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We use a version of the NOAA Climate Forecast System with enhanced (up to 1‐m) ocean model vertical resolution to investigate the mean diurnal cycles of upper ocean temperature and currents. The model sea surface temperature diurnal cycle agrees well with a global observational analysis. The simulated time‐depth profiles of temperature and current also match closely observations from densely instrumented moorings in the tropical Pacific. Our analyses provide new insights into subsurface ocean diurnal cycles. Significant temperature diurnal range occurs, with seasonal modulation, at depths greater than 10 m across broad areas of the subtropical and midlatitude oceans. Significant current diurnal cycles are evident below 30 m across parts of the tropics, including in areas where deep‐cycle turbulence has been observed. Plain Language Summary: We used computer model simulations of Earth's atmosphere and ocean to understand how ocean temperatures and currents vary by time of day. The model has 12 levels in the top 20 m of the ocean—greater than normal for this kind of simulation. This allows realistic simulated diurnal variations of sea surface temperature (compared to global observations), and realistic changes in temperature and current at and below the surface (compared to mooring observations). These results give us confidence in the global simulations of currents, which provide new insights into diurnal variations of surface ocean velocity and turbulence below the surface. Key Points: A 1‐m vertical resolution ocean model accurately simulates global patterns of sea surface temperature mean diurnal cycleThe model gives new insights into modes of subsurface ocean temperature and current diurnal variationGlobal maps of current diurnal cycle extend understanding past that from relatively sparse observations [ABSTRACT FROM AUTHOR]

Given the increasing attention in forecasting weather and climate on the subseasonal time scale in recent years, National Oceanic and Atmospheric Administration (NOAA) announced to support Climate Process Teams (CPTs) which aim to improve the Madden‐Julian Oscillation (MJO) prediction by NOAA's global forecasting models. Our team supported by this CPT program focuses primarily on the improvement of upper ocean mixing parameterization and air‐sea fluxes in the NOAA Climate Forecast System (CFS). Major improvement includes the increase of the vertical resolution in the upper ocean and the implementation of General Ocean Turbulence Model (GOTM) in CFS. In addition to existing mixing schemes in GOTM, a newly developed scheme based on observations in the tropical ocean, with further modifications, has been included. A better performance of ocean component is demonstrated through one‐dimensional ocean model and ocean general circulation model simulations validated by the comparison with in‐situ observations. These include a large sea surface temperature (SST) diurnal cycle during the MJO suppressed phase, intraseasonal SST variations associated with the MJO, ocean response to atmospheric cold pools, and deep cycle turbulence. Impact of the high‐vertical resolution of ocean component on CFS simulation of MJO‐associated ocean temperature variations is evident. Also, the magnitude of SST changes caused by high‐resolution ocean component is sufficient to influence the skill of MJO prediction by CFS. Plain Language Summary: The idea of Climate Process Teams (CPTs) has been suggested in early 2000 to accelerate the development of numerical models for prediction of weather and climate. Members of CPTs consist of observationalists, theoreticians, process‐oriented modelers, and scientists at modeling centers, and thus knowledge obtained from observational and process‐oriented researches can be transferred to the improvement of physical process representations in global climate models. The CPT program initiated by NOAA in 2015 specifically aims to improve prediction of Madden‐Julian Oscillation (MJO) which is the major intraseasonal (30–90 days) fluctuation in the tropical atmosphere. Our CPT primarily focuses on improving the representation of upper ocean processes relevant to the MJO in NOAA's operational climate prediction system: Climate Forecast System (CFS). Performance of the improved ocean component of CFS is evaluated through a comparison of model simulations with high‐quality in‐situ data collected during the recent field campaign which was designed to monitor ocean and atmospheric variability associated with the MJO. The improvement includes the realistic model simulation of large upper ocean warming during daytime through implementing high vertical resolution mixing schemes near the surface. The results demonstrate a significant impact of the high‐vertical resolution ocean component in CFS on the MJO prediction skill. Key Points: Upper ocean processes relevant to MJO simulations in the Climate Forecast System are improved through our Climate Process Team projectRealistic model simulations of diurnal warming and its dependence on mixing schemes are demonstrated by the comparison with in situ dataA series of CFS simulations indicate a positive impact of high vertical resolution near the ocean surface on MJO prediction skills [ABSTRACT FROM AUTHOR]

Deep cycle turbulence (DCT) is a diurnally oscillating turbulence that penetrates into a stratified shear layer below the surface mixed layer, which is often observed in the eastern Pacific and Atlantic above the Equatorial Undercurrent (EUC). Here we present the simulation of DCT by a global ocean general circulation model (OGCM) for the first time. As the k‐ε vertical mixing scheme is used in the OGCM, the simulation of observed DCT structure based on in situ microstructure measurements can be explicitly demonstrated. The simulated DCT is found in all equatorial ocean basins, and its characteristics agree very well with observations. Zonal and meridional variations of DCT in the entire equatorial Pacific and Atlantic are described through constructing the composite diurnal cycle. In the central Pacific where the maximum shear associated with EUC is deep, the separation of DCT from the surface mixed layer is much more prominent than other areas. Plain Language Summary: Deep cycle turbulence (DCT) is a nighttime intensified turbulence that develops in the stratified layer below the base of the surface mixed layer. It is often observed below the equatorial Pacific and Atlantic cold tongue regions above the Equatorial Undercurrent (EUC). Mixing caused by DCT is essential in modulating sea surface temperature (SST), which could have a large impact on air‐sea interaction and thus global climate variability. However, simulations of DCT in global ocean models have not been demonstrated so far, and the spatial variation of DCT characteristics in the entire equatorial oceans is not well known. This study presents the first global ocean general circulation model simulation of DCT, demonstrated by the comparison of simulated turbulence with that derived from in situ observations. The simulated DCT is found in all equatorial ocean basins, and its characteristics agree very well with observations. Large‐scale spatial variability of DCT in the equatorial Pacific and Atlantic is described through the analysis of model output. The DCT layer completely separated from surface mixed layer is found at locations where the EUC is deep, such as the central equatorial Pacific near the dateline. Key Points: Deep cycle turbulence is well simulated for the first time by a global ocean general circulation model in all equatorial ocean basinsLarge‐scale zonal and meridional variations of the deep cycle turbulence in equatorial Pacific and Atlantic are fully describedSeparation of deep cycle turbulence from the surface mixed layer is most prominent in the central Pacific near the dateline [ABSTRACT FROM AUTHOR]

This study investigates the impact of different specification of the underlying sea surface temperature (SST) on the prediction of intraseasonal rainfall variation associated with strong Monsoon Intraseasonal Oscillation (MISO) events in the northern Indian Ocean. A series of forecast experiments forced with observed hourly, daily, or seasonal SSTs are performed for three selected strong MISO events using the National Centers for Environmental Predictions (NCEP) atmospheric Global Forecast System (GFS). The comparison between these GFS forecasts shows that the intraseasonal SST variability is more important than its diurnal variability in the MISO prediction. The GFS experiments forced with daily SST which includes intraseasonal variability has higher prediction skill and faster speed in the northward propagation of the MISO intraseasonal rainfall anomalies than those forced with seasonal SST that do not include intraseasonal variability. No significant difference is found in the MISO prediction when GFS was forced by SST with or without SST diurnal cycle. The GFS runs forced with warmer and colder seasonal SSTs which mimic possible biases in SST prediction have comparable skill in the MISO prediction. A modified version of the NCEP Climate Forecast System coupled model (CFSm5) with 1- and 10-m vertical resolutions in the upper ocean is then used to examine their performance in the MISO prediction when all aspects of SST are actively included. The CFSm5 with 1-m vertical resolution in the upper ocean (CFSm501) shows larger amplitude of intraseasonal SST anomaly, with higher prediction skill in both intraseasonal SST and rainfall than the CFSm5 with the typical 10-m vertical resolution in the upper ocean (CFSm510) does. Compared with the uncoupled GFS, both CFSm501 and CFSm510, despite errors in predicted SSTs, have better prediction skill and more reasonable rainfall variability, which is attributed to the inclusion of active air–sea interaction. These results suggest the importance of intraseasonal variability of SST and air–sea interaction in improving the intraseasonal rainfall prediction associated with the MISO. [ABSTRACT FROM AUTHOR]

The boreal-summer MJOs exhibit three distinctive downstream evolutions on their passages over the Maritime Continent (MC): (i) smoothly transitioning from Indian Ocean (IO) to western Pacific (type-I); (ii) rapidly decayed over the MC (type-II); and (iii) significantly intensified over the MC (type-III). During the life cycles of type-I and -III MJOs, basin-wide overturning circulations connect the convective dipoles between western and eastern north Pacific. For type-II MJO, regional overturning circulations connect the convective dipoles between the IO and Western North Pacific (WNP). For type-I and -III MJOs, the associated convection over eastern IO is able to generate strong Kevin-wave responses and significant positive SST anomalies over the MC and western Pacific. On the other hand, for type-II MJO in the IO, a persistent convection lingers around the WNP. It intensifies westerly monsoon flows, thus preventing the generation of coherent positive SST anomalies ahead of the IO MJO convection. It also blocks the formation of a robust Kelvin-wave response over the MC and western Pacific by delaying the northeastward migrations of the dry phase. The convection of type-I and -III MJOs at eastern IO is able to generate positive boundary-layer moisture anomalies along with positive column-integrated moisture tendencies over the MC and western Pacific, thus allowing them to propagate eastward smoothly. On the other hand, the type-II MJO convection in eastern IO is associated with a very dry troposphere and negative column-integrated moisture tendency over western Pacific, which result in the rapid decay of convection over the MC. These results emphasize the important roles of robust Kelvin-wave responses and positive SST anomalies on the eastward propagation of boreal-summer MJOs over the MC through moistening the troposphere ahead of the convection. [ABSTRACT FROM AUTHOR]

Northward-propagating Indian summer monsoon intraseasonal oscillations (MISOs) are a major origin of the active–break spells of the monsoon rainfall. Forecast results for 28 active and 27 break spells from the National Centers for Environmental Prediction (NCEP) Climate Forecast System, version 2 (CFSv2), during 1999–2010 are analyzed. CFSv2 forecasts are able to represent the propagation of MISOs from the equator to central India, showing improvements in many aspects compared to its previous version. Systematic biases for MISOs, however, still exist, exhibiting apparently weaker amplitude and slower northward propagation compared to observations. The eastern Arabian Sea (EAS)–western Bay of Bengal (WBB) region (EAS–WBB region; 12°–20°N, 65°–85°E) is found to be critical for the MISO prediction. In that region, the forecast and observed MISO trajectories begin to bifurcate from each other, and forecast errors grow rapidly. Further diagnosis reveals that local air–sea interaction in that region is severely underrepresented in CFSv2. Sea surface temperature (SST) response to surface heat flux forcing and convection response to SST forcing are both too weak, leading to the underestimated MISO amplitude. The relationship between precipitation and SST in CFSv2 is much more chaotic than in observation. The misrepresentation of air–sea coupling results in longer MISO periods in the EAS–WBB region, manifesting as slower propagation and delayed arrival of MISOs in central India. Refining the air–sea coupling processes is crucial for improving the CFSv2 forecast. This includes taking into account the ocean skin layer, better resolving the diurnal cycle, and improving the ocean model physics. [ABSTRACT FROM AUTHOR]

Because sea ice thickness is known to influence future patterns of sea ice concentration, it is likely that an improved initialization of sea ice thickness in a coupled ocean-atmosphere model would improve Arctic sea ice cover forecasts. Here, two sea ice thickness datasets as possible candidates for forecast initialization were investigated: the Climate Forecast System Reanalysis (CFSR) and the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS). Using Ice, Cloud, and Land Elevation Satellite (ICESat) data, it was shown that the PIOMAS dataset had a more realistic representation of sea ice thickness than CFSR. Subsequently, both March CFSR and PIOMAS sea ice thicknesses were used to initialize hindcasts using the Climate Forecast System, version 2 (CFSv2), model. A second set of model runs was also done in which the original model physics were modified to more physically reasonable settings-namely, increasing the number of marine stratus clouds in the Arctic region and enabling realistic representation of the ice-ocean heat flux. Hindcasts were evaluated using sea ice concentration observations from the National Aeronautics and Space Administration (NASA) Team and Bootstrap algorithms. Results show that using PIOMAS initial sea ice thickness in addition to the physics modifications yielded significant improvement in the prediction of September Arctic sea ice extent along with increased interannual predictive skill. Significant local improvements in sea ice concentration were also seen in distinct regions for the change to PIOMAS initial thickness or the physics adjustments, with the most improvement occurring when these changes were applied concurrently. [ABSTRACT FROM AUTHOR]

Previous observational analysis and modeling studies indicate that air-sea coupling plays an essential role in improving MJO simulations and extending MJO forecasting skills. However, whether the SST feedback plays an indispensable role for the existence of the MJO remains controversial, and the precise physical processes through which the SST feedback may lead to better MJO simulations and forecasts remain elusive. The DYNAMO/Cooperative Indian Ocean Experiment on Intraseasonal Variability in the Year 2011 (CINDY) field campaign recently completed over the Indian Ocean reveals a new perspective and provides better data to improve understanding of the MJO. It is found that among the five MJO events that occurred during the DYNAMO/CINDY field campaign, only two MJO events (the November and March ones) have robust SST anomalies associated with them. For the other three MJO events (the October, December, and January ones), no coherent SST anomalies are observed. This observational scenario suggests that the roles of air-sea coupling on the MJO vary greatly from event to event. To elucidate the varying roles of air-sea coupling on different MJO events, a suite of hindcast experiments was conducted with a particular focus on the October and November MJO events. The numerical results confirm that the October MJO is largely controlled by atmospheric internal dynamics, while the November MJO is strongly coupled with underlying ocean. For the November MJO event, the positive SST anomalies significantly improve MJO forecasting by enhancing the response of a Kelvin-Rossby wave couplet, which prolongs the feedback between convection and large-scale circulations, and thus favors the development of stratiform rainfall, in turn, facilitating the production of eddy available potential energy and significantly amplifying the intensity of the model November MJO. [ABSTRACT FROM AUTHOR]

The present study assesses the forecast skill of the Madden-Julian Oscillation (MJO) observed during the period of DYNAMO (Dynamics of the MJO)/CINDY (Cooperative Indian Ocean Experiment on Intraseasonal Variability in Year 2011) field campaign in the GFS (NCEP Global Forecast System), CFSv2 (NCEP Climate Forecast System version 2) and UH (University of Hawaii) models, and revealed their strength and weakness in forecasting initiation and propagation of the MJO. Overall, the models forecast better the successive MJO which follows the preceding event than that with no preceding event (primary MJO). The common modeling problems include too slow eastward propagation, the Maritime Continent barrier and weak intensity. The forecasting skills of MJO major modes reach 13, 25 and 28 days, respectively, in the GFS atmosphere-only model, the CFSv2 and UH coupled models. An equal-weighted multi-model ensemble with the CFSv2 and UH models reaches 36 days. Air-sea coupling plays an important role for initiation and propagation of the MJO and largely accounts for the skill difference between the GFS and CFSv2. A series of forecasting experiments by forcing UH model with persistent, forecasted and observed daily SST further demonstrate that: (1) air-sea coupling extends MJO skill by about 1 week; (2) atmosphere-only forecasts driven by forecasted daily SST have a similar skill as the coupled forecasts, which suggests that if the high- resolution GFS is forced with CFSv2 forecasted daily SST, its forecast skill can be much higher than its current level as forced with persistent SST; (3) atmosphere-only forecasts driven by observed daily SST reaches beyond 40 days. It is also found that the MJO-TC (Tropical Cyclone) interactions have been much better represented in the UH and CFSv2 models than that in the GFS model. Both the CFSv2 and UH coupled models reasonably well capture the development of westerly wind bursts associated with November 2011 MJO and the cyclogenesis of TC05A in the Indian Ocean with a lead time of 2 weeks. However, the high-resolution GFS atmosphere-only model fails to reproduce the November MJO and the genesis of TC05A at 2 weeks' lead. This result highlights the necessity to get MJO right in order to ensure skillful extended-range TC forecasting. [ABSTRACT FROM AUTHOR]

While fully coupled atmosphere-ocean models have been used to study the seasonal predictability of sea ice variations within the context of models' own variability, their capability in predicting the observed sea ice at the seasonal time scales is not well assessed. In this study, sea ice predictions from the recently developed NCEP Climate Forecast System, version 2 (CFSv2), a fully coupled atmosphere-ocean model including an interactive dynamical sea ice component, are analyzed. The focus of the analysis is the performance of CFSv2 in reproducing observed Northern Hemisphere sea ice extent (SIE). The SIE climatology, long-term trend, interannual variability, and predictability are assessed. CFSv2 contains systematic biases that are dependent more on the forecast target month than the initial month, with a positive SIE bias for the forecast for January-September and a negative SIE bias for the forecast for October-December. A large source of seasonal prediction skill is from the long-term trend, which is underestimated in the CFSv2. Prediction skill of interannual SIE anomalies is found to be primarily within the first three target months and is largest in the summer and early fall. The performance of the prediction of sea ice interannual variations varies from year to year and is found to be related to initial sea ice thickness. Potential predictability based on the forecast ensemble, its dependence on model deficiencies, and implications of the results from this study for improvements in the seasonal sea ice prediction are discussed. [ABSTRACT FROM AUTHOR]

This study analyzes factors affecting the predictability of seasonal-mean precipitation over the tropical Indian Ocean. The analysis focuses on the contributions from the local sea surface temperature (SST) forcing in the Indian Ocean, the remote SST forcing related to ENSO in the tropical eastern Pacific, and the role of local air-sea coupling. To understand the impacts of the individual factors, the prediction skill over the tropical Indian Ocean for four model simulations, but with different treatments for the ocean, are compared. The seasonality in precipitation skill, the local precipitation-SST relationship, and prediction skill related to Indian Ocean dipole mode (IODM) are examined. It is found that the importance of the accuracy of local SST and the presence of local air-sea coupling in the Indian Ocean has a strong seasonal dependence. Accurate local SSTs are important during the boreal fall season, whereas the local air-sea coupling is important during the boreal spring. The precipitation skill over the Indian Ocean during boreal winter is primarily from ENSO. However, ENSO impacts are better realized with the inclusion of an interactive ocean. For all four seasons, the simulation without the interannual variations of local SST in the Indian Ocean shows the least precipitation skill and a much weaker seasonality. It is also found that, for the simulation where the global SSTs are relaxed to the observations and hence maintain some level of active air-sea coupling, the observed seasonal cycle of precipitation-SST relationship is reproduced reasonably well. In addition, the analysis also shows that simulations with accurate SST forcing display high precipitation skill during strong IODM events, indicating that IODM SST acts as a forcing for the atmospheric variability. [ABSTRACT FROM AUTHOR]

The differences in tropical air-sea interactions and global climate connection as well as the hindcast skills for the warm pool (WP) and cold tongue (CT) El Niños are investigated based on observed, (re)analyzed, and model hindcast data. The robustness of observed global climate connection is established from the model simulations. Lastly, variations of atmosphere and ocean conditions in the recent decades, and their possible connection with the frequency increase of the WP El Niño are discussed. Consistent with previous results, our individual case study and composite results suggest that stronger (weaker) and more eastward extended (westward confined) westerly wind along the equatorial Pacific in early months of a year is associated with active (suppressed) air-sea interaction over the cold tongue/the Intertropical Convergence Zone complex, as well as more (less) intensive oceanic thermocline feedback, favoring the CT (WP) El Niño development. The preceding westerly wind signal and air-sea interaction differences may be responsible for the predication skill difference with higher (lower) overall hindcast skill for the CT (WP) El Niño in the Climate Forecast System of National Centers for Environmental Prediction. Our model experiments show that, in addition to the tropics, the eastern Pacific, North America and North Atlantic are the major regions having robust climate differences between the CT and WP El Niños. Nevertheless, the climate contrasts seem not robust over the Eurasian continent. Also, the frequency increase of the WP El Niño in the recent decades may not be directly connected with the linear trend of the tropical climate. [ABSTRACT FROM AUTHOR]

In this work, the authors analyze the air-sea interaction processes associated with the persistent atmospheric and oceanic anomalies in the North Atlantic Ocean during summer 2009-summer 2010 with a record-breaking positive sea surface temperature anomaly (SSTA) in the hurricane Main Development Region (MDR) in the spring and summer of 2010. Contributions to the anomalies from the El Niño-Southern Oscillation (ENSO), the North Atlantic Oscillation (NAO), and a long-term trend are identified. The warming in the tropical North Atlantic during summer 2009-summer 2010 represented a typical response to ENSO, preconditioned and amplified by the influence of a strong and persistent negative phase of the NAO. The long-term trends enhanced the warming in the high and low latitudes and weakened the cooling in the midlatitudes. The persistent negative phase of the NAO was associated with active thermodynamic air-sea interaction in the North Atlantic basin. Surface wind anomalies associated with the NAO altered the ocean surface heat flux and changed the SSTA, which was likely further enhanced by the positive wind speed-evaporation-SST feedback. The total heat flux was dominated by the latent and sensible heat fluxes, while the shortwave radiation contributed to the tropical SSTA to a lesser degree. Sensitivity experiments with an atmospheric general circulation model forced by observed SST in the Atlantic Ocean alone suggested that the Atlantic SSTA, which was partly forced by the NAO, had some positive contribution to the persistence of the negative phase of the NAO. Therefore, the persistent NAO condition is partly an outcome of the global climate anomalies and the ocean-atmosphere feedback within the Atlantic basin. The combination of the ENSO, NAO, and long-term trend resulted in the record-breaking positive SSTA in the MDR in the boreal spring and summer of 2010. On the basis of the statistical relationship, the SSTA pattern in the North Atlantic was reasonably well predicted by using the preceding ENSO and NAO as predictors. [ABSTRACT FROM AUTHOR]

Predictability of intraseasonal oscillation (ISO) relies on both initial conditions and lower boundary conditions (or atmosphere--ocean interaction). The atmospheric reanalysis datasets are commonly used as initial conditions. Here, the biases of three reanalysis datasets [the NCEP reanalysis 1 and 2 (NCEP-R1 and -R2) and the ECMWF Re-Analysis Interim (ERA-Interim)] in describing ISO were briefly revealed and the impacts of these biases as initial conditions on ISO prediction skills were assessed. A signal-recovery method is proposed to improve ISO prediction. Although all three reanalyses underestimate the intensity of the equatorial eastward-propagating ISO, the overall quality of the ERA-Interim is better than the NCEP-R1 and -R2. When these reanalyses are used as initial conditions in the ECHAM4-University of Hawaii hybrid coupled model (UH-HCM), skillful ISO prediction reaches only about 1 week for both the 850-hPa zonal winds (U850) and rainfall over Southeast Asia and the global tropics. An enhanced nudging of the divergence field is shown to significantly improve the initial conditions, resulting in an extension of the skillful rainfall prediction by 2--4 days and U850 prediction by 5--10 days. After recovering the ISO signals in the original reanalyses, the resultant initial conditions contain ISO strength closer to the observed, whereas the rainfall spatial pattern correlation in the ERA-Interim reanalysis drops. The resultant ISO prediction skills, however, are consistently extended for all the NCEP and ERA-Interim reanalyses. Using these signal-recovered reanalyses as initial conditions, the boreal summer ISO prediction skill measured with the Wheeler--Hendon index reaches 14 days. The U850 and rainfall prediction skills, respectively, reach 23 and 18 days over Southeast Asia. It is also found that small-scale synoptic weather disturbances in initial conditions generally increase ISO prediction skills. Both the UH-HCM and NCEP Climate Forecast System (CFS) suffer the prediction barrier over the Maritime Continent. [ABSTRACT FROM AUTHOR]

This work examines the performance of Madden–Julian oscillation (MJO) forecasts from NCEP's coupled and uncoupled general circulation models (GCMs) and statistical models. The forecast skill from these methods is evaluated in near–real time. Using a projection of El Niño–Southern Oscillation (ENSO)-removed variables onto the principal patterns of MJO convection and upper- and lower-level circulations, MJO-related signals in the dynamical model forecasts are extracted. The operational NCEP atmosphere–ocean fully coupled Climate Forecast System (CFS) model has useful skill (>0.5 correlation) out to ∼15 days when the initial MJO convection is located over the Indian Ocean. The skill of the CFS hindcast dataset for the period from 1995 to 2004 is nearly comparable to that from a lagged multiple linear regression model, which uses information from the previous five pentads of the leading two principal components (PCs). In contrast, the real-time analysis for the MJO forecast skill for the period from January 2005 to February 2006 using the lagged multiple linear regression model is reduced to∼10–12 days. However, the operational CFS forecast for this period is skillful out to ∼17 days for the winter season, implying that the coupled dynamical forecast has some usefulness in predicting the MJO compared to the statistical model. It is shown that the coupled CFS model consistently, but only slightly, outperforms the uncoupled atmospheric model (by one to two days), indicating that only limited improvement is gained from the inclusion of the coupled air–sea interaction in the MJO forecast in this model. This slight improvement may be the result of the existence of a propagation barrier around the Maritime Continent and the far western Pacific in the NCEP Global Forecast System (GFS) and CFS models, as shown in several previous studies. This work also suggests that the higher horizontal resolution and finer initial data might contribute to improving the forecast skill, presumably as a result of an enhanced representation of the Maritime Continent region. [ABSTRACT FROM AUTHOR]

Predictability of intraseasonal oscillation (ISO) relies on both initial conditions and lower boundary conditions (or atmosphere--ocean interaction). The atmospheric reanalysis datasets are commonly used as initial conditions. Here, the biases of three reanalysis datasets [the NCEP reanalysis 1 and 2 (NCEP-R1 and -R2) and the ECMWF Re-Analysis Interim (ERA-Interim)] in describing ISO were briefly revealed and the impacts of these biases as initial conditions on ISO prediction skills were assessed. A signal-recovery method is proposed to improve ISO prediction. Although all three reanalyses underestimate the intensity of the equatorial eastward-propagating ISO, the overall quality of the ERA-Interim is better than the NCEP-R1 and -R2. When these reanalyses are used as initial conditions in the ECHAM4-University of Hawaii hybrid coupled model (UH-HCM), skillful ISO prediction reaches only about 1 week for both the 850-hPa zonal winds (U850) and rainfall over Southeast Asia and the global tropics. An enhanced nudging of the divergence field is shown to significantly improve the initial conditions, resulting in an extension of the skillful rainfall prediction by 2--4 days and U850 prediction by 5--10 days. After recovering the ISO signals in the original reanalyses, the resultant initial conditions contain ISO strength closer to the observed, whereas the rainfall spatial pattern correlation in the ERA-Interim reanalysis drops. The resultant ISO prediction skills, however, are consistently extended for all the NCEP and ERA-Interim reanalyses. Using these signal-recovered reanalyses as initial conditions, the boreal summer ISO prediction skill measured with the Wheeler--Hendon index reaches 14 days. The U850 and rainfall prediction skills, respectively, reach 23 and 18 days over Southeast Asia. It is also found that small-scale synoptic weather disturbances in initial conditions generally increase ISO prediction skills. Both the UH-HCM and NCEP Climate Forecast System (CFS) suffer the prediction barrier over the Maritime Continent. [ABSTRACT FROM AUTHOR]