scholarly journals Statistical bias correction on the climate model for el nino index prediction

2021 ◽  
Vol 12 (2) ◽  
pp. 273-282
Author(s):  
Sri Nurdiati ◽  
Ardhasena Sopaheluwakan ◽  
Yoga Abdi Pratama ◽  
Mohamad Khoirun Najib
Keyword(s):  
Prediction Model ◽  
El Niño ◽  
Bias Correction ◽  
Lead Time ◽  
El Nino ◽  
Sea Surface ◽  
Linear Scaling ◽  
Statistical Bias ◽  
Distribution Mapping ◽  
Statistical Bias Correction

El Nino can harm many sectors in Indonesia by reducing precipitation levels in some areas. The occurrence of El Nino can be estimated by observing the sea surface temperature in Nino 3.4 region. Therefore, an accurate model on sea surface temperature prediction in Nino 3.4 region is needed to optimize the estimation of the occurrence of El Nino, such as ECMWF. However, the prediction model released by ECMWF still consists of some systematic errors or biases. This research aims to correct these biases using statistical bias correction techniques and evaluate the prediction model before and after correction. The statistical bias correction uses linear scaling, variance scaling, and distribution mapping techniques. The results show that statistical bias correction can reduce the prediction model bias. Also, the distribution mapping and variance scaling are more accurate than the linear scaling technique. Distribution mapping has better RMSE in December-March, and variance scaling has better RMSE in April-June also in October and November. However, in July-September, prediction from ECMWF has better RMSE. The application of statistical bias correction techniques has the highest refinement in January-March at the first lead time and in April at the fifth until the seventh lead time. 

scholarly journals Statistical Bias Correction for Predictions of Indian Ocean Dipole Index with Quantile Mapping Approach

2021 ◽  
Author(s):  
Sri Nurdiati ◽  
Ardhasena Sopaheluwakan ◽  
Mohamad Khoirun Najib
Keyword(s):  
Indian Ocean ◽  
Prediction Model ◽  
Bias Correction ◽  
Indian Ocean Dipole ◽  
Sea Surface ◽  
Statistical Bias ◽  
Quantile Mapping ◽  
Ecmwf Model ◽  
The Impact ◽  
Statistical Bias Correction

The Indian Ocean Dipole (IOD) is a phenomenon of ocean-atmosphere interaction that affects climate conditions in Indonesia. The IOD index shows the difference between the western and eastern Indian Ocean sea surface temperature. The impact of the IOD can increase the risk of forest fires, floods and crop failure. Thus, an IOD index prediction model is needed to anticipate the impact of the IOD. One of prediction models of sea surface temperature is the ECMWF prediction model. However, this prediction model has systematic errors that can be corrected using a quantile mapping approach. This method corrects the systematic error of the ECMWF model by connecting the distribution between the ECMWF model and OISST in a transfer function, such as different of quantile and polynomial function. Based on the results, the linear function has the highest chance to improve the accuracy of the model. Moreover, the result shows that statistical bias correction is a good method to improve the accuracy of the ECMWF model especially in Januari-April and September-December.

scholarly journals Studies of the seasonal prediction of heavy late spring rainfall over southeastern China

2021 ◽  
Author(s):  
Shouwen Zhang ◽  
Hui Wang ◽  
Hua Jiang ◽  
Wentao Ma
Keyword(s):  
Indian Ocean ◽  
El Niño ◽  
Lead Time ◽  
Heavy Rainfall ◽  
El Nino ◽  
Late Spring ◽  
Southeastern China ◽  
River Region ◽  
El Niño Events ◽  
El Nino Events

AbstractThe late spring rainfall may account for 15% of the annual total rainfall, which is crucial to early planting in southeastern China. A better understanding of the precipitation variations in the late spring and its predictability not only greatly increase our knowledge of related mechanisms, but it also benefits society and the economy. Four models participating in the North American Multi-Model Ensemble (NMME) were selected to study their abilities to forecast the late spring rainfall over southeastern China and the major sources of heavy rainfall from the perspective of the sea surface temperature (SST) field. We found that the models have better abilities to forecast the heavy rainfall over the middle and lower reaches of the Yangtze River region (MLYZR) with only a 1-month lead time, but they failed for a 3-month lead time since the occurrence of the heavy rainfall was inconsistent with the observations. The observations indicate that the warm SST anomalies in the tropical eastern Indian Ocean are vital to the simultaneously heavy rainfall in the MLYZR in May, but an El Niño event is not a necessary condition for determining the heavy rainfall over the MLYZR. The heavy rainfall over the MLYZR in May is always accompanied by warming of the northeastern Indian Ocean and of the northeastern South China Sea (NSCS) from April to May in the models and observations, respectively. In the models, El Niño events may promote the warming processes over the northeastern Indian Ocean, which leads to heavy rainfall in the MLYZR. However, in the real world, El Niño events are not the main reason for the warming of the NSCS, and further research on the causes of this warming is still needed.

scholarly journals Two Independent Triggers for the Indian Ocean Dipole/Zonal Mode in a Coupled GCM

2005 ◽  
Vol 18 (17) ◽  
pp. 3428-3449 ◽  
Author(s):  
Albert S. Fischer ◽  
Pascal Terray ◽  
Eric Guilyardi ◽  
Silvio Gualdi ◽  
Pascale Delecluse
Keyword(s):  
Indian Ocean ◽  
El Niño ◽  
Indian Ocean Dipole ◽  
El Nino ◽  
Tropical Indian Ocean ◽  
Sea Surface ◽  
Dynamical Response ◽  
Thermocline Depth ◽  
Second Phase ◽  
The Indian Ocean

Abstract The question of whether and how tropical Indian Ocean dipole or zonal mode (IOZM) interannual variability is independent of El Niño–Southern Oscillation (ENSO) variability in the Pacific is addressed in a comparison of twin 200-yr runs of a coupled climate model. The first is a reference simulation, and the second has ENSO-scale variability suppressed with a constraint on the tropical Pacific wind stress. The IOZM can exist in the model without ENSO, and the composite evolution of the main anomalies in the Indian Ocean in the two simulations is virtually identical. Its growth depends on a positive feedback between anomalous equatorial easterly winds, upwelling equatorial and coastal Kelvin waves reducing the thermocline depth and sea surface temperature off the coast of Sumatra, and the atmospheric dynamical response to the subsequently reduced convection. Two IOZM triggers in the boreal spring are found. The first is an anomalous Hadley circulation over the eastern tropical Indian Ocean and Maritime Continent, with an early northward penetration of the Southern Hemisphere southeasterly trades. This situation grows out of cooler sea surface temperatures in the southeastern tropical Indian Ocean left behind by a reinforcement of the late austral summer winds. The second trigger is a consequence of a zonal shift in the center of convection associated with a developing El Niño, a Walker cell anomaly. The first trigger is the only one present in the constrained simulation and is similar to the evolution of anomalies in 1994, when the IOZM occurred in the absence of a Pacific El Niño state. The presence of these two triggers—the first independent of ENSO and the second phase locking the IOZM to El Niño—allows an understanding of both the existence of IOZM events when Pacific conditions are neutral and the significant correlation between the IOZM and El Niño.

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