Spring Predictability Barrier Research Articles

The Potential Role of Seasonal Surface Heating on the Chaotic Origins of the El Niño/Southern Oscillation Spring Predictability Barrier

AbstractThe Spring Predictability Barrier (SPB) phenomenon is characterized by the reduced accuracy of El Niño/Southern Oscillation (ENSO) forecasts during the spring, which substantially limits our ability to predict ENSO events. By investigating the nonlinear dynamic characteristics of ENSO systems simulated by a box model, we found that the strong surface heating process in spring may contribute to the SPB by regulating the different coupling processes between the ocean and atmosphere. Specifically, the intensified springtime surface heating increases the Sea Surface Temperature (SST), further amplifying the thermal damping effect of SST anomalies and reducing the dynamic connection between zonal SST gradient and upwelling process, and finally increasing the chaotic degree of ENSO systems simulated by the box model. The enhanced chaotic degree of ENSO systems leads to a more rapid growth of initial errors in the forecast model in spring, potentially leading to the SPB phenomenon.

Decadal preference of seasonal ENSO transition through a southern hemisphere climate mode

El Niño Southern Oscillation (ENSO) is the leading interannual coupled climate mode in the tropical Pacific. The seasonal transition of ENSO from boreal winter to the following summer can significantly affect the global climate. One of the major hurdles in understanding the seasonal transition of ENSO is the spring predictability barrier. Here, we show that ENSO’s seasonal transition is modulated by a multidecadal climate mode of boreal spring sea-level pressure (SLP) in the extratropical Southern Hemisphere. This ENSO transition mode (ETM), when characterised by a decrease in SLP and associated clockwise circulation of the surface winds centred over the southeastern subtropical Pacific Ocean, produces westerly anomalies at the equator. These wind anomalies in the equatorial eastern Pacific aid the seasonal warming of Niño3.4 sea surface temperature anomalies (N34SST) from boreal winter to the following summer. The ETM time series shows prominent multidecadal variations at around 50 years. This creates a conducive environment for alternate cold and warm seasonal transitions leading to multidecadal variations in boreal summer N34SST. Thus, ETM provides a physical insight into the seasonal transition of ENSO and leads to a new paradigm for ENSO evolution beyond its peak. This has implications for seasonal ENSO forecasts and decadal climate predictions.

Central-Pacific El Niño-Southern Oscillation less predictable under greenhouse warming

El Niño-Southern Oscillation (ENSO) is the dominant mode of interannual climate variability in the tropical Pacific, whose nature nevertheless may change significantly in a warming climate. Here, we show that the predictability of ENSO may decrease in the future. Across the models in the Coupled Model Intercomparison Project Phase 6 (CMIP6), we find a robust decrease of the persistence and predictability for the Central Pacific (CP) ENSO under global warming, notably in passing through the boreal spring. The strength of spring predictability barrier will be increased by 25% in the future. The reduced predictability of CP ENSO is caused by the faster warming over surface ocean in tropical Pacific and, in turn, the enhanced thermodynamical damping rate on CP ENSO in response to global warming. In contrast, the predictability of Eastern Pacific ENSO will not change. Our results suggest that future greenhouse warming will make the prediction of CP ENSO more challenging, with far-reaching implications on future climate predictions.

Drying Over Eastern China Driven by the Depletion of Arctic Stratospheric Ozone During Boreal Spring

AbstractGiven the significant importance of spring precipitation for agricultural production in China and the presence of the spring predictability barrier, scientists have dedicated extensive efforts to understand the factors influencing spring precipitation variability and explore new predictors. However, the effects of Arctic stratospheric ozone (ASO) on precipitation in China during boreal spring, if any, and the underlying mechanisms remain unclear. We found the robust influences of March ASO on the differences in the precipitation and evaporation in April over Eastern China during 1980–2020. When ASO decreases in March, it tends to result in a higher and colder tropopause in the polar, a stronger subtropical jet stream, an intensified local Hadley circulation accompanied by anomalous downward motion over Eastern China, and consequently, drying in this region, and vice versa. These findings suggest that the likelihood of April moistening over East Asia may be potentially predicted by employing the ASO index.

Deep learning with autoencoders and LSTM for ENSO forecasting

AbstractEl Niño Southern Oscillation (ENSO) is the prominent recurrent climatic pattern in the tropical Pacific Ocean with global impacts on regional climates. This study utilizes deep learning to predict the Niño 3.4 index by encoding non-linear sea surface temperature patterns in the tropical Pacific using an autoencoder neural network. The resulting encoded patterns identify crucial centers of action in the Pacific that serve as predictors of the ENSO mode. These patterns are utilized as predictors for forecasting the Niño 3.4 index with a lead time of at least 6 months using the Long Short-Term Memory (LSTM) deep learning model. The analysis uncovers multiple non-linear dipole patterns in the tropical Pacific, with anomalies that are both regionalized and latitudinally oriented that should support a single inter-tropical convergence zone for modeling efforts. Leveraging these encoded patterns as predictors, the LSTM - trained on monthly data from 1950 to 2007 and tested from 2008 to 2022 - shows fidelity in predicting the Niño 3.4 index. The encoded patterns captured the annual cycle of ENSO with a 0.94 correlation between the actual and predicted Niño 3.4 index for lag 12 and 0.91 for lags 6 and 18. Additionally, the 6-month lag predictions excel in detecting extreme ENSO events, achieving an 85% hit rate, outperforming the 70% hit rate at lag 12 and 55% hit rate at lag 18. The prediction accuracy peaks from November to March, with correlations ranging from 0.94 to 0.96. The average correlations in the boreal spring were as large as 0.84, indicating the method has the capability to decrease the spring predictability barrier.

Enhancing ENSO predictions with self-attention ConvLSTM and temporal embeddings

El Niño-Southern Oscillation (ENSO), a cyclic climate phenomenon spanning interannual and decadal timescales, exerts substantial impacts on the global weather patterns and ecosystems. Recently, deep learning has brought considerable advances in the accurate prediction of ENSO occurrence. However, the current models are insufficient to characterize the evolutionary behavior of the ENSO, particularly lacking comprehensive modeling of local-range and longrange spatiotemporal interdependencies, and the incorporation of calendar monthly and seasonal properties. To make up this gap, we propose a Two-Stage SpatioTemporal (TSST) autoregressive model that couples the meteorological factor prediction with ENSO indicator prediction. The first stage predicts the meteorological time series by leveraging self-attention ConvLSTM network which captures both the local and the global spatial-temporal dependencies. The temporal embeddings of calendar months and seasonal information are further incorporated to preserves repeatedly-occurring-yet-hidden patterns in meteorological series. The second stage uses multiple layers to extract higher level of features from predicted meteorological factors progressively to generate ENSO indicators. The results demonstrate that our model outperforms the state-of-the-art ENSO prediction models, effectively predicting ENSO up to 24 months and mitigating the spring predictability barrier.

The Indian Ocean Weakens the ENSO Spring Predictability Barrier: Role of the Indian Ocean Basin and Dipole Modes

Abstract Prediction of El Niño–Southern Oscillation (ENSO) is hindered by a spring predictability barrier (SPB). In this paper, we investigate the effects of the Indian Ocean (IO) on the SPB. Using a seasonally varying extended IO–ENSO recharge oscillator model, we find that the SPB is much weakened when IO is coupled with ENSO. To gauge the relative role of the Indian Ocean dipole (IOD) and the Indian Ocean Basin (IOB) modes in weakening ENSO SPB, we develop an empirical dynamical model, the linear inverse model (LIM). By coupling/decoupling the IOB or IOD mode with ENSO, we show that the IOB significantly weakens eastern Pacific and central Pacific ENSO SPBs, while the IOD plays a weaker role. The evolution of the optimum initial structures also illustrates the importance of the IOB in ENSO SPB. Moreover, the IOB strongly influences the forecast skill of La Niña SPB rather than El Niño SPB. This point is also identified through six coupled models from the North American multimodel ensemble. It may be related to the role of the IO in the asymmetry in the duration of El Niño and La Niña. The IOB-induced easterly wind anomalies are conducive to the development of La Niña and thus the prediction of La Niña events, whereas these anomalous easterlies are less important during the development of El Niño and the related forecast of El Niño events.

Improvements in September Arctic Sea Ice Predictions Via Assimilation of Summer CryoSat‐2 Sea Ice Thickness Observations

AbstractBecause of a spring predictability barrier, the seasonal forecast skill of Arctic summer sea ice is limited by the availability of melt‐season sea ice thickness (SIT) observations. The first year‐round SIT observations, retrieved from CryoSat‐2 from 2011 to 2020, are assimilated into the GFDL ocean–sea ice model. The model's SIT anomaly field is brought into significantly better agreement with the observations, particularly in the Central Arctic. Although the short observational period makes forecast assessment challenging, we find that the addition of May–August SIT assimilation improves September local sea ice concentration (SIC) and extent forecasts similarly to SIC‐only assimilation. Although most regional forecasts are improved by SIT assimilation, the Chukchi Sea forecasts are degraded. This degradation is likely due to the introduction of negative correlations between September SIC and earlier SIT introduced by SIT assimilation, contrary to the increased correlations found in other regions.

A new subsurface precursor across the spring predictability barrier for the ENSO prediction

Despite decades of research on forecasting the El Niño–Southern Oscillation (ENSO) in the past decades, skillfully predicting the ENSO across the spring predictability barrier remains challenging. In this study, we utilize the ensemble of four model products to identify a new subsurface precursor consisting of the anomalous equatorial zonal velocity over 180°–150°W and 140–180 m and potential temperature in the western Pacific (over 120°–140°E and 120–160 m), which could predict the ENSO before spring by applying regression analysis. Such a precursor can predict the Niño 3.4 index in December at a lead time of 13 months with a regressed correlation of 0.79. The anomalous subsurface zonal velocity in the central Pacific favors the eastward migration of the warm water during the development of El Niño. Further validations with diverse training and application periods indicate that for initialization in November, this new precursor shows high skills in predicting the Niño 3.4 index at a lead time of 6–18 months over 1993–2016, outperforming counterparts for the other traditional precursors. Our study can help highlight the importance of subsurface processes in the ENSO development, improving our general ability to predict the ENSO, and enabling better preparedness for the implications of its occurrence.

Sea Surface Salinity Strongly Weakens ENSO Spring Predictability Barrier

AbstractPrevious studies suggested that tropical sea surface salinity (SSS) can influence tropical Pacific sea surface temperature (SST) through mixing and entrainment and thus it may be a signal for El Niño‐Southern Oscillation (ENSO) prediction. This paper explores the influence of SSS on ENSO spring predictability barrier (SPB) using an empirical dynamic model ‐ Linear Inverse Model (LIM). By coupling and decoupling SSS in the LIM, we find that tropical Pacific SSS plays a significant role in weakening both Central‐Pacific and Eastern‐Pacific ENSO SPB. The evolution of optimal initial structure also shows the importance of SSS dynamics in ENSO. We found an SSS mode that plays the dominant role in SSS impacting ENSO prediction. By the analysis of lead‐lag correlation, we find that this mode can induce easterlies during the spring, which finally leads to a La Niña‐like SST pattern in the winter through zonal advective and thermocline feedbacks.

Forecasting the El Niño type well before the spring predictability barrier

El Niño events represent anomalous episodic warmings, which can peak in the equatorial Central Pacific (CP events) or Eastern Pacific (EP events). The type of an El Niño (CP or EP) has a major influence on its impact and can even lead to either dry or wet conditions in the same areas on the globe. Here we show that the difference of the sea surface temperature anomalies between the equatorial western and central Pacific in December enables an early forecast of the type of an upcoming El Niño (p-value < 10−3). Combined with a previously introduced climate network-based approach that allows to forecast the onset of an El Niño event, both the onset and type of an upcoming El Niño can be efficiently forecasted. The lead time is about 1 year and should allow early mitigation measures. In December 2022, the combined approach forecasted the onset of an EP event in 2023.

Improved Seasonal Forecast Skill of Pan-Arctic and Regional Sea Ice Extent in CanSIPS Version 2

Abstract This study assesses the forecast skill of the Canadian Seasonal to Interannual Prediction System (CanSIPS), version 2, in predicting Arctic sea ice extent on both the pan-Arctic and regional scales. In addition, the forecast skill is compared to that of CanSIPS, version 1. Overall, there is a net increase of forecast skill when considering detrended data due to the changes made in the development of CanSIPSv2. The most notable improvements are for forecasts of late summer and autumn target months that have been initialized in the months of April and May that, in previous studies, have been associated with the spring predictability barrier. By comparison of the skills of CanSIPSv1 and CanSIPSv2 to that of an intermediate version of CanSIPS, CanSIPSv1b, we can attribute skill differences between CanSIPSv1 and CanSIPSv2 to two main sources. First, an improved initialization procedure for sea ice initial conditions markedly improves forecast skill on the pan-Arctic scale as well as regionally in the central Arctic, Laptev Sea, Sea of Okhotsk, and Barents Sea. This conclusion is further supported by analysis of the predictive skill of the sea ice volume initialization field. Second, the change in model combination from CanSIPSv1 to CanSIPSv2 (exchanging the constituent CanCM3 model for GEM-NEMO) improves forecast skill in the Bering, Kara, Chukchi, Beaufort, East Siberian, Barents, and the Greenland–Iceland–Norwegian (GIN) Seas. In Hudson and Baffin Bay, as well as the Labrador Sea, there is limited and unsystematic improvement in forecasts of CanSIPSv2 as compared to CanSIPSv1.

El Niño and La Niña asymmetry in short-term predictability on springtime initial condition

El Niño-Southern Oscillation (ENSO) asymmetry in predictability on springtime initial condition remains unclear. From the perspective of the spring predictability barrier (SPB), this paper investigates the ENSO asymmetry in SPB and explores the potential factors that may lead to this asymmetry. Both the observation and 29 Coupled Model Intercomparison Project Phase 6 (CMIP6) models show that the spring sea surface temperature (SST) persistence is significantly higher in El Niño years than that in La Niña years, and the SPB intensity is stronger in La Niña years than that in El Niño years. Through the recharge oscillator model, observation and CMIP6 models, we demonstrate that the nonlinear wind stress response to SST anomalies in spring is the main cause of the asymmetric SPB intensity. By the mixed-layer heat budget of the tropical Pacific in the spring, we further identify that a stronger response of zonal wind stress in El Niño events can cause a stronger zonal advection feedback, which finally leads to a weaker SPB and enhances the predictability of El Niño. In contrast, the cooling SST in the spring only leads to weak easterly anomalies, the zonal advection feedback is relatively weaker, thus SPB is stronger and the predictability of La Niña is lower. From the perspective of SPB, we suggest that El Niño is more predictable than La Niña.

Understanding Arctic Sea Ice Thickness Predictability by a Markov Model

Abstract The Arctic sea ice decline and associated change in maritime accessibility have created a pressing need for sea ice thickness (SIT) predictions. This study developed a linear Markov model for the seasonal prediction of model-assimilated SIT. It tested the performance of physically relevant predictors by a series of sensitivity tests. As measured by the anomaly correlation coefficient (ACC) and root-mean-square error (RMSE), the SIT prediction skill was evaluated in different Arctic regions and across all seasons. The results show that SIT prediction has better skill in the cold season than in the warm season. The model performs best in the Arctic basin up to 12 months in advance with ACCs of 0.7–0.8. Linear trend contributions to model skill increase with lead months. Although monthly SIT trends contribute largely to the model skill, the model remains skillful up to 2-month leads with ACCs of 0.6 for detrended SIT predictions in many Arctic regions. In addition, the Markov model’s skill generally outperforms an anomaly persistence forecast even after all trends were removed. It also shows that, apart from SIT itself, upper-ocean heat content (OHC) generally contributes more to SIT prediction skill than other variables. Sea ice concentration (SIC) is a relatively less sensitive predictor for SIT prediction skill than OHC. Moreover, the Markov model can capture the melt-to-growth season reemergence of SIT predictability and does not show a spring predictability barrier, which has previously been observed in regional dynamical model forecasts of September sea ice area, suggesting that the Markov model is an effective tool for SIT seasonal predictions.

Multi-decadal variation of ENSO forecast skill since the late 1800s

Diagnosing El Niño-Southern Oscillation (ENSO) predictability within operational forecast models is hindered by computational expense and the need for initialization with three-dimensional fields generated by global data assimilation. We instead examine multi-year ENSO predictability since the late 1800s using the model-analog technique, which has neither limitation. We first draw global coupled model states from pre-industrial control simulations, from the Coupled Model Intercomparison Project Phase 6, that are chosen to initially match observed monthly sea surface temperature and height anomalies in the Tropics. Their subsequent 36-month model evolution are the hindcasts, whose 20th century ENSO skill is comparable to twice-yearly hindcasts generated by a state-of-the-art European operational forecasting system. Despite the so-called spring predictability barrier, present throughout the record, there is substantial second-year ENSO skill, especially after 1960. Overall, ENSO exhibited notably high values of both amplitude and skill towards the end of the 19th century, and again in recent decades.

Using an ensemble nonlinear forcing singular vector data assimilation approach to address the ENSO forecast uncertainties caused by the “spring predictability barrier” and El Niño diversity

Using an ensemble nonlinear forcing singular vector data assimilation approach to address the ENSO forecast uncertainties caused by the “spring predictability barrier” and El Niño diversity

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.

Reducing the Spring Barrier in Predicting Summer Arctic Sea Ice Concentration

AbstractThe predictive skill of summer sea ice concentration (SIC) in the Arctic presents a steep decline when initialized before June, which is the so‐called spring predictability barrier for Arctic sea ice. This study explores the potential influence of surface heat flux, cloud and water vapor anomalies on monthly to seasonal predictions of Arctic SIC anomalies. The results show an enhancement in skill predicting Arctic September SIC in the models that use surface fluxes, clouds, or water vapor in combination with SIC and surface sea temperature as predictors when initialized in boreal spring. This result shows the potential to reduce the spring barrier for Arctic SIC predictions by including the surface heat budget. The enhanced predictive skill can be very likely linked to the improved representation of the thermodynamics associated with water vapor and cloudiness anomalies in spring.

Contrasting El Niño–La Niña Predictability and Prediction Skill in 2-Year Reforecasts of the Twentieth Century

Abstract Despite the growing demand for long-range ENSO predictions beyond 1 year, quantifying the skill at these lead times remains limited. This is partly due to inadequate long records of seasonal reforecasts that make skill estimates of irregular ENSO events quite challenging. Here, we investigate ENSO predictability and the dependency of prediction skill on the ENSO cycle using 110 years of 24-month-long 10-member ensemble reforecasts from ECMWF’s coupled model (SEAS5-20C) initialized on 1 November and 1 May during 1901–2010. Results show that Niño-3.4 SST can be skillfully predicted up to ∼18 lead months when initialized on 1 November, but skill drops at ∼12 lead months for May starts that encounter the boreal spring predictability barrier in year 2. The skill beyond the first year is highly conditioned to the phase of ENSO: Forecasts initialized at peak El Niño are more skillful in year 2 than those initialized at peak La Niña, with the transition to La Niña being more predictable than to El Niño. This asymmetry is related to the subsurface initial conditions in the western equatorial Pacific: peak El Niño states evolving into La Niña are associated with strong upper-ocean heat discharge of the western Pacific, the memory of which stays beyond 1 year. In contrast, the western Pacific recharged state associated with La Niña is usually weaker and shorter-lived, being a weaker preconditioner for subsequent El Niño, the year after. High prediction skill of ENSO events beyond 1 year provides motivation for extending the lead time of operational seasonal forecasts up to 2 years.

How Well Can Current Climate Models Simulate the Connection of the Early Spring Aleutian Low to the Following Winter ENSO?

Abstract A recent study revealed an impact of the intensity of early spring Aleutian low (AL) on the succeeding winter ENSO. This study examines the ability of 41 climate models that participated in CMIP6 in simulating the early spring AL–winter ENSO connection. It is shown that there exists a large diversity among the models in simulating this AL–ENSO linkage. A number of models capture well the observed AL–ENSO connection and the associated physical processes. However, the AL–ENSO relation in several models is opposite to the observed. Diversity of the AL–ENSO connection is related to the spread in the spatial structure of AL-related atmospheric anomalies over the North Pacific. In the models that capture the observed AL–ENSO connection, weakened AL induces an anomalous anticyclone over the northern middle and high latitudes and an anomalous cyclone over the subtropical North Pacific. The resultant westerly wind anomalies over the tropical western-central Pacific (TWCP) induce an El Niño sea surface temperature (SST) anomaly pattern in the following winter. By contrast, in the models with the AL–ENSO relation opposite to the observations, the AL-associated anomalous anticyclone over the North Pacific extends too southward. As such, the subtropical North Pacific is dominated by northeasterly wind anomalies and SST cooling. The subtropical North Pacific SST cooling induces easterly wind anomalies over the TWCP via wind–evaporation–SST feedback, and leads to a La Niña anomaly pattern in the following winter. The spread in the spatial structure of the AL-associated atmospheric anomalies over the North Pacific is partly due to the diversity in the amplitude of the climatological mean flow. Significance Statement A recent study suggested that variation of the AL intensity in early spring could exert a significant impact on the following winter ENSO. It indicated that inclusion of the early spring AL signal could improve the prediction of ENSO and to some extent help reduce the spring predictability barrier of ENSO. To employ the AL as a predictor in the ENSO prediction and forecast, the current climate model should have the ability to simulate realistically the early spring AL variation as well as the physical process linking the early spring AL with the subsequent winter ENSO. Hence, this study examines the performance of the current coupled climate models that participated in the phase 6 of the Coupled Model Intercomparison Project (CMIP6) in simulating the linkage between the early spring AL and the following winter ENSO. We show that there exists a large diversity among the CMIP6 models in simulating the early spring AL–winter ENSO connection. A number of models capture well the observed AL–ENSO connection and the associated physical processes. However, the AL–ENSO relation in several models is opposite to the observed. The factors leading to the spread are further examined. Results of this study would have implications in improving our understanding of the impact of extratropical atmospheric forcing on the ENSO and improving the seasonal forecasting of the ENSO.