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Angelika Werner and Neil J. Holbrook

Abstract

A new and potentially skillful seasonal forecast model of tropical cyclone formation [tropical cyclogenesis (TCG)] is developed for the Australian region. The model is based on Poisson regression using the Bayesian approach. Predictor combinations are chosen using a step-by-step predictor selection. The three-predictor model based on derived indices of June–August average convective available potential energy, May–July average meridional winds at 850 hPa (V 850), and July–September geopotential height at 500 hPa produces the smallest standard error (se = 0.36) and root-mean-squared error (RMSE = 5.20) for the leave-one-out cross-validated TCG hindcasts over the 40-yr record between 1968/69–2007/08. The corresponding correlation coefficient between observed annual TCG totals and cross-validated model hindcasts is r = 0.73. Using fourfold cross validation, model hindcast skill is robust, with 85% of the observed seasonal TCG totals hindcast within the model standard deviations. Seasonal TCG totals during ENSO events are typically well captured with RMSE = 5.14 during El Niño, and RMSE = 6.04 during La Niña years. The model is shown to be valuable in hindcasting seasonal TCG totals in the eastern Australian subregion (r = 0.73) and also provides some skill for the western Australian region (r = 0.42), while it not useful for the northern region. In summary, the authors find that the three-predictor Bayesian model provides substantial improvement over existing statistical TCG forecast models, with remarkably skillful hindcasts (forecasts) of Australian region and subregional seasonal TCG totals provided one month ahead of the TC season.

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Eric C. J. Oliver and Neil J. Holbrook

Abstract

Spatially and temporally homogeneous measurements of ocean temperature variability at high resolution on the continental shelf are scarce. Daily estimates of large-scale ocean properties are readily available from global ocean reanalysis products. However, the ocean models that underpin these reanalysis products tend not to have been designed for the simulation of complex coastal ocean variability. Hence, across-shelf values are often poorly represented. This study involved developing a statistical approach to more accurately and robustly represent SST on the continental shelf informed by large-scale satellite observations and reanalysis data or model output. Using the southeastern Australian shelf region as a case study, this paper demonstrates that this statistical model approach generates more accurate estimates of the inshore SST using (i) offshore SST from Bluelink Reanalysis (BRAN) and (ii) the statistical relationship between inshore and offshore SST in observations from the Advanced Very High Resolution Radiometer. SST is separated into the mean, seasonal cycle, and residual variability, and separate models are developed for each component. The offshore locations used to inform the model are determined by taking into account (i) the quality of BRAN at each location, (ii) the strength between the inshore and offshore variability, and (iii) the proximity of the inshore and offshore locations. Model predictions are made for the continental shelf around southeastern Australia. The role of the mean circulation in providing connectivity between the shelf and the offshore regions is discussed, and how this information can be used to better inform the choice of model predictor locations, leading to a hybrid statistical–connectivity model.

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Matthew L. Perkins and Neil J. Holbrook

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This study attempts to reproduce the salient features of the variability in the depth of the thermocline in the marginally eddy-resolving Parallel Ocean Climate Model (POCM) of Semtner and Chervin, using a simple linear vorticity model that only permits local Ekman pumping and the propagation of long Rossby waves. The dynamic upper-ocean variability in the POCM is examined in response to changes in daily European Centre for Medium-Range Weather Forecasts wind stresses across the tropical and subtropical Pacific Ocean (31°S–31°N) between 1983 and 1989. The POCM provides a complete and physically consistent representation of the state of the Pacific Ocean, with the phase of the thermocline depth anomalies being consistent with the observed El Niño/La Niña variations in the near-equatorial zone and southwest Pacific during the decade.

A series of vorticity model sensitivity experiments, incorporating scaled Rossby wave speeds based on recent observations from the TOPEX/Poseidon satellite altimeter, is used to examine and compare the phase and amplitude variations in the depth of the internal surface against changes in the depth of the 14°C isotherm (D14, used as a proxy for the depth of the thermocline, or pycnocline) as simulated in the POCM. This study demonstrates that the simple linear vorticity model can reproduce the Pacific Ocean thermocline depth anomalies in the interior of the subtropical gyres as simulated by the POCM. These variations are both qualitatively and quantitatively consistent with an ocean forced by only Ekman pumping and Rossby waves that traverse the basin, with isolated topographic and background influences. Further, a number of experiments demonstrate that the phase similarities, from correlation analyses, between results from the POCM and those from the simple dynamical model are statistically significant (at the 95% level) across the majority of the 11°S, 11°N, and 21°N transects in the western, central, or eastern Pacific basin. At 11° and 21° latitude, the amplitude of the variability is similarly comparable across much of the basin. The model is generally less successful at 31° latitude where higher baroclinic modes of the mean flow become important.

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Neil J. Holbrook and Nathaniel L. Bindoff

Abstract

This paper presents a modified objective mapping technique that takes advantage of the strong vertical correlations in ocean temperature profiles. This technique has been used here successfully to generate a uniformly gridded upper-ocean temperature dataset in the southwest Pacific Ocean region from most of the available bathythermograph casts collected between 0°–50°S and 140°E–180°, covering the period from 1955 to 1988. Important advantages of this technique over most previous objective methods are its (i) ability to deal with four-dimensional data (space and time), (ii) improved estimate of the first-guess (polynomial) mean, (iii) preservation of the vertical structure of the ocean temperature data, (iv) computational efficiency, and (v) objective error analysis.

The technique combines empirical orthogonal function (EOF) analysis, using the singular value decomposition, and objective mapping. In this application of the method, a digital “atlas” of upper-ocean temperatures has been created on a grid 2° × 2°, at 5-m depth intervals, and comprises a monthly climatology and two three-monthly time series (January, April, July, and October). The time series include a dataset from 1955 to 1988 to 100-m depth, and a shorter period, deeper dataset from 1973 to 1988 to 450-m depth. Only the first five vertical EOFs are needed to explain about 90% of the total variance in the data and to within the a priori noise estimates. The full four-dimensional temperature field was reconstructed using objective maps of the horizontal coefficients corresponding to each of the significant vertical EOFs. Although the method is statistically suboptimal, the final mapped temperature fields are unbiased and consistent with the a priori noise. In this application, the computing time is reduced by a factor of 36, making the mapping procedure feasible on modern workstations.

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Neil J. Holbrook and Nathaniel L. Bindoff

Abstract

The spatial and temporal variability of the southwest Pacific Ocean is examined with the aim of describing the physical processes operating on interannual and decadal timescales. The study takes advantage of a new temperature atlas of the upper 450 m of the southwest Pacific Ocean, obtained from 40 000 bathythermograph profiles between 1955 and 1988. Rotated principal components analysis was used to filter the important spatial and temporal scales of temperature variability in the data. Three different analyses are presented. They include two intraocean analyses and a joint analysis of subsurface ocean temperature, sea level pressure, and surface winds.

The dominant El Niño mode describes the large vertical excursions of the thermocline in the western tropical Pacific in response to atmospheric forcing at a 3–6-month lag. More importantly, most of the retained modes, outside of the equatorial region, have time variations that correlate with El Niño. One ocean mode, with a spatial pattern representing sea surface temperature anomalies in the western Coral Sea (linked to the interannual migration of the South Pacific convergence zone), correlates significantly with (at the 99% level) and leads (by 3–6 months) the Southern Oscillation index (SOI), suggesting that sea surface temperature anomalies in this region may be a useful indicator for the onset of El Niño. A separate mode whose spatial pattern corresponds to the main oceanographic gyre also shows statistically significant temperature variations in phase with, or slightly leading, the SOI.

The main decadal variations occur in the midlatitudes, in the subtropical gyre, and in another mode associated with sub-Antarctic mode water (SAMW). The subtropical gyre warmed to a maximum in the mid-1970s and has been cooling since. In the SAMW a long-term warming of the upper 100 m of the southwest Tasman Sea is identified between 1955 and 1988. The depth-integrated warming in this region is found to be about 0.015°C yr−1, representing a contribution to sea level rise, through thermal expansion, of about 0.3 mm yr−1.

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Neil J. Holbrook and Nathaniel L. Bindoff

Abstract

Climatological monthly upper-ocean temperature anomalies from the annual mean in the subtropical southwest Pacific Ocean show a characteristic out-of-phase relationship between the mixed layer and the underlying water. The mixed layer temperature anomalies in the subtropical gyre and midlatitudes are consistent in the spatial distribution and phase expected from solar radiation. However, below the mixed layer, the temperature anomalies between 10°S and 30°S are coherent throughout the water column to 450-m depth and are almost 180° out of phase with the mixed layer temperatures. This pattern of temperature anomalies describes vertical movements of the thermocline more closely linked to the seasonal variations in the wind stress curl.

To test this hypothesis, a one-dimensional linear vorticity model was forced using the Hellerman and Rosenstein monthly wind stresses across the entire width of the South Pacific Ocean. This simple wind-driven model has considerable skill in predicting the gyre-scale pattern of change in the phase and amplitude associated with thermocline variations in the subtropical gyre. Experiments, varying the Rossby wave speed, showed that a better representation is achieved with speeds of 2 to 2.5 times that observed from altimeter observations. Overall, the inclusion of long Rossby waves appears to be a very important contribution to the amplitude of the thermocline depth variations in the southwest Pacific. Furthermore, this important Rossby wave contribution is supported by the large-scale anomaly patterns obtained from more sophisticated three-dimensional dynamical ocean models.

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Katrina A. McDonnell and Neil J. Holbrook

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This paper seeks to address some of the limitations in previous statistical forecast models of tropical cyclogenesis through the development of a series of Poisson regression models on a 2° latitude × 5° longitude spatial grid and a monthly grid in time. The “Gray” parameters [low-level relative vorticity, vertical wind shear parameter, ocean thermal energy, (saturated) equivalent potential temperature gradient, and middle-troposphere humidity] were analyzed as potential predictors of tropical cyclogenesis for the Australian–southwest Pacific Ocean region. Various predictor lead times of up to 5 months were investigated, with the most significant Poisson regression models being cross validated, and the skill of their hindcasts evaluated.

The Poisson regression model incorporating a combination of saturated equivalent potential temperature gradients at various leads was found to be the most skillful in hindcasting the temporal (phase and amplitude) variability of tropical cyclogenesis for the Australian–southwest Pacific region, with a correlation coefficient between the observed and cross-validated hindcast time series of 0.54 (significant at the 99% level), and a root- mean-square error 26% better than climatology. Models using the thermal (ocean thermal energy, saturated equivalent potential temperature gradient, and middle-troposphere humidity) and all (thermal plus low-level relative vorticity and vertical wind shear parameter) predictor variables showed the most skill in hindcasting the spatial distribution of cyclogenesis in this region.

The model hindcast skill in predicting individual tropical cyclone occurrences and nonoccurrences was also examined. The all-Gray parameter Poisson regression model was found to correctly hindcast up to 72.6% of cyclogenesis events and nearly 70% of nonoccurrences in the Australian—southwest Pacific region. The model design enabled the investigation of tropical cyclogenesis on subregional/subseasonal scales, with promising model hindcast skill evident. The results presented herein suggest that useful and more detailed forecasts may be possible in the future in addition to those currently provided at the basin-wide and seasonal scales.

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Jiale Lou, Terence J. O’Kane, and Neil J. Holbrook

Abstract

A multivariate linear inverse model (LIM) is developed to demonstrate the mechanisms and seasonal predictability of the dominant modes of variability from the tropical and South Pacific Oceans. We construct a LIM whose covariance matrix is a combination of principal components derived from tropical and extratropical sea surface temperature, and South Pacific Ocean vertically averaged temperature anomalies. Eigen-decomposition of the linear deterministic system yields stationary and/or propagating eigenmodes, of which the least damped modes resemble El Niño–Southern Oscillation (ENSO) and the South Pacific decadal oscillation (SPDO). We show that although the oscillatory periods of ENSO and SPDO are distinct, they have very close damping time scales, indicating that the predictive skill of the surface ENSO and SPDO is comparable. The most damped noise modes occur in the midlatitude South Pacific Ocean, reflecting atmospheric eastward-propagating Rossby wave train variability. We argue that these ocean wave trains occur due to the high-frequency atmospheric variability of the Pacific–South American pattern imprinting onto the surface ocean. The ENSO spring predictability barrier is apparent in LIM predictions initialized in March–May (MAM) but displays a significant correlation skill of up to ~3 months. For the SPDO, the predictability barrier tends to appear in June–September (JAS), indicating remote but delayed influences from the tropics. We demonstrate that subsurface processes in the South Pacific Ocean are the main source of decadal variability and further that by characterizing the upper ocean temperature contribution in the LIM, the seasonal predictability of both ENSO and the SPDO variability is increased.

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Jiale Lou, Neil J. Holbrook, and Terence J. O’Kane

Abstract

The South Pacific decadal oscillation (SPDO) characterizes the Southern Hemisphere contribution to the Pacific-wide interdecadal Pacific oscillation (IPO) and is analogous to the Pacific decadal oscillation (PDO) centered in the North Pacific. In this study, upper ocean variability and potential predictability of the SPDO is examined in HadISST data and an atmosphere-forced ocean general circulation model. The potential predictability of the IPO-related variability is investigated in terms of both the fractional contribution made by the decadal component in the South, tropical and North Pacific Oceans and in terms of a doubly integrated first-order autoregressive (AR1) model. Despite explaining a smaller fraction of the total variance, we find larger potential predictability of the SPDO relative to the PDO. We identify distinct local drivers in the western subtropical South Pacific, where nonlinear baroclinic Rossby wave–topographic interactions act to low-pass filter decadal variability. In particular, we show that the Kermadec Ridge in the southwest Pacific enhances the decadal signature more prominently than anywhere else in the Pacific basin. Applying the doubly integrated AR1 model, we demonstrate that variability associated with the Pacific–South American pattern is a critically important atmospheric driver of the SPDO via a reddening process analogous to the relationship between the Aleutian low and PDO in the North Pacific—albeit that the relationship in the South Pacific appears to be even stronger. Our results point to the largely unrecognized importance of South Pacific processes as a key source of decadal variability and predictability.

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Jiale Lou, Terence J. O’Kane, and Neil J. Holbrook

Abstract

A stochastically forced linear inverse model (LIM) of the combined modes of variability from the tropical and South Pacific Oceans is used to investigate the linear growth of optimal initial perturbations and to identify the spatiotemporal features of the stochastic forcing associated with the atmospheric Pacific–South American patterns 1 and 2 (PSA1 and PSA2). Optimal initial perturbations are shown to project onto El Niño–Southern Oscillation (ENSO) and South Pacific decadal oscillation (SPDO), where the inclusion of subsurface South Pacific Ocean temperature variability significantly increases the multiyear linear predictability of the deterministic system. We show that the optimal extratropical sea surface temperature (SST) precursor is associated with the South Pacific meridional mode, which takes from 7 to 9 months to linearly evolve into the final ENSO and SPDO peaks in both the observations and as simulated in an atmosphere-forced ocean model. The optimal subsurface precursor resembles its peak phase, but with a weak amplitude, representing oceanic Rossby waves in the extratropical South Pacific. The stochastic forcing is estimated as the residual by removing the deterministic dynamics from the actual tendency under a centered difference approximation. The resulting stochastic forcing time series satisfies the Gaussian white noise assumption of the LIM. We show that the PSA-like variability is strongly associated with stochastic SST forcing in the tropical and South Pacific Oceans and contributes not only to excite the optimal initial perturbations associated with ENSO and the SPDO but in general to activate the entire stochastic SST forcing, especially in austral summer.

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