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Currently most ocean forecast systems provide daily eddy resolving predictions 7 days or so in advance. We first investigate the potential for longer range daily ocean forecast up to 28 days using the coupled system ACCESS-S1 in which the ocean component is eddy permitting. The daily ocean forecasts over the hindcast period 1990–2012 in the Indo-Pacific oceans have been evaluated, using persistence skill as a reference. Our results show that the sea surface temperature (SST) biases are in the range of ±0.3 °C in most regions within the first 28 days of the forecast. The model's predicted correlation skill of SST anomaly (SSTA) is higher than persistence skill in most areas beyond the first few days up until the 28-day lead time. The skill of SSTA indices averaged in different domains suggest that the model produces more skilful predictions in the North Indian Ocean, South Indian Ocean, western tropical Pacific and Leeuwin Current regions than the corresponding persistence forecasts. The model's SSTA prediction skill in the eastern tropical Pacific is comparable to the persistence skill which is very high. The model's correlation skill for sea surface height anomaly (SSHA) can beat persistence skill beyond the first week and the model's skill has a great advantage with a longer lead time. The daily prediction of the larger mesoscale eddies that shed off the East Australia Current is also investigated here. The spatial correlation between the model predicted and observed SSHA is applied to measure the model prediction skill for these eddies. Our results suggest that the averaged correlation for all forecasting cases at the same lead time is above 0.5 up to 14-day lead. It indicates that ACCESS-S1 has some skill in predicting these eddies, since the strength and track of the eddies in the Tasman Sea are highly complicated. [ABSTRACT FROM AUTHOR]

Craniovertebral junction (CVJ) trauma is a challenging clinical condition. Being a highly mobile functional unit at the junction of the skull and the vertebral column, traumatic events in this area may produce devastating neurological complications and death. Additionally, many of the CVJ traumatic injuries can be left undiagnosed or even raise difficult treatment dilemmas. We present a literature review in the format of recommendations on the diagnosis and management of different scenarios for upper cervical trauma and produce recommendations, which can be applicable to various areas of the globe. [ABSTRACT FROM AUTHOR]

Cervical spondylotic myelopathy (CSM) is a common cause of adult spinal cord dysfunction. Although the therapeutic options for moderate to severe CSM patients have been established well, the existing guidelines for therapeutic decisions in mild cases of CSM are unclear. We present a review of literature on conservative treatment and surgery for CSM and suggest general recommendations applicable in various clinical presentations and in different geographic locations across the globe, with due considerations to available resources and locally prevalent practices. [ABSTRACT FROM AUTHOR]

The impact of realistic atmospheric initialisation on the seasonal prediction of tropical Pacific sea surface temperatures is explored with the Predictive Ocean-Atmosphere Model for Australia (POAMA) dynamical seasonal forecast system. Previous versions of POAMA used data from an Atmospheric Model Intercomparison Project (AMIP)-style simulation to initialise the atmosphere for the hindcast simulations. The initial conditions for the hindcasts did not, therefore, capture the true intra-seasonal atmospheric state. The most recent version of POAMA has a new Atmosphere and Land Initialisation scheme (ALI), which captures the observed intra-seasonal atmospheric state. We present the ALI scheme and then compare the forecast skill of two hindcast datasets, one with AMIP-type initialisation and one with realistic initial conditions from ALI, focussing on the prediction of El Niño. For eastern Pacific (Niño3) sea surface temperature anomalies (SSTAs), both experiments beat persistence and have useful SSTA prediction skill (anomaly correlations above 0.6) at all lead times (forecasts are 9 months duration). However, the experiment with realistic atmospheric initial conditions from ALI is an improvement over the AMIP-type initialisation experiment out to about 6 months lead time. The improvements in skill are related to improved initial atmospheric anomalies rather than an improved initial mean state (the forecast drift is worse in the ALI hindcast dataset). Since we are dealing with a coupled system, initial atmospheric errors (or differences between experiments) are amplified though coupled processes which can then lead to long lasting errors (or differences). [ABSTRACT FROM AUTHOR]

A new ensemble ocean data assimilation system, developed for the Predictive Ocean Atmosphere Model for Australia (POAMA), is described. The new system is called PEODAS, the POAMA Ensemble Ocean Data Assimilation System. PEODAS is an approximate form of an ensemble Kalman filter system. For a given assimilation cycle, a central forecast is integrated, along with a small ensemble of forecasts that are forced with perturbed surface fluxes. The small ensemble is augmented with multiple small ensembles from previous assimilation cycles, yielding a larger ensemble that consists of perturbed forecasts from the last month. This larger ensemble is used to represent the system''s time-dependent background error covariance. At each assimilation cycle, a central analysis is computed utilizing the ensemble-based covariance. Each of the perturbed ensemble members are nudged toward the central analysis to control the ensemble spread and mean. The ensemble-based covariances generated by PEODAS potentially yield dynamically balanced analysis increments. The time dependence of the ensemble-based covariance yields spatial structures that change for different dynamical regimes, for example during El Niño and La Niña conditions. These differences are explored in terms of the dominant dynamics and the system''s errors. The performance of PEODAS during a 27-yr reanalysis is evaluated through a series of comparisons with assimilated and independent observations. When compared to its predecessor, POAMA version 1, and a simulation with no assimilation of subsurface observations, PEODAS demonstrates a quantitative improvement in skill. PEODAS will form the basis of Australia''s next operational seasonal prediction system. [ABSTRACT FROM AUTHOR]

The impact of stochastic intraseasonal variability on the onset of the 1997/98 El Niño was examined using a large ensemble of forecasts starting on 1 December 1996, produced using the Australian Bureau of Meteorology Predictive Ocean Atmosphere Model for Australia (POAMA) seasonal forecast coupled model. This coupled model has a reasonable simulation of El Niño and the Madden–Julian oscillation, so it provides an ideal framework for investigating the interaction between the MJO and El Niño. The experiment was designed so that the ensemble spread was simply a result of internal stochastic variability that is generated during the forecast. For the initial conditions used here, all forecasts led to warm El Niño–type conditions with the amplitude of the warming varying from 0.5° to 2.7°C in the Niño-3.4 region. All forecasts developed an MJO event during the first 4 months, indicating that perhaps the background state favored MJO development. However, the details of the MJOs that developed during December 1996–March 1997 had a significant impact on the subsequent strength of the El Niño event. In particular, the forecasts with the initial MJOs that extended farther into the central Pacific, on average, led to a stronger El Niño, with the westerly winds in the western Pacific associated with the MJO leading the development of SST and thermocline anomalies in the central and eastern Pacific. These results imply a limit to the accuracy with which the strength of El Niño can be predicted because the details of individual MJO events matter. To represent realistic uncertainty, coupled models should be able to represent the MJO, including its propagation into the central Pacific so that forecasts produce sufficient ensemble spread. [ABSTRACT FROM AUTHOR]

Simulations using an atmospheric model forced with observed SST climatology and the same atmospheric model coupled to a slab-ocean model are used to investigate the role of air–sea interaction on the dynamics of the MJO. Slab-ocean coupling improved the MJO in Australia’s Bureau of Meteorology atmospheric model over the Indo-Pacific warm pool by reducing its period from 70–100 to 45–70 days, thereby showing better agreement with the 30–80-day observed oscillation. Air–sea coupling improves the MJO by increasing the moisture flux in the lower troposphere prior to the passage of active convection, which acts to promote convection and precipitation on the eastern flank of the main convective center. This process is triggered by an increase in surface evaporation over positive SST anomalies ahead of the MJO convection, which are driven by the enhanced shortwave radiation in the region of suppressed convection. This in turn generates enhanced convergence into the region, which supports evaporation–wind feedback in the presence of weak background westerly winds. A subsequent increase in low-level moisture convergence acts to further moisten the lower troposphere in advance of large-scale convection in a region of reduced atmospheric pressure. This destabilizing mechanism is referred to as enhanced moisture convergence–evaporation feedback (EMCEF) and is utilized to understand the role of air–sea coupling on the observed MJO. The EMCEF mechanism also reconciles traditionally opposing ideas on the roles of frictional wave–conditional instability of the second kind (CISK) and wind–evaporation feedback. These results support the idea that the MJO is primarily an atmospheric phenomenon, with air–sea interaction improving upon, but not critical for, its existence in the model. [ABSTRACT FROM AUTHOR]

Aims To conduct a randomized placebo controlled double-blind crossover trial in order to evaluate a loratadine-pseudoephedrine combination (L+PS) in children with seasonal allergic rhinitis. Methods Forty children (15 males; 25 females), aged 3–15 years, were included in this study. They were randomized to receive L+PS (0.2 mg kg[sup -1] body weight–2.4 mg kg[sup -1] body weight respectively) or placebo (PLA) for 14 days. After 7 days of washout, patients were shifted to the other treatment for a further 14 days. Nasal symptoms (sneezing/itching, congestion, nasal dripping) and signs (turbinal swelling, retronasal drainage), rated on a scale ranging from: 1. absent to 5. very intense, and their sum or mean total symptom score (MTSS) were used as efficacy measurement. Results Significant relief was observed; post-treatment MTSS difference and its percent change were respectively; L+PS=-4.29; 95% CI: -3.64 and -4.94 (27.8%), and PLA=-1.63; 95% CI: -0.95 and -2.31 (10.7%) (P<0.001 baseline vs endpoint and between treatments). Furthermore, L+PS and PLA significantly modified symptoms, but only L+PS significantly modified signs. No clinical changes were observed during the trial; only one patient showed slight transient insomnia when receiving L+PS. Conclusions It is concluded that L+PS is useful and well tolerated in children with seasonal allergic rhinitis. However, elements such as placebo effect must be taken into account for planning future trials. [ABSTRACT FROM AUTHOR]

Background and Aim: Dobutamine stress echocardiography (DSE) is a well-established noninvasive investigation for significant coronary artery disease (CAD). The aim of this study was to evaluate the accuracy of cardiac Doppler parameters in predicting CAD. Methods: We prospectively studied 103 consecutive patients with suspected CAD based on typical symptoms; 59 proved to have CAD, and 44 patients proved to have no-CAD (n = 44). All patients underwent a complete stress Doppler echocardiographic examination. Total isovolumic time (T-IVT) as a marker of cavity dyssynchrony and wall motion score index (WMSI) were also calculated. Results: At peak dobutamine stress, the compromised LV longitudinal excursion (MAPSE), systolic septal and lateral velocities (s'), and diastolic indices were more pronounced in the CAD patients compared with those without CAD, but LV dimension did not differ between groups (p > 0.05). The WMSI was higher and t-IVT more prolonged in patients with CAD (p < 0.01 for both). Similarly, the changes were more pronounced in patients with significant CAD compared with insignificant CAD. On multivariate model, Δ mean s', OR 2.016 (1.610 to 3.190; p < 0.001), Δ E velocity OR 2.502 (1.179 to 1.108; p < 0.001), Δ t-IVT 2.206 (1.180 to 2.780; p < 0.001) and Δ WMSI OR 1.911 (1.401 to 3.001; p = 0.001) were the most powerful independent predictors of the presence of CAD, particularly when significant (>75%). Δ mean s' < 5.0 was 85% sensitive, 89% specific with AUC 0.92. Respective values for Δ E velocity <6.0 cm/s were 82%, 90% and 0.91; for Δ t-IVT > 4.5, 78%, 77% and 0.81 and for Δ FT ≥ 150 ms, 76%, 78% and 0.84 in predicating significant CAD. WMSI ≥ 0.7 was 75% sensitive, 77% specific with AUC of 0.81 in predicting significant CAD. The accuracy of DSE was higher in significant CAD compared to insignificant CAD (80% vs. 74%; p = 0.03). Conclusions: Compromised LV longitudinal systolic function, lower delta E wave, prolonged t-IVT, and increased WMSI were the most powerful independent predictors of the presence and significance of CAD. These finding strengthen the role of comprehensive DSE analysis in diagnosing ischemic disturbances secondary to significant CAD. [ABSTRACT FROM AUTHOR]

Background: The early management of polytrauma patients with traumatic spinal cord injury (tSCI) is a major challenge. Sparse data is available to provide optimal care in this scenario and worldwide variability in clinical practice has been documented in recent studies. Methods: A multidisciplinary consensus panel of physicians selected for their established clinical and scientific expertise in the acute management of tSCI polytrauma patients with different specializations was established. The World Society of Emergency Surgery (WSES) and the European Association of Neurosurgical Societies (EANS) endorsed the consensus, and a modified Delphi approach was adopted. Results: A total of 17 statements were proposed and discussed. A consensus was reached generating 17 recommendations (16 strong and 1 weak). Conclusions: This consensus provides practical recommendations to support a clinician's decision making in the management of tSCI polytrauma patients. [ABSTRACT FROM AUTHOR]

We quantify seasonal prediction skill of tropical winter rainfall in 14 climate forecast systems. High levels of seasonal prediction skill exist for year‐to‐year rainfall variability in all tropical ocean basins. The tropical East Pacific is the most skilful region, with very high correlation scores, and the tropical West Pacific is also highly skilful. Predictions of tropical Atlantic and Indian Ocean rainfall show lower but statistically significant scores. We compare prediction skill (measured against observed variability) with model predictability (using single forecasts as surrogate observations). Model predictability matches prediction skill in some regions but it is generally greater, especially over the Indian Ocean. We also find significant inter‐basin connections in both observed and predicted rainfall. Teleconnections between basins due to El Niño–Southern Oscillation (ENSO) appear to be reproduced in multi‐model predictions and are responsible for much of the prediction skill. They also explain the relative magnitude of inter‐annual variability, the relative magnitude of predictable rainfall signals and the ranking of prediction skill across different basins. These seasonal tropical rainfall predictions exhibit a severe wet bias, often in excess of 20% of mean rainfall. However, we find little direct relationship between bias and prediction skill. Our results suggest that future prediction systems would be best improved through better model representation of inter‐basin rainfall connections as these are strongly related to prediction skill, particularly in the Indian and West Pacific regions. Finally, we show that predictions of tropical rainfall alone can generate highly skilful forecasts of the main modes of extratropical circulation via linear relationships that might provide a useful tool to interpret real‐time forecasts. Tropical rainfall and its year‐to‐year variability. Seasonal mean climatological rainfall (top left) and inter‐annual variability (top right) of global rainfall from GPCP observations. Tropical rainfall is analysed in the boxed regions for the Indian (45°–100°E, 5°S–10°N), West Pacific (110°–140°E, 5°S–25°N), East Pacific (200 –90 W, 5°S–10°N) and Atlantic (60°–0 W, 5°S–5°N) regions. Mean and standard deviation of ensemble member rainfall over the same years (1993–2012) from a single example prediction system (bottom panels, GloSea5). Note the logarithmic contour interval. Units are mm/day. [ABSTRACT FROM AUTHOR]

Since 2017, the Northern Australia Climate Program (NACP) has assisted the pastoral grazing industry to better manage drought risk and climate variability. The NACP funding is sourced from the beef cattle industry, government, and academia, representing the program's broad range of aims and target beneficiaries. The program funds scientists in the United Kingdom and Australia, in addition to extension advisers called "Climate Mates" across a region that supports 15 million head of cattle. Many Climate Mates are employed in the cattle sector and have existing relationships in their communities and capacity to meaningfully engage with the program's intended beneficiaries—red meat producers. The NACP is a prime example of a successful end-to-end program, integrating climate model improvements (research) with tailored forecast products (development), through to direct stakeholder engagement (extension), on-ground application of technologies (adoption), and improvement in industry and community resilience (impact). The climate information needs of stakeholders also feed back to the research and development components, ensuring the scientific research directly addresses end-user requirements. For any scientific research program, ensuring that research output has measurable real-world impact represents a key challenge. This is more difficult in cases where the scientific research is several steps away from the customer's needs. This paper gives an overview of the NACP and research highlights, discussing how the end-to-end framework could be adapted and applied in other regions and industries. It seeks to provide a roadmap for other groups to follow to produce more targeted research with identifiable real-world benefits. [ABSTRACT FROM AUTHOR]

Predictive skill for El Niño in the equatorial eastern Pacific across a range of forecast models declined sharply in the early twenty-first century relative to what was achieved in the late twentieth century despite ongoing improvements of forecast systems. This decline coincided with a shift in Pacific climate to an enhanced east-west surface temperature gradient across the Pacific and a stronger Walker circulation at the end of the twentieth century. Using seasonal forecast sensitivity experiments with the Australian Bureau of Meteorology coupled model POAMA2.4, the authors show that this shift in background climate acted to weaken key ocean-atmosphere feedbacks that amplify eastern Pacific El Niño, thus resulting in weaker variability that is less predictable. These results indicate that extreme El Niños, such as those that occurred in 1982/83 and 1997/98, were conditioned by the background climate and so were favored to occur in the late twentieth century. However, anticipating future changes in El Niño variability and predictability is an outstanding challenge because causes and prediction of low-frequency variations of Pacific climate have not yet been demonstrated. [ABSTRACT FROM AUTHOR]

In order to explore temporal changes of predictability of El Niño Southern Oscillation (ENSO), a novel set of global biennial climate reforecasts for the historical period 1901–2010 has been generated using a modern initialized coupled forecasting system. We find distinct periods of enhanced long‐range skill at the beginning and at the end of the twentieth century, and an extended multi‐decadal epoch of reduced skill during the 1930s–1950s. Once the forecast skill extends beyond the first spring barrier, the predictability limit is much enhanced and our results provide support for the feasibility of skillful ENSO forecasts up to 18 months. Changes in the mean state, variability (amplitude), persistence, seasonal cycle and predictability suggest that multi‐decadal variations in the dynamical characteristics of ENSO rather than the data coverage and quality of the observations have primarily driven the reported non‐monotonic skill modulations. Plain Language Summary: El Niño Southern Oscillation (ENSO) is an irregular periodic variation in ocean temperatures and winds over the tropical Pacific Ocean that occurs every few years with varying intensity of its several‐month‐long cold and warm phases. Using a modern forecast model as well as observations of the climate system at the start of the forecast, seasonal forecasts draw on the predictability arising from these slows ENSO fluctuations to provide regular predictions of the atmosphere and ocean for the whole globe 1–2 seasons ahead. Such forecasts are important to warn us about potentially dangerous weather and climate conditions in the months to come. How well can we predict ENSO and what confidence do we have in our future abilities to forecast it? Here we present results from an unprecedented extensive model data set of retrospective ENSO forecasts 2 years into the future which were repeated every year from 1901 to 2010. By comparison with observations, we find that our ability to forecast ENSO did not simply degrade the further back in time we went. Rather, the skill had a distinct minimum during the 1930s to 1950s with better forecasts before and after. We explore factors that play a role in explaining this unexpected behavior. Key Points: Novel 2‐year‐long global coupled retrospective forecast ensemble from 1901 to 2010 to study predictability of El Niño Southern OscillationPeriods of very high skill at the beginning and end of the twentieth century but extended multi‐decadal mid‐century epoch of reduced skillNon‐monotonic skill modulations cannot be explained by better recent observations and are likely linked to background changes instead [ABSTRACT FROM AUTHOR]

The simulation and prediction of extreme heat over Australia on intraseasonal timescales in association with the El Niño-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) is assessed using the Bureau of Meteorology's Predictive Ocean Atmosphere Model for Australia (POAMA). The analysis is based on hindcasts over 1981-2010 and focuses on weeks 2 and 3 of the forecasts, i.e. beyond a typical weather forecast. POAMA simulates the observed increased probabilities of extreme heat during El Niño events, focussed over south eastern and southern Australia in SON and over northern Australia in DJF, and the decreased probabilities of extreme heat during La Niña events, although the magnitude of these relationships is smaller than observed. POAMA also captures the signal of increased probabilities of extreme heat during positive phases of the IOD across southern Australia in SON and over Western Australia in JJA, but again underestimates the strength of the relationship. Shortcomings in the simulation of extreme heat in association with ENSO and the IOD over southern Australia may be linked to deficiencies in the teleconnection with Indian Ocean SSTs. Forecast skill for intraseasonal episodes of extreme heat is assessed using the Symmetric Extremal Dependence Index. Skill is highest over northern Australia in MAM and JJA and over south-eastern and eastern Australia in JJA and SON, whereas skill is generally poor over south-west Western Australia. Results show there are windows of forecast opportunity related to the state of ENSO and the IOD, where the skill in predicting extreme temperatures over certain regions is increased. [ABSTRACT FROM AUTHOR]

We assess the impact of improved ocean initial conditions for predicting El Niño-Southern Oscillation (ENSO) and Indian Ocean dipole (IOD) using the Bureau of Meteorology's Predictive Ocean Atmosphere Model for Australia (POAMA) coupled seasonal prediction model for the period 1982-2006. The new ocean initial conditions are provided by an ensemble-based analysis system that assimilates subsurface temperatures and salinity and which is a clear improvement over the previous optimal interpolation system which used static error covariances and was univariate (temperature only). Hindcasts using the new ocean initial conditions have better skill at predicting sea surface temperature (SST) variations associated with ENSO than do the hindcasts initialized with the old ocean analyses. The improvement derives from better prediction of subsurface temperatures and the largest improvements come during ENSO-IOD neutral years. We show that improved prediction of the Niño3.4 SST index derives from improved initial depiction of the thermocline and halocline in the equatorial Pacific but as lead time increases the improved depiction of the initial salinity field in the western Pacific become more important. Improved ocean initial conditions do not translate into improved skill for predicting the IOD but we do see an improvement in the prediction of subsurface temperatures in the Indian Ocean (IO). This result reflects that the coupling between subsurface and surface temperature variations is weaker in the IO than in the Pacific, but coupled model errors may also be limiting predictive skill in the IO. [ABSTRACT FROM AUTHOR]

The authors assess the sensitivity of the simulated mean state and coupled variability to systematic initial state salinity errors in seasonal forecasts using the Australian Bureau of Meteorology Predictive Ocean Atmosphere Model for Australia (POAMA) coupled model. This analysis is based on two sets of hindcasts that were initialized from old and new ocean initial conditions, respectively. The new ocean initial conditions are provided by an ensemble multivariate analysis system that assimilates subsurface temperatures and salinity and is a clear improvement over the previous system, which was based on univariate optimal interpolation, using static error covariances and assimilating only temperature without updating salinity. Large systematic errors in the salinity field around the thermocline region of the tropical western and central Pacific produced by the old assimilation scheme are shown to have strong impacts on the predicted mean state and variability in the tropical Pacific for the entire 9 months of the forecast. Forecasts initialized from the old scheme undergo a rapid and systematic adjustment of density that causes large persistent changes in temperature both locally in the western and central Pacific thermocline, but also remotely in the eastern Pacific via excitation of equatorial waves. The initial subsurface salinity errors in the western and central Pacific ultimately result in an altered surface climate because of induced temperature changes in the thermocline that trigger a coupled feedback in the eastern Pacific. These results highlight the importance of accurately representing salinity in initial conditions for climate prediction on seasonal and potentially multiyear time scales. [ABSTRACT FROM AUTHOR]

In light of the growing recognition of the role of surface temperature variations in the Indian Ocean for driving global climate variability, the predictive skill of the sea surface temperature (SST) anomalies associated with the Indian Ocean dipole (IOD) is assessed using ensemble seasonal forecasts from a selection of contemporary coupled climate models that are routinely used to make seasonal climate predictions. The authors assess predictions from successive versions of the Australian Bureau of Meteorology Predictive Ocean-Atmosphere Model for Australia (POAMA 15b and 24), successive versions of the NCEP Climate Forecast System (CFSv1 and CFSv2), the ECMWF seasonal forecast System 3 (ECSys3), and the Frontier Research Centre for Global Change system (SINTEX-F) using seasonal hindcasts initialized each month from January 1982 to December 2006. The lead time for skillful prediction of SST in the western Indian Ocean is found to be about 5-6 months while in the eastern Indian Ocean it is only 3-4 months when all start months are considered. For the IOD events, which have maximum amplitude in the September-November (SON) season, skillful prediction is also limited to a lead time of about one season, although skillful prediction of large IOD events can be longer than this, perhaps up to about two seasons. However, the tendency for the models to overpredict the occurrence of large events limits the confidence of the predictions of these large events. Some common model errors, including a poor representation of the relationship between El Niño and the IOD, are identified indicating that the upper limit of predictive skill of the IOD has not been achieved. [ABSTRACT FROM AUTHOR]

The importance of initializing atmospheric intra-seasonal (stochastic) variations for prediction of the onset of the 1997/1998 El Niño is examined using the Australian Bureau of Meteorology coupled seasonal forecast model. A suite of 9-month forecasts was initialized on the 1st December 1996. Observed ocean initial conditions were used together with five different atmospheric initial conditions that sample a range of possible initial states of intra-seasonal (stochastic) variability, especially the Madden-Julian Oscillation (MJO), which is considered the primary stochastic variability of relevance to El Niño evolution. The atmospheric initial states were generated from a suite of atmosphere-only integrations forced by observed sea surface temperatures (SST). To the extent that low frequency variability of the tropical atmosphere is forced by slow variations in SST, these atmospheric states should all represent realistic low frequency atmospheric variability that was present in December 1996. However, to the extent that intra-seasonal variability is not constrained by SST, they should capture a range of intra-seasonal states, especially variations in the activity, phase and amplitude of the MJO. For each of these five states, a 20-member ensemble of coupled model forecasts was generated by the addition of small random perturbations to the SST field at the initial time. The ensemble mean from all five sets of forecasts resulted in El Niño but three of the sets produced substantially greater warming by months 4-5 in the NINO3.4 region compared to the other two. The warmer group stemmed from stronger intra-seasonal westerly wind anomalies associated with the MJO that propagated eastward into the central Pacific during the first 1-2 months of the forecast. These were largely absent in the colder group; the weakest of the colder group developed strong easterly wind anomalies, relative to the grand ensemble mean, that propagated into the central Pacific early in the forecast, thereby generating significantly weaker downwelling Kelvin waves in comparison to the warmer group. The strong reduction in downwelling Kelvin waves in the weakest case acted to limit the warming in the eastern Pacific, resulting in a 'Modoki' type El Niño that is more focused in the central Pacific. Our results suggest that the intra-seasonal stochastic component of the atmospheric initial condition has an important and potentially predictable impact on the forecasts of the initial warming and flavour of the 1997/1998 El Niño. However, to the extent that atmospheric intra-seasonal variability is not predictable beyond a month or two, these results imply a limit to the accuracy with which the strength and perhaps the spatial distribution of an El Niño can ultimately be predicted. These results do not preclude a predictable role of the MJO and other intra-seasonal stochastic variability at longer lead times if some aspects of the stochastic variability are preconditioned by the evolving state of El Niño or other low frequency boundary forcing. [ABSTRACT FROM AUTHOR]

The evolution of the Indian Ocean during El Niño–Southern Oscillation is investigated in a 100-yr integration of an Australian Bureau of Meteorology coupled seasonal forecast model. During El Niño, easterly anomalies are induced across the eastern equatorial Indian Ocean. These act to suppress the equatorial thermocline to the west and elevate it to the east and initially cool (warm) the sea surface temperature (SST) in the east (west). Subsequently, the entire Indian Ocean basin warms, mainly in response to the reduced latent heat flux and enhanced shortwave radiation that is associated with suppressed rainfall. This evolution can be partially explained by the excitation of an intrinsic coupled mode that involves a feedback between anomalous equatorial easterlies and zonal gradients in SST and rainfall. This positive feedback develops in the boreal summer and autumn seasons when the mean thermocline is shallow in the eastern equatorial Indian Ocean in response to trade southeasterlies. This positive feedback diminishes once the climatological surface winds become westerly at the onset of the Australian summer monsoon. ENSO is the leading mechanism that excites this coupled mode, but not all ENSO events are efficient at exciting it. During the typical El Niño (La Niña) event, easterly (westerly) anomalies are not induced until after boreal autumn, which is too late in the annual cycle to instigate strong dynamical coupling. Only those ENSO events that develop early (i.e., before boreal summer) instigate a strong coupled response in the Indian Ocean. The coupled mode can also be initiated in early boreal summer by an equatorward shift of the subtropical ridge in the southern Indian Ocean, which stems from uncoupled extratropical variability. [ABSTRACT FROM AUTHOR]

Background: Multiple myeloma (MM) presents with spinal lesions in 60% of cases. The combination of osteolytic lesions with multifactorial osteopenia raises specific surgical treatment challenges. Minimally invasive spine surgery (MISS) could be a potential option for MM spinal lesions treatment. Objective: The objective of this study is to evaluate MISS techniques to treat patients presenting with spine fractures due to MM Methods: Retrospective analysis of consecutive patients with histology-proven pathological fractures caused by MM treated with MISS between 2009 and 2018. We collected the data from the clinical records on epidemiology, topography of spine lesions, surgical techniques, blood loss, operation time, complications, mean in-hospital time, and clinical evolution. Results: Twenty-one patients were studied – 13 males and 8 females, with a mean age of 64 years (range 43–83). Mean preoperative spinal instability neoplastic score was 9.8 ± 6 (range 5–16). All cases had a thoracolumbar location – 15 patients underwent kyphoplasty (KP) or vertebroplasty (VP) and 6 were treated with other more complex procedures. All patients had a reduction of pain and/or analgesic load. Vertebral body height increased by a mean of 2.9 mm after VP/KP. Mean hospital stay was 1.3 days for KP/VP and 5.0 days for other MISS procedures. Three patients had complications. Conclusions: The heterogeneity of techniques used reflected the variety of spine involvement by MM. KP and VP led to shorter hospital stays and less complications, being adequate for lesions without major instability. More complex MISS techniques offer an effective treatment with short delay for starting MM adjuvant treatment. [ABSTRACT FROM AUTHOR]

Seasonal climate prediction to date has largely focussed on probabilistic forecasts for above‐ and below‐average conditions in climate means. Here, we examine the possibility of making sub‐seasonal to seasonal outlooks for daily‐scale precipitation extremes in Australia. We first use observational data to show that significant relationships exist between climate modes, such as the El Niño–Southern Oscillation, and indices representing rainfall extremes across much of Australia. The strong observed teleconnections between climate modes and daily rainfall extremes suggest the potential for predictability on seasonal scales. The current Australian Bureau of Meteorology seasonal prediction system (ACCESS‐S1) is examined for performance in predicting rainfall extreme indices using a range of measures. Ensemble hindcasts, consisting of 11 members initialised every month during 1990–2012, perform well for some extreme rainfall indices on short lead‐times (up to 1 month). We note that at short lead‐times, forecasts are aided by skilful weather prediction, so forecast performance drops at lead‐times of a week or more. Forecast performance is lower in austral summer than other seasons and greater in the north and interior of the continent, particularly in the dry season, than elsewhere. The ACCESS‐S1 ensemble is overconfident but exhibits some reliability in probabilistic forecasts of above‐ or below‐average number of wet days and intensity of the highest daily maximum precipitation, especially in northern Australia. ACCESS‐S1 captures the broad pattern of relationships between climate modes and rainfall extremes that are observed. For two case‐studies of unusually extreme precipitation, ACCESS‐S exhibits contrasting performance for forecasts of extreme rainfall anomalies beyond the first month. These results suggest that ACCESS‐S1 may be used to produce outlooks for some rainfall indices, such as the number of wet days and the intensity of the wettest day, for the month ahead. [ABSTRACT FROM AUTHOR]

El Nino and La Nina strongly affect the year-to-year variability of global climate, which exerts intense pressure on water resources and environmental management in a changing climate. Well-known examples include the severe floods in Australia in 2010-11 and 1998-2000 in association with multi-yearly persistent La Nina events, and a strong drought in 2002 in association with El Nino. Growing evidence suggests that some ENSO events may be predictable at lead times beyond one to two years, which is much longer than the forecasts currently issued by most operational forecast centers worldwide. However, it is still unclear how well ENSO can be predicted at multi-year timescales and what essential dynamics underpins such multiyear predictability. Based on a high-resolution coupled model forecast system (60km grid mesh for atmosphere and 25km grid mesh for ocean), we conducted three sets of ensemble hindcast experiments with lead times up to 16, 36, and 66 months, respectively. Results suggest that, despite considerable model bias in simulating the annual mean states and seasonal cycle of the Indo-Pacific climate, ENSO over the past 35 years can be skillfully predicted out to at least 16 months ahead. Some ENSO events can be well predicted at a multi-year lead. In addition, the results also show good skill in predicting global warming related signals at multi-year timescales. [ABSTRACT FROM AUTHOR]

We present a first assessment of the Sea Surface Temperature (SST) and 300m Heat Content (HC) ensemble mean skill of the Australian Community Climate and Earth-System Simulator-Seasonal v1.0 (ACCESS-S1) around New Zealand on seasonal timescales, using a set of retrospective ensemble forecasts for 1990-2012. This was verified against Reynolds AVHRR analysis and Bluelink ReANalysis 3.5 (BRAN3.5). For inshore areas with depth <300m, the model shows skill forecasting for summer at a lead time of 0 months, with 66% and 65% of model grid cells having correlation coefficients exceeding 0.6 for SST and 300m HC respectively. Regions of lower skill seem to be associated with the model representation of the complex and variable frontal systems around New Zealand, and an overly-strong response to the El Niño Southern-Oscillation (ENSO). Nevertheless, the skill of ACCESS-S1 around New Zealand suggests there is potential for the development of useful seasonal forecast products for the region. [ABSTRACT FROM AUTHOR]

Soil water is a critical resource in many rain-fed agricultural systems. Climate variability represents a significant risk in these systems, which has been addressed in the past through seasonal weather outlooks. This study undertakes a pilot assessment of the potential to extend seasonal weather outlooks to plant available soil water (PASW). We analyse 20 sites in the southeast Australian wheat belt using seasonal weather outlooks from the Predictive Ocean-Atmosphere Model for Australia (POAMA; (the operational seasonal model of the Australian Bureau of Meteorology), which were downscaled and used in conjunction with the Agricultural Production Simulator (APSIM). Hindcast rainfall, potential evapotranspiration ( PET ) and PASW outlooks were produced on a monthly basis for 33 years at a point scale. The outlooks were assessed using a range of ensemble verification tools. The results showed hit rates that outperformed climatology for rainfall and PET in the short-term (0–2 months), and for PASW with longer lead times (2–5 months). Continuous rank probability skill scores (CRPSS) were generally statistically worse than climatology for rainfall and PET and statistically better than climatology for PASW over 1–3 months. The influence of initial soil water is seasonally dependent, with longer dependence in low evapotranspiration periods. Improved weather model downscaling approaches would transition to climatology and could improve both weather and PASW outlooks. PASW outlooks were strongly reliant on initial conditions, indicating the importance of understanding current soil water status, which needs to be interpreted in a seasonal context as its influence varies over the year. Expanded operational soil water monitoring would be important if PASW outlooks are to become routine. [ABSTRACT FROM AUTHOR]

Predictive skills of the Somali cross-equatorial flow (CEF) and the Maritime Continent (MC) CEF during boreal summer are assessed using three ensemble seasonal forecasting systems, including the coarse-resolution Predictive Ocean Atmospheric Model for Australia (POAMA, version 2), the intermediate-resolution Scale Interaction Experiment–Frontier Research Center for Global Change (SINTEX-F), and the high-resolution seasonal prediction version of the Australian Community Climate and Earth System Simulator (ACCESS-S1) model. Retrospective prediction results suggest that prediction of the Somali CEF is more challenging than that of the MC CEF. While both the individual models and the multimodel ensemble (MME) mean show useful skill (with the anomaly correlation coefficient being above 0.5) in predicting the MC CEF up to 5-month lead, only ACCESS-S1 and the MME can skillfully predict the Somali CEF up to 2-month lead. Encouragingly, the CEF seesaw index (defined as the difference of the two CEFs as a measure of the negative phase relation between them) can be skillfully predicted up to 4–5 months ahead by SINTEX-F, ACCESS-S1, and the MME. Among the three models, the high-resolution ACCESS-S1 model generally shows the highest skill in predicting the individual CEFs, the CEF seesaw, as well as the CEF seesaw index–related precipitation anomaly pattern in Asia and northern Australia. Consistent with the strong influence of ENSO on the CEFs, the skill in predicting the CEFs depends on the model’s ability in predicting not only the eastern Pacific SST anomaly but also the anomalous Walker circulation that brings ENSO’s influence to bear on the CEFs. [ABSTRACT FROM AUTHOR]

Three different estimates of shortwave attenuation depth (SWAD) of photosynthetically active radiation (PAR) derived from remotely sensed ocean colour data have been tested in an ocean general circulation model (OGCM) forced with interannual atmospheric forcings. Two estimates (referred to as [ K d (PAR) ] 1 - 1 and [ K d (PAR) ] 2 - 1 ) are calculated from different algorithms based on the diffusive attenuation coefficient at 490 nm and the third one ( [ K d (AVE) ] 1 - 1 ) is just an average of [ K d (PAR) ] 1 - 1 and [ K d (PAR) ] 2 - 1 . [ K d (PAR) ] 2 - 1 is larger than [ K d (PAR) ] 1 - 1 almost everywhere in the tropical oceans. Our results show that the OGCM with [ K d (PAR) ] 2 - 1 produces warmer sea surface temperature (SST) in the eastern equatorial Pacific and Atlantic and leads to reduce a cold bias in the equatorial cold tongue regions. It has warmer subsurface temperatures in the low latitude, a slower meridional velocity and Pacific equatorial undercurrent (EUC) than the model with [ K d (PAR) ] 1 - 1 . These results are similar to previous studies, although we use a different model and different methods. This study has further analysis and firstly reveals that slower EUC and meridional velocity in the model with [ K d (PAR) ] 2 - 1 are mainly related to the changes of the acceleration due to zonal density gradient. This acceleration driving the EUC eastward in the subsurface becomes smaller in the subsurface along the equatorial Pacific. However, near the sea surface, the zonally averaged accelerations over the different ocean basins are larger in the model with [ K d (PAR) ] 2 - 1 than that with [ K d (PAR) ] 1 - 1 , which pushes back the poleward meridional transport. The interannual variability in the model with [ K d (PAR) ] 2 - 1 is generally weaker than that in the experiment with [ K d (PAR) ] 1 - 1 due to a deeper mixed layer depth. The vertical temperature errors averaged horizontally within the domain of 30°S to 30°N in the experiment with [ K d (AVE) ] 1 - 1 are almost in the middle of errors of the other two experiments. This indicates that the effect of the SWAD on the simulation of the vertical temperature profile is largely linear. [ABSTRACT FROM AUTHOR]

The drastic reduction in the number of observation data from the Tropical Atmospheric Ocean (TAO)/Triangle Trans-Ocean Buoy Network (TRITON) array since 2012 has given rise to a need to assess the impact of those data in ocean data assimilation (DA) systems. This article provides a review of existing studies evaluating the impacts of data from the TAO/TRITON array and other components of the Tropical Pacific Observing System (TPOS) on current ocean DA systems used for a variety of operational and research applications. It can be considered as background information that can guide the evaluation exercise of TPOS. Temperature data from TAO/TRITON array are assimilated in most ocean DA systems which cover the tropical Pacific in order to constrain the ocean heat content, stratification, and circulation. It is shown that the impacts of observation data depend considerably on the system and application. The presence of model error often makes the results difficult to interpret. Nevertheless there is consensus that the data from TAO/TRITON generally have positive impacts complementary to Argo floats. In the equatorial Pacific, the impacts are generally around the same level or larger than those of Argo. We therefore conclude that, with the current configuration of TPOS, the loss of the TAO/TRITON data is having a significant detrimental impact on many applications based on ocean DA systems. This conclusion needs to be kept under review because the equatorial coverage by Argo is expected to improve in the future. [ABSTRACT FROM AUTHOR]

Three different estimates of shortwave attenuation depth (SWAD) of photosynthetically active radiation (PAR) derived from remotely sensed ocean colour data have been tested in an ocean general circulation model (OGCM) forced with interannual atmospheric forcings. Two estimates (referred to as [Kd(PAR)1-1 and [Kd(PAR)]2-1) are calculated from different algorithms based on the diffusive attenuation coefficient at 490 nm and the third one ([Kd (AVE)]1-1 is just an average of [Kd(PAR)]1-1 and [Kd(PAR)]2-1. [Kd (PAR)]2-1 is larger than [Kd(PAR)]1-1 almost everywhere in the tropical oceans. Our results show that the OGCM with [Kd(PAR)]1-1 produces warmer sea surface temperature (SST) in the eastern equatorial Pacific and Atlantic and leads to reduce a cold bias in the equatorial cold tongue regions. It has warmer subsurface temperatures in the low latitude, a slower meridional velocity and Pacific equatorial undercurrent (EUC) than the model with [Kd(PAR)]1-1. These results are similar to previous studies, although we use a different model and different methods. This study has further analysis and firstly reveals that slower EUC and meridional velocity in the model with [Kd (PAR)]2-1 are mainly related to the changes of the acceleration due to zonal density gradient. This acceleration driving the EUC eastward in the subsurface becomes smaller in the subsurface along the equatorial Pacific. However, near the sea surface, the zonally averaged accelerations over the different ocean basins are larger in the model with [Kd(PAR)]2-1 than that with [Kd(PAR)]1-1, which pushes back the poleward meridional transport. The interannual variability in the model with [Kd(PAR)]2-1 is generally weaker than that in the experiment with [Kd(PAR)]1-1 due to a deeper mixed layer depth. The vertical temperature errors averaged horizontally within the domain of 30°S to 30°N in the experiment with [Kd(AVE)]1-1 are almost in the middle of errors of the other two experiments. This indicates that the effect of the SWAD on the simulation of the vertical temperature profile is largely linear. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

AbstractCyclosporin A (CsA), an immunosuppressive medication traditionally used in the prevention of post-transplant rejection, is a promising neuroprotective agent for traumatic brain injury (TBI). Preliminary studies in animals and humans describe the efficacy and safety of CsA when administered following neurotrauma. The objective of this study is to describe CsA exposure in adults with severe TBI by assessing concentrations in whole blood, cerebrospinal fluid (CSF), and brain extracellular fluid (ECF) dialysate as measured by brain microdialysis. Severe TBI patients were enrolled in a randomized controlled trial following the written informed consent of their legal guardians. Patients received either CsA 5 mg/kg as a continuous infusion over 24 h, or matching placebo. Noncompartmental exposure analyses were performed using CsA concentrations in whole blood, CSF, and ECF dialysate. There were 37 patients randomized to the CsA arm of the trial and included in this exposure analysis. CsA was detected in the ECF dialysate and CSF at a fraction of the whole blood concentration. Mean CsA maximum concentrations were achieved at 24 and 30 h from the start of the 24 h infusion, in the CSF and ECF dialysate, respectively. A correlation was found between ECF dialysate and CSF concentrations. CsA was detected in the blood, CSF, and brain ECF dialysate. CsA exposure characteristic differences exist for whole blood, CSF, and ECF dialysate in severe TBI patients when administered as a continuous intravenous infusion. These exposure characteristics should be used for safer CsA dose optimization to achieve target CsA concentrations for neuroprotection in future TBI studies. [ABSTRACT FROM AUTHOR]

Abstract: Seasonal rainfall forecasts have been shown to have significant skill in many parts of the world. In this study, seasonal forecasts were used together with a crop simulation model and a simple pasture growth curve to inform several decisions on year-to-year farm management, including on land-allocations in a mixed wheat–sheep farming system. In seasons where “above-median” rainfall was forecast, N fertiliser applications in cropping were increased to support the higher grain yield potential, sheep stocking rates were increased to take advantage of higher pasture growth and unused pasture land was made available for cropping. In seasons where “below–median” rainfall was forecast, N fertiliser applications in cropping were reduced to minimise costs, and traditional conservative sheep stocking rates were used. Application of the Predictive Ocean Atmosphere Model for Australia (POAMA) seasonal rainfall forecast yielded additional profit of up to A$66ha−1 per annum, equivalent to A$200,000y−1 for an average size farm in Western Australia. The largest benefit from applying a forecast in wheat–sheep farms comes from the increase of more profitable cropping during “above-median” rainfall seasons. This is well above the benefit of previous single-commodity forecast applications, and therefore has widespread potential to improve decision making on mixed crop–livestock farms. With the projected decline of rainfall in the Australian and other rain-fed crop–livestock regions of the world, skilful seasonal forecasting systems will become increasingly valuable as they will assist farm managers to capture the benefits in the declining number of potentially high-production seasons and minimise the input costs in the increasing number of low-production seasons. [Copyright &y& Elsevier]

We assess the ability of the Predictive Ocean Atmosphere Model for Australia (POAMA) to simulate and predict the Southern Annular Mode (SAM) and its influence on Australian intra-seasonal climate using a 27-year hindcast dataset. The analysis consists of three stages: (1) prediction of the SAM, (2) simulation of SAM climate anomalies over Australia, and (3) prediction of Australian climate anomalies in association with the SAM. POAMA achieves skilful prediction of the SAM index for lead times out to about 2 weeks with little skill seen beyond 3 weeks when calculated over all hindcast start months; the inherent strong persistence of the SAM appears to be a key factor for its extended-range predictability in a dynamical forecast model. POAMA also simulates SAM climate anomalies over Australia reasonably well despite notable biases in its representation of the SAM to the south and east of the continent. The model reproduces Australian rainfall anomalies most effectively throughout June-November, and least effectively throughout March-May. Skilful prediction of the SAM index, together with realistic simulation of SAM climate anomalies over Australia, translates into more skilful forecasts of rainfall and maximum temperature at intra-seasonal timescales during austral winter and spring. When the SAM is strong in the initial conditions, there is higher skill in forecasting rainfall anomalies over eastern Australia and maximum temperature anomalies over most of the continent during June-November at lead times of 2-3 weeks, compared with when the SAM is weak. The SAM thus contributes to intra-seasonal prediction skill in the Australian region in POAMA. [ABSTRACT FROM AUTHOR]

We assess the ability of the Predictive Ocean Atmosphere Model for Australia (POAMA) to simulate and predict weekly rainfall associated with the MJO using a 27-year hindcast dataset. After an initial 2-week atmospheric adjustment, the POAMA model is shown to simulate well, both in pattern and in intensity, the weekly-mean rainfall variation associated with the evolution of the MJO over the tropical Indo-Pacific. The simulation is most realistic in December-February (austral summer) and least realistic in March-May (austral autumn). Regionally, the most problematic area is the Maritime Continent, which is a common problem area in other models. Coupled with our previous demonstration of the ability of POAMA to predict the evolution of the large-scale structure of the MJO for up to about 3 weeks, this ability to simulate the regional rainfall evolution associated with the MJO translates to enhanced predictability of rainfall regionally throughout much of the tropical Indo-Pacific when the MJO is present in the initial conditions during October-March. We also demonstrate enhanced prediction skill of rainfall at up to 3 weeks lead time over the north-east Pacific and north Atlantic, which are areas of pronounced teleconnections excited by the MJO-modulation of tropical Indo-Pacific rainfall. Failure to simulate and predict the modulation of rainfall in such places as the Maritime Continent and tropical Australia by the MJO indicates, however, there is still much room for improvement of the prediction of the MJO and its teleconnections. [ABSTRACT FROM AUTHOR]

Capture of the target, bycatch, and protected species in fisheries is often regulated through spatial measures that partition fishing effort, including areal closures. In eastern Australian waters, southern bluefin tuna (SBT, ) are a quota-limited species in a multispecies longline fishery; minimizing capture by nonquota holders is an important management concern. A habitat preference model (conditioned with electronic tag data) coupled with ocean reanalysis data has been used since 2003 to generate real-time predicted maps of SBT distribution (nowcasts). These maps are used by fishery managers to restrict fisher access to areas with high predicted SBT distribution. Here we use the coupled ocean-atmosphere model, POAMA (predictive ocean atmosphere model for Australia), and a habitat model to forecast SBT distribution at lead times of up to 4 months. These forecasts are comparable with nowcasts derived from the operational system, and show skill in predicting SBT habitat boundaries out to lead-times of 3-4 months. For this fishery, seasonal forecasts can provide managers and fishers with valuable insights into future habitat distributions for the upcoming months, to better inform operational decisions. [ABSTRACT FROM AUTHOR]

Impacts on the coupled variability of the Indo-Pacific by including the effects of surface currents on surface stress are explored in four extended integrations of an experimental version of the Bureau of Meteorology's coupled seasonal forecast model POAMA. The first pair of simulations differs only in their treatment of momentum coupling: one version includes the effects of surface currents on the surface stress computation and the other does not. The version that includes the effect of surface currents has less mean-state bias in the equatorial Pacific cold tongue but produces relatively weak coupled variability in the Tropics, especially that related to the Indian Ocean dipole (IOD) and El Niño/Southern Oscillation (ENSO). The version without the effects of surface currents has greater bias in the Pacific cold tongue but stronger IOD and ENSO variability. In order to diagnose the role of changes in local coupling from changes in remote forcing by ENSO for causing changes in IOD variability, a second set of simulations is conducted where effects of surface currents are included only in the Indian Ocean and only in the Pacific Ocean. IOD variability is found to be equally reduced by inclusion of the local effects of surface currents in the Indian Ocean and by the reduction of ENSO variability as a result of including effects of surface currents in the Pacific. Some implications of these results for predictability of the IOD and its dependence on ENSO, and for ocean subsurface data assimilation are discussed. [ABSTRACT FROM AUTHOR]

The prediction skill of the Australian Bureau of Meteorology dynamical seasonal forecast model Predictive Ocean Atmosphere Model for Australia (POAMA) is assessed for probabilistic forecasts of spring season rainfall in Australia and the feasibility of increasing forecast skill through statistical postprocessing is examined. Two statistical postprocessing techniques are explored: calibrating POAMA prediction of rainfall anomaly against observations and using dynamically predicted mean sea level pressure to infer regional rainfall anomaly over Australia (referred to as ''bridging''). A ''homogeneous'' multimodel ensemble prediction method (HMME) is also introduced that consists of the combination of POAMA''s direct prediction of rainfall anomaly together with the two statistically postprocessed predictions. Using hindcasts for the period 1981--2006, the direct forecasts from POAMA exhibit skill relative to a climatological forecast over broad areas of eastern and southern Australia, where El Niño and the Indian Ocean dipole (whose behavior POAMA can skillfully predict at short lead times) are known to exert a strong influence in austral spring. The calibrated and bridged forecasts, while potentially offering improvement over the direct forecasts because of POAMA''s ability to predict the main drivers of springtime rainfall (e.g., El Niño and the Southern Oscillation), show only limited areas of improvement, mainly because strict cross-validation limits the ability to capitalize on relatively modest predictive signals with short record lengths. However, when POAMA and the two statistical--dynamical rainfall forecasts are combined in the HMME, higher deterministic and probabilistic skill is achieved over any of the single models, which suggests the HMME is another useful method to calibrate dynamical model forecasts. [ABSTRACT FROM AUTHOR]

Predictions of the Madden-Julian oscillation (MJO) are assessed using a 10-member ensemble of hindcasts from POAMA, the Australian Bureau of Meteorology coupled ocean-atmosphere seasonal prediction system. The ensemble of hindcasts was initialised from observed atmosphere and ocean initial conditions on the first of each month during 1980-2006. The MJO is diagnosed using the Wheeler-Hendon Real-time Multivariate MJO (RMM) index, which involves projection of daily data onto the leading pair of eigenmodes from an analysis of zonal winds at 200 and 850 hPa and outgoing longwave radiation (OLR) averaged about the equator. Forecasts of the two component (RMM1 and RMM2) index are quantitatively compared with observed behaviour derived from NCEP reanalyses and satellite OLR using the bivariate correlation skill, root-mean-square error (RMSE), and measures of the MJO amplitude and phase error. Comparison is also made with a simple vector autoregressive (VAR) prediction model of RMM as a benchmark. Using the full hindcast set, we find that the MJO can be predicted with the POAMA ensemble out to about 21 days as measured by the bivariate correlation exceeding 0.5 and the bivariate RMSE remaining below ~1.4 (which is the value for a climatological forecast). The VAR model, by comparison, drops to a correlation of 0.5 by about 12 days. The prediction limit from POAMA increases by less than 2 days for times when the MJO has large initial amplitude, and has little sensitivity to the initial phase of the MJO. The VAR model, on the other hand, shows a somewhat larger increase in skill for times of strong MJO variability and has greater sensitivity to initial phase, with lower skill for times when MJO convection is developing in the Indian Ocean. The sensitivity to season is, however, greater for POAMA, with maximum skill occurring in the December-January-February season and minimum skill in June-July-August. Examination of the MJO amplitudes shows that individual POAMA members have slightly above observed amplitude after a spin-up of about 10 days, whereas examination of the MJO phase error reveals that the model has a consistent tendency to propagate the MJO slightly slower than observed. Finally, an estimate of potential predictability of the MJO in POAMA hindcasts suggests that actual MJO prediction skill may be further improved through continued development of the dynamical prediction system. [ABSTRACT FROM AUTHOR]

The relationship between variations of Indo-Pacific sea surface temperatures (SSTs) and Australian springtime rainfall over the last 30 years is investigated with a focus on predictability of inter–El Niño variations of SST and associated rainfall anomalies. Based on observed data, the leading empirical orthogonal function (EOF) of Indo-Pacific SST represents mature El Niño conditions, while the second and fourth modes depict major east–west shifts of individual El Niño events. These higher-order EOFs of SST explain more rainfall variance in Australia, especially in the southeast, than does the El Niño mode. Furthermore, intense springtime droughts tend to be associated with peak warming in the central Pacific, as captured by EOFs 2 and 4, together with warming in the eastern Pacific as depicted by EOF1. The ability to predict these inter–El Niño variations of SST and Australian rainfall is assessed with the Australian Bureau of Meteorology dynamical coupled model seasonal forecast system, the Predictive Ocean and Atmospheric Model for Australia (POAMA). A 10-member ensemble of 9-month hindcasts was generated for the period 1980–2006. For the September–November season, the leading 2 EOFs of SST are predictable with lead times of 3–6 months, while SST EOF4 is predictable out to a lead time of 1 month. The teleconnection between the leading EOFs of SST and Australian rainfall is also well depicted in the model. Based on this ability to predict major east–west variations of El Niño and the teleconnection to Australian rainfall, springtime rainfall over eastern Australia, and major drought events are predictable up to a season in advance. [ABSTRACT FROM AUTHOR]

The article reports on the Experimental Madden-Julian Oscillation (MJO) Prediction Project (EMPP) during 2006. The project provides real-time weather and climate information and predictions for different type of applications. MJO represents a repeatable, low-frequency phenomenon. The article describes the history, objectives, status, capabilities, and plans of EMPP. Samples of forecast products are presented and explained. Verifications of tropical velocity potential for different weather forecasts models are also discussed.