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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.
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.
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.
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.
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.
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.
Abstract
Three aspects of space–time spectral analysis are explored for diagnosis of the organization of tropical convection by the Madden–Julian oscillation (MJO) and other equatorial wave modes: 1) definition of the background spectrum upon which spectral peaks are assessed, 2) alternate variance preserving display of the spectra, and 3) the space–time coherence spectrum. Here the background spectrum at each zonal wavenumber is assumed to result from a red noise process. The associated decorrelation time for the red noise process for tropical convection is found to be half as long as for zonal wind, reflecting the different physical processes controlling each field. The significance of spectral peaks associated with equatorial wave modes for outgoing longwave radiation (OLR), which is a proxy for precipitating deep convection, and zonal winds that stand out above the red background spectrum is similar to that identified using a background spectrum resulting from ad hoc smoothing of the original spectrum. A variance-preserving display of the space–time power spectrum with a logarithmic frequency axis is useful for directly detecting Kelvin waves (periods 5–15 days for eastward zonal wavenumbers 1–5) and for highlighting their distinction from the MJO. The space–time coherence of OLR and zonal wind is predominantly associated with the MJO and other equatorial waves. The space–time coherence is independent of estimating the background spectrum and is quantifiable; thus, it is suggested as a useful metric for the MJO and other equatorial waves in observations and simulations. The space–time coherence is also used to quantify the association of Kelvin waves in the stratosphere with convective variability in the troposphere and for detection of barotropic Rossby–Haurwitz waves.
Abstract
Three aspects of space–time spectral analysis are explored for diagnosis of the organization of tropical convection by the Madden–Julian oscillation (MJO) and other equatorial wave modes: 1) definition of the background spectrum upon which spectral peaks are assessed, 2) alternate variance preserving display of the spectra, and 3) the space–time coherence spectrum. Here the background spectrum at each zonal wavenumber is assumed to result from a red noise process. The associated decorrelation time for the red noise process for tropical convection is found to be half as long as for zonal wind, reflecting the different physical processes controlling each field. The significance of spectral peaks associated with equatorial wave modes for outgoing longwave radiation (OLR), which is a proxy for precipitating deep convection, and zonal winds that stand out above the red background spectrum is similar to that identified using a background spectrum resulting from ad hoc smoothing of the original spectrum. A variance-preserving display of the space–time power spectrum with a logarithmic frequency axis is useful for directly detecting Kelvin waves (periods 5–15 days for eastward zonal wavenumbers 1–5) and for highlighting their distinction from the MJO. The space–time coherence of OLR and zonal wind is predominantly associated with the MJO and other equatorial waves. The space–time coherence is independent of estimating the background spectrum and is quantifiable; thus, it is suggested as a useful metric for the MJO and other equatorial waves in observations and simulations. The space–time coherence is also used to quantify the association of Kelvin waves in the stratosphere with convective variability in the troposphere and for detection of barotropic Rossby–Haurwitz waves.
Abstract
A forecast product focusing on the onset of the north Australian wet season using a dynamical ocean–atmosphere model is developed and verified. Onset is defined to occur when a threshold rainfall accumulation of 50 mm is reached from 1 September. This amount has been shown to be useful for agricultural applications, as it is about what is required to generate new plant growth after the usually dry period of June–August. The normal (median) onset date occurs first around Darwin in the north and Cairns in the east in late October, and is progressively later for locations farther inland away from these locations. However, there is significant interannual variability in the onset, and skillful predictions of this can be valuable. The potential of the Predictive Ocean–Atmosphere Model for Australia (POAMA), version 2, for making probabilistic predictions of onset, derived from its multimember ensemble, is shown. Using 50 yr of hindcasts, POAMA is found to skillfully predict the variability of onset, despite a generally dry bias, with the “percent correct” exceeding 70% over about a third of the Northern Territory. In comparison to a previously developed statistical method based solely on El Niño–Southern Oscillation, the POAMA system shows improved skill scores, suggesting that it gains from additional sources of predictability. However, the POAMA hindcasts do not reproduce the observed long-term trend in onset dates over inland regions to an earlier date despite being initialized with the observed warming ocean temperatures. Understanding and modeling this trend should lead to further enhancements in skill.
Abstract
A forecast product focusing on the onset of the north Australian wet season using a dynamical ocean–atmosphere model is developed and verified. Onset is defined to occur when a threshold rainfall accumulation of 50 mm is reached from 1 September. This amount has been shown to be useful for agricultural applications, as it is about what is required to generate new plant growth after the usually dry period of June–August. The normal (median) onset date occurs first around Darwin in the north and Cairns in the east in late October, and is progressively later for locations farther inland away from these locations. However, there is significant interannual variability in the onset, and skillful predictions of this can be valuable. The potential of the Predictive Ocean–Atmosphere Model for Australia (POAMA), version 2, for making probabilistic predictions of onset, derived from its multimember ensemble, is shown. Using 50 yr of hindcasts, POAMA is found to skillfully predict the variability of onset, despite a generally dry bias, with the “percent correct” exceeding 70% over about a third of the Northern Territory. In comparison to a previously developed statistical method based solely on El Niño–Southern Oscillation, the POAMA system shows improved skill scores, suggesting that it gains from additional sources of predictability. However, the POAMA hindcasts do not reproduce the observed long-term trend in onset dates over inland regions to an earlier date despite being initialized with the observed warming ocean temperatures. Understanding and modeling this trend should lead to further enhancements in skill.
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.
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.
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.
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.
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.
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.
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.
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.
Abstract
The modulation of tropical cyclone activity by the Madden–Julian oscillation (MJO) is explored using an empirical genesis potential (GP) index. Composite anomalies of the genesis index associated with the different MJO phases are consistent with the composite anomalies in TC genesis frequency that occur in the same phases, indicating that the index captures the changes in the environment that are at least in part responsible for the genesis frequency changes. Of the four environmental variables that enter the genesis potential index, the midlevel relative humidity makes the largest contribution to the MJO composite GP anomalies. The second largest contribution comes from the low-level absolute vorticity, and only very minor contributions come from the vertical wind shear and potential intensity.
When basin-integrated MJO composite anomalies of the GP index are regressed against basin-integrated composite anomalies of TC genesis frequency, the results differ quantitatively from those obtained from the analogous calculation performed on the annual climatologies in the two quantities. The GP index captures the MJO modulation of TC genesis to a lesser degree than the climatological annual cycle of genesis (to which it was originally tuned). This may be due to weaknesses of the reanalysis or indicative of the importance of precursor disturbances, not well captured in the GP index computed from weekly data, to the intraseasonal TC genesis frequency fluctuations.
Abstract
The modulation of tropical cyclone activity by the Madden–Julian oscillation (MJO) is explored using an empirical genesis potential (GP) index. Composite anomalies of the genesis index associated with the different MJO phases are consistent with the composite anomalies in TC genesis frequency that occur in the same phases, indicating that the index captures the changes in the environment that are at least in part responsible for the genesis frequency changes. Of the four environmental variables that enter the genesis potential index, the midlevel relative humidity makes the largest contribution to the MJO composite GP anomalies. The second largest contribution comes from the low-level absolute vorticity, and only very minor contributions come from the vertical wind shear and potential intensity.
When basin-integrated MJO composite anomalies of the GP index are regressed against basin-integrated composite anomalies of TC genesis frequency, the results differ quantitatively from those obtained from the analogous calculation performed on the annual climatologies in the two quantities. The GP index captures the MJO modulation of TC genesis to a lesser degree than the climatological annual cycle of genesis (to which it was originally tuned). This may be due to weaknesses of the reanalysis or indicative of the importance of precursor disturbances, not well captured in the GP index computed from weekly data, to the intraseasonal TC genesis frequency fluctuations.
