Madden–Julian Oscillation Prediction Skill Research Articles

Understanding the Factors Controlling MJO Prediction Skill across Events

Abstract The prediction skill for individual Madden–Julian oscillation (MJO) events is highly variable, but the key factors behind this remain unclear. Using the latest hindcast results from the subseasonal-to-seasonal (S2S) phase II models, this study attempts to understand the diverse prediction skill for the MJO events with an enhanced convective anomaly over the eastern Indian Ocean (IO) at the forecast start date, by investigating the preference of the prediction skill to the MJO-associated convective anomalies and low-frequency background states (LFBS). Compared to the low-skill MJO events, the high-skill events are characterized by a stronger intraseasonal convection–circulation couplet over the IO before the forecast start date, which could result in a longer zonal propagation range during the forecast period, thereby leading to a higher score for assessing the prediction skill. The difference in intraseasonal fields can further be attributed to the LFBS of IO sea surface temperature (SST) and quasi-biannual oscillation (QBO), with the high-skill (low-skill) events corresponding to a warmer (colder) IO and easterly (westerly) QBO phase. The physical link is that a warm IO could increase the low-level convective instability and thus amplify MJO convection over the IO, whereas an easterly QBO phase could weaken the Maritime Continent barrier effect by weakening the static stability near the tropopause, thus favoring eastward propagation of the MJO. It is also found that the combined effects of IO SST and QBO phases are more effective in influencing MJO prediction skill than individual LFBS. Significance Statement Given the importance of the Madden–Julian oscillation (MJO) in the subseasonal predictability of global weather and climate, how well the MJO itself can be predicted is a matter of concern. This study reveals the critical observational factors that determine the variation in MJO prediction skill across events. Without making any presumptions about what the factors would be, the observed MJO events are separated according to their individual prediction skill, and the difference between the MJO events with higher and lower skill is then investigated. The results show that the low-frequency background states of the Indian Ocean sea surface temperature and the quasi-biannual oscillation are good indicators for MJO prediction skill, for their modulatory role in the MJO propagation range.

The Role of Subtropical Rossby Waves in Amplifying the Divergent Circulation of the Madden–Julian Oscillation

Abstract The composite structure of the Madden–Julian oscillation (MJO) has long been known to feature pronounced Rossby gyres in the subtropical upper troposphere, whose existence can be interpreted as the forced response to convective heating anomalies in the presence of a subtropical westerly jet. The question of interest here is whether these forced gyre circulations have any subsequent effects on divergence patterns in the tropics and the Kelvin-mode component of the MJO. A nonlinear spherical shallow water model is used to investigate how the introduction of different background jet profiles affects the model’s steady-state response to an imposed MJO-like stationary thermal forcing. Results show that a stronger jet leads to a stronger Kelvin-mode response in the tropics up to a critical jet speed, along with stronger divergence anomalies in the vicinity of the forcing. To understand this behavior, additional calculations are performed in which a localized vorticity forcing is imposed in the extratropics, without any thermal forcing in the tropics. The response is once again seen to include pronounced equatorial Kelvin waves, provided the jet is of sufficient amplitude. A detailed analysis of the vorticity budget reveals that the zonal-mean zonal wind shear plays a key role in amplifying the Kelvin-mode divergent winds near the equator, with the effects of nonlinearities being of negligible importance. These results help to explain why the MJO tends to be strongest during boreal winter when the Indo-Pacific jet is typically at its strongest. Significance Statement The MJO is a planetary-scale convectively coupled equatorial disturbance that serves as a primary source of atmospheric predictability on intraseasonal time scales (30–90 days). Due to its dominance and spontaneous recurrence, the MJO has a significant global impact, influencing hurricanes in the tropics, storm tracks, and atmosphere blocking events in the midlatitudes, and even weather systems near the poles. Despite steady improvements in subseasonal-to-seasonal (S2S) forecast models, the MJO prediction skill has still not reached its maximum potential. The root of this challenge is partly due to our lack of understanding of how the MJO interacts with the background mean flow. In this work, we use a simple one-layer atmospheric model with idealized heating and vorticity sources to understand the impact of the subtropical jet on the MJO amplitude and its horizontal structure.

The Idealized Aquaplanet Maritime Continent Barrier Effect on the MJO Predictability

Abstract Studies have indicated exaggerated Maritime Continent (MC) barrier effect in simulations of the Madden–Julian oscillation (MJO), a dominant source of subseasonal predictability in the tropics. This issue has plagued the modeling and operational forecasting communities for decades, while the sensitivity of MC barrier on MJO predictability has not been addressed quantitatively. In this study, perfect-model ensemble forecasts are conducted with an aquaplanet configuration of the Community Earth System Model version 2 (CESM2) in which both basic state and tropical modes of variability are reasonably simulated with a warm pool–like SST distribution. When water-covered terrain mimicking MC landmasses is added to the warm pool–like SST framework, the eastward propagation of the MJO is disturbed by the prescribed MC aqua-mountain. The MJO predictability estimate with the perfect-model experiment is about 6 weeks but reduces to about 4 weeks when the MJO is impeded by the MC aqua-mountain. Given that the recent operational forecasts show an average of 3–4 weeks of MJO prediction skill, we can conclude that improving the MJO propagation crossing the MC could improve the MJO skill to 5–6 weeks, close to the potential predictability found in this study (6 weeks). Therefore, more effort toward understanding and improving the MJO propagation is needed to enhance the MJO and MJO-related forecasts to improve the subseasonal-to-seasonal prediction.

Skill improvement of the yearly updated reforecasts in ECMWF S2S prediction from 2016 to 2022

Hazardous weather events are often accompanied by subseasonal processes, but the forecast skills of subseasonal prediction are still limited. To assess the skill improvement of the constantly updated model version in ECMWF subseasonal-seasonal (S2S) prediction from 2016 to 2022, the performance of yearly updated reforecasts was evaluated against ERA5 reanalysis data using the temporal anomaly correlation coefficient (TCC) as a metric. The newly updated reforecasts exhibit stable superiority at the weather scale of the first two weeks, regardless of whether the 2-m temperature or precipitation forecast is being considered. At the subseasonal time scale starting from the third week, some slight improvements in prediction skills are only found in several tropical regions. Generally, the week-3 TCC values averaged over global land grids still reflect an advancement in prediction skills for updated reforecasts. For the Madden–Julian Oscillation (MJO), reforecasts can reproduce the characteristics of eastward propagation, but there are deviations in the intensity and propagation range of convection anomalies for reforecasts of all seven years. Based on an evaluation of MJO prediction skill using the bivariate anomaly correlation coefficient and bivariate root-mean-square error, some differences are apparent in the MJO prediction skills among the updated reforecasts, but the improvements do not increase monotonically year by year. Despite the inherent limitation of S2S prediction, positive progress has already been achieved via the constantly updated S2S prediction in ECMWF, which reinforces the confidence in further collaboratively improving S2S prediction in the future.摘要在2016年至2022年间, ECMWF次季节预测系统不断升级并逐年完成新的回报试验. 本文考察该预测系统逐年升级带来的预测技巧提升潜力. 从2米气温和降水来看, 在起报之后的前两周内天气尺度上预测技巧表现出逐年稳定提升的趋势; 在从第三周开始的次季节时间尺度上, 预测技巧的提升仅限于热带部分区域. MJO预测技巧并不随着模式升级而逐年单调提升. 尽管目前S2S预测技巧存在局限性, 但目前已有的进展增强了在未来深入合作以提高S2S预测技术的信心.

S2S Prediction in GFDL SPEAR: MJO Diversity and Teleconnections

Abstract A subseasonal-to-seasonal (S2S) prediction system was recently developed using the GFDL Seamless System for Prediction and Earth System Research (SPEAR) global coupled model. Based on 20-yr hindcast results (2000–19), the boreal wintertime (November–April) Madden–Julian oscillation (MJO) prediction skill is revealed to reach 30 days measured before the anomaly correlation coefficient of the real-time multivariate (RMM) index drops to 0.5. However, when the MJO is partitioned into four distinct propagation patterns, the prediction range extends to 38, 31, and 31 days for the fast-propagating, slow-propagating, and jumping MJO patterns, respectively, but falls to 23 days for the standing MJO. A further improvement of MJO prediction requires attention to the standing MJO given its large gap with its potential predictability (38 days). The slow-propagating MJO detours southward when traversing the Maritime Continent (MC), and confronts the MC prediction barrier in the model, while the fast-propagating MJO moves across the central MC without this prediction barrier. The MJO diversity is modulated by stratospheric quasi-biennial oscillation (QBO): the standing (slow-propagating) MJO coincides with significant westerly (easterly) phases of QBO, partially explaining the contrasting MJO prediction skill between these two QBO phases. The SPEAR model shows its capability, beyond the propagation, in predicting their initiation for different types of MJO along with discrete precursory convection anomalies. The SPEAR model skillfully predicts the observed distinct teleconnections over the North Pacific and North America related to the standing, jumping, and fast-propagating MJO, but not the slow-propagating MJO. These findings highlight the complexities and challenges of incorporating MJO prediction into the operational prediction of meteorological variables.

Seasonality in Prediction Skill of the Madden‐Julian Oscillation and Associated Dynamics in Version 2 of NASA's GEOS‐S2S Forecast System

AbstractThe prediction skill of the Madden‐Julian Oscillation (MJO) in Version 2 of NASA's Global Earth Observing System Subseasonal to Seasonal (GEOS‐S2S) forecast system is investigated for winter and summer focusing on moistening‐related processes crucial for eastward propagating MJO activity. It is found that the annual bivariate correlation of the Real‐time Multivariate MJO time series between prediction and observation is ∼0.70, ∼0.57, and ∼0.50 at 20‐, 25‐, and 30‐day forecast leads. Correlation at long‐leads (>30 days) is noticeably higher for boreal summer initial conditions (June‐September [JJAS]), with correlations remaining above 0.5 at 35–40 days leads. Correlations are lower for boreal winter initial conditions from January through March (JFM), dropping to ∼0.5 at 25‐day lead, still comparable to the skills in the other reliable S2S forecast systems.The predicted eastward MJO propagation across the Indo‐western Pacific sector is well captured in JJAS, but is slower than observed in JFM. Investigations of the moisture field and advection and moisture sink, moist static energy (MSE) budget, and tropical circulation/pressure responses to the MJO convective heating reveal that, in JFM, those responses and moistening processes, especially the vertical MSE advection, are underestimated over and to the east of the Maritime Continent when the MJO anomaly approaches from the west. In contrast, those processes are well represented in JJAS, although moistening is overestimated due to large surface evaporation. This study suggests that improvement of the moistening tendency over that region in the boreal winter could contribute to further increases in MJO prediction skill of the GEOS‐S2S system.

NAO Influence on the MJO and its Prediction Skill in the Subseasonal-to-Seasonal Prediction Models

AbstractBased on the database of the Subseasonal to Seasonal (S2S) Prediction project of the World Weather Research Programme (WWRP) / World Climate Research Programme (WCRP), the influence of the North Atlantic Oscillation (NAO) on the Madden- Julian Oscillation (MJO) and its forecast skill is investigated. It is found that most models can capture the MJO phase changes following positive and negative NAO events. About 20 days after initialized with a positive (negative) NAO, the forecast MJO appears more frequently in phase 7 (3), which corresponds to reduced (enhanced) convection in the tropical Indian Ocean and enhanced (suppressed) convection in the western Pacific. In most S2S models the MJO prediction skill is dependent on the NAO amplitude and phase in the initial condition. A strong NAO leads to a better MJO forecast skill than a weak NAO. The MJO skill tends to be higher when the forecast starts from a negative NAO than a positive NAO. These results indicates that there is a strong Northern extratropical influence on the MJO and its forecast skill. It is important for numerical models to better represent the NAO influence to improve the simulation and prediction of the MJO.

Assessing the role of air-sea coupling in predicting Madden-Julian Oscillation with an atmosphere-ocean coupled model

AbstractThe Madden-Julian Oscillation (MJO) provides an important source of sub-seasonal to seasonal (S2S) predictability. Improved MJO prediction can be beneficial to S2S prediction of global climate and associated weather extremes. In this study, hindcasts based on an atmosphere-ocean coupled general circulation model (CGCM) are compared to those based on atmosphere general circulation models (AGCMs) to investigate influences of air-sea interactions on MJO prediction. Our results suggest that MJO prediction skill can be extended about one week longer in the CGCM hindcasts than AGCM-only experiments, particularly for boreal winter predictions.Further analysis suggests that improved MJO prediction in the CGCM is closely associated with improved representation of moistening processes. Compared to the AGCM experiments, the CGCM better predicts the boundary-layer moisture preconditioning to the east of MJO convection, which is generally considered crucial for triggering MJO deep convection. Meanwhile, the widely extended east-west asymmetric structure in free-tropospheric moisture tendency anomalies relative to the MJO convection center as seen in the observations is also well predicted in the CGCM. Improved prediction of MJO moisture processes in CGCM is closely associated with better representation of the zonal scale of MJO circulation and stronger Kelvin waves to the east of MJO convection, both of which have been recently suggested conductive for MJO eastward propagation. The above improvements by including air-sea coupling could be largely attributed to the realistic MJO-induced SST fluctuations through the convection-SST feedback. This study confirms a critical role of atmosphere-ocean coupling for the improvement of MJO prediction.

The impact of coupled data assimilation on Madden-Julian Oscillation predictability initialized from coupled satellite-era reanalysis

AbstractThis study investigates the impact of coupled initialization on the extended-range prediction of the Madden-Julian Oscillation (MJO). A set of reforecasts using combinations of the oceanic and atmospheric initial conditions produced with coupled and uncoupled data assimilation (DA) are conducted to evaluate the impact of coupling in the different domains, from the perspective of MJO forecasts. The coupled initial conditions are provided by CERA-SAT pilot coupled reanalysis for the satellite era recently produced by ECMWF. We focus on the prediction skill of the MJO using the Real-time Outgoing Long-wave Radiation (OLR) MJO index in a series of re-forecasts. The impact of atmospheric initial conditions produced by coupled DA shows slight benefit for the MJO prediction. However, compared with the operational ocean reanalysis, the ocean initial conditions created by CERA-SAT degrade the MJO prediction skill during the first 2-3 weeks of the re-forecast by 1.5% to 5.8%. A moist static energy budget analysis revealed that the underestimation of 0.2 K sea surface temperature, 1.4 W m-2 top of atmosphere downward longwave radiation, and 3.8 W m-2 latent heat flux over the Maritime Continent lead to small but statistically significant degradation of the MJO forecast skill. The results demonstrate that the MJO is sensitive to ocean initial conditions, and illustrate the value of the extended range MJO prediction for evaluating the quality of coupled data assimilation, and suggest that future efforts on coupled data assimilation pay special attention to the balance of air-sea interaction processes over the warm pool area, in terms of modeling, observational needs and system.

Deep learning for bias correction of MJO prediction

Producing accurate weather prediction beyond two weeks is an urgent challenge due to its ever-increasing socioeconomic value. The Madden-Julian Oscillation (MJO), a planetary-scale tropical convective system, serves as a primary source of global subseasonal (i.e., targeting three to four weeks) predictability. During the past decades, operational forecasting systems have improved substantially, while the MJO prediction skill has not yet reached its potential predictability, partly due to the systematic errors caused by imperfect numerical models. Here, to improve the MJO prediction skill, we blend the state-of-the-art dynamical forecasts and observations with a Deep Learning bias correction method. With Deep Learning bias correction, multi-model forecast errors in MJO amplitude and phase averaged over four weeks are significantly reduced by about 90% and 77%, respectively. Most models show the greatest improvement for MJO events starting from the Indian Ocean and crossing the Maritime Continent.

Assessing Predictive Potential Associated with the MJO during the Boreal Winter

AbstractIn this paper, the question of potential predictability in meteorological variables associated with skillful prediction of the Madden–Julian oscillation (MJO) during boreal winter is analyzed. The analysis is motivated by the fact that dynamical prediction systems are now capable of predicting MJO up to 30 days or earlier (measured in terms of anomaly correlation for RMM indices). Translating recent gains in MJO prediction skill and relating them back to potential for predicting meteorological variables—for example, precipitation and surface temperature—is not straightforward because of a chain of steps that go into the computation and evaluation of RMM indices. This paper assesses potential predictability in meteorological variables that could be attributed to skillful prediction of the MJO. The analysis is based on the observational data alone and assesses the upper limit of MJO-associated predictability that could be achieved.

MJO Prediction Skill Using IITM Extended Range Prediction System and Comparison with ECMWF S2S

Eastward propagating Madden–Julian Oscillation (MJO) is a dominant mode of the intraseasonal variability and hence a potential source of intraseasonal predictability. Therefore, advancing MJO prediction using state-of-the-art dynamical model is of utmost importance for improving intraseasonal prediction. The prediction skill and predictability of MJO are assessed using 44 members ensemble hindcast (16 years data; 2001–2016) of CFSv2 based extended range prediction (ERP) system of IITM as well as 10 member ensemble hindcast (16 years data; 2001–2016) of ECMWF S2S dataset. The MJO is diagnosed using a newly developed Extended Empirical Orthogonal Function (EEOF) analysis. Near equatorial (15° S–15° N) model anomaly fields are projected onto the leading pair of observed eigen modes. The leading pair of observed eigen modes are obtained based on the EEOF analysis of the combined field of zonal wind at 200 hPa (U200), zonal wind at 850 hPa (U850) and velocity potential at 200 hPa (chi200). Model forecasted principal components (PCs) are quantitatively compared with observed PCs using bivariate correlation coefficient and root mean square error (RMSE). We find that MJO could be predicted up to around 22 days (around 31 days) for IITM ERP system (ECMWF S2S dataset) as measured by anomaly correlation coefficient remains larger than 0.5 and RMSE remains lower than 1.4. This prediction skill is quite low compared to potential predictability, which is estimated as more than 40 days both for IITM-ERP and ECMWF system. MJO prediction skill varies with initial MJO phase, particularly at the longer lead. This variation is more significant for the ECMWF system. Model (both for IITM-ERP and ECMWF) predicted amplitude drops at a faster rate and phase propagation speed for almost all initial phase is slower and amplitude is weaker compared to the observation. It could be concluded that even the state-of-the-art models [IITM-ERP (basically NCEP CFSv2) and ECMWF] are also not free from systematic errors/biases. Hence, there is an enormous space for improving MJO prediction skill by reducing these errors/biases in the dynamical model and error in the initial condition.

Effects of Moisture Initialization on MJO and its Teleconnection Prediction in BCC Subseasonal Coupled Model

AbstractIt is well recognized that moisture is of great importance in the representation of the Madden‐Julian Oscillation (MJO). In this study, a moisture initialization scheme is developed using the Beijing Climate Center (BCC) subseasonal operational coupled model, and its effects on MJO predictions are investigated. Three sets of hindcast experiments are conducted: a reference experiment without moisture initialization (REF) and two sensitivity experiments with moderate and strong moisture nudging schemes. We show that the prediction skill of the real‐time multivariate MJO index is significantly improved in the two sensitivity experiments, where the skillful prediction can reach up to 20 days using the moderate nudging scheme. Larger improvements can be achieved when MJO was initiated from Phases 2–3 and 5–7 or during La Niña episodes, implying that the MJO prediction skill is more sensitive to moisture conditions in active convection regions and backgrounds. The moisture budget analysis indicated that the MJO prediction improvement using the moisture initialization schemes comes from the realistic representation of vertical moisture structure and vertical moisture advection to the east of the MJO convection in the planetary boundary layer. The improved MJO prediction is also demonstrated to enable a more reliable subseasonal prediction of extratropical circulation and precipitation through a more realistic description of MJO‐related teleconnections. In addition, the ensemble mean of the three schemes can extend the MJO prediction skill to more than 22 days, showing the potential benefits of ensemble prediction of multiinitialization schemes.

Insignificant QBO-MJO Prediction Skill Relationship in the SubX and S2S Subseasonal Reforecasts.

The impact of the stratospheric quasi‐biennial oscillation (QBO) on the prediction of the tropospheric Madden‐Julian oscillation (MJO) is evaluated in reforecasts from nine models participating in subseasonal prediction projects, including the Subseasonal Experiment (SubX) and Subseasonal to Seasonal (S2S) projects. When MJO prediction skill is analyzed for December to February, MJO prediction skill is higher in the easterly phase of the QBO than the westerly phase, consistent with previous studies. However, the relationship between QBO phase and MJO prediction skill is not statistically significant for most models. This insignificant QBO‐MJO skill relationship is further confirmed by comparing two subseasonal reforecast experiments with the Community Earth System Model v1 using both a high‐top (46‐level) and low‐top (30‐level) version of the Community Atmosphere Model v5. While there are clear differences in the forecasted QBO between the two model top configurations, a negligible change is shown in the MJO prediction, indicating that the QBO in this model may not directly control the MJO prediction and supporting the insignificant QBO‐MJO skill relationship found in SubX and S2S models.

Influence of the QBO on MJO prediction skill in the subseasonal-to-seasonal prediction models

Recent studies have shown that the Madden-Julian Oscillation (MJO) is significantly modulated by the stratospheric Quasi-Biennial Oscillation (QBO). In general, boreal winter MJO becomes more active during the easterly phase of the QBO (EQBO) than during the westerly phase (WQBO). Based on this finding, here we examine the possible impacts of the QBO on MJO prediction skill in the operational models that participated in the WCRP/WWRP subseasonal-to-seasonal (S2S) prediction project. All models show a higher MJO prediction skill during EQBO winters than during WQBO winters. For the bivariate anomaly correlation coefficient of 0.5, the MJO prediction skill during EQBO winters is enhanced by up to 10 days. This enhancement is insensitive to the initial MJO amplitude, indicating that the improved MJO prediction skill is not simply the result of a stronger MJO. Instead, a longer persistence of the MJO during EQBO winters likely induces a higher prediction skill by having a higher prediction limit.

Prediction of the Madden–Julian Oscillation: A Review

AbstractThere has been an accelerating interest in forecasting the weather and climate within the subseasonal time range. The Madden–Julian oscillation (MJO), an organized envelope of tropical convection, is recognized as one of the leading sources of subseasonal predictability. This review synthesizes the latest progress regarding the MJO predictability and prediction. During the past decade, the MJO prediction skill in dynamical prediction systems has exceeded the skill of empirical predictions. Such improvement has been mainly attributed to more observations and computer resources, advances in theoretical understanding, and improved numerical models aided in part by multinational efforts through field campaigns and multimodel experiments. The state-of-the-art dynamical forecasts have shown MJO prediction skill up to 5 weeks. Prediction skill can be extended by improving the ensemble generation approach tailored for MJO prediction and by averaging multiensembles or multimodels. MJO prediction skill can be influenced by the tropical mean state and low-frequency climate mode variations, as well as by the extratropical circulation. MJO prediction skill is proven to be sensitive to model physics, ocean–atmosphere coupling, and quality of initial conditions, while the impact of the model resolution seems to be marginal. Remaining challenges and recommendations on new research avenues to fully realize the predictability of the MJO are discussed.

Representation of Boreal Winter MJO and Its Teleconnection in a Dynamical Ensemble Seasonal Prediction System

This study examines the representation of the Madden–Julian oscillation (MJO) and its teleconnection in boreal winter in the Global Seasonal Forecast System, version 5 (GloSea5), using 20 years (1991–2010) of hindcast data. The sensitivity of the performance to the polarity of El Niño–Southern Oscillation (ENSO) is also investigated. The real-time multivariate MJO index of Wheeler and Hendon is used to assess MJO prediction skill while intraseasonal 200-hPa streamfunction anomalies are used to evaluate the MJO teleconnection. GloSea5 exhibits significant MJO prediction skill up to 25 days of forecast lead time. MJO prediction skill in GloSea5 also depends on initial MJO phases, with relatively enhanced (degraded) performance when the initial MJO phase is 2 or 3 (8 or 1) during the first 2 weeks of the hindcast period. GloSea5 depicts the observed MJO teleconnection patterns in the extratropics realistically up to 2 weeks albeit weaker than the observed. The ENSO-associated basic-state changes in the tropics and in the midlatitudes are reasonably represented in GloSea5. MJO prediction skill during the first 2 weeks of the hindcast is slightly higher in neutral and La Niña years than in El Niño years, especially in the upper-level zonal wind anomalies. Presumably because of the better representation of MJO-related tropical heating anomalies, the Northern Hemispheric MJO teleconnection patterns in neutral and La Niña years are considerably better than those in El Niño years.

Evaluating the MJO prediction skill from different configurations of NCEP GEFS extended forecast

NOAA is accelerating its efforts to improve the numerical guidance and prediction capability for extended range (weeks 3 and 4) prediction in its seamless forecast system. Madden Julian Oscillation (MJO) is the dominant mode of sub-seasonal variability in tropics and prediction skill of MJO is investigated in this paper. We used different configurations of the NCEP Global Ensemble Forecast System (GEFS) to perform the experiments. The configurations include (1) the operational version of the stochastic perturbation forced with operational Sea Surface Temperatures (SSTs); (2) an updated stochastic physics forced with operational SSTs; (3) an updated stochastic physics forced with bias-corrected SSTs that are from Climate Forecast System (Version 2); and (4) as in (3) but with the addition of a scale aware-convection scheme. We evaluated MJO prediction skill from the experiments using Wheeler–Hendon indices and also examined the performance of the forecast system on prediction of key MJO components. We found that using the updated stochastic scheme improved the MJO prediction lead-time by about 4 days. Further updating the underlying SSTs with the bias corrected CFSv2 forecast increased the MJO prediction lead time by another 1.7 days. The best configuration of the four experiments is the last configuration which extends forecast lead time by ~ 9 days. Further investigation shows that upper and lower level zonal wind over the tropics has larger improvement than the outgoing longwave radiation (OLR). The improvement of the MJO prediction skill appears to be related primarily to the improvement in the representation associated circulations and OLR over the tropical West Pacific.

MJO Prediction Skill of the Subseasonal-to-Seasonal Prediction Models

Abstract The Madden–Julian oscillation (MJO), the dominant mode of tropical intraseasonal variability, provides a major source of tropical and extratropical predictability on a subseasonal time scale. This study conducts a quantitative evaluation of the MJO prediction skill in state-of-the-art operational models, participating in the subseasonal-to-seasonal (S2S) prediction project. The relationship of MJO prediction skill with model biases in the mean moisture fields and in the longwave cloud–radiation feedbacks is also investigated. The S2S models exhibit MJO prediction skill out to a range of 12 to 36 days. The MJO prediction skills in the S2S models are affected by both the MJO amplitude and phase errors, with the latter becoming more important at longer forecast lead times. Consistent with previous studies, MJO events with stronger initial MJO amplitude are typically better predicted. It is found that the sensitivity to the initial MJO phase varies notably from model to model. In most models, a notable dry bias develops within a few days of forecast lead time in the deep tropics, especially across the Maritime Continent. The dry bias weakens the horizontal moisture gradient over the Indian Ocean and western Pacific, likely dampening the organization and propagation of the MJO. Most S2S models also underestimate the longwave cloud–radiation feedbacks in the tropics, which may affect the maintenance of the MJO convective envelope. The models with smaller bias in the mean horizontal moisture gradient and the longwave cloud–radiation feedbacks show higher MJO prediction skills, suggesting that improving those biases would enhance MJO prediction skill of the operational models.

MJO prediction skill, predictability, and teleconnection impacts in the Beijing Climate Center Atmospheric General Circulation Model

This study evaluates performance of Madden–Julian oscillation (MJO) prediction in the Beijing Climate Center Atmospheric General Circulation Model (BCC_AGCM2.2). By using the real-time multivariate MJO (RMM) indices, it is shown that the MJO prediction skill of BCC_AGCM2.2 extends to about 16–17days before the bivariate anomaly correlation coefficient drops to 0.5 and the root-mean-square error increases to the level of the climatological prediction. The prediction skill showed a seasonal dependence, with the highest skill occurring in boreal autumn, and a phase dependence with higher skill for predictions initiated from phases 2–4. The results of the MJO predictability analysis showed that the upper bounds of the prediction skill can be extended to 26days by using a single-member estimate, and to 42days by using the ensemble-mean estimate, which also exhibited an initial amplitude and phase dependence. The observed relationship between the MJO and the North Atlantic Oscillation was accurately reproduced by BCC_AGCM2.2 for most initial phases of the MJO, accompanied with the Rossby wave trains in the Northern Hemisphere extratropics driven by MJO convection forcing. Overall, BCC_AGCM2.2 displayed a significant ability to predict the MJO and its teleconnections without interacting with the ocean, which provided a useful tool for fully extracting the predictability source of subseasonal prediction.