Debra Hudson

Prospects for predicting two flavors of El Niño

[1]xa0Global climatic impacts of El Nino are sensitive to details of the surface warming of the equatorial Pacific Ocean, which vary between each El Nino event. The ability to predict the differences in pattern of anomalous ocean temperatures is explored for two prominent types of El Nino, traditional cold tongue events that have maximum surface warming in the eastern Pacific, and warm pool events that have maximum warming in the central Pacific. We assess seasonal predictions of the two types of El Nino using the Australian Bureau of Meteorology coupled ocean-atmosphere seasonal forecast model. Prediction of the major differences in pattern of anomalous ocean surface temperature between the two types of El Nino is limited to less than 1 season lead time, which is much shorter than for prediction of the occurrence of El Nino but which does have important practical application for prediction of regional climate. Improved understanding of the mechanisms of warm pool events and reduction of systematic model biases of the mean state and the coupled modes of variability in the Pacific warm pool/cold tongue should lead to improved skill for predicting regional climate variability associated with El Nino.

Improving Intraseasonal Prediction with a New Ensemble Generation Strategy

AbstractThe 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 compar...

Dynamical Forecast of Inter–El Niño Variations of Tropical SST and Australian Spring Rainfall

Abstract 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 Nino variations of SST and associated rainfall anomalies. Based on observed data, the leading empirical orthogonal function (EOF) of Indo-Pacific SST represents mature El Nino conditions, while the second and fourth modes depict major east–west shifts of individual El Nino events. These higher-order EOFs of SST explain more rainfall variance in Australia, especially in the southeast, than does the El Nino 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 Nino variations of SST and Australian rainfall is assessed with the Australian Bureau of Meteorology dynamical coupled model seasonal forecast system, the Pr...

Intra-seasonal drivers of extreme heat over Australia in observations and POAMA-2

We assess the occurrence and probability of extreme heat over Australia in association with the Southern Annular Mode (SAM), persistent anticyclones over the Tasman Sea, and the Madden–Julian Oscillation (MJO), which have previously been shown to be key drivers of intra-seasonal variations of Australian climate. In this study, extreme heat events are defined as occurring when weekly-mean maximum temperature anomalies exceed the 90th percentile. The observed probability of exceedance is reduced during the positive phase of the SAM and enhanced during the negative phase of the SAM over most of Australia. Persistent anticyclones over the Tasman Sea are described in terms of (1) split-flow blocking at 160°E and (2) high pressure systems located in the vicinity of the subtropical ridge (STRHs), about 10° north of the split-flow blocking region, for which we devise a simple index. Split-flow blocks and STRHs have contrasting impacts on the occurrence of extreme heat over Australia, with STRHs showing enhanced probability of upper decile heat events over southern Australia in all seasons. The observed probability of an upper decile heat event varies according to MJO phase and time of year, with the greatest impact of the MJO on extreme heat occurring over southern Australia (including the Mallee agricultural region) in spring during phases 2–3. We show that this modulation of the probability of extreme heat by the SAM, persistent anticyclones over the Tasman Sea, and the MJO is well simulated in the Bureau of Meteorology dynamical intra-seasonal/seasonal forecast model POAMA-2 at lead times of 2–3xa0weeks. We further show that predictability of heat extremes increases in association with the negative SAM phase, STRH and MJO, thus providing a basis for skilful intra-seasonal prediction of heat extremes.

Simulation and prediction of the Southern Annular Mode and its influence on Australian intra-seasonal climate in POAMA

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 2xa0weeks with little skill seen beyond 3xa0weeks 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 2–3xa0weeks, compared with when the SAM is weak. The SAM thus contributes to intra-seasonal prediction skill in the Australian region in POAMA.

Seamless Precipitation Prediction Skill in the Tropics and Extratropics from a Global Model

Theskill withwhichacoupledocean‐atmospheremodelisabletopredictprecipitation overarangeoftime 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 408‐608 latitude and lowest around 208, 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 Ni~ 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.

ENSO, the IOD and the intraseasonal prediction of heat extremes across Australia using POAMA-2

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.

Simulation and prediction of blocking in the Australian region and its influence on intra-seasonal rainfall in POAMA-2

AbstractnWe assess the depiction and prediction of blocking at 140°E and its impact on Australian intra-seasonal climate variability in the Bureau of Meteorology’s dynamical intra-seasonal/seasonal forecast model Predictive Ocean Atmosphere Model for Australia version 2 (POAMA-2). The model simulates well the strong seasonality of blocking but underestimates its strength and frequency increasingly with lead time, particularly after the first fortnight of the hindcast, in connection with the model’s drifting basic state. POAMA-2 reproduces well the large-scale structure of weekly-mean blocking anomalies and associated rainfall anomalies over Australia; the depiction of total blocking in POAMA-2 may be improved with the reduction of biases in the distribution of Indian Ocean rainfall via a tropical-extratropical wave teleconnection linking blocking activity at 140°E with tropical variability near Indonesia. POAMA-2 demonstrates the ability to skilfully predict the daily blocking index out to 16xa0days lead time for the ensemble mean hindcast, surpassing the average predictive skill of the individual hindcast members (5xa0days), the skill obtained from persistence of observed (2xa0days), and the decorrelation timescale of blocking (3xa0days). This skilful prediction of the blocking index, together with effective simulation of blocking rainfall anomalies, translates into higher skill in forecasting rainfall in weeks 2 and 3 over much of Australia when blocking is high at the initial time of the hindcast, compared to when the blocking index is small. POAMA-2 is thus capable of providing forecast skill for blocking rainfall on the intra-seasonal timescale to meet the needs of Australian farming communities, whose management practises often rely upon decisions being made a few weeks ahead.

Seasonal and Decadal Prediction

Dynamical seasonal prediction has grown rapidly over the last decade or so. At present, a number of operational centres issue routine seasonal forecasts produced with coupled ocean-atmosphere models. These require real-time knowledge of the state of the global ocean since the potential for climate predictability at seasonal time scales resides mostly in information provided by the ocean initial conditions, in particular the upper thermal structure. The primary aim of the coupled model is to predict sea surface temperature variability and how this variability impacts regional climate through large scale teleconnections.

Visualizing and Verifying Probabilistic Forecasts of the Madden-Julian Oscillation

We describe a new approach for presenting probabilistic forecasts of the Madden-Julian Oscillation (MJO) based on the community standard Real-time Multivariate MJO (RMM) index, using forecasts from version 2 of the Predictive Ocean Atmosphere Model for Australia. This new display overcomes the difficulty of interpreting a dispersive ensemble plume and directly quantifies the probability for the MJO to occur in each of its eight RMM-defined phases as well as the weak phase. Beyond monitoring and interpreting predictions of the MJO, this new approach also provides a basis for forecast verification using probability-based skill scores. Here we present a clear and concise quantitative summary of this innovative method for accessing probability of the state of the MJO in an ensemble forecast. This new method compliments the traditional MJO ensemble forecast display and verification and will benefit global forecasting centers, international MJO working groups, and the World Meteorological Organization Subseasonal to Seasonal Project.