ORCID Profile
0000-0002-8054-8636
Current Organisation
Australian Bureau of Meteorology
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Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2015
Publisher: Stockholm University Press
Date: 12-2015
Publisher: Informa UK Limited
Date: 31-10-2018
Publisher: Elsevier BV
Date: 11-2012
Publisher: IEEE
Date: 12-2016
Publisher: Springer Science and Business Media LLC
Date: 30-04-2010
Publisher: American Meteorological Society
Date: 02-2011
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 Niño–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 captured by the model. In particular, strong observed correlations between peak ENSO indices and SST in the Caribbean in the following summer were reproduced. The model also shows skill in predicting ocean conditions conducive to bleaching in non-ENSO years, capturing the anomalously warm conditions in the Caribbean region in 2005. Probabilistic forecasts of DHM values above certain thresholds for the Caribbean show useful skill and could be valuable in the assessment of the likelihood of bleaching for the region.
Publisher: Springer Science and Business Media LLC
Date: 26-10-2013
Publisher: Elsevier BV
Date: 2010
Publisher: IEEE
Date: 12-2015
Publisher: Canadian Science Publishing
Date: 05-2011
DOI: 10.1139/F2011-031
Abstract: 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, Thunnus maccoyii ) 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.
Publisher: Springer Science and Business Media LLC
Date: 15-08-2015
Publisher: American Meteorological Society
Date: 07-03-2016
Abstract: Interannual variations of upper-ocean salinity in the tropical Pacific and relationships with ENSO are investigated using the Bureau of Meteorology (Australia) POAMA Ensemble Ocean Data Assimilation System (PEODAS) reanalyses. Empirical orthogonal function (EOF) analysis reveals the systematic evolution of salinity and temperature during ENSO. EOF1 and EOF2 of both temperature and salinity capture the mature phase of El Niño and the discharge and recharge phase, respectively. Typical El Niño and La Niña evolution captured by the leading pair of EOFs depicts eastward or westward migration of the eastern edge of the warm/fresh pool in the western Pacific. Increased or decreased freshness in the western Pacific mixed layer occurs in the recharge/discharge phase. EOF3 captures extreme El Niño, when the strong positive temperature anomaly extends to the South American coast and the fresh pool detaches from the western Pacific and shifts into the central Pacific. Large loadings on EOF3 occurred only during 1982/83 and 1997/98, which suggests that eastern Pacific El Niño is actually the exception, whereas moderate central Pacific El Niño and La Niña are more typical. The eastward expansion of the warm/fresh pool during El Niño is also associated with a continuous eastward displacement of the barrier layer, indicating an active role of the barrier layer not just at the onset of an event. The barrier layer and fresh pool shift much farther eastward during strong El Niño, which could contribute to the eastward shift of strong events. The prior enhancement of the barrier layer in the western Pacific is also more concentrated and stronger, which might portend development of extreme El Niño.
Publisher: IEEE
Date: 05-2014
Publisher: Wiley
Date: 07-10-2018
DOI: 10.1002/JOC.5855
Publisher: Springer Science and Business Media LLC
Date: 10-12-2010
Publisher: IEEE
Date: 10-2014
Publisher: Springer Science and Business Media LLC
Date: 10-12-2013
Publisher: American Meteorological Society
Date: 02-09-2016
Abstract: 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 lify 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.
Publisher: Springer Science and Business Media LLC
Date: 19-02-2014
Publisher: American Meteorological Society
Date: 15-05-2009
Abstract: 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 litude 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 in idual 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.
Publisher: Springer Science and Business Media LLC
Date: 21-10-2009
Publisher: American Meteorological Society
Date: 2013
Abstract: 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.
Publisher: American Meteorological Society
Date: 03-2011
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 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.
Publisher: IEEE
Date: 07-2016
Publisher: American Meteorological Society
Date: 06-2016
Abstract: There has been increasing demand in Australia for extended-range forecasts of extreme heat events. An assessment is made of the subseasonal experimental guidance provided by the Bureau of Meteorology’s seasonal prediction system, Predictive Ocean Atmosphere Model for Australia (POAMA, version 2), for the three most extreme heat events over Australia in 2013, which occurred in January, March, and September. The impacts of these events included devastating bushfires and damage to crops. The outlooks performed well for January and September, with forecasts indicating increased odds of top-decile maximum temperature over most affected areas at least one week in advance for the fortnightly averaged periods at the start of the heat waves and for forecasts of the months of January and September. The March event was more localized, affecting southern Australia. Although the anomalously high sea surface temperature around southern Australia in March (a potential source of predictability) was correctly forecast, the forecast of high temperatures over the mainland was restricted to the coastline. September was associated with strong forcing from some large-scale atmospheric climate drivers known to increase the chance of having more extreme temperatures over parts of Australia. POAMA-2 was able to forecast the sense of these drivers at least one week in advance, but their magnitude was weaker than observed. The reasonably good temperature forecasts for September are likely due to the model being able to forecast the important climate drivers and their teleconnection to Australian climate. This study adds to the growing evidence that there is significant potential to extend and augment traditional weather forecast guidance for extreme events to include longer-lead probabilistic information.
Publisher: Springer Science and Business Media LLC
Date: 19-02-2011
Publisher: American Meteorological Society
Date: 04-2006
Publisher: American Meteorological Society
Date: 03-2011
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 dynamically balanced analysis increments. The time dependence of the ensemble-based covariance yields spatial structures that change for different dynamical regimes, for ex le 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.
Publisher: Springer Science and Business Media LLC
Date: 28-10-2010
Publisher: Bureau of Meteorology, Australia
Date: 03-2011
DOI: 10.22499/2.6101.001
Publisher: Springer Science and Business Media LLC
Date: 25-02-2010
Publisher: Stockholm University Press
Date: 2017
Publisher: IEEE
Date: 07-2015
Publisher: Springer Netherlands
Date: 27-09-2013
Publisher: Springer Science and Business Media LLC
Date: 10-2011
Publisher: American Geophysical Union (AGU)
Date: 09-2013
DOI: 10.1002/JGRC.20317
Publisher: The Oceanography Society
Date: 09-2009
Publisher: Elsevier BV
Date: 05-2015
Publisher: American Meteorological Society
Date: 12-2012
Abstract: 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 litude 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.
Publisher: Elsevier BV
Date: 12-2019
Publisher: American Meteorological Society
Date: 03-2008
Abstract: 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.
Publisher: Wiley
Date: 10-2006
DOI: 10.1256/QJ.05.113
Publisher: IEEE
Date: 07-2015
Publisher: American Meteorological Society
Date: 11-2009
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 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 in idual 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.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2016
Publisher: Wiley
Date: 28-07-2015
DOI: 10.1002/QJ.2579
Publisher: American Meteorological Society
Date: 25-11-2013
Abstract: 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.
Publisher: Wiley
Date: 04-04-2013
DOI: 10.1002/JOC.3486
Publisher: Springer Science and Business Media LLC
Date: 09-07-2011
Publisher: American Geophysical Union (AGU)
Date: 10-2009
DOI: 10.1029/2009GL040100
Publisher: Springer Science and Business Media LLC
Date: 03-06-2010
Publisher: Elsevier BV
Date: 05-2018
Publisher: Springer Science and Business Media LLC
Date: 06-02-2017
Publisher: American Meteorological Society
Date: 04-2009
Abstract: The ocean dynamics of the Madden–Julian oscillation (MJO) and its interaction with El Niño–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 Niño, 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 Niño events. The model shows a strong relationship between ENSO and the MJO near the peak of El Niño. 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 Niño, however, when Pacific Ocean SST anomalies are near zero, an increase in MJO activity is associated with Kelvin wave activity and stronger subsequent ENSO warming. A significant increase in the number of MJO events, rather than the strength of in idual MJO events, leads to stronger eastern Pacific warming the MJO appears not to be responsible for the occurrence of El Niño itself, but, rather, is important for influencing its development thus. This research supports a role for downwelling oceanic Kelvin waves and subsequent deepening of the thermocline in contributing to eastern Pacific warming during the onset of El Niño.
Publisher: IEEE
Date: 12-2015
Publisher: American Meteorological Society
Date: 09-2005
DOI: 10.1175/JCLI3493.1
Abstract: 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.
Publisher: Springer Science and Business Media LLC
Date: 28-03-2015
Publisher: Elsevier BV
Date: 06-2016
Publisher: Wiley
Date: 21-03-2011
DOI: 10.1002/QJ.769
Publisher: Springer Science and Business Media LLC
Date: 23-05-2017
DOI: 10.1038/S41598-017-01479-9
Abstract: Multi-year La Niña events often induce persistent cool and wet climate over global lands, altering and in some case mitigating regional climate warming impacts. The latest event lingered from mid-2010 to early 2012 and brought about intensive precipitation over many land regions of the world, particularly Australia. This resulted in a significant drop in global mean sea level despite the background upwards trend. This La Niña event is surprisingly predicted out to two years ahead in a few coupled models, even though the predictability of El Niño-Southern Oscillation during 2002–2014 has declined owing to weakened ocean-atmosphere interactions. However, the underlying mechanism for high predictability of this multi-year La Niña episode is still unclear. Experiments based on a climate model that demonstrates a successful two-year forecast of the La Niña support the hypothesis that warm sea surface temperature (SST) anomalies in the Atlantic and Indian Oceans act to intensify the easterly winds in the central equatorial Pacific and largely contribute to the occurrence and two-year predictability of the 2010–2012 La Niña. The results highlight the importance of increased Atlantic-Indian Ocean SSTs for the multi-year La Niña’s predictability under global warming.
Publisher: Springer Science and Business Media LLC
Date: 16-09-2008
Publisher: Springer Science and Business Media LLC
Date: 28-05-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2017
Publisher: Springer Science and Business Media LLC
Date: 11-12-2013
Publisher: IEEE
Date: 05-2016
Publisher: Springer Science and Business Media LLC
Date: 14-10-2017
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Oscar Alves.