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Heat extremes frequently hit different regions synchronously during boreal summer over the Eurasian continent. A remarkable coupling is first revealed between Eurasian heat extreme occurrence and individual extratropical intraseasonal oscillation (EISO). Further, the combined EISOs facilitate and largely increase the occurrence probabilities of synchronous Eurasian heat extremes. These dominant combined EISOs together contribute 20–45% to the total heat extreme days over the five Eurasian regions where the climatological heat extremes occur most frequently. A multi-model hindcast further shows that the subseasonal prediction exhibits higher skills for synchronous heat extremes over the combined-EISO hotspot regions when the associated combined EISOs are active, supporting the notion that the monitoring and prediction of EISOs are crucial for heat extremes' early warning. Skillful prediction of EISOs opens a pathway for heat extremes' prediction by extending it from the weather to the subseasonal timescales. [ABSTRACT FROM AUTHOR]

An analysis of diabatic heating and moistening processes from 12 to 36 h lead time forecasts from 12 Global Circulation Models are presented as part of the "Vertical structure and physical processes of the Madden-Julian Oscillation (MJO)" project. A lead time of 12-36 h is chosen to constrain the large-scale dynamics and thermodynamics to be close to observations while avoiding being too close to the initial spin-up of the models as they adjust to being driven from the Years of Tropical Convection (YOTC) analysis. A comparison of the vertical velocity and rainfall with the observations and YOTC analysis suggests that the phases of convection associated with the MJO are constrained in most models at this lead time although the rainfall in the suppressed phase is typically overestimated. Although the large-scale dynamics is reasonably constrained, moistening and heating profiles have large intermodel spread. In particular, there are large spreads in convective heating and moistening at midlevels during the transition to active convection. Radiative heating and cloud parameters have the largest relative spread across models at upper levels during the active phase. A detailed analysis of time step behavior shows that some models show strong intermittency in rainfall and differences in the precipitation and dynamics relationship between models. The wealth of model outputs archived during this project is a very valuable resource for model developers beyond the study of the MJO. In addition, the findings of this study can inform the design of process model experiments, and inform the priorities for field experiments and future observing systems.

A unique set of relaxation experiments with a forecast model initialized during December–January 1999–2019 is used to explore tropical influence on the Northern Hemisphere polar stratosphere and stratosphere–troposphere coupling, to quantify predictability benefits due to the perfect knowledge of the tropical variability. On average, predictability of the polar stratosphere, as represented by the 50 hPa geopotential height anomalies north of 50°N (Z50), increases from 17 days in freely running (control) forecasts to 21 days in the tropical relaxation experiments. At sub‐seasonal time‐scales, a statistically significant improvement in weekly mean skill scores can be demonstrated in 14%–20% of individual forecast ensembles, mostly in cases when the skill of the corresponding control forecast is worse than average. In these forecasts, root‐mean‐square errors and forecast spread of Z50 during forecast weeks 3–5 are decreased by 10%–15%. Stratospheric improvements are detected during periods of both vortex strengthening and vortex weakening, including most major Sudden Stratospheric Warmings that occurred during the study period, via modulation of the upward wave activity fluxes. An active Madden–Julian Oscillation (MJO) is found in most of these events with MJO phase 5–7 preceding vortex weakening and MJO phase 3–4 preceding vortex strengthening. Forecasts with improved stratospheric circulation also have improved tropospheric circulation during and after the periods when improvements are detected in the stratosphere. We attribute these improvements to both stratosphere–troposphere coupling and tropospheric tropical teleconnections. [ABSTRACT FROM AUTHOR]

The tropical Pacific plays an important role in modulating the global climate through its prevailing sea-surface temperature spatial structure and dominant climate modes like El Niño-Southern Oscillation (ENSO), the Madden-Julian Oscillation (MJO), and their teleconnections. These modes of variability, including their oceanic anomalies, are considered to provide sources of prediction skill on subseasonal timescales in the Tropics. Therefore, this study aims to examine how assimilating in situ ocean observations influences the initial ocean sea-surface temperature (SST) and mixed-layer depth (MLD) and their subseasonal forecasts. We analyze two subseasonal forecast systems generated with the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS), where the ocean states were initialized using two Observing-System Experiment (OSE) reanalyses. We find that the SST differences between forecasts with and without ocean data assimilation grow with time, resulting in a reduced cold-tongue bias when assimilating ocean observations. Two mechanisms related to air-sea coupling are considered to contribute to this growth of SST differences. One is a positive feedback between the zonal SST gradient, pressure gradient, and surface wind. The other is the difference in Ekman suction and mixing at the Equator due to surface wind-speed differences. While the initial mixed-layer depth (MLD) can be improved through ocean data assimilation, this improvement is not maintained in the forecasts. Instead, the MLD in both experiments shoals rapidly at the beginning of the forecast. These results emphasize how initialization and model biases influence air-sea interaction and the accuracy of subseasonal forecasts in the tropical Pacific. [ABSTRACT FROM AUTHOR]

This study combines operational reforecasts (2001–21) with results from a lower-resolution 41-yr reforecast (1980–2020) to provide a robust assessment of wintertime Euro-Atlantic regimes and their modulation by tropospheric and stratospheric teleconnection pathways in the European Centre for Medium-Range Weather Forecasts (ECMWF) Subseasonal to Seasonal Prediction project (S2S). In both operational and lower-resolution reforecasts, the climatological properties of wintertime Euro-Atlantic regimes, including regime structures, frequencies, and transition probabilities, are accurately simulated at S2S lead times. However, the 41-yr reforecasts allow us to diagnose substantial errors in regime statistics when conditioned on modes of intraseasonal-to-interannual variability. In particular, ECMWF reforecasts underestimate the response of the North Atlantic Oscillation (NAO) to the Madden–Julian oscillation (MJO) and fail to reproduce the modulation of MJO–NAO teleconnections by El Niño–Southern Oscillation (ENSO). Teleconnection and atmospheric wave diagnostics highlight two specific issues that are likely to contribute to these conditional errors in ECMWF reforecasts: (i) insufficient propagation of Rossby wave activity from the Pacific to the Atlantic following MJO phase 3 during El Niño conditions, when the direct tropospheric teleconnection pathway is most active; and (ii) an underestimated response of the stratospheric polar vortex following MJO phase 8 during La Niña conditions, when the indirect stratospheric teleconnection pathway is most active. Improving the representation of tropospheric and stratospheric teleconnection pathways is thus a priority for improving ECMWF forecasts of extratropical weather regimes and their associated surface impacts. Significance Statement: Subseasonal to Seasonal Prediction project (S2S) forecasts are used operationally at ECMWF to provide early warning of cold conditions in Europe associated with persistent large-scale circulation patterns known as weather regimes. On average, ECMWF reforecasts accurately simulate wintertime Euro-Atlantic regime structures and frequencies at S2S lead times. However, regime forecasts show substantial errors when we restrict our analysis to certain phases of intraseasonal and interannual variability, such as El Niño–Southern Oscillation (ENSO). These errors are related to deficiencies in the simulated response of weather regimes to well-predicted variability in the tropics. Improving the representation of such tropical–extratropical teleconnections will improve predictions of extratropical weather regimes and their associated surface impacts. [ABSTRACT FROM AUTHOR]

Previous studies suggest that boreal summer intraseasonal variations along the subtropical westerly jet (SJ), featuring quasi-biweekly periodicity, frequently modulate downstream subseasonal variations over East Asia (EA). Based on subseasonal hindcasts from six dynamical models, this study discovered that the leading two–three-week prediction skills for surface air temperature (SAT) are significantly higher in summer with stronger intraseasonal oscillation along the SJ, which are best demonstrated over the eastern Tibetan Plateau, Southwest Basin, and North China. The reasons are that the enhanced quasi-biweekly wave and its energy dispersion along the SJ cause more regular quasi-biweekly periodic variations of downstream SAT, which potentially increase regional predictability. This study suggests that the strengthened intraseasonal periodic signals along the SJ would enhance the subseasonal predictability in downstream regions, which could provide a window of opportunity for achieving better subseasonal prediction for EA SAT. [ABSTRACT FROM AUTHOR]

Subseasonal‐to‐seasonal (S2S) forecasts of atmospheric rivers (ARs) are in high demand in the water supply management and flood control communities. This study focuses on a new metric, the accumulated water vapor transport associated with ARs, which is closely related to the winter precipitation over the western U.S., and provides a multi‐model S2S prediction skill assessment. The prediction skill is evaluated at lead time 1–4 weeks in four dynamical model hindcast data sets from National Centers for Environmental Prediction (NCEP), European Center for Medium‐Range Weather Forecasts (ECMWF), Environment and Climate Change Canada (ECCC), and Global Modeling and Assimilation Office (GMAO) at National Aeronautics and Space Administration. Three reanalysis data sets are used to evaluate the uncertainty of prediction skills related to the choice of references. The AR‐related water vapor transport is underestimated in ECMWF and ECCC over most of the investigated region, while its maximum has a southeastward shift in NCEP and GMAO at lead time 3–4 weeks. The root mean square error, anomaly correlation coefficient, and Brier skill score are calculated to quantify the prediction skill in both deterministic and probabilistic sense. At week‐3 lead time, the models have significant skill near the lower latitudes (<40°N) of the eastern North Pacific, extending northeastward to the California coastal area. Models have higher skills in forecasting no and strong AR cases than weak cases. The Madden–Julian Oscillation can modulate the prediction skill at week‐3 lead over central and Southern California, but with large uncertainties across models. Plain Language Summary: An atmospheric river (AR) is a long and narrow corridor of strong horizontal water vapor transport, which plays a key role in transporting water vapor from the tropics and subtropics to feed precipitation over the U.S. West Coast. Therefore, subseasonal‐to‐seasonal (S2S) forecasts of ARs are in high demand in water supply management and flood control communities. This study provides an S2S prediction skill assessment for the weekly accumulated water vapor transport from ARs at 1–4 weeks lead times in four dynamical model hindcast data sets. Three reanalysis data sets are used as a reference to evaluate the model hindcasts and test the impacts of different reference data sets on the evaluation results. Several metrics are used to quantify the prediction skills. The results show that the models have some useful skills in predicting the weekly water vapor transport related to ARs at week‐3 lead over the lower latitudes of the eastern North Pacific, extending northeastward to the California coastal area. Models have higher skills in forecasting no and strong AR weeks than weak AR weeks. The Madden–Julian Oscillation has some impacts on the prediction skill at week‐3 lead over central and Southern California, but the impacts vary across models. Key Points: Dynamical models are skillful in forecasting weekly water vapor transport by atmospheric rivers (ARs) at week‐3 lead over the western U.SModels have higher skill in forecasting no and strong AR‐related water vapor transport cases than weak cases at weeks 1–4 lead timeMadden‐Julian Oscillation can modulate the forecast skill of water vapor transport by ARs at week 3 over central and Southern California [ABSTRACT FROM AUTHOR]

Despite the well‐recognized initial value nature of the subseasonal forecasts, the role of subsurface ocean initialization in subseasonal forecasts remains underexplored. Using observing system experiments, this study investigates the impact of ocean in situ data assimilation on the propagation of Madden–Julian Oscillation (MJO) events across the Maritime Continent in the European Centre for Medium‐Range Weather Forecasts (ECMWF) subseasonal forecast system. Two sets of twin experiments are analyzed, which only differ on the use or not of in situ ocean observations in the initial conditions. Besides using the Real‐time Multivariate MJO Index (RMMI) to evaluate the forecast performance, we also develop a new MJO tracking method based on outgoing longwave radiation anomalies (OLRa) for forecast evaluation. We find that the ocean initialization with in situ data assimilation, though having an impact on the forecasted ocean mean state, does not improve the relatively low MJO forecast skill across the Maritime Continent. Moist static energy budget analysis further suggests that a significant underestimation in the meridional moisture advection in the model forecast may hinder the potential role played by the ocean state differences associated with data assimilation. Bias of the intraseasonal meridional winds in the model is a more important factor for such underestimation than the mean state moisture biases. This finding suggests that atmospheric model biases dominate the forecast error growth, and the atmospheric circulation bias is one of the major sources of the MJO prediction error and should be a target for improving the ECMWF subseasonal forecast model. Plain Language Summary: Assimilating ocean subsurface observations can provide a better ocean state estimate for initializing weather and climate forecast in models. This study assesses the impact of the ocean subsurface data assimilation on subseasonal forecasts from the European Centre for Medium‐Range Weather Forecasts (ECMWF). For the subseasonal prediction, the Madden–Julian Oscillation (MJO)—the dominant tropical atmospheric variability at the subseasonal time scale—is commonly used to evaluate the model skill. This study specifically focuses on the MJO propagation across the Maritime Continent. By using two quantitative methods, we find ocean in situ observations have no impact on forecasting the MJO propagation. Furthermore, we find the coupled forecast model has atmospheric biases in its meridional wind field, which leads to an underestimation in the meridional moisture advection, thus potentially reducing the MJO prediction skill. Such an atmospheric origin of the model error may hinder the potential impact of ocean subsurface data assimilation. While previous research emphasized the importance of improving the mean state moisture representation in the model for MJO forecast, our findings emphasize the importance of improving the meridional wind representation. Key Points: Ocean in situ data assimilation has an impact on subseasonal sea surface temperature forecasts but has little impact on forecasting Madden–Julian Oscillation (MJO) propagation across Maritime ContinentMoist static energy budget analysis reveals that the MJO forecast errors are likely due to the underestimated meridional moisture advectionThe intraseasonal meridional wind biases contribute more to the underestimated meridional moisture advection than the systematic dry biases [ABSTRACT FROM AUTHOR]

This study evaluates the ability of state-of-the-art subseasonal-to-seasonal (S2S) forecasting systems to represent and predict the teleconnections of the Madden-Julian oscillation and their effects on weather in terms of midlatitude weather patterns and North Atlantic tropical cyclones. This evaluation of forecast systems applies novel diagnostics developed to track teleconnections along their preferred pathways in the troposphere and stratosphere, and to measure the global and regional responses induced by teleconnections across both the Northern and Southern Hemispheres. Results of this study will help the modeling community understand to what extent the potential to predict the weather on S2S time scales is achieved by the current generation of forecasting systems, while informing where to focus further development efforts. The findings of this study will also provide impact modelers and decision-makers with a better understanding of the potential of S2S predictions related to MJO teleconnections. [ABSTRACT FROM AUTHOR]

Record‐breaking above‐normal temperatures were observed across western North America in June–July 2021. In this study, our ability to predict this heatwave 2–3 weeks in advance is assessed based on 10 Subseasonal‐to‐Seasonal prediction models. It is found that the above normal temperature in Western Canada during June 28–July 4 was predicted by most models as early as June 10. However, for the forecasts initialized earlier than June 17, not a single ensemble member of all the models is able to capture the magnitude of the observed temperature anomaly. We identify two important processes: an upper tropospheric wave train associated with the boreal summer intraseasonal oscillation in Southeast Asia and an anomalous North Pacific atmospheric river leading to high moisture conditions. Most models are able to predict the wave train across the North Pacific. A realistic representation of moisture transport and its pattern appears crucial for the extended‐range forecast of this heatwave. Plain Language Summary: Record‐breaking high temperatures were observed across western Canada and the U.S. Pacific Northwest from late June to early July 2021. The heatwave caused more than 1,400 deaths, and led to numerous extensive wildfires and widespread smoke pollution. An accurate long‐range prediction of such heatwaves is needed and remains an enormous challenge. In this study, we assess our current ability in predicting this heatwave 2–3 weeks in advance. We identify two important processes for this heatwave: influence of the boreal summer monsoon variability in Southeast Asia and moisture transport across the North Pacific. We investigate why some forecasts are more successful than others and how the forecast could be improved. Key Points: The ability of current state‐of‐the‐art operational Subseasonal‐to‐Seasonal systems to predict an extreme heat wave 2–3 weeks in advance is assessedTwo important dynamical processes for the extreme heatwave are identifiedA realistic representation of moisture transport and its pattern is important for the extended‐range forecast of heatwaves [ABSTRACT FROM AUTHOR]

This study examines the relationship between the Madden‐Julian oscillation (MJO) and stratospheric quasi‐biennial oscillation (QBO) in a state‐of‐the‐art global numerical weather forecast model. A set of 61‐day model integrations, with 15 ensemble members, is performed across 84 start dates during December–February of 1989–2016. For 28 of those dates—every 1 January—the stratosphere is initialized from observation, and the model simulates stronger MJO events during observed easterly QBO phases (QBOE) than westerly QBO phases (QBOW). However, in these "control experiments," the QBO's impact on the MJO is already present in the initial conditions, and the direct influence of the model stratosphere during the simulation is unclear. To explore this more directly, the model was rerun with an artificially imposed QBOE and QBOW state, replacing the existing stratospheric initial condition above 150 hPa while leaving the troposphere unaltered. Though the imposed QBO states weaken faster in the model than in observations, their persistence is comparable to the control simulations. The MJO is stronger during imposed‐QBOE experiments than imposed‐QBOW, and differences are statistically significant by several metrics, though magnitude of the differences is smaller than in observations. Analysis suggests that the strength of the MJO response to the QBO increases for simulations with stronger upper‐tropospheric temperature differences and for simulations in which the MJO at the initialization time is strong and active over the Maritime Continent. However, tropospheric conditions still appear to have a dominant effect in explaining the apparent QBO influence in this model. Key Points: Forecasted MJO events are stronger in the QBO easterly phase than the westerly phase in a stratosphere‐resolving modelChanging initial QBO states artificially leads to small but significantly greater MJO amplitudes in QBO easterly versus westerly phasesMJO differences in different QBO phases still appear to be mainly driven by tropospheric initial conditions, not the model stratosphere [ABSTRACT FROM AUTHOR]

The impact of the quasi‐biennial oscillation (QBO) on the prediction of tropical intraseasonal convection, including the Madden Julian Oscillation (MJO) and Boreal Summer Intraseasonal Oscillation (BSISO), is assessed in the WMO Subseasonal to Seasonal (S2S) forecast database using the real‐time OLR based MJO (ROMI) index. It is shown that the ROMI prediction skill for the boreal winter MJO, measured by the maximum time at which the anomaly correlation coefficient exceeds 0.6, is higher by 5 to 10 days in the QBO easterly phase than its westerly phase. This difference occurs even in models with low tops and poorly resolved stratospheres. MJO predictability, as measured by signal to noise ratio in the S2S ensemble, also shows a similar difference between the two QBO phases, and results from a simple linear regression model show consistent behavior as well. Analysis of the ROMI index derived from observations indicates that the MJO is more coherent and stronger in the QBO easterly phase than in the westerly phase. These results suggest that the skill dependence on QBO phase results from the initial state of the MJO, the regularity of its propagation in the verifying observations, or most likely a combination of the two, but not on an actual stratospheric influence on the MJO within the model simulations. In contrast to the robust QBO‐MJO connection in boreal winter, the BSISO prediction skill exhibited by the S2S models in boreal summer is greater in the QBO westerly phase than in the easterly phase during the 1999 to 2010 period. This is consistent with the observation that BSISO OLR anomalies are stronger in the QBO westerly phase during that period. However, this relationship between the QBO and BSISO in boreal summer changes in recent decades: BSISO is weaker in QBO westerly than easterly during 1979–2000. Correspondingly, the QBO impact on BSISO prediction in boreal summer also reverses in that period as well in a statistical model, whereas this statistical model shows a consistent QBO impact on MJO prediction in boreal winter over the past four decades. Key Points: Prediction skill of boreal winter MJO convection is higher by 5–10 days in QBO easterly than QBO westerly in the WMO/S2S modelsModel forecasted stratosphere has little influence on MJO predictionBSISO prediction skill in QBO westerly is higher than easterly in 2000–2010 but reverses before 1999 [ABSTRACT FROM AUTHOR]

A multimodel evaluation of subseasonal‐to‐seasonal (S2S) hindcast skill of atmospheric rivers (ARs) out to 4‐week lead over the western United States is presented for three operational hindcast systems: European Centre for Medium‐Range Weather Forecasts (ECMWF; Europe), National Centers for Environmental Prediction (NCEP; U.S.), and Environment and Canada Climate Change (ECCC; Canada). Ensemble mean biases and Brier Skill Scores are examined for no, moderate, and high levels of AR activity (0, 1–2, and 3–7 AR days/week, respectively). All hindcast systems are more skillful in predicting no and high AR activity relative to moderate activity. There are isolated regions of skill at week‐3 over 150–125°W, 25–35°N for the no and high AR activity levels, with larger magnitude and spatial extent of the skill in ECMWF and ECCC compared to NCEP. The spatial pattern of this skill suggests that for high AR activity, a southwest‐to‐northeast orientation is more predictable at subseasonal lead times than other orientations, and for no AR activity, more skill exists in the subtropical North Pacific, upstream of central and southern California. AR hindcast skill along the western U.S. is most strongly increased in hindcasts initialized during Madden‐Julian Oscillation (MJO) Phases 1 and 8, and hindcast skill is substantially decreased over California in hindcasts initialized during MJO Phase 4. Skill modulations in the ECMWF hindcast system conditioned on El Niño‐Southern Oscillation phase are weaker than those conditioned on particular MJO phases. This work provides hindcast skill benchmarks and uncertainty quantification for experimental real‐time forecasts of AR activity during winters 2019–2021 as part of the S2S Prediction Project Real‐time Pilot Initiative in collaboration with the California Department of Water Resources. Key Points: A multimodel subseasonal‐to‐seasonal (S2S) hindcast skill assessment of atmospheric river (AR) activity over the western United States is presentedThe modulation of hindcast skill conditioned on phases of active MJO and ENSO events is quantifiedAn experimental S2S AR activity forecast product designed with California Department of Water Resources stakeholders is introduced [ABSTRACT FROM AUTHOR]

The Stochastically Perturbed Parametrization Tendencies (SPPT) scheme is used in ensemble forecast systems to represent uncertainties due to the atmospheric physics parametrizations. The scheme works by introducing random perturbations to the tendencies from the physics schemes. The size of the perturbations is modulated by the size of the net tendencies from the full physics suite. Recent work has identified an inconsistency in this approach to attributing model uncertainty: greater uncertainty is attributed to the night‐time sky than to the daytime sky in clear‐sky conditions, which is not deemed physically realistic. This paper documents the details of a revised configuration of SPPT, which became active in the recent operational version of the ensemble forecast system ("CY45R1") at the European Centre for Medium‐Range Weather Forecasts (ECMWF) — released in June 2018. In this work, a modification to SPPT is outlined such that the clear‐sky contribution from the radiation scheme is subtracted from the physics tendencies that are acted on by SPPT. Inspection of individual vertical profiles of physics tendencies provides a greater understanding of the interactions between processes, and helps demonstrate that the revision gives a more realistic description of the associated uncertainties. Forecast experiments with the revised version of SPPT demonstrate neutral to positive impacts on the skill of the ensemble forecasts in the medium‐range; and in the extended‐range, a significantly improved match between the spread and error of the principal components describing the Madden–Julian Oscillation. In addition, it is shown that including the revisions in the ensemble of data assimilations leads to increases in spread in the boundary layer. [ABSTRACT FROM AUTHOR]

Several monsoon indices have been applied to multiple models from the sub-seasonal to seasonal ( S2S) prediction project database during the period May to October 1999-2010 to assess their ability to simulate the Asian monsoon. The bivariate anomaly correlation ( BAC) of the Boreal Summer IntraSeasonal Oscillation ( BSISO) index suggests that the operational models can predict the BSISO1 and BSISO2 events up to 6-24.5 and 6.5-14 days in advance respectively, although the models tend to underestimate the amplitude of BSISO as the lead time increases. For the strong BSISO events, BSISO1 ( BSISO2) display lower skill mostly in phases 3-5 for all the models, suggesting that the BSISO1 ( BSISO2) is not easy to predict when it is located over India and the Maritime Continent (South China Sea and Bay of Bengal). On the other hand, the higher skills appear in different phases for different models. For instance, the limit of predictive skill of strong BSISO1 and BSISO2 events in phases 6-7 for the ECMWF ensemble forecast could exceed 30 and 28 days, respectively. The comparisons of the BSISO life cycle among the ECMWF, NCEP and CMA models also indicate that the ECMWF model can better predict the evolution of strong BSISO events. Predictions of additional monsoon circulation indices including the Webster-Yang index ( WY), Indian Summer Monsoon index ( ISM), South Asian Monsoon index ( SAM), and SouthEast Asian Monsoon index ( SEAM) in the S2S models have statistically significant skill over the corresponding monsoon regions up to 9-31, 3-17, 7-13 and 7-14 days, respectively. However, the significant skill of summer monsoon precipitation over the SAM and SEAM regions varies significantly among the models, with the skill ranging from 2 days to 2 weeks lead time. [ABSTRACT FROM AUTHOR]

Information is provided on the International Workshop on the Madden-Julian Oscillation (MJO) held from August 6 to 9, 2016 in Chengdu, China. Topics include the impact of MJO on global weather and climate, its predictability to measure the progress in the study of MJO, and general framework frictional convergence theory.

The characteristics of the MJO propagation across the Maritime Continent are investigated using a 20-yr reforecast dataset from the ECMWF ensemble prediction system. Analysis of the MJO events initialized over the Indian Ocean (phase 2) shows that the initial MJO amplitude and prediction skill relationship is not linear, particularly when the predictions start in moderate (between strong and weak) MJO amplitude category. To examine the key factors that determine the prediction skill, reforecasts in the moderate category are grouped into high- and low-skill events, and the differences in their ocean-atmospheric conditions as well as the physical processes during reforecast period are examined. The initial distribution of OLR anomalies in high-skill events shows a clear dipole pattern of convection with an enhanced convective anomalies over the Indian Ocean and strongly suppressed convective anomalies in the western Pacific Ocean. This dipole mode may support the MJO propagation across the Maritime Continent via the Rossby wave response and associated meridional moisture advection. Prominent ocean-atmosphere coupled processes are also simulated during the propagation of high-skill events. However, in low-skill events, the convective signal over the western Pacific is almost absent and less organized, and the ocean-atmosphere coupled processes are not simulated correctly. It is found that in both high- and low-skill events, the amplitude of the convective anomaly decreases significantly after about day 15, possibly due to the systematic mean model bias. A strong wet bias in the vicinity of the Maritime Continent, a cold SST bias in the equatorial Pacific, and associated circulation biases make the west Pacific area unfavorable for MJO propagation, thus limiting its prediction skill. [ABSTRACT FROM AUTHOR]

This study introduces a concept of global versus local forecast skill of the Madden-Julian oscillation (MJO). The global skill, measured by a commonly used MJO index [the Real-time Multivariate MJO (RMM)], evaluates the model's capability of forecasting global patterns of the MJO, with an emphasis on the zonal wind fields. The local skill is measured by a method of tracking the eastward propagation of MJO precipitation. It provides quantitative information of the strength, propagation speed, and timing of MJO precipitation in a given region, such as the Indian Ocean. Both global and local MJO forecast skills are assessed for ECMWF forecasts of three MJO events during the 2011-12 Dynamics of the MJO (DYNAMO) field campaign. Characteristics of error growth differ substantially between global and local MJO forecast skills, and between the three MJO quantities (strength, speed, and timing) of the local skill measure. They all vary considerably among the three MJO events. Deterioration in global forecast skill for these three events appears to be related to poor local skill in forecasting the propagation speed of MJO precipitation. The global and local MJO forecast skill measures are also applied to evaluate numerical experiments of observation denial, humidity relaxation, and forcing by daily perturbations in sea surface temperature (SST). The results suggest that forecast skill or errors of convective initiation of the three MJO events have global origins. Effects of local (Indian Ocean) factors, such as enhanced observations in the initial conditions, variability of tropospheric humidity and tropical SST, on forecasts of MJO initiation and propagation are limited. [ABSTRACT FROM AUTHOR]

The intraseasonal oscillation (e.g., the MJO and the BSISO) is the major source of predictability of extreme weather in a S2S time scale (e.g., 3-4 weeks). Many authors examined the predictability of the MJO and the BSISO in subjectively selected months. Recently, Kikuchi et al. (2016) applied a method which can objectively determine which ISO mode is dominant in a given day to a cloud-system resolving climate simulation data. They pointed out that the model has the BSISO bias even in boreal winter and simulated amplitude of ISO is about half of the observation. Considering that the models drift toward their own climatology in some timescales, it is helpful for model improvement to know how the drift occurs. Here we applied Kikuchi's method to the S2S data. All the S2S models examined show the BSISO bias even in boreal winter and some of those monotonically increase in the BSISO appearance frequency with the lead time. This result obviously indicates that the BSISO bias in boreal winter occurs from the drift of the model climatology. We will discuss what causes the drift at the presentation. [ABSTRACT FROM AUTHOR]

The finite resolution of general circulation models of the coupled atmosphere-ocean system and the effects of sub-grid-scale variability present a major source of uncertainty in model simulations on all time scales. The European Centre for Medium-Range Weather Forecasts has been at the forefront of developing new approaches to account for these uncertainties. In particular, the stochastically perturbed physical tendency scheme and the stochastically perturbed backscatter algorithm for the atmosphere are now used routinely for global numerical weather prediction. The European Centre also performs long-range predictions of the coupled atmosphere-ocean climate system in operational forecast mode, and the latest seasonal forecasting system--System 4--has the stochastically perturbed tendency and backscatter schemes implemented in a similar way to that for the medium-range weather forecasts. Here, we present results of the impact of these schemes in System 4 by contrasting the operational performance on seasonal time scales during the retrospective forecast period 1981-2010 with comparable simulations that do not account for the representation of model uncertainty. We find that the stochastic tendency perturbation schemes helped to reduce excessively strong convective activity especially over the Maritime Continent and the tropical Western Pacific, leading to reduced biases of the outgoing longwave radiation (OLR), cloud cover, precipitation and near-surface winds. Positive impact was also found for the statistics of the Madden-Julian oscillation (MJO), showing an increase in the frequencies and amplitudes of MJO events. Further, the errors of El Niño southern oscillation forecasts become smaller, whereas increases in ensemble spread lead to a better calibrated system if the stochastic tendency is activated. The backscatter scheme has overall neutral impact. Finally, evidence for noise-activated regime transitions has been found in a cluster analysis of mid-latitude circulation regimes over the Pacific-North America region. [ABSTRACT FROM AUTHOR]