scholarly journals Application of Data Assimilation and the Relationship between ENSO and Precipitation

2021 ◽  
Vol 26 (1) ◽  
pp. 24
Author(s):  
Sittisak Injan ◽  
Angkool Wangwongchai ◽  
Usa Humphries
Keyword(s):  
Data Assimilation ◽  
Correlation Coefficient ◽  
Southern Oscillation ◽  
Three Dimensional ◽  
Coupled Model ◽  
Heavy Precipitation ◽  
Sea Surface ◽  
Intermediate Coupled Model ◽  
The Relationship ◽  
Better Than

Climate change in Thailand is related to the El Niño and Southern Oscillation (ENSO) phenomenon, in particular drought and heavy precipitation. The data assimilation method is used to improve the accuracy of the Ensemble Intermediate Coupled Model (EICM) that simulates the sea surface temperature (SST). The four-dimensional variational (4D-Var) and three-dimensional variational (3D-Var) schemes have been used for data assimilation purposes. The simulation was performed by the model with and without data assimilation from satellite data in 2011. The result shows that the model with data assimilation is better than the model without data assimilation. The 4D-Var scheme is the best method, with a Root Mean Square Error (RMSE) of 0.492 and a Correlation Coefficient of 0.684. The relationship between precipitation in Thailand and the ENSO area in Niño 3.4 was consistent for seven months, with a correlation coefficient of −0.882.

scholarly journals Reinitializing Sea Surface Temperature in the Ensemble Intermediate Coupled Model for Improved Forecasts

Axioms ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 189
Author(s):  
Sittisak Injan ◽  
Angkool Wangwongchai ◽  
Usa Humphries ◽  
Amir Khan ◽  
Abdullahi Yusuf
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Southern Oscillation ◽  
Coupled Model ◽  
Sea Surface ◽  
In Situ Data ◽  
The Pacific ◽  
Intermediate Coupled Model ◽  
Sea Surface Temperature Anomalies

The Ensemble Intermediate Coupled Model (EICM) is a model used for studying the El Niño-Southern Oscillation (ENSO) phenomenon in the Pacific Ocean, which is anomalies in the Sea Surface Temperature (SST) are observed. This research aims to implement Cressman to improve SST forecasts. The simulation considers two cases in this work: the control case and the Cressman initialized case. These cases are simulations using different inputs where the two inputs differ in terms of their resolution and data source. The Cressman method is used to initialize the model with an analysis product based on satellite data and in situ data such as ships, buoys, and Argo floats, with a resolution of 0.25 × 0.25 degrees. The results of this inclusion are the Cressman Initialized Ensemble Intermediate Coupled Model (CIEICM). Forecasting of the sea surface temperature anomalies was conducted using both the EICM and the CIEICM. The results show that the calculation of SST field from the CIEICM was more accurate than that from the EICM. The forecast using the CIEICM initialization with the higher-resolution satellite-based analysis at a 6-month lead time improved the root mean square deviation to 0.794 from 0.808 and the correlation coefficient to 0.630 from 0.611, compared the control model that was directly initialized with the low-resolution in-situ-based analysis.

Interdecadal Change in Properties of El Niño–Southern Oscillation in an Intermediate Coupled Model

2005 ◽  
Vol 18 (9) ◽  
pp. 1369-1380 ◽  
Author(s):  
Rong-Hua Zhang ◽  
Antonio J. Busalacchi
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
Coupled Model ◽  
Ocean Model ◽  
Sea Surface ◽  
Subsurface Temperature ◽  
Intermediate Coupled Model

Abstract The role of subsurface temperature variability in modulating El Niño–Southern Oscillation (ENSO) properties is examined using an intermediate coupled model (ICM), consisting of an intermediate dynamic ocean model and a sea surface temperature (SST) anomaly model. An empirical procedure is used to parameterize the temperature of subsurface water entrained into the mixed layer (Te) from sea level (SL) anomalies via a singular value decomposition (SVD) analysis for use in simulating sea surface temperature anomalies (SSTAs). The ocean model is coupled to a statistical atmospheric model that estimates wind stress anomalies also from an SVD analysis. Using the empirical Te models constructed from two subperiods, 1963–79 (T63–79e) and 1980–96 (T80–96e), the coupled system exhibits strikingly different properties of interannual variability (the oscillation period, spatial structure, and temporal evolution). For the T63–79e model, the system features a 2-yr oscillation and westward propagation of SSTAs on the equator, while for the T80–96e model, it is characterized by a 5-yr oscillation and eastward propagation. These changes in ENSO properties are consistent with the behavior shift of El Niño observed in the late 1970s. Heat budget analyses further demonstrate a controlling role played by the vertical advection of subsurface temperature anomalies in determining the ENSO properties.

scholarly journals Roles of initial ocean surface and subsurface states on successfully predicting 2006–2007 El Niño with an intermediate coupled model

Ocean Science ◽  
2015 ◽  
Vol 11 (1) ◽  
pp. 187-194 ◽  
Author(s):  
F. Zheng ◽  
J. Zhu
Keyword(s):  
El Niño ◽  
El Nino ◽  
Surface Condition ◽  
Coupled Model ◽  
Ocean Surface ◽  
Sea Surface ◽  
Initial Surface ◽  
El Niño Event ◽  
Successful Prediction ◽  
Intermediate Coupled Model

Abstract. The 2006–2007 El Niño event, an unusually weak event, was predicted by most models only after the warming in the eastern Pacific had commenced. In this study, on the basis of an El Niño prediction system, roles of the initial ocean surface and subsurface states on predicting the 2006–2007 El Niño event are investigated to determine conditions favorable for predicting El Niño growth and are isolated in three sets of hindcast experiments. The hindcast is initialized through assimilation of only the sea surface temperature (SST) observations to optimize the initial surface condition, only the sea level (SL) data to update the initial subsurface state, or both the SST and SL data. Results highlight that the hindcasts with three different initial states can all successfully predict the 2006–2007 El Niño event 1 year in advance and that the hindcast initialized by both the SST and SL data performs best. A comparison between the various sets of hindcast results further demonstrates that successful prediction is more significantly affected by the initial subsurface state than by the initial surface condition. The accurate initial surface state can trigger the easier prediction of the 2006–2007 El Niño, whereas a more reasonable initial subsurface state can contribute to improving the prediction in the growth of the warm event.

scholarly journals The Correlation of Southern Oscillation Index (SOI) with the Rainfall at Rainy and Dry Season Peak in Kintamani-Bangli Region period 1986-2015

2019 ◽  
Vol 20 (2) ◽  
pp. 6
Author(s):  
Wayan Mita Restitiasih ◽  
I Ketut Sukarasa ◽  
I Wayan Andi Yuda
Keyword(s):  
Correlation Coefficient ◽  
El Niño ◽  
Southern Oscillation Index ◽  
El Nino ◽  
Southern Oscillation ◽  
Management Plan ◽  
Correlation Study ◽  
Dry Season ◽  
Rainfall Data ◽  
The Relationship

A correlation study of the Southern Oscillation Index (SOI) on rainfall at the peak of the wet and dry season in the Kintamani-Bangli region has been carried out by taking SOI values and rainfall data for the period 1986-2015. The rainfall data used were recorded at 2 rain posts, namely Kembangsari and Kintamani. The research aimed to determine the relationship of fluctuations in the value of SOI with the intensity of rainfall, so that it can be used as a regional management plan when El Nino occurs. The method used in this study is correlation. The results obtained from the correlation that is the relationship between SOI value and rainfall in February were quite strong in the Kembangsari post with correlation coefficient of 0.409. Whereas for the Kintamani post the correlation obtained was weak with a correlation coefficient of 0.308. Then in August a weak correlation occurred in the Kembangsari post with a correlation coefficient of 0.2398 and was quite strong in the Kintamani post with a correlation coefficient of 0.4662. So that the influence of El Nino in the Kintamani area in February was more dominant in the Kembangsari post and in August at the Kintamani post.

The relationship between Indian Ocean sea–surface temperature and East African rainfall

2005 ◽  
Vol 363 (1826) ◽  
pp. 43-47 ◽  
Author(s):  
Emily Black
Keyword(s):  
Indian Ocean ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Southern Oscillation ◽  
Sea Surface ◽  
East African ◽  
African Rainfall ◽  
The Pacific ◽  
The Indian Ocean ◽  
The Relationship

Knowledge of the processes that control East African rainfall is essential for the development of seasonal forecasting systems, which may mitigate the effects of flood and drought. This study uses observational data to unravel the relationship between the Indian Ocean Dipole (IOD), the El Niño Southern Oscillation (ENSO) and rainy autumns in East Africa. Analysis of sea–surface temperature data shows that strong East African rainfall is associated with warming in the Pacific and Western Indian Oceans and cooling in the Eastern Indian Ocean. The resemblance of this pattern to that which develops during IOD events implies a link between the IOD and strong East African rainfall. Further investigation suggests that the observed teleconnection between East African rainfall and ENSO is a manifestation of a link between ENSO and the IOD.

scholarly journals Analisis Pola Sebaran Area Upwelling di Selatan Indonesia Menggunakan Citra Modis Level 2

2021 ◽  
Vol 4 (1) ◽  
pp. 56
Author(s):  
I Gede Mita Anjas Swara ◽  
I Wayan Gede Astawa Karang ◽  
Gede Surya Indrawan
Keyword(s):  
Surface Temperature ◽  
Southern Oscillation ◽  
Sea Surface ◽  
The South ◽  
Transition Season ◽  
Overlay Technique ◽  
Upwelling Intensity ◽  
The Relationship ◽  
Level 2 ◽  
Upwelling Area

This research aimed to find out the pattern of spasio-temporal upwelling and its relation with El Nino Southern Oscillation (ENSO) in the southern waters of Java to the East Nusa Tenggara. Two indicators namely sea surface temperature (SST) and chlorofil-a data obtained from oceancolor database were used as an indicator of upwelling occurences. The overlay technique and correlation analyses were used to describe the relationship between upwelling and ENSO. The results showed the phenomenon of upwelling occurred along southern Java and East Nusa Tenggara. The appearance of upwelling occurred in the South Easth Monsoon until transition season which began in June and ended in November. The pattern of upwelling area for 17 years varies each season, where for the South Easth Monsoon in June it gained 6986 km2, in July 78294 km2, and in August 254212 km2. As for transition season II in September 166767 km2, in October 72033 km2, and November 1949 km2. The results also showed that upwelling intensity was influenced by ENSO indicated by correlation values that matched the correlation value between SST - ENSO was -0.78 and chlorophyll-a-ENSO was 0.98.

scholarly journals Interactions of ENSO, the IOD, and the SAM in CMIP3 Models

2011 ◽  
Vol 24 (6) ◽  
pp. 1688-1704 ◽  
Author(s):  
Wenju Cai ◽  
Arnold Sullivan ◽  
Tim Cowan
Keyword(s):  
Southern Oscillation ◽  
Global Climate ◽  
Coupled Model ◽  
Southern Annular Mode ◽  
Enso Events ◽  
Intercomparison Project ◽  
Wave Trains ◽  
Induced Signal ◽  
The Impact ◽  
The Relationship

Abstract Simulations of individual global climate drivers using models from the Coupled Model Intercomparison Project phase 3(CMIP3) have been examined; however, the relationship among them has not been assessed. This is carried out to address several important issues, including the likelihood of the southern annular mode (SAM) forcing Indian Ocean dipole (IOD) events and the possible impact of the IOD on El Niño–Southern Oscillation (ENSO) events. Several conclusions emerge from statistics based on multimodel outputs. First, ENSO signals project strongly onto the SAM, although ENSO-forced signals tend to peak before ENSO. This feature is similar to the situation associated with the IOD. The IOD-induced signal over southern Australia, through stationary equivalent Rossby barotropic wave trains, peak before the IOD itself. Second, there is no control by the SAM on the IOD, in contrast to what has been suggested previously. Indeed, no model produces a SAM–IOD relationship that supports a positive (negative) SAM driving a positive (negative) IOD event. This is the case even in models that do not simulate a statistically significant relationship between ENSO and the IOD. Third, the IOD does have an impact on ENSO. The relationship between ENSO and the IOD in the majority of models is far weaker than the observed. However, the ENSO’s influence on the IOD is boosted by a spurious oceanic teleconnection, whereby ENSO discharge–recharge signals transmit to the Sumatra–Java coast, generating thermocline anomalies and changing IOD properties. Without the spurious oceanic teleconnection, the influence of the IOD on ENSO is comparable to the impact of ENSO on the IOD. Other model deficiencies are discussed.

Sign in / Sign up

Export Citation Format

Share Document