scholarly journals ACCESS-S1 The new Bureau of Meteorology multi-week to seasonal prediction system

2017 ◽  
Vol 67 (3) ◽  
pp. 132 ◽  
Author(s):  
Debra Hudson ◽  
Oscar Alves ◽  
Harry H. Hendon ◽  
Eun-Pa Lim ◽  
Guoqiang Liu ◽  
...  
Keyword(s):  
Indian Ocean ◽  
Land Surface ◽  
Southern Oscillation ◽  
Seasonal Prediction ◽  
Maximum Temperature ◽  
Prediction System ◽  
Seasonal Forecasts ◽  
Austral Spring ◽  
Eastern Australia ◽  
The Indian Ocean

ACCESS-S1 will be the next version of the Australian Bureau of Meteorology's seasonal prediction system, due to become operational in early 2018. The multiweek and seasonal performance of ACCESS-S1 has been evaluated based on a 23-year hindcast set and compared to the current operational system, POAMA. The system has considerable enhancements compared to POAMA, including higher vertical and horizontal resolution of the component models and state-ofthe-art physics parameterisation schemes. ACCESS-S1 is based on the UK Met Office GloSea5-GC2 seasonal prediction system, but has enhancements to the ensemble generation strategy to make it appropriate for multi-week forecasting, and a larger ensemble size.ACCESS-S1 has markedly reduced biases in the mean state of the climate, both globally and over Australia, compared to POAMA. ACCESS-S1 also better predicts the early stages of the development of the El Niño Southern Oscillation (through the predictability barrier) and the Indian Ocean Dipole, as well as multi-week variations of the Southern Annular Mode and the Madden-Julian Oscillation — all important drivers of Australian climate variability. There is an overall improvement in the skill of the forecasts of rainfall, maximum temperature (Tmax) and minimum temperature (Tmin) over Australia on multi-week timescales compared to POAMA. On seasonal timescales the differences between the two systems are generally less marked. ACCESS-S1 has improved seasonal forecasts over Australia for the austral spring season compared to POAMA, with particularly good forecast reliability for rainfall and Tmax. However, forecasts of seasonal mean Tmax are noticeably less skilful over eastern Australia for forecasts of late autumn and winter compared to POAMA.The study has identified scope for improvement of ACCESS-S in the future, particularly 1) reducing rainfall errors in the Indian Ocean and Maritime Continent regions, and 2) initialising the land surface with realistic soil moisture rather than climatology. The latter impacts negatively on the skill of the temperature forecasts over eastern Australia and is being addressed in the next version of the system, ACCESS-S2.

scholarly journals Corrigendum to: ACCESS-S1: The new Bureau of Meteorology multi-week to seasonal prediction system

2020 ◽  
Vol 70 (1) ◽  
pp. 393
Author(s):  
Debra Hudson ◽  
Oscar Alves ◽  
Harry H. Hendon ◽  
Eun-Pa Lim ◽  
Guoqiang Liu ◽  
...  
Keyword(s):  
Indian Ocean ◽  
Land Surface ◽  
Southern Oscillation ◽  
Seasonal Prediction ◽  
Maximum Temperature ◽  
Prediction System ◽  
Seasonal Forecasts ◽  
Austral Spring ◽  
Eastern Australia ◽  
The Indian Ocean

ACCESS-S1 will be the next version of the Australian Bureau of Meteorology's seasonal prediction system, due to become operational in early 2018. The multiweek and seasonal performance of ACCESS-S1 has been evaluated based on a 23-year hindcast set and compared to the current operational system, POAMA. The system has considerable enhancements compared to POAMA, including higher vertical and horizontal resolution of the component models and state-ofthe-art physics parameterisation schemes. ACCESS-S1 is based on the UK Met Office GloSea5-GC2 seasonal prediction system, but has enhancements to the ensemble generation strategy to make it appropriate for multi-week forecasting, and a larger ensemble size.ACCESS-S1 has markedly reduced biases in the mean state of the climate, both globally and over Australia, compared to POAMA. ACCESS-S1 also better predicts the early stages of the development of the El Niño Southern Oscillation (through the predictability barrier) and the Indian Ocean Dipole, as well as multi-week variations of the Southern Annular Mode and the Madden-Julian Oscillation — all important drivers of Australian climate variability. There is an overall improvement in the skill of the forecasts of rainfall, maximum temperature (Tmax) and minimum temperature (Tmin) over Australia on multi-week timescales compared to POAMA. On seasonal timescales the differences between the two systems are generally less marked. ACCESS-S1 has improved seasonal forecasts over Australia for the austral spring season compared to POAMA, with particularly good forecast reliability for rainfall and Tmax. However, forecasts of seasonal mean Tmax are noticeably less skilful over eastern Australia for forecasts of late autumn and winter compared to POAMA.The study has identified scope for improvement of ACCESS-S in the future, particularly 1) reducing rainfall errors in the Indian Ocean and Maritime Continent regions, and 2) initialising the land surface with realistic soil moisture rather than climatology. The latter impacts negatively on the skill of the temperature forecasts over eastern Australia and is being addressed in the next version of the system, ACCESS-S2.

scholarly journals Teleconnection Pathways of ENSO and the IOD and the Mechanisms for Impacts on Australian Rainfall

2011 ◽  
Vol 24 (15) ◽  
pp. 3910-3923 ◽  
Author(s):  
Wenju Cai ◽  
Peter van Rensch ◽  
Tim Cowan ◽  
Harry H. Hendon
Keyword(s):  
Indian Ocean ◽  
Rossby Wave ◽  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
Australian Climate ◽  
Eastern Australia ◽  
The Indian Ocean ◽  
Wave Trains ◽  
Australian Rainfall

Abstract Impacts of El Niño–Southern Oscillation (ENSO) and the Indian Ocean dipole (IOD) on Australian rainfall are diagnosed from the perspective of tropical and extratropical teleconnections triggered by tropical sea surface temperature (SST) variations. The tropical teleconnection is understood as the equatorially trapped, deep baroclinic response to the diabatic (convective) heating anomalies induced by the tropical SST anomalies. These diabatic heating anomalies also excite equivalent barotropic Rossby wave trains that propagate into the extratropics. The main direct tropical teleconnection during ENSO is the Southern Oscillation (SO), whose impact on Australian rainfall is argued to be mainly confined to near-tropical portions of eastern Australia. Rainfall is suppressed during El Niño because near-tropical eastern Australia directly experiences subsidence and higher surface pressure associated with the western pole of the SO. Impacts on extratropical Australian rainfall during El Niño are argued to stem primarily from the Rossby wave trains forced by convective variations in the Indian Ocean, for which the IOD is a primary source of variability. These equivalent-barotropic Rossby wave trains emanating from the Indian Ocean induce changes to the midlatitude westerlies across southern Australia, thereby affecting rainfall through changes in mean-state baroclinicity, west–east steering, and possibly orographic effects. Although the IOD does not mature until austral spring, its impact on Australian rainfall during winter is also ascribed to this mechanism. Because ENSO is largely unrelated to the IOD during austral winter, there is limited impact of ENSO on rainfall across southern latitudes of Australia in winter. A strong impact of ENSO on southern Australia rainfall in spring is ascribed to the strong covariation of ENSO and the IOD in this season. Implications of this pathway from the tropical Indian Ocean for impacts of both the IOD and ENSO on southern Australian climate are discussed with regard to the ability to make seasonal climate predictions and with regard to the role of trends in tropical SST for driving trends in Australian climate.

scholarly journals Mechanisms associated with September to November (SON) rainfall over Uganda during the recent decades

2021 ◽  
Vol 25 (1) ◽  
pp. 10-23
Author(s):  
Hamida Ngoma ◽  
Wang Wen ◽  
Brian Ayugi ◽  
Rizwan Karim ◽  
Makula Kisesa
Keyword(s):  
Indian Ocean ◽  
Extreme Events ◽  
Southern Oscillation ◽  
Walker Circulation ◽  
Dominant Mode ◽  
Strong Positive Correlation ◽  
Seasonal Forecasts ◽  
Circulation Mode ◽  
The Indian Ocean ◽  
Upper Level

This study revisits teleconnections associated with the anomalous events of September to November (SON) rainfall over Uganda during 1981-2019, owing to the recent intensification of extreme events. Empirical Orthogonal Function (EOF), Composite and Correlation analysis are employed to examine the variability of SON rainfall over the study domain and associated circulations anomalies. The first EOF mode (dominant mode) displays a positive monopole pattern and explains 67.2% of the variance. The results revealed that SON rainfall is largely influenced by a Walker circulation mode over the Indian Ocean, whereby, wet events are associated with an ascending limb of the Walker circulation on the western part of the Indian Ocean characterized by convergence at low levels and divergence at upper level. The study showed that SON rainfall is positively (negatively) correlated with Indian Ocean (Atlantic Ocean) sea surface temperatures (SST). Furthermore, Indian Ocean Dipole (IOD) events have impact on SON rainfall with strong positive correlation, whereas Southern Oscillation Index (SOI) revealed negative correlation. The results also reveal that there is a lag in ENSO and IOD episodes during wet/dry events over the region. ENSO and IOD also tend to extend the rainfall season of SON and thus study of extreme events may not be well captured by studies focusing on SON. Future studies might consider the season of October to December or December to February. These phenomena need to be closely monitored and considered when making seasonal forecasts.

scholarly journals Indian Ocean impact on ENSO evolution 2014–2016 in a set of seasonal forecasting experiments

2021 ◽  
Author(s):  
Michael Mayer ◽  
Magdalena Alonso Balmaseda
Keyword(s):  
Pacific Ocean ◽  
Indian Ocean ◽  
El Niño ◽  
Initial Conditions ◽  
El Nino ◽  
Southern Oscillation ◽  
Heat Content ◽  
Tropical Pacific ◽  
Decadal Variations ◽  
The Indian Ocean

AbstractThis study investigates the influence of the anomalously warm Indian Ocean state on the unprecedentedly weak Indonesian Throughflow (ITF) and the unexpected evolution of El Niño-Southern Oscillation (ENSO) during 2014–2016. It uses 25-month-long coupled twin forecast experiments with modified Indian Ocean initial conditions sampling observed decadal variations. An unperturbed experiment initialized in Feb 2014 forecasts moderately warm ENSO conditions in year 1 and year 2 and an anomalously weak ITF throughout, which acts to keep tropical Pacific ocean heat content (OHC) anomalously high. Changing only the Indian Ocean to cooler 1997 conditions substantially alters the 2-year forecast of Tropical Pacific conditions. Differences include (i) increased probability of strong El Niño in 2014 and La Niña in 2015, (ii) significantly increased ITF transports and (iii), as a consequence, stronger Pacific ocean heat divergence and thus a reduction of Pacific OHC over the two years. The Indian Ocean’s impact in year 1 is via the atmospheric bridge arising from altered Indian Ocean Dipole conditions. Effects of altered ITF and associated ocean heat divergence (oceanic tunnel) become apparent by year 2, including modified ENSO probabilities and Tropical Pacific OHC. A mirrored twin experiment starting from unperturbed 1997 conditions and several sensitivity experiments corroborate these findings. This work demonstrates the importance of the Indian Ocean’s decadal variations on ENSO and highlights the previously underappreciated role of the oceanic tunnel. Results also indicate that, given the physical links between year-to-year ENSO variations, 2-year-long forecasts can provide additional guidance for interpretation of forecasted year-1 ENSO probabilities.

scholarly journals Tropical Cyclone Count Forecasting Using a Dynamical Seasonal Prediction System: Sensitivity to Improved Ocean Initialization

2011 ◽  
Vol 24 (12) ◽  
pp. 2963-2982 ◽  
Author(s):  
Andrea Alessandri ◽  
Andrea Borrelli ◽  
Silvio Gualdi ◽  
Enrico Scoccimarro ◽  
Simona Masina
Keyword(s):  
Tropical Cyclone ◽  
Indian Ocean ◽  
Wind Shear ◽  
Large Scale ◽  
Initial Conditions ◽  
North Pacific Ocean ◽  
Convective Available Potential Energy ◽  
Seasonal Prediction ◽  
Boreal Summer ◽  
Prediction System

Abstract This study investigates the predictability of tropical cyclone (TC) seasonal count anomalies using the Centro Euro-Mediterraneo per i Cambiamenti Climatici–Istituto Nazionale di Geofisica e Vulcanologia (CMCC-INGV) Seasonal Prediction System (SPS). To this aim, nine-member ensemble forecasts for the period 1992–2001 for two starting dates per year were performed. The skill in reproducing the observed TC counts has been evaluated after the application of a TC location and tracking detection method to the retrospective forecasts. The SPS displays good skill in predicting the observed TC count anomalies, particularly over the tropical Pacific and Atlantic Oceans. The simulated TC activity exhibits realistic geographical distribution and interannual variability, thus indicating that the model is able to reproduce the major basic mechanisms that link the TCs’ occurrence with the large-scale circulation. TC count anomalies prediction has been found to be sensitive to the subsurface assimilation in the ocean for initialization. Comparing the results with control simulations performed without assimilated initial conditions, the results indicate that the assimilation significantly improves the prediction of the TC count anomalies over the eastern North Pacific Ocean (ENP) and northern Indian Ocean (NI) during boreal summer. During the austral counterpart, significant progresses over the area surrounding Australia (AUS) and in terms of the probabilistic quality of the predictions also over the southern Indian Ocean (SI) were evidenced. The analysis shows that the improvement in the prediction of anomalous TC counts follows the enhancement in forecasting daily anomalies in sea surface temperature due to subsurface ocean initialization. Furthermore, the skill changes appear to be in part related to forecast differences in convective available potential energy (CAPE) over the ENP and the North Atlantic Ocean (ATL), in wind shear over the NI, and in both CAPE and wind shear over the SI.

scholarly journals A Probabilistic Multimodel Ensemble Approach to Seasonal Prediction

2009 ◽  
Vol 24 (3) ◽  
pp. 812-828 ◽  
Author(s):  
Young-Mi Min ◽  
Vladimir N. Kryjov ◽  
Chung-Kyu Park
Keyword(s):  
Real Time ◽  
Gaussian Approximation ◽  
Seasonal Prediction ◽  
Asia Pacific ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Sampling Errors ◽  
Seasonal Forecasts ◽  
Multimodel Ensemble ◽  
Ensemble Prediction System

Abstract A probabilistic multimodel ensemble prediction system (PMME) has been developed to provide operational seasonal forecasts at the Asia–Pacific Economic Cooperation (APEC) Climate Center (APCC). This system is based on an uncalibrated multimodel ensemble, with model weights inversely proportional to the errors in forecast probability associated with the model sampling errors, and a parametric Gaussian fitting method for the estimate of tercile-based categorical probabilities. It is shown that the suggested method is the most appropriate for use in an operational global prediction system that combines a large number of models, with individual model ensembles essentially differing in size and model weights in the forecast and hindcast datasets being inconsistent. Justification for the use of a Gaussian approximation of the precipitation probability distribution function for global forecasts is also provided. PMME retrospective and real-time forecasts are assessed. For above normal and below normal categories, temperature forecasts outperform climatology for a large part of the globe. Precipitation forecasts are definitely more skillful than random guessing for the extratropics and climatological forecasts for the tropics. The skill of real-time forecasts lies within the range of the interannual variability of the historical forecasts.

scholarly journals Merits of a 108-Member Ensemble System in ENSO and IOD Predictions

2019 ◽  
Vol 32 (3) ◽  
pp. 957-972 ◽  
Author(s):  
Takeshi Doi ◽  
Swadhin K. Behera ◽  
Toshio Yamagata
Keyword(s):  
Seasonal Prediction ◽  
Ensemble Prediction ◽  
Economic Losses ◽  
Prediction System ◽  
Ensemble Size ◽  
Ensemble Prediction System ◽  
Skill Scores ◽  
Operational Systems ◽  
The Indian Ocean ◽  
Climate Events

This paper explores merits of 100-ensemble simulations from a single dynamical seasonal prediction system by evaluating differences in skill scores between ensembles predictions with few (~10) and many (~100) ensemble members. A 100-ensemble retrospective seasonal forecast experiment for 1983–2015 is beyond current operational capability. Prediction of extremely strong ENSO and the Indian Ocean dipole (IOD) events is significantly improved in the larger ensemble. It indicates that the ensemble size of 10 members, used in some operational systems, is not adequate for the occurrence of 15% tails of extreme climate events, because only about 1 or 2 members (approximately 15% of 12) will agree with the observations. We also showed an ensemble size of about 50 members may be adequate for the extreme El Niño and positive IOD predictions at least in the present prediction system. Even if running a large-ensemble prediction system is quite costly, improved prediction of disastrous extreme events is useful for minimizing risks of possible human and economic losses.

scholarly journals How Predictable is the Indian Ocean Dipole?

2012 ◽  
Vol 140 (12) ◽  
pp. 3867-3884 ◽  
Author(s):  
Li Shi ◽  
Harry H. Hendon ◽  
Oscar Alves ◽  
Jing-Jia Luo ◽  
Magdalena Balmaseda ◽  
...  
Keyword(s):  
Indian Ocean ◽  
Surface Temperature ◽  
Indian Ocean Dipole ◽  
Lead Time ◽  
Research Centre ◽  
Model Errors ◽  
Seasonal Forecasts ◽  
Forecast System ◽  
Predictive Skill ◽  
The Indian Ocean

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 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.

scholarly journals Triggering the Indian Ocean Dipole from the Southern Hemisphere

2021 ◽  
Author(s):  
Lian-Yi Zhang ◽  
Yan Du ◽  
Wenju Cai ◽  
Zesheng Chen ◽  
Tomoki Tozuka ◽  
...  
Keyword(s):  
Indian Ocean ◽  
Southern Hemisphere ◽  
Indian Ocean Dipole ◽  
Southern Oscillation ◽  
Southern Annular Mode ◽  
Triggering Mechanism ◽  
Phase Development ◽  
The Indian Ocean ◽  
High System ◽  
The Tropics

<p>This study identifies a new triggering mechanism of the Indian Ocean Dipole (IOD) from the Southern Hemisphere. This mechanism is independent from the El Niño/Southern Oscillation (ENSO) and tends to induce the IOD before its canonical peak season. The joint effects of this mechanism and ENSO may explain different lifetimes and strengths of the IOD. During its positive phase, development of sea surface temperature cold anomalies commences in the southern Indian Ocean, accompanied by an anomalous subtropical high system and anomalous southeasterly winds. The eastward movement of these anomalies enhances the monsoon off Sumatra-Java during May-August, leading to an early positive IOD onset. The pressure variability in the subtropical area is related with the Southern Annular Mode, suggesting a teleconnection between high-latitude and mid-latitude climate that can further affect the tropics. To include the subtropical signals may help model prediction of the IOD event.</p>

scholarly journals Land surface phenological response to decadal climate variability across Australia using satellite remote sensing

2014 ◽  
Vol 11 (5) ◽  
pp. 7685-7719 ◽  
Author(s):  
M. Broich ◽  
A. Huete ◽  
M. G. Tulbure ◽  
X. Ma ◽  
Q. Xin ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Variability ◽  
Land Management ◽  
Land Surface ◽  
Vegetation Index ◽  
Southern Oscillation ◽  
Quantitative Information ◽  
Decadal Climate Variability ◽  
Eastern Australia ◽  
Phenological Cycle

Abstract. Land surface phenological cycles of vegetation greening and browning are influenced by variability in climatic forcing. Quantitative information on phenological cycles and their variability is important for agricultural applications, wildfire fuel accumulation, land management, land surface modeling, and climate change studies. Most phenology studies have focused on temperature-driven Northern Hemisphere systems, where phenology shows annually reoccurring patterns. Yet, precipitation-driven non-annual phenology of arid and semi-arid systems (i.e. drylands) received much less attention, despite the fact that they cover more than 30% of the global land surface. Here we focused on Australia, the driest inhabited continent with one of the most variable rainfall climates in the world and vast areas of dryland systems. Detailed and internally consistent studies investigating phenological cycles and their response to climate variability across the entire continent designed specifically for Australian dryland conditions are missing. To fill this knowledge gap and to advance phenological research, we used existing methods more effectively to study geographic and climate-driven variability in phenology over Australia. We linked derived phenological metrics with rainfall and the Southern Oscillation Index (SOI). We based our analysis on Enhanced Vegetation Index (EVI) data from the MODerate Resolution Imaging Spectroradiometer (MODIS) from 2000 to 2013, which included extreme drought and wet years. We conducted a continent-wide investigation of the link between phenology and climate variability and a more detailed investigation over the Murray–Darling Basin (MDB), the primary agricultural area and largest river catchment of Australia. Results showed high inter- and intra-annual variability in phenological cycles. Phenological cycle peaks occurred not only during the austral summer but at any time of the year, and their timing varied by more than a month in the interior of the continent. The phenological cycle peak magnitude and integrated greenness were most significantly correlated with monthly SOI within the preceding 12 months. Correlation patterns occurred primarily over north-eastern Australia and within the MDB predominantly over natural land cover and particularly in floodplain and wetland areas. Integrated greenness of the phenological cycles (surrogate of productivity) showed positive anomalies of more than two standard deviations over most of eastern Australia in 2009–2010, which coincided with the transition between the El Niño induced decadal droughts to flooding caused by La Niña. The quantified spatial-temporal variability in phenology across Australia in response to climate variability presented here provides important information for land management and climate change studies and applications.

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