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Miloud Bessafi
and
Matthew C. Wheeler

Abstract

The subseasonal modulation of tropical cyclone (TC) genesis by large-scale atmospheric wave modes is studied using data from the south Indian Ocean region. The modes considered are the Madden–Julian oscillation (MJO), and the convectively coupled equatorial Rossby (ER), Kelvin, and mixed Rossby–gravity (MRG) waves. Analysis of all TCs west of 100°E reveals a large and statistically significant modulation by the MJO and ER waves, a small yet significant modulation by Kelvin waves, and a statistically insignificant modulation by MRG waves. Attribution of the observed TC modulation was made through examination of the wave-induced perturbations to the dynamical fields of low-level vorticity, vertical shear, and deep convection. Possible thermodynamic influences on TC genesis were neglected. Different combinations of the three dynamical fields were necessary for successful attribution for each of the large-scale wave modes. For example, for the MJO, the modulation was best attributable to its perturbations to both the vorticity and shear fields, while for the ER wave, it was its perturbations to the convection and vorticity fields that appeared to best be able to explain the modulation. It appears that there is no single factor that can be used for the attribution of all subseasonal TC variability. Finally, it is shown that the modulation of TCs by at least the MJO and ER waves is large enough to warrant further investigation for prediction on the weekly time scale.

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Anne Leroy
and
Matthew C. Wheeler

Abstract

A statistical prediction scheme, employing logistic regression, is developed to predict the probability of tropical cyclone (TC) formation in zones of the Southern Hemisphere during forthcoming weeks. Through physical reasoning, examination of previous research, and some new analysis, five predictors were chosen for this purpose: one representing the climatological seasonal cycle of TC activity in each zone, two representing the eastward propagation of the Madden–Julian oscillation (MJO), and a further two representing the leading patterns of interannual sea surface temperature variability in the Indo-Pacific Oceans. Cross-validated hindcasts were generated, being careful to use the predictors at lags that replicate what can be performed in real time. All predictors contribute significantly to the skill of the hindcasts for at least some leads in the majority of zones. In particular, it is found that inclusion of indices of the MJO as predictors leads to increased skill out to about the third week. Beyond the third week, the skill asymptotically approaches that which can be achieved through consideration of the seasonal cycle and interannual variability alone. Furthermore, the importance of a simple consideration of the seasonal cycle of TC activity for intraseasonal TC prediction, for all forecast leads, is demonstrated.

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Matthew C. Wheeler
and
Harry H. Hendon

Abstract

A seasonally independent index for monitoring the Madden–Julian oscillation (MJO) is described. It is based on a pair of empirical orthogonal functions (EOFs) of the combined fields of near-equatorially averaged 850-hPa zonal wind, 200-hPa zonal wind, and satellite-observed outgoing longwave radiation (OLR) data. Projection of the daily observed data onto the multiple-variable EOFs, with the annual cycle and components of interannual variability removed, yields principal component (PC) time series that vary mostly on the intraseasonal time scale of the MJO only. This projection thus serves as an effective filter for the MJO without the need for conventional time filtering, making the PC time series an effective index for real-time use.

The pair of PC time series that form the index are called the Real-time Multivariate MJO series 1 (RMM1) and 2 (RMM2). The properties of the RMM series and the spatial patterns of atmospheric variability they capture are explored. Despite the fact that RMM1 and RMM2 describe evolution of the MJO along the equator that is independent of season, the coherent off-equatorial behavior exhibits strong seasonality. In particular, the northward, propagating behavior in the Indian monsoon and the southward extreme of convection into the Australian monsoon are captured by monitoring the seasonally independent eastward propagation in the equatorial belt. The previously described interannual modulation of the global variance of the MJO is also well captured.

Applications of the RMM series are investigated. One application is through their relationship with the onset dates of the monsoons in Australia and India; while the onsets can occur at any time during the convectively enhanced half of the MJO cycle, they rarely occur during the suppressed half. Another application is the modulation of the probability of extreme weekly rainfall; in the “Top End” region around Darwin, Australia, the swings in probability represent more than a tripling in the likelihood of an upper-quintile weekly rainfall event from the dry to wet MJO phase.

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Frédéric Vitart
,
Anne Leroy
, and
Matthew C. Wheeler

Abstract

The skill of the European Centre for Medium-Range Weather Forecasts (ECMWF) forecast system to predict the occurrence of tropical cyclones (TCs) over the Southern Hemisphere during weekly periods has been evaluated and compared to the skill of a state-of-the-art statistical model. Probabilistic skill scores have been applied to a common series of hindcasts produced with the dynamical and statistical models. The ECMWF hindcasts have higher relative operating characteristic (ROC) scores than the statistical model for the first three weeks of integrations. The dynamical model also has skill over the Indian Ocean in week 4.

The ECMWF hindcasts have lower Brier skill scores than the statistical model after week 2, which is likely because this version of the ECMWF model creates about 30% more TCs than observations and therefore generates a large number of false alarms. A simple calibration has been applied to the ECMWF probabilistic forecasts that significantly improves their reliability, but at the expense of the sharpness. The calibrated dynamical model has higher Brier skill scores than the statistical model during the first three weeks, although the statistical model remains more reliable.

The multimodel combination of the calibrated dynamical forecasts with the statistical forecasts helps to improve the reliability of the ECMWF forecasts. The Brier skill score of the multimodel exceeds the Brier skill scores of the individual models, but with less sharpness than the calibrated dynamical model. This result suggests that the statistical model can be useful as a benchmark for dynamical models and as a component of a multimodel combination to improve the skill of the dynamical model. Potential economic value diagrams confirm that the multimodel forecasts are useful up to week 3 over the Southern Hemisphere.

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Jeffrey S. Gall
,
William M. Frank
, and
Matthew C. Wheeler

Abstract

This two-part series of papers examines the role of equatorial Rossby (ER) waves in tropical cyclone (TC) genesis. To do this, a unique initialization procedure is utilized to insert n = 1 ER waves into a numerical model that is able to faithfully produce TCs. In this first paper, experiments are carried out under the idealized condition of an initially quiescent background environment. Experiments are performed with varying initial wave amplitudes and with and without diabatic effects. This is done to both investigate how the properties of the simulated ER waves compare to the properties of observed ER waves and explore the role of the initial perturbation strength of the ER wave on genesis. In the dry, frictionless ER wave simulation the phase speed is slightly slower than the phase speed predicted from linear theory. Large-scale ascent develops in the region of low-level poleward flow, which is in good agreement with the theoretical structure of an n = 1 ER wave. The structures and phase speeds of the simulated full-physics ER waves are in good agreement with recent observational studies of ER waves that utilize wavenumber–frequency filtering techniques. Convection occurs primarily in the eastern half of the cyclonic gyre, as do the most favorable conditions for TC genesis. This region features sufficient midlevel moisture, anomalously strong low-level cyclonic vorticity, enhanced convection, and minimal vertical shear. Tropical cyclogenesis occurs only in the largest initial-amplitude ER wave simulation. The formation of the initial tropical disturbance that ultimately develops into a tropical cyclone is shown to be sensitive to the nonlinear horizontal momentum advection terms. When the largest initial-amplitude simulation is rerun with the nonlinear horizontal momentum advection terms turned off, tropical cyclogenesis does not occur, but the convectively coupled ER wave retains the properties of the ER wave observed in the smaller initial-amplitude simulations. It is shown that this isolated wave-only genesis process only occurs for strong ER waves in which the nonlinear advection is large. will look at the more realistic case of ER wave–related genesis in which a sufficiently intense ER wave interacts with favorable large-scale flow features.

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Fiona Lo
,
Matthew C. Wheeler
,
Holger Meinke
, and
Alexis Donald

Abstract

The amount and timing of early wet-season rainfall are important for the management of many agricultural industries in north Australia. With this in mind, a wet-season onset date is defined based on the accumulation of rainfall to a predefined threshold, starting from 1 September, for each square of a 1° gridded analysis of daily rainfall across the region. Consistent with earlier studies, the interannual variability of the onset dates is shown to be well related to the immediately preceding July–August Southern Oscillation index (SOI). Based on this relationship, a forecast method using logistic regression is developed to predict the probability that onset will occur later than the climatological mean date. This method is expanded to also predict the probabilities that onset will be later than any of a range of threshold dates around the climatological mean. When assessed using cross-validated hindcasts, the skill of the predictions exceeds that of climatological forecasts in the majority of locations in north Australia, especially in the Top End region, Cape York, and central Queensland. At times of strong anomalies in the July–August SOI, the forecasts are reliably emphatic. Furthermore, predictions using tropical Pacific sea surface temperatures (SSTs) as the predictor are also tested. While short-lead (July–August predictor) forecasts are more skillful using the SOI, long-lead (May–June predictor) forecasts are more skillful using Pacific SSTs, indicative of the longer-term memory present in the ocean.

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Hongyan Zhu
,
Matthew C. Wheeler
,
Adam H. Sobel
, and
Debra Hudson

Abstract

The skill with which a coupled ocean–atmosphere model is able to predict precipitation over a range of time scales (days to months) is analyzed. For a fair comparison across the seamless range of scales, the verification is performed using data averaged over time windows equal in length to the lead time. At a lead time of 1 day, skill is greatest in the extratropics around 40°–60° latitude and lowest around 20°, and has a secondary local maximum close to the equator. The extratropical skill at this short range is highest in the winter hemisphere, presumably due to the higher predictability of winter baroclinic systems. The local equatorial maximum comes mostly from the Pacific Ocean, and thus appears to be mostly from El Niño–Southern Oscillation (ENSO). As both the lead time and averaging window are simultaneously increased, the extratropical skill drops rapidly with lead time, while the equatorial maximum remains approximately constant, causing the equatorial skill to exceed the extratropical at leads of greater than 4 days in austral summer and 1 week in boreal summer. At leads longer than 2 weeks, the extratropical skill flattens out or increases, but remains below the equatorial values. Comparisons with persistence confirm that the model beats persistence for most leads and latitudes, including for the equatorial Pacific where persistence is high. The results are consistent with the view that extratropical predictability is mostly derived from synoptic-scale atmospheric dynamics, while tropical predictability is primarily derived from the response of moist convection to slowly varying forcing such as from ENSO.

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James S. Risbey
,
Michael J. Pook
,
Peter C. McIntosh
,
Matthew C. Wheeler
, and
Harry H. Hendon

Abstract

This work identifies and documents a suite of large-scale drivers of rainfall variability in the Australian region. The key driver in terms of broad influence and impact on rainfall is the El Niño–Southern Oscillation (ENSO). ENSO is related to rainfall over much of the continent at different times, particularly in the north and east, with the regions of influence shifting with the seasons. The Indian Ocean dipole (IOD) is particularly important in the June–October period, which spans much of the wet season in the southwest and southeast where IOD has an influence. ENSO interacts with the IOD in this period such that their separate regions of influence cover the entire continent. Atmospheric blocking also becomes most important during this period and has an influence on rainfall across the southern half of the continent. The Madden–Julian oscillation can influence rainfall in different parts of the continent in different seasons, but its impact is strongest on the monsoonal rains in the north. The influence of the southern annular mode is mostly confined to the southwest and southeast of the continent. The patterns of rainfall relationship to each of the drivers exhibit substantial decadal variability, though the characteristic regions described above do not change markedly. The relationships between large-scale drivers and rainfall are robust to the selection of typical indices used to represent the drivers. In most regions the individual drivers account for less than 20% of monthly rainfall variability, though the drivers relate to a predictable component of this variability. The amount of rainfall variance explained by individual drivers is highest in eastern Australia and in spring, where it approaches 50% in association with ENSO and blocking.

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Xianan Jiang
,
Duane E. Waliser
,
Matthew C. Wheeler
,
Charles Jones
,
Myong-In Lee
, and
Siegfried D. Schubert

Abstract

Motivated by an attempt to augment dynamical models in predicting the Madden–Julian oscillation (MJO), and to provide a realistic benchmark to those models, the predictive skill of a multivariate lag-regression statistical model has been comprehensively explored in the present study. The predictors of the benchmark model are the projection time series of the leading pair of EOFs of the combined fields of equatorially averaged outgoing longwave radiation (OLR) and zonal winds at 850 and 200 hPa, derived using the approach of Wheeler and Hendon. These multivariate EOFs serve as an effective filter for the MJO without the need for bandpass filtering, making the statistical forecast scheme feasible for the real-time use. Another advantage of this empirical approach lies in the consideration of the seasonal dependence of the regression parameters, making it applicable for forecasts all year-round. The forecast model exhibits useful extended-range skill for a real-time MJO forecast. Predictions with a correlation skill of greater than 0.3 (0.5) between predicted and observed unfiltered (EOF filtered) fields still can be detected over some regions at a lead time of 15 days, especially for boreal winter forecasts. This predictive skill is increased significantly when there are strong MJO signals at the initial forecast time. The analysis also shows that predictive skill for the upper-tropospheric winds is relatively higher than for the low-level winds and convection signals. Finally, the capability of this empirical model in predicting the MJO is further demonstrated by a case study of a real-time “hindcast” during the 2003/04 winter. Predictive skill demonstrated in this study provides an estimate of the predictability of the MJO and a benchmark for the dynamical extended-range models.

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Michael J. Ventrice
,
Matthew C. Wheeler
,
Harry H. Hendon
,
Carl J. Schreck III
,
Chris D. Thorncroft
, and
George N. Kiladis

Abstract

A new Madden–Julian oscillation (MJO) index is developed from a combined empirical orthogonal function (EOF) analysis of meridionally averaged 200-hPa velocity potential (VP200), 200-hPa zonal wind (U200), and 850-hPa zonal wind (U850). Like the Wheeler–Hendon Real-time Multivariate MJO (RMM) index, which was developed in the same way except using outgoing longwave radiation (OLR) data instead of VP200, daily data are projected onto the leading pair of EOFs to produce the two-component index. This new index is called the velocity potential MJO (VPM) indices and its properties are quantitatively compared to RMM. Compared to the RMM index, the VPM index detects larger-amplitude MJO-associated signals during boreal summer. This includes a slightly stronger and more coherent modulation of Atlantic tropical cyclones. This result is attributed to the fact that velocity potential preferentially emphasizes the planetary-scale aspects of the divergent circulation, thereby spreading the convectively driven component of the MJO’s signal across the entire globe. VP200 thus deemphasizes the convective signal of the MJO over the Indian Ocean warm pool, where the OLR variability associated with the MJO is concentrated, and enhances the signal over the relatively drier longitudes of the equatorial Pacific and Atlantic. This work provides a useful framework for systematic analysis of the strengths and weaknesses of different MJO indices.

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