scholarly journals Toward a better multi-model ensemble prediction of East Asian and Australasian precipitation during non-mature ENSO seasons

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Soo-Jin Sohn ◽  
WonMoo Kim
Keyword(s):  
Southern Oscillation ◽  
East Asian ◽  
Precipitation Variability ◽  
Prediction Skill ◽  
Ensemble Prediction ◽  
Boreal Winter ◽  
Model Ensemble ◽  
Boreal Spring ◽  
Mature Phase ◽  
Regional Prediction

AbstractAn effective and reliable way for better predicting the seasonal Australasian and East Asian precipitation variability in a multi-model ensemble (MME) prediction system is newly designed, in relation to the performance of predicting El Niño-Southern Oscillation (ENSO) and its impact. While ENSO is a major predictability source of global and regional precipitation variation, the prediction skill of precipitation is not solely due to typical ENSO alone, of which variability and predictability exhibit strong seasonality. The first mode of ENSO variability has large variance with high prediction skill for boreal winter and small variance with low skill for spring and summer, while the second mode shows the opposite phase. The regional prediction skills for Australasian and East Asian precipitation also show such seasonal dependence, with low skill and large spread of individual models’ skills during the boreal spring to summer and high skill and small spread during winter. Using the individual models’ reproducibility of the association between ENSO and regional precipitation, the prediction skills of the MME with selected models can improve at regional levels, compared to those for all-inclusive MME, during boreal spring to summer. While typical ENSO as a predictability source may still dominate during boreal winter, consideration of complex ENSO structure and its diverse impact can lead to a better prediction of regional precipitation variability during non-mature phase of ENSO seasons.

scholarly journals Dominant Interannual Covariations of the East Asian–Australian Land Precipitation during Boreal Winter

2019 ◽  
Vol 32 (11) ◽  
pp. 3279-3296 ◽  
Author(s):  
Lin Liu ◽  
Jianping Guo ◽  
Wen Chen ◽  
Renguang Wu ◽  
Lin Wang ◽  
...  
Keyword(s):  
Indian Ocean ◽  
General Circulation ◽  
Southern Oscillation ◽  
East Asian ◽  
Circulation Model ◽  
Boreal Winter ◽  
Dipole Structure ◽  
Second Mode ◽  
Precipitation Anomalies ◽  
Sst Anomalies

AbstractThe present study applies the empirical orthogonal function (EOF) method to investigate the interannual covariations of East Asian–Australian land precipitation (EAALP) during boreal winter based on observational and reanalysis datasets. The first mode of EAALP variations is characterized by opposite-sign anomalies between East Asia (EA) and Australia (AUS). The second mode features an anomaly pattern over EA similar to the first mode, but with a southwest–northeast dipole structure over AUS. El Niño–Southern Oscillation (ENSO) is found to be a primary factor in modulating the interannual variations of land precipitation over EA and western AUS. By comparison, the Indian Ocean subtropical dipole mode (IOSD) plays an important role in the formation of precipitation anomalies over northeastern AUS, mainly through a zonal vertical circulation spanning from the southern Indian Ocean (SIO) to northern AUS. In addition, the ENSO-independent cold sea surface temperature (SST) anomalies in the western North Pacific (WNP) impact the formation of the second mode. Using the atmospheric general circulation model ECHAM5, three 40-yr numerical simulation experiments differing in specified SST forcings verify the impacts of the IOSD and WNP SST anomalies. Further composite analyses indicate that the dominant patterns of EAALP variability are largely determined by the out-of-phase and in-phase combinations of ENSO and IOSD. These results suggest that in addition to ENSO, IOSD should be considered as another crucial factor influencing the EAALP variability during the boreal winter, which has large implications for improved prediction of EAALP land precipitation on the interannual time scale.

scholarly journals Hindcasts of Tropical Atlantic SST Gradient and South American Precipitation: The Influences of the ENSO Forcing and the Atlantic Preconditioning

2009 ◽  
Vol 22 (9) ◽  
pp. 2405-2421 ◽  
Author(s):  
Huei-Ping Huang ◽  
Andrew W. Robertson ◽  
Yochanan Kushnir ◽  
Shiling Peng
Keyword(s):  
South America ◽  
General Circulation ◽  
Southern Oscillation ◽  
Boreal Winter ◽  
South American ◽  
Primary Role ◽  
Tropical Atlantic ◽  
Boreal Spring ◽  
Sst Anomaly ◽  
Sst Gradient

Abstract Hindcast experiments for the tropical Atlantic sea surface temperature (SST) gradient G1, defined as tropical North Atlantic SST anomaly minus tropical South Atlantic SST anomaly, are performed using an atmospheric general circulation model coupled to a mixed layer ocean over the Atlantic to quantify the contributions of the El Niño–Southern Oscillation (ENSO) forcing and the preconditioning in the Atlantic to G1 in boreal spring. The results confirm previous observational analyses that, in the years with a persistent ENSO SST anomaly from boreal winter to spring, the ENSO forcing plays a primary role in determining the tendency of G1 from winter to spring and the sign of G1 in late spring. In the hindcasts, the initial perturbations in Atlantic SST in boreal winter are found to generally persist beyond a season, leaving a secondary but nonnegligible contribution to the predicted Atlantic SST gradient in spring. For 1993/94, a neutral year with a large preexisting G1 in winter, the hindcast using the information of Atlantic preconditioning alone is found to reproduce the observed G1 in spring. The seasonal predictability in precipitation over South America is examined in the hindcast experiments. For the recent events that can be validated with high-quality observations, the hindcasts produced dryness in boreal spring 1983, wetness in spring 1996, and wetness in spring 1994 over northern Brazil that are qualitatively consistent with observations. An inclusion of the Atlantic preconditioning is found to help the prediction of South American rainfall in boreal spring. For the ENSO years, discrepancies remain between the hindcast and observed precipitation anomalies over northern and equatorial South America, an error that is partially attributed to the biased atmospheric response to ENSO forcing in the model. The hindcast of the 1993/94 neutral year does not suffer this error. It constitutes an intriguing example of useful seasonal forecast of G1 and South American rainfall anomalies without ENSO.

scholarly journals Representation of Boreal Winter MJO and Its Teleconnection in a Dynamical Ensemble Seasonal Prediction System

2018 ◽  
Vol 31 (21) ◽  
pp. 8803-8818 ◽  
Author(s):  
Hyerim Kim ◽  
Myong-In Lee ◽  
Daehyun Kim ◽  
Hyun-Suk Kang ◽  
Yu-Kyung Hyun
Keyword(s):  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
Seasonal Prediction ◽  
La Niña ◽  
Prediction Skill ◽  
Boreal Winter ◽  
Teleconnection Patterns ◽  
La Nina ◽  
Forecast Lead Time

This study examines the representation of the Madden–Julian oscillation (MJO) and its teleconnection in boreal winter in the Global Seasonal Forecast System, version 5 (GloSea5), using 20 years (1991–2010) of hindcast data. The sensitivity of the performance to the polarity of El Niño–Southern Oscillation (ENSO) is also investigated. The real-time multivariate MJO index of Wheeler and Hendon is used to assess MJO prediction skill while intraseasonal 200-hPa streamfunction anomalies are used to evaluate the MJO teleconnection. GloSea5 exhibits significant MJO prediction skill up to 25 days of forecast lead time. MJO prediction skill in GloSea5 also depends on initial MJO phases, with relatively enhanced (degraded) performance when the initial MJO phase is 2 or 3 (8 or 1) during the first 2 weeks of the hindcast period. GloSea5 depicts the observed MJO teleconnection patterns in the extratropics realistically up to 2 weeks albeit weaker than the observed. The ENSO-associated basic-state changes in the tropics and in the midlatitudes are reasonably represented in GloSea5. MJO prediction skill during the first 2 weeks of the hindcast is slightly higher in neutral and La Niña years than in El Niño years, especially in the upper-level zonal wind anomalies. Presumably because of the better representation of MJO-related tropical heating anomalies, the Northern Hemispheric MJO teleconnection patterns in neutral and La Niña years are considerably better than those in El Niño years.

Predictability of the Madden–Julian Oscillation in the Intraseasonal Variability Hindcast Experiment (ISVHE)*

2014 ◽  
Vol 27 (12) ◽  
pp. 4531-4543 ◽  
Author(s):  
J. M. Neena ◽  
June Yi Lee ◽  
Duane Waliser ◽  
Bin Wang ◽  
Xianan Jiang
Keyword(s):  
Dynamic Models ◽  
Intraseasonal Variability ◽  
Prediction Skill ◽  
Ensemble Prediction ◽  
Future Research ◽  
Boreal Winter ◽  
Madden Julian Oscillation ◽  
Maritime Continent ◽  
Coupled Models ◽  
Ensemble Mean

Abstract The Madden–Julian oscillation (MJO) represents a primary source of predictability on the intraseasonal time scales and its influence extends from seasonal variations to weather and extreme events. While the last decade has witnessed marked improvement in dynamical MJO prediction, an updated estimate of MJO predictability from a contemporary suite of dynamic models, in conjunction with an estimate of their corresponding prediction skill, is crucial for guiding future research and development priorities. In this study, the predictability of the boreal winter MJO is revisited based on the Intraseasonal Variability Hindcast Experiment (ISVHE), a set of dedicated extended-range hindcasts from eight different coupled models. Two estimates of MJO predictability are made, based on single-member and ensemble-mean hindcasts, giving values of 20–30 days and 35–45 days, respectively. Exploring the dependence of predictability on the phase of MJO during hindcast initiation reveals a slightly higher predictability for hindcasts initiated from MJO phases 2, 3, 6, or 7 in three of the models with higher prediction skill. The estimated predictability of MJO initiated in phases 2 and 3 (i.e., convection in Indian Ocean with subsequent propagation across Maritime Continent) being equal to or higher than other MJO phases implies that the so-called Maritime Continent prediction barrier may not actually be an intrinsic predictability limitation. For most of the models, the skill for single-member (ensemble mean) hindcasts is less than the estimated predictability limit by about 5–10 days (15–25 days), implying that significantly more skillful MJO forecasts can be afforded through further improvements of dynamical models and ensemble prediction systems (EPS).

Diverse Influences of ENSO on the East Asian–Western Pacific Winter Climate Tied to Different ENSO Properties in CMIP5 Models

2015 ◽  
Vol 28 (6) ◽  
pp. 2187-2202 ◽  
Author(s):  
Hainan Gong ◽  
Lin Wang ◽  
Wen Chen ◽  
Debashis Nath ◽  
Gang Huang ◽  
...  
Keyword(s):  
Rossby Wave ◽  
Southern Oscillation ◽  
East Asian ◽  
Coupled Model ◽  
Western Pacific ◽  
Cmip5 Models ◽  
Boreal Winter ◽  
Winter Climate ◽  
Climatic Responses ◽  
Longitudinal Extension

Abstract The influence of El Niño–Southern Oscillation (ENSO) on the East Asian–western Pacific (EAWP) climate in boreal winter is investigated in the phase 5 of the Coupled Model Intercomparison Project (CMIP5) model results and then compared to that in the phase 3 (CMIP3) results. In particular, the role played by the differences among models in ENSO properties, including the amplitude and longitudinal extension of ENSO’s sea surface temperature (SST) pattern, is analyzed. Results show that an eastward shrinking of ENSO’s SST pattern leads to quite weak circulation and climatic responses over the EAWP regions in the models. On the contrary, a westward expansion of the SST pattern shifts the anomalous Walker circulation too far west. The resultant precipitation anomalies and lower-tropospheric atmospheric Rossby wave responses both extend unrealistically into the Indian Ocean, and the hemispheric asymmetry of the Rossby wave response is missing. All these features lead to unrealistic climatic impacts of ENSO over the EAWP regions. In contrast to the above two cases, a reasonable longitudinal extension of ENSO’s SST pattern corresponds to better ENSO teleconnections over the EAWP regions. Nevertheless, the atmospheric responses over the western Pacific are still located farther west than observed, implying a common bias of CMIP5 models. In this case, a larger amplitude of ENSO variability to some extent helps to reduce model biases and facilitate better climatic responses to ENSO in the EAWP regions. Compared with CMIP3 models, CMIP5 models perform better in representing ENSO’s impacts on the East Asian winter climate.

scholarly journals Seasonal prediction skill of East Asian summer monsoon in CMIP5-Models

2017 ◽  
Author(s):  
Bo Huang ◽  
Ulrich Cubasch ◽  
Christopher Kadow
Keyword(s):  
Summer Monsoon ◽  
Zonal Wind ◽  
East Asian Summer Monsoon ◽  
Southern Oscillation ◽  
Asian Summer Monsoon ◽  
East Asian ◽  
Prediction Skill ◽  
Cmip5 Models ◽  
Coupled Mode ◽  
Asian Summer

Abstract. The East Asian summer monsoon (EASM) is an important part of the global climate system and plays a vital role in the Asian climate. Its sub-seasonal-to-seasonal predictability is a long-standing issue within the monsoon scientist community. In this study, we analyse the seasonal (with six months lead time) prediction skill of the EASM rainfall and its associated general circulation in non-initialised and initialised simulations for the years 1979–2005 performed by six prediction systems (i.e., the BCC-CSM1-1, the CanCM4, the GFDL-CM2p1, the HadCM3, the MIROC5 and the MPI-ESM-LR) from the Coupled Model Intercomparison Project phase 5 (CMIP 5). We find that the simulation of the zonal wind is significantly improved in initialised simulations compared to non-initialized simulations. Based on the knowledge that zonal wind indices can be used as potential predictors for the EASM, we selected an EASM index based upon the zonal wind for further analysis. The assessment show that the GFDL-CM2p1 and the MIROC5 add prediction skill in simulating the EASM index with initialisation, the BCC-CSM1-1, the CanCM4, and the MPI-ESM-LR change the skill insignificantly, and the HadCM3 indicates a decreased skill score. The different response to the initialisation can be traced back to the ability of the models to capture the ENSO (El Niño-Southern Oscillation)-EASM coupled mode, particularly the Southern Oscillation-EASM coupled mode. In summary, we find that the GFDL-CM2p1 and the MIROC5 are capable to predict the EASM on a seasonal time-scale after initialisation.

scholarly journals Effect of the air–sea coupled system change on the ENSO evolution from boreal spring

2021 ◽  
Author(s):  
Xiang-Hui Fang ◽  
Fei Zheng
Keyword(s):  
Regime Shift ◽  
Southern Oscillation ◽  
Coupled System ◽  
Tropical Pacific ◽  
Enso Prediction ◽  
Boreal Spring ◽  
Mature Phase ◽  
Spring Predictability Barrier ◽  
The Impact ◽  
The Tropical Pacific

AbstractRealistic simulation and accurate prediction of El Niño-Southern Oscillation (ENSO) is still a challenge. One fundamental obstacle is the so-called spring predictability barrier (SPB), which features a low predictive skill of the ENSO with prediction across boreal spring. Our observational analysis shows that the leading empirical orthogonal function mode of the seasonal Niño3.4 index evolution (i.e., from May to the following April) explains nearly 90% of its total variance, and the principle component is almost identical to the Niño3.4 index in the mature phase. This means a good ENSO prediction for a year ranging May-next April can be achieved if the Niño3.4 index in the mature phase is accurately obtained in advance. In this work, by extracting physically oriented variables in the spring, a linear regression approach that can reproduce the mature ENSO phases in observation is firstly proposed. Further investigation indicates that the specific equation, however, is significantly modulated by an interdecadal regime shift in the air–sea coupled system in the tropical Pacific. During 1980–1999, ocean adjustment and vertical processes were dominant, and the recharge oscillator theory was effective to capture the ENSO evolutions. While, during 2000–2018, zonal advection and thermodynamics became important, and successful prediction essentially relies on the wind stress information and their controlled processes, both zonally and meridionally. These results imply that accounting for the interdecadal regime shift of the tropical Pacific coupled system and the dominant processes in spring in modulating the ENSO evolution could reduce the impact of SPB and improve ENSO prediction.

scholarly journals Seasonal prediction skill of East Asian summer monsoon in CMIP5 models

2018 ◽  
Vol 9 (3) ◽  
pp. 985-997
Author(s):  
Bo Huang ◽  
Ulrich Cubasch ◽  
Christopher Kadow
Keyword(s):  
Summer Monsoon ◽  
Zonal Wind ◽  
East Asian Summer Monsoon ◽  
Southern Oscillation ◽  
Asian Summer Monsoon ◽  
East Asian ◽  
Prediction Skill ◽  
Coupled Mode ◽  
Asian Summer ◽  
Prediction Systems

Abstract. The East Asian summer monsoon (EASM) is an important part of the global climate system and plays a vital role in the Asian climate. Its seasonal predictability is a long-standing issue within the monsoon scientist community. In this study, we analyse the seasonal (the leading time is at least 6 months) prediction skill of the EASM rainfall and its associated general circulation in non-initialised and initialised simulations for the years 1979–2005, which are performed by six prediction systems (i.e. the BCC-CSM1-1, the CanCM4, the GFDL-CM2p1, the HadCM3, the MIROC5, and the MPI-ESM-LR) from the Coupled Model Intercomparison Project phase 5 (CMIP 5). We find that most prediction systems of simulated zonal wind over 850 and 200 hPa are significantly improved in the initialised simulations compared to non-initialised simulations. Based on the knowledge that zonal wind indices can be used as potential predictors for the EASM, we select an EASM index based upon the zonal wind over 850 hPa for further analysis. This assessment shows that the GFDL-CM2p1 and the MIROC5 added prediction skill in simulating the EASM index with initialisation, the BCC-CSM1-1, the CanCM4, and the MPI-ESM-LR changed the skill insignificantly, and the HadCM3 indicates a decreased skill score. The different responses to initialisation can be traced back to the ability of the models to capture the ENSO (El Niño–Southern Oscillation) and EASM coupled mode, particularly the Southern Oscillation–EASM coupled mode. As is known from observation studies, this mode links the oceanic circulation and the EASM rainfall. Overall, the GFDL-CM2p1 and the MIROC5 are capable of predicting the EASM on a seasonal timescale under the current initialisation strategy.

scholarly journals Seasonal-to-Interannual Variability of Ethiopia/Horn of Africa Monsoon. Part II: Statistical Multimodel Ensemble Rainfall Predictions

2015 ◽  
Vol 28 (9) ◽  
pp. 3511-3536 ◽  
Author(s):  
Zewdu T. Segele ◽  
Michael B. Richman ◽  
Lance M. Leslie ◽  
Peter J. Lamb
Keyword(s):  
Addis Ababa ◽  
Monsoon Season ◽  
Rainfall Variability ◽  
Skill Score ◽  
Monthly Rainfall ◽  
Prediction Skill ◽  
Ensemble Prediction ◽  
Regional Prediction ◽  
Leave One Out ◽  
Prediction Approach

Abstract An ensemble-based multiple linear regression technique is developed to assess the predictability of regional and national June–September (JJAS) anomalies and local monthly rainfall totals for Ethiopia. The ensemble prediction approach captures potential predictive signals in regional circulations and global sea surface temperatures (SSTs) two to three months in advance of the monsoon season. Sets of 20 potential predictors are selected from visual assessments of correlation maps that relate rainfall with regional and global predictors. Individual predictors in each set are utilized to initialize specific forward stepwise regression models to develop ensembles of equal number of statistical model estimates, which allow quantifying prediction uncertainties related to individual predictors and models. Prediction skill improvement is achieved through error minimization afforded by the ensemble. For retroactive validation (RV), the ensemble predictions reproduce well the observed all-Ethiopian JJAS rainfall variability two months in advance. The ensemble mean prediction outperforms climatology, with mean square error reduction (SSClim) of 62%. The skill of the prediction remains high for leave-one-out cross validation (LOOCV), with the observed–predicted correlation r (SSClim) being +0.81 (65%) for 1970–2002. For tercile predictions (below, near, and above normal), the ranked probability skill score is 0.45, indicating improvement compared to climatological forecasts. Similarly high prediction skill is found for local prediction of monthly rainfall total at Addis Ababa (r = +0.72) and Combolcha (r = +0.68), and for regional prediction of JJAS standardized rainfall anomalies for northeastern Ethiopia (r = +0.80). Compared to the previous generation of rainfall forecasts, the ensemble predictions developed in this paper show substantial value to benefit society.

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