Intraseasonal Forecast Research Articles

The Indo-Pacific Maritime Continent Barrier Effect on MJO Prediction

Abstract The Madden–Julian oscillation (MJO) is often observed to weaken or sometimes completely decay as its convective anomaly moves from the Indian Ocean over to the Maritime Continent (MC), which is known as the MC barrier effect on the MJO. This barrier effect is often exaggerated in numerical models. Using 23 years of the retrospective intraseasonal forecast from two coupled model systems with useful MJO prediction skills, we show that the predictive skill of the real-time multivariate MJO (RMM) index for the continuously propagating MJO events across the MC region is higher than for the blocked MJO events. The greater prediction skill is not related to the higher initial RMM amplitude for the continuous MJO events. Rather the higher skill arises from the more persistent behavior of the propagating MJO events as the convective anomaly moves through the MC region into the western Pacific. The potential predictability is similar for both types of MJO events, suggesting the forecast models hardly differentiate the two types of MJO events in prediction; they only maintain higher RMM magnitudes of the continuously propagating events. The global reanalysis dataset indicates that the blocked events are often associated with persistent higher surface pressures over colder sea surface temperatures in the central Pacific, suggesting the large-scale environment plays a role in promoting or inhibiting the MJO propagation across the MC region. Caveats in the models to reproduce the observed MJO events are also discussed.

Evaluation of the RegCM regional model driven by CFSv2 in intra-seasonal forecasts for the Alcântara Launch Center

Meteorological forecasts with a predictability window greater than 15 days, characterize the intra-seasonal forecast, which requires initial state conditions of the atmosphere and slower variability related to the temperature of the sea surface. Driving regional models by global models, it is possible to generate intra-seasonal forecasts. For the following study, we used the “regional climate model” (RegCM) nested in the global climate model “Climate Prediction System” (CFSv2) to predict temperature and the wind magnitude at 10 meters. For South America, 10 projections were made for the months of April and September. The dexterity of the model was attested by comparing it with the data from the ERA5 reanalysis model. This study evaluates the effectiveness of the model in describing the main atmospheric characteristics in force in South America, for operational purposes of the Alcântara Launch Center (CLA) in Maranhão. The evaluated scenarios were promising, where the temperature and wind intensity data showed low anomaly values for almost the entire continent. Generally, the model can prescribe well the conditions of circulation near the surface, being of great importance for the planning of launches of space vehicles.

A Statistical Intraseasonal Prediction Model of Extended Boreal Summer Western North Pacific Tropical Cyclone Genesis

Abstract An L2 regularized logistic regression model is developed in this study to predict weekly tropical cyclone (TC) genesis over the western North Pacific (WNP) Ocean and subregions of the WNP including the South China Sea (SCS), the western WNP (WWNP), and the eastern WNP (EWNP). The potential predictors for the TC genesis model include a time-varying TC genesis climatology, the Madden–Julian oscillation (MJO), the quasi-biweekly oscillation (QBWO), and ENSO. The relative importance of the predictors in a constructed L2 regression model is justified by a forward stepwise selection procedure for each region from a 0-week to a 7-week lead. Cross-validated hindcasts are then generated for the corresponding prediction schemes out to a 7-week lead. The TC genesis climatology generally improves the regional model skill, and the importance of intraseasonal oscillations and ENSO is regionally dependent. Over the WNP, there is increased model skill over the time-varying climatology in predicting weekly TC genesis out to a 4-week lead by including the MJO and QBWO, whereas ENSO has a limited impact. On a regional scale, ENSO and then either the MJO or QBWO are the two most important predictors over the EWNP and WWNP after the TC genesis climatology. The MJO is found to be the most important predictor over the SCS. The logistic regression model is shown to have comparable reliability and forecast skill scores to the ECMWF dynamical model on intraseasonal time scales. Significance Statement Skillful forecasts of tropical cyclone activity on time scales from short-range to seasonal are now issued operationally. Although there has been great progress in the understanding of physical mechanisms driving tropical cyclone (TC) activity, intraseasonal prediction of TCs remains a significant scientific challenge. This study develops a statistically based intraseasonal model to predict weekly TC genesis over the western North Pacific Ocean basin. The intraseasonal prediction model developed here for TC genesis over the western North Pacific basin shows skill extending out to four weeks. We discuss the regional dependence of the model skill on ENSO and other subseasonal climate oscillations. This approach provides skillful intraseasonal forecasting of TCs over the western North Pacific basin.

Maximum covariance analysis to identify intraseasonal oscillations over tropical Brazil

A reliable prognosis of extreme precipitation events in the tropics is arguably challenging to obtain due to the interaction of meteorological systems at various time scales. A pivotal component of the global climate variability is the so-called intraseasonal oscillations, phenomena that occur between 20 and 100 days. The Madden–Julian Oscillation (MJO), which is directly related to the modulation of convective precipitation in the equatorial belt, is considered the primary oscillation in the tropical region. The aim of this study is to diagnose the connection between the MJO signal and the regional intraseasonal rainfall variability over tropical Brazil. This is achieved through the development of an index called Multivariate Intraseasonal Index for Tropical Brazil (MITB). This index is based on Maximum Covariance Analysis (MCA) applied to the filtered daily anomalies of rainfall data over tropical Brazil against a group of covariates consisting of: outgoing longwave radiation and the zonal component u of the wind at 850 and 200 hPa. The first two MCA modes, which were used to create the { MITB}_1 and { MITB}_2 indices, represent 65 and 16 % of the explained variance, respectively. The combined multivariate index was able to satisfactorily represent the pattern of intraseasonal variability over tropical Brazil, showing that there are periods of activation and inhibition of precipitation connected with the pattern of MJO propagation. The MITB index could potentially be used as a diagnostic tool for intraseasonal forecasting.

Predicting Summer Arctic Sea Ice Concentration Intraseasonal Variability Using a Vector Autoregressive Model*

Abstract Recent Arctic sea ice changes have important societal and economic impacts and may lead to adverse effects on the Arctic ecosystem, weather, and climate. Understanding the predictability of Arctic sea ice melting is thus an important task. A vector autoregressive (VAR) model is evaluated for predicting the summertime (May–September) daily Arctic sea ice concentration on the intraseasonal time scale, using only the daily sea ice data and without direct information of the atmosphere and ocean. The intraseasonal forecast skill of Arctic sea ice is assessed using the 1979–2012 satellite data. The cross-validated forecast skill of the VAR model is found to be superior to both the anomaly persistence and damped anomaly persistence at lead times of ~20–60 days, especially over northern Eurasian marginal seas and the Beaufort Sea. The daily forecast of ice concentration also leads to predictions of ice-free dates and September mean sea ice extent. In addition to capturing the general seasonal melt of sea ice, the model is also able to capture the interannual variability of the melting, from partial melt of the marginal sea ice in the beginning of the period to almost a complete melt in the later years. While the detailed mechanism leading to the high predictability of intraseasonal sea ice concentration needs to be further examined, the study reveals for the first time that Arctic sea ice can be predicted statistically with reasonable skill at the intraseasonal time scales given the small signal-to-noise ratio of daily data.

Prediction of northern summer low-frequency circulation using a high-order vector auto-regressive model

A data-driven, high-order vector auto-regressive (VAR) model is evaluated for predicting the Northern Hemisphere summer time (May through September) low frequency (>10 days or so) variability. The VAR model is suitable for linear stationary time series, similar to the commonly used linear inverse model (LIM), with additional temporal information incorporated to improve forecast skill. The intraseasonal forecast skill of the 250/750 hPa streamfunction is investigated using observational data since 1979, which shows significant improvements in high-order VAR models than the first-order model LIM. Furthermore, the tropical diabatic heating is found to significantly improve the forecast skill of the atmospheric low frequency circulation when included in the VAR model. The forecast skill of 250 hPa streamfunction at Arabian Peninsula is particularly enhanced for up to 5 weeks lead-time through circumglobal wave propagation associated with the persistent tropical eastern Pacific and equatorial Atlantic heating anomalies and the intraseasonal evolution of the tropical Indian Ocean and western Pacific heating anomalies.

Comparison of intra-seasonal forecast of Indian summer monsoon between two versions of NCEP coupled models

The real-time forecasting of monsoon activity over India on extended range time scale (about 3 weeks) is analyzed for the monsoon season of 2012 during June to September (JJAS) by using the outputs from latest (CFSv2 [Climate Forecast System version 2]) and previous version (CFSv1 [Climate Forecast System version 1]) of NCEP coupled modeling system. The skill of monsoon rainfall forecast is found to be much better in CFSv2 than CFSv1. For the country as a whole the correlation coefficient (CC) between weekly observed and forecast rainfall departure was found to be statistically significant (99 % level) at least for 2 weeks (up to 18 days) and also having positive CC during week 3 (days 19–25) in CFSv2. The other skill scores like the mean absolute error (MAE) and the root mean square error (RMSE) also had better performance in CFSv2 compared to that of CFSv1. Over the four homogeneous regions of India the forecast skill is found to be better in CFSv2 with almost all four regions with CC significant at 95 % level up to 2 weeks, whereas the CFSv1 forecast had significant CC only over northwest India during week 1 (days 5–11) forecast. The improvement in CFSv2 was very prominent over central India and northwest India compared to other two regions. On the meteorological subdivision level (India is divided into 36 meteorological subdivisions) the percentage of correct category forecast was found to be much higher than the climatology normal forecast in CFSv2 as well as in CFSv1, with CFSv2 being 8–10 % higher in the category of correct to partially correct (one category out) forecast compared to that in CFSv1. Thus, it is concluded that the latest version of CFS coupled model has higher skill in predicting Indian monsoon rainfall on extended range time scale up to about 25 days.

Improving Intraseasonal Prediction with a New Ensemble Generation Strategy

Abstract The Australian Bureau of Meteorology has recently enhanced its capability to make coupled model forecasts of intraseasonal climate variations. The Predictive Ocean Atmosphere Model for Australia (POAMA, version 2) seasonal prediction forecast system in operations prior to March 2013, designated P2-S, was not designed for intraseasonal forecasting and has deficiencies in this regard. Most notably, the forecasts were only initialized on the 1st and 15th of each month, and the growth of the ensemble spread in the first 30 days of the forecasts was too slow to be useful on intraseasonal time scales. These deficiencies have been addressed in a system upgrade by initializing more often and through enhancements to the ensemble generation. The new ensemble generation scheme is based on a coupled-breeding approach and produces an ensemble of perturbed atmosphere and ocean states for initializing the forecasts. This scheme impacts favorably on the forecast skill of Australian rainfall and temperature compared to P2-S and its predecessor (version 1.5). In POAMA-1.5 the ensemble was produced using time-lagged atmospheric initial conditions but with unperturbed ocean initial conditions. P2-S used an ensemble of perturbed ocean initial conditions but only a single atmospheric initial condition. The improvement in forecast performance using the coupled-breeding approach is primarily reflected in improved reliability in the first month of the forecasts, but there is also higher skill in predicting important drivers of intraseasonal climate variability, namely the Madden–Julian oscillation and southern annular mode. The results illustrate the importance of having an optimal ensemble generation strategy.

Intraseasonal Forecasting of the Asian Summer Monsoon in Four Operational and Research Models*

Abstract The boreal summer intraseasonal oscillation (BSISO) is a dominant tropical mode with a period of 30–60 days, which offers an opportunity for intraseasonal forecasting of the Asian summer monsoon. The present study provides a preliminary, yet up-to-date, assessment of the prediction skill of the BSISO in four state-of-the-art models: the ECMWF model, the University of Hawaii (UH) model, the NCEP Climate Forecast System, version 2 (CFSv2), and version 1 for the 2008 summer (CFSv1), which is a common year of two international programs: the Year of Tropical Convection (YOTC) and Asian Monsoon Years (AMY). The mean prediction skill over the global tropics and Southeast Asia for first three models reaches about 1–2 (3) weeks for BSISO-related rainfall (850-hPa zonal wind), measured as the lead time when the spatial anomaly correlation coefficient drops to 0.5. The skill of CFSv1 is consistently lower than the other three. The strengths and weaknesses of the CFSv2, UH, and ECMWF models in forecasting the BSISO for this specific year are further revealed. The ECMWF and UH have relatively better performance for northward-propagating BSISO when the initial convection is near the equator, although they suffer from an early false BSISO onset when initial convection is in the off-equatorial monsoon trough. However, CFSv2 does not have a false onset problem when the initial convection is in monsoon trough, but it does have a problem with very slow northward propagation. After combining the forecasts of CFSv2 and UH into an equal-weighted multimodel ensemble, the resultant skill is slightly better than that of individual models. An empirical model shows a comparable skill with the dynamical models. A combined dynamical–empirical ensemble advances the intraseasonal forecast skill of BSISO-related rainfall to three weeks.

Intraseasonal forecasting of the 2009 summer and winter Australian heat waves using POAMA

Extreme heat waves occurred over much of southern and eastern Australia during the summer (27 January–8 February) and winter (14–31 August) of 2009. The summer heat wave resulted in many temperature records across southeastern Australia, as well as devastating bushfires in Victoria that caused losses of life and property. The winter heat wave primarily affected subtropical areas of eastern Australia and produced major disruptions to agricultural industries. In this study the ability of the Bureau of Meteorology’s dynamical seasonal prediction model, the Predictive Ocean Atmosphere Model for Australia (POAMA), to forecast fortnightly means for two periods corresponding to the heat waves (25 January–7 February and 15–28 August, respectively) is assessed. The forecasts are based on 10-member daily lagged-ensemble forecasts, initialized up to 30 days prior to the verification date. Ensemble mean and probabilistic forecasts are assessed. The paper forms part of a larger study investigating the use of POAMA for filling the current prediction capability gap between weather forecasts and seasonal outlooks for Australia. The most successful forecasts were initialized up to 2 weeks prior to the verification date, when POAMA demonstrated the ability to predict widespread warming over southeastern Australia for the summer event and over central and eastern Australia for the winter event. At these lead times, the ensemble mean forecast captured the midtropospheric subtropical anticyclonic anomaly over Australia that characterized both heat waves. The magnitude of the peak daytime warming in the ensemble mean forecast was, however, underpredicted, more so for the summer heat wave, and maximum warming occurred farther east than observed in both events. POAMA was able to predict temperature anomalies similar to those observed over the southeast in the ensemble initialized 0–9 days prior to the forecast verification date for the winter heat wave, thus being of potential benefit to the grain-producing regions of southeastern Australia.

Intraseasonal Forecasting of the 2009 Summer and Winter Australian Heat Waves Using POAMA

AbstractExtreme heat waves occurred over much of southern and eastern Australia during the summer (27 January–8 February) and winter (14–31 August) of 2009. The summer heat wave resulted in many temperature records across southeastern Australia, as well as devastating bushfires in Victoria that caused losses of life and property. The winter heat wave primarily affected subtropical areas of eastern Australia and produced major disruptions to agricultural industries. In this study the ability of the Bureau of Meteorology’s dynamical seasonal prediction model, the Predictive Ocean Atmosphere Model for Australia (POAMA), to forecast fortnightly means for two periods corresponding to the heat waves (25 January–7 February and 15–28 August, respectively) is assessed. The forecasts are based on 10-member daily lagged-ensemble forecasts, initialized up to 30 days prior to the verification date. Ensemble mean and probabilistic forecasts are assessed. The paper forms part of a larger study investigating the use of P...

Bridging the gap between weather and seasonal forecasting: intraseasonal forecasting for Australia

AbstractThis study examines the potential use of the Predictive Ocean Atmosphere Model for Australia (POAMA), the Bureau of Meteorology's dynamical seasonal forecast system, as an intraseasonal prediction tool for Australia. This would fill the current prediction capability gap between weather forecasts and seasonal outlooks for Australia. The intraseasonal forecast skill of a 27‐year hindcast dataset is investigated, focusing on precipitation and minimum and maximum temperatures over Australia in the second fortnight (average days 15–28 of the forecast). Most of the skill for forecasting precipitation and maximum temperature in the second fortnight is focused over eastern Australia, during austral winter and spring for precipitation and during spring for maximum temperature. For this region and seasons the forecast of the second fortnight performs generally better than using climatology, persistence of observed, or persistence of the forecast for the first fortnight (average days 1–14). The model has generally poor skill in predicting minimum temperatures. The role of key drivers of Australian climate variability for providing predictability at intraseasonal time‐scales is investigated. This is done for the austral winter and spring seasons, when POAMA's skill for predicting precipitation is highest. Forecast skill is found to be increased during extremes of the El Niño Southern Oscillation, the Indian Ocean Dipole and the Southern Annular Mode. The regions of impact of these modes of climate variability on forecast skill are similar to those regions identified in observed studies as being influenced by the respective drivers. In contrast, there is no significant relationship between intraseasonal forecast skill for precipitation and the amplitude of the Madden Julian Oscillation (MJO) in winter and spring, although the analysis does not distinguish between the phases of the MJO. The results indicate that the use of POAMA for intraseasonal forecasting is promising. Copyright © 2011 Royal Meteorological Society

Impact of the Madden‐Julian Oscillation on the intraseasonal forecast skill of the North Atlantic Oscillation

Using the output of the intraseasonal hindcast experiment conducted with the GEM global atmospheric model during 24 extended winters, the association between the forecast skill of the NAO and the amplitude and phase of the MJO in the initial condition is investigated. It is found that with a lead time up to about one month the NAO forecast skill is significantly influenced by the existence of the MJO signal in the initial condition. A strong MJO leads to a better NAO forecast skill than a weak MJO. An initial state with an MJO phase corresponding to a dipole tropical convection anomaly in the eastern Indian Ocean and western Pacific favors a more skillful NAO forecast than an MJO phase with a single tropical convection anomaly near 120°E. These results indicate that it is possible to increase the skill of the NAO and the extratropical surface air temperature intraseasonal forecast with an improved tropical initialization, a better prediction of the tropical MJO and a better representation of the tropical‐extratropical interaction in dynamical models.

Correction to “Impacts of initial conditions on monsoon intraseasonal forecasting”

[1] In the paper ‘‘Impacts of initial conditions on monsoon intraseasonal forecasting’’ by Xiouhua Fu et al. (Geophys. Res. Lett., 36, L08801, doi:10.1029/2009GL037166, 2009), the ‘NCEP_R2’ label in Figure 1 (bottom) is incorrect. The corrected label should be ‘NCEP_R1’ [Kalnay et al., 1996]. [2] Acknowledgment. NCEP reanalysis data (NCEP_R1) are provided by the NOAA/OAR/ESRL/PSD, Boulder, Colorado, USA, from their web site at http://www.cdc.noaa.gov/.

Impacts of initial conditions on monsoon intraseasonal forecasting

Sensitivity of monsoon intraseasonal forecasting to different initial conditions is examined with the University of Hawaii Hybrid Coupled Model (UH_HCM). The target period is May to September 2004. We found that in the NCEP reanalysis the amplitudes of the convective activities associated with monsoon intraseasonal oscillation (MISO) are smaller than the observed counterparts by a factor of two to three. Motivated by this fact, we carried out a suite of forecasting experiments to explore the impacts of initial conditions on intraseasonal forecast skills. Our results reveal that with the original NCEP reanalysis as initial conditions the monsoon intraseasonal forecast skills of 850‐hPa zonal winds and rainfall are only about a week over the global tropics (30°S–30°N) and Southeast Asia (10°N–30°N, 60°E–120°E). The predictability increases steadily with increased amplitudes of MISO in the initial conditions. When the MISO signals in initial conditions are recovered to a level similar to that in the observations, monsoon intraseasonal forecast skills reach 25 days for 850‐hPa zonal winds and 15 days for rainfall over both the global tropics and Southeast Asia. It is also found that high‐frequency weather in initial conditions generally extends rainfall predictability by about 5 days.