is described well by two leading EOF-modes, which roughly reflect the fundamental recharge-discharge mechanism of ENSO. These modes exhibit strong seasonal cycles associated with ENSO phase locking while the revealed nonlinear dependencies between amplitudes of these cycles reflect ENSO asymmetry.We also assess and compare the predictability of observed and simulated ENSO based on linear inverse modelling. We find that the improved INMCM6 model has significant benefits in simulating described features of observed ENSO as compared with the previous INMCM5 model. The improvements of the INMCM6 model providing such benefits are discussed. We argue that proper cloud parameterization scheme is crucial for accurate simulation of ENSO dynamics with numerical climate models.

Millennial and suborbital-scale El Niño/Southern Oscillation control of climate variability in southern China over the past 70 ka

Hydroclimatic changes in East Asia on suborbital and millennial time scales have been shown to be highly uncertain. The El Niño-Southern Oscillation (ENSO) activity plays a key role in precipitation in low-latitude East Asia on interannual and interdecadal time scales. However, there remained some ambiguity about their connection on millennial and suborbital time scales. This study comprehensively explored the multifaceted relationships between ENSO activities, suborbital and millennial-scale drought events, and hydroclimatic variations within the Huguangyan Maar Lake (HML) region during the past 70 ka. Four suborbital-scale dry and wet hydroclimatic phases interspersed with ten millennial-scale extreme dry intervals were revealed by the ratio of hematite and goethite (Hem/Goe). These stages and intervals unexpectedly coupled with the zonal sea surface temperature (ΔSST) of the tropical Pacific Ocean, underscored the complex spatial and temporal dynamics of ENSO impacts. We propose the “glacial-El Niño-arid” and “interglacial-La Niña-humid” hydroclimate patterns on the suborbital scale. While on the millennial-scale, La Niña-like fluctuations during the persistent cold period (16–40 ka) increased the frequency and intensity of droughts in low-latitude East Asia, which was in stark contrast to the wet climate in northern China. The dynamic relationship underpins a millennia-spanning La Niña-drought pattern, elucidating the intertwined nature of ENSO dynamics and hydroclimatic variability on both sub-orbital and millennial scales. The above findings can be attributed to changes in the location and intensity of the Western Pacific subtropical high (WPSH) over southern China induced by persistent mean ENSO-like states in the tropical Pacific. This also indicates that the hydroclimate in low-latitude East Asia is more sensitive to variations in tropical ENSO compared to northern China.

The Potential Influence of Maritime Continent Deforestation on El Niño‐Southern Oscillation: Insights From Idealized Modeling Experiments

AbstractDuring the past two decades, the Maritime Continent (MC) has experienced increased deforestation. Here we show, with ensemble idealized deforestation experiments, that the MC deforestation could potentially alter the complexity (i.e., event‐to‐event differences) of the El Niño‐Southern Oscillation (ENSO) in terms of its spatial pattern and temporal evolution. The deforestation model run increases the occurrences of the Central Pacific and multi‐year types of ENSO compared to the control experiments. This change in ENSO complexity can be attributed to MC's intensification of the subtropical ENSO dynamics, commonly known as the seasonal footprinting mechanism. The deforestation amplifies the mean state of the subtropical high over the northeastern Pacific, leading to an increased dominance of subtropical ENSO dynamics in determining the ENSO pattern and evolution. This idealized coupled climate modeling study suggests that MC deforestation has a potential to alter ENSO's complexity, making El Niño more complex and less predictable.

ENSO Spread Caused by Uncertainties in Initial Condition and High-Frequency Perturbations in the Tropical Pacific

Abstract Uncertainties both in the initial condition (IC) and surface wind high-frequency perturbations (HFPs) contribute to the prediction spread of El Niño–Southern Oscillation (ENSO), yet the relative roles of the two are hard to separate, mostly due to the nonlinearity in ENSO dynamics. In this study, we conducted two ensemble experiments using the CESM model to analyze the source of ENSO spread arising from the tropical Pacific. The HFPs were precluded using an online low-pass-filtering scheme in one experiment. Results showed that the uncertainty in ENSO prediction depends on both the IC and HFPs. By comparing simulations with and without HFPs, it is found that the predicted ENSO spread decreased in magnitude when HFPs are removed, especially in the first four prediction months. At integration time longer than that, the additional impact of HFPs cannot be detected on top of that of the uncertainty in IC. To the ENSO spread anomaly, we found that it is tightly associated with uncertainty in the sea surface temperature and surface wind in the central-eastern equatorial Pacific at the short integration time, and associated with the HFPs in the western equatorial Pacific at the long integration time. When the HFPs are removed, the precursor of the ENSO spread anomaly at the long lead time emerges in the northeastern tropical Pacific, the area where the Pacific meridional mode originates from. Our results indicate that the uncertainty in ENSO prediction depends primarily on IC, and the HFPs in the western equatorial Pacific play the secondary role.

ENSO phase space dynamics with an improved estimate of the thermocline depth

The recharge oscillator model of the El Niño Southern Oscillation (ENSO) describes the ENSO dynamics as an interaction between the eastern tropical Pacific sea surface temperatures (T) and subsurface heat content (thermocline depth; h), defining a dynamical cycle with different phases. h is often approximated on the basis of the depth of the 20 °C isotherm (Z20). In this study we will address how the estimation of h affects the representation of ENSO dynamics. We will compare the ENSO phase space with h estimated based on Z20 and based on the maximum gradient in the temperature profile (Zmxg). The results illustrate that the ENSO phase space is much closer to the idealised recharge oscillator model if based on Zmxg than if based on Z20. Using linear and non-linear recharge oscillator models fitted to the observed data illustrates that the Z20 estimate leads to artificial phase dependent structures in the ENSO phase space, which result from an in-phase correlation between h and T. Based on the Zmxg estimate the ENSO phase space diagram show very clear non-linear aspects in growth rates and phase speeds. Based on this estimate we can describe the ENSO cycle dynamics as a non-linear cycle that grows during the recharge and El Nino state, and decays during the La Nina states. The most extreme ENSO states are during the El Nino and discharge states, while the La Nina and recharge states do not have extreme states. It further has faster phase speeds after the El Nino state and slower phase speeds during and after the La Nina states. The analysis suggests that the ENSO phase speed is significantly positive in all phases, suggesting that ENSO is indeed a cycle. However, the phase speeds are closest to zero during and after the La Nina state, indicating that the ENSO cycle is most likely to stall in these states.

ENSO vs glacial-interglacial-induced changes in the Kuroshio-Oyashio transition zone during the Pleistocene

The subarctic front (SAF) in the pelagic North Pacific is the northernmost front of the Kuroshio-Oyashio transition zone separating the subpolar and subtropical gyres and is marked by a strong sea surface temperature gradient. A complex interplay of e.g. variations of currents, the wind system and other forcing mechanisms causes shifts of the SAF’s position on timescales from orbital to interannual. In this study, we present proxy data from the Emperor Seamount chain, which reveal a link between long-term ENSO (El Niño/Southern Oscillation) dynamics in the tropics and shifts of the SAF. Based on sediment core SO264-45-2 from Jimmu Seamount (46°33.792’N, 169°36.072’E) located close to the modern position of the SAF, we reconstruct changes in (sub)surface temperature ((sub)SSTMg/Ca) and δ18Osw-ivc (approximating salinities) via combined Mg/Ca and δ18O analyses of the shallow-dwelling foraminifera Globigerina bulloides and the near-thermocline-dwelling Neogloboquadrina pachyderma, biological productivity (XRF-based Ba/Ti ratios), and terrigenous input via dust (XRF-based Fe). From ~600 to ~280 ka BP we observe significantly higher SSTMg/Ca than after an abrupt change at 280 ka BP. We assume that during this time warmer water from the Kuroshio-Oyashio transition zone reached the core site, reflecting a shift of the SAF from a position at or even north of our study site prior to 280 ka BP to a position south of our study site after 280 ka BP. We propose that such a northward displacement of the SAF between 600-280 ka BP was induced by sustained La Niña-like conditions, which led to increased transport of tropical ocean heat into the Kuroshio-Oyashio transition zone via the Kuroshio Current. After ~280 ka BP, the change to more El Niño-like conditions led to less heat transfer via the Kuroshio Current with the SAF remaining south of the core location. In contrast, our productivity record shows a clear glacial-interglacial pattern that is common in the North Pacific. We assume that this pattern is connected to changes in nutrient supply or utilization, which are not primarily driven by changes of the Kuroshio and Oyashio Currents or the SAF.

Changes in ENSO Characteristics in Model Simulations with Considerably Altered Background Climate States

Abstract Changes in the background climate are known to affect El Niño–Southern Oscillation (ENSO) by altering feedbacks that control ENSO’s characteristics. Here, the sensitivity of ENSO variability to the background climate is investigated by utilizing two Community Earth System Model, version 1 (CESM1), simulations in which the solar constant is altered by ±25 W m−2. The resulting stable warm and cold climate mean state simulations differ in terms of ENSO amplitude, frequency, diversity, asymmetry, and seasonality. In the warm run, ENSO reveals a larger amplitude and occurs at higher frequencies relative to the cold and control runs as well as observations. The warm run also features more eastern Pacific El Niños, an increased asymmetry, and a stronger seasonal phase locking. These changes are linked to changes in the mean state via the amplifying and damping feedbacks. In the warm run, a shallower mean thermocline results in a stronger subsurface–surface coupling, whereas the cold run reveals reduced ENSO variability due to a reduced Bjerknes feedback in accordance with a deeper mean thermocline and enhanced surface wind stress. A strong zonal advective and upwelling feedback further contribute to the large ENSO amplitude in the run with a warmer mean state. In the cold run, ENSO events are partly forced by anomalous shortwave radiation. However, in light of the large temperature contrast between the simulations of up to 6 K in the tropical Pacific, the relatively small changes in ENSO variability highlight the robustness of ENSO dynamics under vastly different climate mean states.

ENSO coupling to the equatorial Atlantic: Analysis with an extended improved recharge oscillator model

IntroductionObservational and modeling studies have examined the interactions between El Niño-Southern Oscillation (ENSO) and the equatorial Atlantic variability as incorporated into the classical charge-recharge oscillator model of ENSO. These studies included the role of the Atlantic in the predictability of ENSO but assumed stationarity in the relationships, i.e., that models’ coefficients do not change over time. A recent work by the authors has challenged the stationarity assumption in the ENSO framework but without considering the equatorial Atlantic influence on ENSO.MethodsThe present paper addresses the changing relationship between ENSO and the Atlantic El Niño using an extended version of the recharge oscillator model. The classical two-variable model of ENSO is extended by adding a linear coupling on the SST anomalies in the equatorial Atlantic. The model’s coefficients are computed for different periods. This calculation is done using two methods to fit the model to the data: (1) the traditional method (ReOsc), and (2) a novel method (ReOsc+) based on fitting the Fisher’s Z transform of the auto and cross-correlation functions.ResultsWe show that, during the 20th century, the characteristic damping rate of the SST and thermocline depth anomalies in the Pacific have decreased in time by a factor of 2 and 3, respectively. Moreover, the damping time of the ENSO fluctuations has doubled from 10 to 20 months, and the oscillation period of ENSO has decreased from 60-70 months before the 1960s to 50 months afterward. These two changes have contributed to enhancing ENSO amplitude. The results also show that correlations between ENSO and the Atlantic SST strengthened after the 70s and the way in which the impact of the equatorial Atlantic is added to the internal ENSO variability.ConclusionsThe remote effects of the equatorial Atlantic on ENSO must be considered in studies of ENSO dynamics and predictability during specific time-periods. Our results provide further insight into the evolution of the ENSO dynamics and its coupling to the equatorial Atlantic, as well as an improved tool to study the coupling of climatic and ecological variables.

Assessing the Impact of Long-Term ENSO, SST, and IOD Dynamics on Extreme Hydrological Events (EHEs) in the Kelani River Basin (KRB), Sri Lanka

Hydrological extremes are common throughout the world and can be considered a globally significant phenomenon with severe environmental and social consequences. In recent decades, especially in the second half of the 20th century, Extreme Hydrological Events (EHEs) have attracted extensive attention. Physiological and anthropogenic factors have increased the frequency and severity of hydrological extremes worldwide in the last few decades. Recently, it has become a significant environmental issue in Sri Lanka. Both floods and droughts are widespread throughout the country, and the influence of floods is becoming more common every year. Currently, the frequency and severity of EHEs in the Kelani River Basin (KRB), Sri Lanka, are very common and have increased due to climate variations. Therefore, this study focused mainly on evaluating the EHEs and the impact of long-term El Niño Southern Oscillation (ENSO), Sea Surface Temperature (SST), and Indian Ocean Dipole (IOD) dynamics on extreme events. Rainfall Anomaly Index (RAI) and Extreme Precipitation Indices (EPIs) were calculated to examine the EHEs and their spatial variability. In addition, the relationships between EHEs and ENSO were investigated using several climate indices based on SST anomalies. Both observed and satellite-derived daily precipitation from 1951 to 2019 were used to assess the EHEs in the KRB. The trend of EHEs and the change points were evaluated using the Pettitt test, and teleconnection with global indices was examined using the correlation coefficient in the R application. The result of the study revealed that the pattern of EHEs varied spatially from 1951 to 2019. The strong La Niña years showed a high degree of teleconnection with EHEs in April (r = 0.622 at 0.05 significance level) and August (r = −0.732 at 0.05 significance level). NINO3.4 and the Southern Oscillation Index (SOI) have shown a significant positive impact on EHEs in the Northeast Monsoon (NEM) period. This research on KRB will be a popular scientific measure that can provide scientific results and solutions for the comprehensive decision-making process in the future. Investigating the global physical changes that influence EHEs is critical to taking the necessary steps to reduce the severity of hydrological extremes in Sri Lanka.

Indonesia’s Natural Rubber Productivity and Technically Specified Natural Rubber 20 Export: The Effect of El Nino Southern Oscillation

El Nino Southern Oscillation (ENSO) causes rainfall anomalies, which may disrupt Indonesia’s natural rubber production by interfering with the trees’ growth and affecting the export volume. This study analyzed the effect of ENSO dynamics on the monthly productivity of natural rubber and Technically Specified Natural Rubber (TSNR) 20 export. Monthly data from January 2006 to December 2019 were collected from the Statistics Indonesia, International Trade Centre (ITC), World Bank, Bank Indonesia, and National Ocean and Atmospheric Administration (NOAA). Descriptive statistics unveiled that strong La Nina increased the average of monthly productivity by 3.37% to 9.68%, while strong El Nino tended to decrease productivity by 1.30% to 9.27%. Moreover, the Vector Error Correction Model (VECM) demonstrated the negative effect of ENSO on Indonesia’s natural rubber export, both in the short and long term.

El Niño–Southern Oscillation (ENSO) predictability in equilibrated warmer climates

Abstract. Responses of El Niño–Southern Oscillation (ENSO) to global warming remain uncertain, which challenges ENSO forecasts in a warming climate. We investigate changes in ENSO characteristics and predictability in idealized simulations with quadrupled CO2 forcing from seven general circulation models. Comparing the warmer climate to control simulations, ENSO variability weakens, with the neutral state lasting longer, while active ENSO states last shorter and skew to favor the La Niña state. The 6-month persistence-assessed ENSO predictability slightly reduces in five models and increases in two models under the warming condition. While the overall changes in ENSO predictability are insignificant, we find significant relationships between changes in predictability and intensity, duration, and skewness of the three individual ENSO states. The maximal contribution to changes in the predictability of El Niño, La Niña and neutral states stems from changes in skewness and events' duration. Our findings show that a robust and significant decrease in ENSO characteristics does not imply a similar change in ENSO predictability in a warmer climate. This could be due to model deficiencies in ENSO dynamics and limitations in the persistence model when predicting ENSO.

The Essential Role of Westerly Wind Bursts in ENSO Dynamics and Extreme Events Quantified in Model “Wind Stress Shaving” Experiments

Abstract Westerly wind bursts (WWBs)—brief but strong westerly wind anomalies in the equatorial Pacific—are believed to play an important role in El Niño–Southern Oscillation (ENSO) dynamics, but quantifying their effects is challenging. Here, we investigate the cumulative effects of WWBs on ENSO characteristics, including the occurrence of extreme El Niño events, via modified coupled model experiments within Community Earth System Model (CESM1) in which we progressively reduce the impacts of wind stress anomalies associated with model-generated WWBs. In these “wind stress shaving” experiments we limit momentum transfer from the atmosphere to the ocean above a preset threshold, thus “shaving off” wind bursts. To reduce the tropical Pacific mean state drift, both westerly and easterly wind bursts are removed, although the changes are dominated by WWB reduction. As we impose progressively stronger thresholds, both ENSO amplitude and the frequency of extreme El Niño decrease, and ENSO becomes less asymmetric. The warming center of El Niño shifts westward, indicating less frequent and weaker eastern Pacific (EP) El Niño events. Removing most wind burst–related wind stress anomalies reduces ENSO mean amplitude by 22%. The essential role of WWBs in the development of extreme El Niño events is highlighted by the suppressed eastward migration of the western Pacific warm pool and hence a weaker Bjerknes feedback under wind shaving. Overall, our results reaffirm the importance of WWBs in shaping the characteristics of ENSO and its extreme events and imply that WWB changes with global warming could influence future ENSO.

Application of Deep Learning to Understanding ENSO Dynamics

Abstract Many deep learning technologies have been applied to the Earth sciences. Nonetheless, the difficulty in interpreting deep learning results still prevents their applications to studies on climate dynamics. Here, we applied a convolutional neural network to understand El Niño–Southern Oscillation (ENSO) dynamics from long-term climate model simulations. The deep learning algorithm successfully predicted ENSO events with a high correlation skill (∼0.82) for a 9-month lead. For interpreting deep learning results beyond the prediction, we present a “contribution map” to estimate how much the grid box and variable contribute to the output and “contribution sensitivity” to estimate how much the output variable is changed to the small perturbation of the input variables. The contribution map and sensitivity are calculated by modifying the input variables to the pretrained deep learning, which is quite similar to the occlusion sensitivity. Based on the two methods, we identified three precursors of ENSO and investigated their physical processes with El Niño and La Niña development. In particular, it is suggested here that the roles of each precursor are asymmetric between El Niño and La Niña. Our results suggest that the contribution map and sensitivity are simple approaches but can be a powerful tool in understanding ENSO dynamics and they might be also applied to other climate phenomena.

Frequency of different types of El Niño events under global warming

AbstractThe El Niño–Southern Oscillation (ENSO) is the dominant natural climate pattern that influences the global climate on subdecadal timescales. Thus, a better understanding of the impacts of global warming on the characteristics of ENSO events offers large socioeconomic benefits. Changes in the frequency of the eastern Pacific (EP), central Pacific (CP) and total El Niño events are analysed in the future period (2051–2100) under the two shared socioeconomic pathways (SSPs) scenarios (i.e., SSP2‐4.5 and SSP5‐8.5) compared to the historical period (1951–2000) using outputs of eight models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6). Types of El Niño events are diagnosed based on pattern correlation coefficients (PCCs) between monthly sea surface temperature (SST) anomalies and the first two leading empirical orthogonal function (EOF) modes of SST anomalies in the tropical Pacific. Based on the ensemble of models, the number of total El Niño events decreases by 26 and 16% under the SSP2‐4.5 and SSP5‐8.5 scenarios, respectively, in the future compared to the historical period. A smaller decrease in the number of total El Niño events under the faster warming rate of the SSP5‐8.5 scenario suggests that the response of ENSO dynamics to global warming is not linear. Analysis of the Extended Reconstructed Sea Surface Temperature, version 5 (ERSSTv5) dataset during the period 1951–2020 indicates that the ratio of the number of CP El Niño to EP El Niño has substantially increased over the past two decades. Nevertheless, under both the SSP2‐4.5 and SSP5‐8.5 scenarios in the future period, the ratio of the number of CP El Niño to EP El Niño does not change considerably compared to that in the historical period. This suggests that the recent increase in the frequency of CP El Niño could be a result of multidecadal variations rather than anthropogenic global warming.

ENSO atmospheric feedbacks under global warming and their relation to mean-state changes

Two atmospheric feedbacks play an important role in the dynamics of the El Niño/Southern Oscillation (ENSO), namely the amplifying zonal wind feedback and the damping heat flux feedback. Here we investigate how and why both feedbacks change under global warming in climate models participating in the 5th and 6th phase of the Coupled Model Intercomparison Project (CMIP5 and CMIP6) under the business-as-usual scenario (RCP8.5 and SSP5-8.5, respectively). The amplifying zonal wind feedback over the western equatorial Pacific (WEP) becomes significantly stronger in two third of the models, on average by 12 ± 7% in these models. The heat flux damping feedback over the eastern and central equatorial Pacific (EEP and CEP, respectively) increases as well in nearly all models, with the damping effect increasing on average by 18 ± 11%. The simultaneous strengthening of the two feedbacks can be explained by the stronger warming in the EEP relative to the WEP and the off-equatorial regions, which shifts the rising branch of the Pacific Walker Circulation to the east and increases the mean convection over the CEP. This in turn leads to a stronger vertical wind response during ENSO events over the CEP that strengthens both atmospheric feedbacks. We separate the climate models into sub-ensembles with STRONG and WEAK ENSO atmospheric feedbacks, as 2/3 of the models underestimate both feedbacks under present-day conditions by more than 40%, causing an error compensation in the ENSO dynamics. The biased mean state in WEAK in 20C constrains the ENSO atmospheric feedback projection in 21C, as the models of the WEAK sub-ensemble also have weaker ENSO atmospheric feedbacks in 21C. Further, due to the more realistic dynamics and teleconnections, we postulate that one should have more confidence in the ENSO predictions with models belonging to the STRONG sub-ensemble. Finally, we analyze the relation between ENSO amplitude change and ENSO atmospheric feedback change. We find that models simulating an eastward shift of the zonal wind feedback and increasing precipitation over the EEP during Eastern Pacific El Niño events tend to predict a larger ENSO amplitude in response to global warming.

Asymmetries in the ENSO phase space

El Niño Southern Oscillation (ENSO) dynamics are best described by the recharge oscillator model, in which the eastern tropical Pacific sea surface temperatures (T) and subsurface heat content (thermocline depth; h) have an out-of-phase relationship. This defines a 2-dimensional phase space diagram between T and h. In an idealized, stochastically forced damped oscillator, the mean phase space diagram should be a perfectly symmetrical circle with a clockwise propagation over time. However, the observed phase space shows strong asymmetries. In this study we illustrate how the ENSO phase space can be used to discuss the phase-dependency of ENSO dynamics. A normalized spherical coordinate system allows the definition of phase-depending ENSO growth rates and phase transition speeds. Based on these we discuss the implications of the observed asymmetries with regards to the dynamics and predictability of ENSO; with a particular focus on the variations in the growth rate and coupling of ENSO along the oscillation cycle. Using linear and non-linear recharge oscillator models we will show how dynamics and noise are driving ENSO at different phases of the ENSO cycles. The results illustrate that the ENSO cycle with positive phase transitions is present in all phases but has strong variations in its strength. Much of these variations result from presenting the ENSO phase space with estimates of h based on the iso-thermal depth, that is not ideal as it is not out-of-phase with T. Future work should address how h can be estimated better, including aspects such as the vertical temperature gradients and the meridional or zonal range. We further illustrated that a non-linear growth rate of T can explain most of the observed non-linear phase space characteristics.

Dynamics of ENSO Phase–Locking and Its Biases in Climate Models

AbstractThe contributions of different oceanic feedbacks to the El Niño–Southern Oscillation (ENSO) phase‐locking are examined by deriving ENSO dynamics based on the recharge‐discharge framework. In observations, the significant winter preference of the ENSO peak is determined by a strong seasonal modulation of SST growth rate, which is controlled by the zonal advective and thermodynamic feedbacks. However, the majority of climate models fail to simulate ENSO phase‐locking because the contribution of zonal advective feedback to the seasonal modulation of the SST growth rate is much smaller compared to observations. The weak annual cycle of the SST‐current coupling coefficient and small annual mean of the negative climatological zonal SST gradient are two factors contributing to the weak‐biased seasonality of zonal advective feedback. Further analysis shows that the Niño3.4 SSTA has better phase‐locking performance than Niño3 SSTA in the climate models due to the better simulation of zonal advection feedback in the Niño3.4 region.

Spatiotemporal changes of precipitation extremes in Bangladesh during 1987–2017 and their connections with climate changes, climate oscillations, and monsoon dynamics

This study provides a comprehensive framework to explore the spatiotemporal changes in precipitation extremes in Bangladesh, an important South Asian country, over the 1987–2017 period and their links with climate changes, large-scale climate oscillations ( i.e. , El Niño–Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD)), and the East Asian Summer Monsoon (EASM) dynamics. Precipitation extremes are measured by ten extreme precipitation indices (EPIs) recommended by the Expert Team on Climate Change Detection and Indices. The comprehensive framework consists of an iterative-based Mann–Kendall trend analysis to explore the trends in the EPIs across the country and a joint analysis based on a Pearson product-moment correlation analysis and a multivariate linear regression to investigate the links of the EPIs with local to global climate changes, ENSO, IOD, and EASM dynamics. This study finds that precipitation extremes show clearly regional and seasonal differential changes across Bangladesh. All of the 28 stations across Bangladesh show positive trends for consecutive dry days, while 21 out of 28 stations show negative trends in annual total wet-day precipitation. Maximum 1-day precipitation amount and 5-day precipitation amount are increasing over the coastal areas during the monsoon season and decreasing in winter for most parts of Bangladesh. The trends of precipitation extremes show a clear spatial dependency on topography and distance from the coastline. Precipitation extremes in Bangladesh appear to respond more actively to IOD, local to global climate changes, and EASM than ENSO, especially for annual total wet-day precipitation, very and extreme wet days, and wet spells. In addition, precipitation extremes in coastal regions are more responsive to global warming than in inland regions. Most of the EPIs show negative associations with local temperature anomalies and the EASM strength. This study provides a new comprehensive look at the important links of extreme precipitations variations with climate changes, large-scale climate oscillations, and EASM dynamics across Bangladesh. • Provided a comprehensive framework to explore the spatiotemporal changes in precipitation extremes in Bangladesh. • Investigated trends in precipitation extremes during 1987–2017. • Identified spatial patterns of extreme precipitation trends and the associated geography effects. • Precipitation extremes show regional and seasonal differential changes.

ENSO-ASC 1.0.0: ENSO deep learning forecast model with a multivariate air–sea coupler

Abstract. The El Niño–Southern Oscillation (ENSO) is an extremely complicated ocean–atmosphere coupling event, the development and decay of which are usually modulated by the energy interactions between multiple physical variables. In this paper, we design a multivariate air–sea coupler (ASC) based on the graph using features of multiple physical variables. On the basis of this coupler, an ENSO deep learning forecast model (named ENSO-ASC) is proposed, whose structure is adapted to the characteristics of the ENSO dynamics, including the encoder and decoder for capturing and restoring the multi-scale spatial–temporal correlations, and two attention weights for grasping the different air–sea coupling strengths on different start calendar months and varied effects of physical variables in ENSO amplitudes. In addition, two datasets modulated to the same resolutions are used to train the model. We firstly tune the model performance to optimal and compare it with the other state-of-the-art ENSO deep learning forecast models. Then, we evaluate the ENSO forecast skill from the contributions of different predictors, the effective lead time with different start calendar months, and the forecast spatial uncertainties, to further analyze the underlying ENSO mechanisms. Finally, we make ENSO predictions over the validation period from 2014 to 2020. Experiment results demonstrate that ENSO-ASC outperforms the other models. Sea surface temperature (SST) and zonal wind are two crucial predictors. The correlation skill of the Niño 3.4 index is over 0.78, 0.65, and 0.5 within the lead time of 6, 12, and 18 months respectively. From two heat map analyses, we also discover the common challenges in ENSO predictability, such as the forecasting skills declining faster when making forecasts through June–July–August and the forecast errors being more likely to show up in the western and central tropical Pacific Ocean in longer-term forecasts. ENSO-ASC can simulate ENSO with different strengths, and the forecasted SST and wind patterns reflect an obvious Bjerknes positive feedback mechanism. These results indicate the effectiveness and superiority of our model with the multivariate air–sea coupler in predicting ENSO and analyzing the underlying dynamic mechanisms in a sophisticated way.