Oscar Alves

An Ensemble Ocean Data Assimilation System for Seasonal Prediction

Abstract 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 d...

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...

Indian Ocean Variability and Its Association with ENSO in a Global Coupled Model

Abstract The evolution of the Indian Ocean during El Nino–Southern Oscillation is investigated in a 100-yr integration of an Australian Bureau of Meteorology coupled seasonal forecast model. During El Nino, 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 posi...

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...

How Predictable is the Indian Ocean Dipole

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,whichhavemaximumamplitudeintheSeptember‐November(SON)season,skillfulpredictionisalso 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, in(

An Enhanced Moisture Convergence–Evaporation Feedback Mechanism for MJO Air–Sea Interaction

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 Australias 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 convergaence-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.

Dynamical, Statistical-Dynamical, and Multimodel Ensemble Forecasts of Australian Spring Season Rainfall

Abstract 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 Nino and th...

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.

Seasonal Prediction of Thermal Stress Accumulation for Coral Bleaching in the Tropical Oceans

Abstract Mass coral bleaching, associated with anomalously warm ocean temperatures over large regions, poses a serious threat to the future health of the world coral reef systems. Seasonal forecasts from coupled ocean–atmosphere models can be a valuable resource for reef management, providing early warning of potential bleaching conditions, allowing for a proactive management response. Here, the ability of a dynamical seasonal forecast model (Predictive Ocean Atmosphere Model for Australia, POAMA) to forecast degree heating months (DHMs) in the tropical oceans is assessed, with particular focus on the 1997/98 El Nino–Southern Oscillation (ENSO) and associated global bleaching events. The model exhibits useful skill in forecasting sea surface temperatures (SSTs) across the tropical oceans for 1982–2006 and reproduced both the magnitude and distribution of DHM values observed during the 1997/98 ENSO event. In general, observed teleconnections between ENSO indices and tropical SST at various lags are well ca...

A Coupled GCM Analysis of MJO Activity at the Onset of El Niño

Abstract The ocean dynamics of the Madden–Julian oscillation (MJO) and its interaction with El Nino–Southern Oscillation (ENSO) are assessed using a flux-corrected coupled model experiment from the Australian Bureau of Meteorology. The model demonstrates the correct oceanic Kelvin wave response to the MJO-related westerly winds in the western Pacific. Although there may be a role for the MJO in influencing the strength of El Nino, its impact is difficult to separate from that of strong heat content preconditioning of ENSO. Hence, the MJO–ENSO relationship is assessed starting from a background state of low heat content anomalies in the western Pacific that are also characteristic of recent observed El Nino events. The model shows a strong relationship between ENSO and the MJO near the peak of El Nino. At this time, the sea surface temperature (SST) anomaly is largest in the central Pacific, and it is difficult to separate cause and effect. Near the onset of El Nino, however, when Pacific Ocean SST anomali...