1. Introduction
The El Niño–Southern Oscillation (ENSO) is used to describe the Sea Surface Temperature Anomaly (SSTA) in the equatorial Pacific Ocean and ocean–atmosphere system fluctuations in the Southern Hemisphere. Scientists now use the term ENSO warm event to describe the phenomenon where the SST in the eastern and central parts of the Pacific region is warmer than normal, while the term ENSO cold event is now used to describe the phenomenon where the SST in the central and eastern parts of the Pacific region is colder than normal. Many countries in the world are affected by these two phenomena, especially countries in the equatorial parts of the Pacific Ocean. The ENSO is also associated with abnormal climatic conditions, leading to droughts in southern Africa and other areas of the Southern Hemisphere, such as Australia; for example, the Australian continent experienced a drought in 1997 as a result of the ENSO phenomenon. At present, the hot weather in Australia is believed to be the cause of forest fires in Victoria and New South Wales. Southeast Asia, comprising Indonesia, the Philippines, Malaysia, Singapore, Brunei, and Papua New Guinea, experienced the greatest incidence of forest fires in 1997–1998. Moreover, other countries in the region, such as Thailand, Laos, Cambodia, and Vietnam, suffered from drought conditions at this time. The ENSO has been identified as the dominant cause of climate variability around the equatorial Pacific Ocean. It connects the air circulation in the atmosphere with the temperature of water flowing into the Pacific Ocean. International research has shown that the ENSO phenomenon affects more than 70% of the global temperature, although it occurs in the Pacific Ocean.
The modelling of ENSO phenomena has improved, in terms of prediction skills, to within a range of 12 months in advance, based on analyses of the relationships between the atmosphere and ocean. Several studies have been conducted to predict ENSO phenomena using different methods [
1,
2,
3,
4,
5,
6]. Studies have reviewed the efficacy of many models, in an attempt to rule out changes related to ENSO phenomena [
7]. The Hybrid Oceanic and Atmospheric System Model (HCM) has been studied to explain climate variability in the tropical Pacific Ocean system [
8]. An intermediate coupled model (ICM) has been studied and developed with a variety of methods, in order to improve ENSO forecasting results [
9]. Scientists in the Institute of Oceanology, Chinese Academy of Sciences, have studied the evolution of the SST in the tropical Pacific Ocean, as predicted using the IOCAS ICM model. A unique feature is how the temperature of the sub-surface water, entrained into the mixed layer, is parameterized [
10]. SST data have been used to predict ENSO phenomena, as an essential geophysical variable that can act as a predictor of atmospheric conditions [
1]. The simplest model that can be used to predict the ENSO phenomenon is the EICM. The EICM is constructed from an Intermediate Ocean Model (IOM), which seeks to couple the ocean with entrainment temperature, SST, and wind stress in the tropical Pacific Ocean; however, the observation of oceanic data is very difficult, for various reasons [
4], and the resulting inaccuracies in the input data result in incorrect ENSO, leading to incorrect assessment of the model status and its predictions [
11]. Therefore, it is necessary to find a procedure that can lead to predictions of the model which are in agreement with the observed data. The unstable data problem may not occur if one uses satellite data, as the model grid resolution is lower than that of the satellite data [
12]. For the above reason, discovering an optimal method is necessary for improving initial data, to make them consistent with observation data. Hence, the Cressman initialization method may serve as a potential means to provide the initial data in the EICM.
The data assimilation method is a technique of statistical combination that combines the forecasted result with the initial observation data. This technique is used to correct the initial data that are to be fed into the EICM [
13,
14]. The process of data assimilation between oceanic and atmospheric improved the El Nino forecasts compared to the forecasting result without data assimilation [
15]. The Cressman method has been used to correct the SST data when there are difficulties in measuring the temperatures at exact locations and exact times over vast areas, with satellite-measured observations of sea surface temperature from the MODIS Aqua spectroradiometer [
16]. The Cressman method may improve results slightly compared to other methods but is suitable for SST, as shown by [
16,
17]. Artur et al. (2015) uses Cressman, but applies it to satellite-measured sea surface temperature from the MODIS Aqua spectroradiometer, using a coupled ecosystem model [
16]. This procedure provides more correct input data, which may lead to more accurate forecasts and more reliable predictions [
18]. Therefore, this work aims to improve the SSTA prediction of the ENSO phenomenon with EICM, using the Cressman initialization method. The Optimum Interpolation Sea Surface Temperature (OISST) data from the Advanced Very High-Resolution Radiometer (AVHRR) was analyzed through the data assimilation process to be used as an input of EICM.
3. Results and Discussion
Simulation was performed for the SSTA data of OISST in the EICM and CIEICM, using historical data spanning from 1995 to 2019. The results of EICM and CIEICM simulations were compared with the observed data from NOAA/PMEL TAO buoy network, in order to determine whether the Cressman method had worked properly. A comparison of the SSTA result samples from each simulation with the observations was carried out, in order to assess the accuracy of the preliminary models. The absorption validation with EICM consists of three parts: First, the results of both simulations are compared with the OISST data to determine if the initial method is working correctly. Second, the results of both simulations are compared with source data, to verify the accuracy of the two simulations. Finally, the results of the model are computed, in SSTA index format, in the Niño 3.4 area and compared with data from Hadley Center’s Sea Ice and Sea Surface Temperature (HadISST) [
39].
Figure 4 shows the Pacific region SSTA data for January 1995, including SSTA data from ERSST (which was the EICM import data, shown in
Figure 4a). The SSTA data of EICM from ICOADS are shown in
Figure 4b. The SSTA data, obtained from OISST, are shown in
Figure 4c, while SSTA data of CIEICM that Cressman initialized from OISST are shown in
Figure 4d.
From
Figure 4, the OISST data yielded SSTAs lower than those from ERSST and EICM. CIEICM found that most SSTAs were lower than EICM, and similar to those from ERSST and OISST, as expected.
Figure 5 shows a comparison of SSTA data from EICM and CIEICM in January 1995, where the blue color means that Cressman Initialized resulted in lower SSTA values. White and red colours indicate that Cressman Initialized increased the SSTA value of the model. The simulation results showed that SSTAs were mostly reduced from EICM, which was consistent with
Figure 4.
Visual comparison may not be sufficient and, therefore, a statistical method must be utilised to assess the validity of the model. The statistics used were the Root Mean Square Deviation, shown as Equation (
7), Correlation Coefficient, shown as Equation (
8), and Standard Deviation of Error, shown in Equation (
9).
Root Mean Square Deviation:
In the above equations,
denotes the SSTA data from the EICM and CIEICM,
denotes the SSTA data from OISST,
denotes the mean SSTA data from EICM and CIEICM,
denotes the mean SSTA data from OISST,
n is the number of grids of data,
is the Error information between the model and OISST
, and
is the mean of the Error. Comparing SSTA data from EICM and CIEICM with the OISST from 1995 to 2019 (i.e., over 25 years), it was found that, when using Cressman Initialized, there was less discrepancy, as the Root Mean Square Deviation value decreased from 0.616 to 0.605. The correlation between the simulation and OISST increased from 0.535 to 0.548, and there was less variation in the error (which decreased from 0.896 to 0.869), as shown in
Table 2.
When comparing the CIEICM with OISST, the absorption algorithm worked correctly. A comparison of the precision of both models and OISST with in situ data from 1995 to 2019 was carried out, in order to validate the model and to correlate the model with situational data, where the in situ data were obtained from the NOAA/PMEL TAO buoy network [
40,
41], for which the buoy locations in the central Pacific Ocean to predict ENSO-related climate variations are shown in
Figure 6.
Figure 7 shows the correlation of SSTA data between OISST and source data, with black lines showing that the OISST data and in situ data were highly correlated. If the SSTA value is below the black line, the OISST data were higher than the source data. If the SSTA value is higher than the black line, the OISST data were lower than the source data. From the figure, the data from OISST had a correlation coefficient of 0.968.
Figure 8a shows the SSTA relationship between EICM or CIEICM and OISST, where blue indicates the relationship between EICM and OISST, and red shows the relationship between CIEICM and OISST. It was found that the EICM was less correlated than that of the CIEICM (at 0.624 and 0.637, respectively).
Figure 8b shows the the SSTA relationship between EICM and CIEICM and in situ data, where the blue colour shows the relationship between EICM and in situ data, and red shows the relationship between CIEICM and in situ data. It was found that the EICM was less correlated than that of the CIEICM (at 0.614 and 0.632, respectively).
Comparison of the accuracy of the two simulations with in situ data for each month from 1995 to 2019 found that the CIEICM was able to reduce the model error. During the period from June to August, the model tolerance was better than in other months, where the
from CIEICM was approximately 0.024 less than EICM. When comparing the model relationships, it was found that the CIEICM was more correlated than that of the model; it was also found that the EICM had a correlation of approximately 0.16, as shown in
Table 3.
The EICM error was compared with the in situ data, and the CIEICM error was compared with 300 in situ data by the Mann–Whitney U statistical method. Considering the statistical value of the Mann–Whitney U Test, it was found that the value was 41,416.5, and the Asymptotic Significance (1-tailed) value was 0.455, which was compared with the statistical significance level to conclude. As a result of the analysis, the Asymptotic Significance (1-tailed) value was less than the significance level of 0.05. The error from the EICM is greater than the error from the CIEICM, as shown in
Table 4.
The Taylor diagram shows a comparison of the SSTA Index in Niño 3.4 (180
E–240
E and 5
S–5
N). The red dot is the SSTA data from the EICM for each ensemble of 100 ensemble and HadISST data. The blue dot is the SSTA data from the CIEICM for each ensemble of 100 ensembles and data from HadISST. The red cross is the SSTA mean from all EICM 100 ensembles and data from HadISST, and the blue cross is the SSTA mean from all CIEICM 100 ensembles and data from HadISST.
Figure 9 shows the prediction results for each month. It was found that, in the forecast using imported data from December 1994 to November 2019, the forecast from January 1995 to December 2019 had a forecast period of 1 month. The
of each ensemble was 0.53 to 0.55 and the
of the Mean Ensemble was 0.49 over a 12-month forecast period. Forecasts for December 1995 through December 2019 had an
of each ensemble from 1.2 to 1.4 and a Mean Ensemble
of 0.8. When forecasting SSTA in the near-term, there was little discrepancy, while the longer period SSTA forecast increased the error.
Figure 10 shows the SSTA Index at Niño 3.4, in order to determine the ENSO phenomenon, where the black line is the SSTA Index data from HadISST, the red line is the SSTA Index data from EICM, and the blue line is the SSTA Index data. If the Niño 3.4 index is greater than 0.5, then the El Niño is defined to occur. If the value of the index is between 0.5 and 1, a weak El Niño is defined to occur. If it is between 1 and 1.5, it is defined to be a moderate El Niño. If it is between 1.5 and 2, it is defined to be strong. Finally, if it is greater than 2, it is defined to be very strong. On the other hand, if the Oceanic Niño index is negative and lower than −0.5, then the La Niña is defined to occur. The level of the phenomenon is defined to be divided into weak, moderate, and strong, similar to that for El Niño. The shorter the forecast is, the less accurate the forecast; the longer the forecast, the greater the error.
Comparing the accuracy of the two simulations with HadISST for each month’s lead time from 1995 to 2019, the CIEICM was able to reduce the model error. With 1–8 months lead time, the CIEICM yielded better forecasts than the EICM. With 9–12 months lead time, the two simulations showed no significant differences in terms of
, with the mean
from CIEICM being approximately 0.017 less than EICM. When comparing the model relationships, it was found that the CIEICM was more correlated than the EICM, with an increase of approximately 0.11. In the ranges 1, 2–6, and 7–12 months lead, the relationship was at the levels of very strong, strong, and moderate, respectively, as shown in
Table 5.
Figure 11 shows the Pacific SSTA data in November 2015, which includes SSTA data from EICM and SSTA data from CIEICM. It was found that, during the El Niño phenomenon, CIEICM simulations yielded higher SSTAs than EICMs in the Niño region, as shown in
Figure 11a,b, respectively.
Figure 11c shows the difference between EICM and CIEICM, where blue means that carrying out Cressman Initialized resulted in a decrease in the model’s SSTA, while white and red meant that Cressman Initialization increased the model’s SSTA. Most SSTA values were reduced from EICM.
Figure 12 shows the Pacific SSTA data in January 2000, which includes SSTA data from EICM and SSTA data from CIEICM. It was found that, during the La Niña phenomenon, CIEICM simulation led to lower SSTA than EICM during Niño region, as shown in
Figure 12a,b.
Figure 12c shows the difference between EICM and CIEICM, where blue means Cressman Initialized decreased the model SSTA, while white and red indicate that Cressman Initialization increased the model SSTA. Most SSTA values were reduced from EICM.
4. Conclusions
Accurately predicting the occurrence of ENSO phenomena several months in advance is a major goal of climate research. It is necessary to find a procedure that can yield model predictions which agree with the observed data. This study, therefore, focused on improving the predictions of ENSO phenomena in the Pacific using the Cressman method to improve SST forecasts with the EICM and CIEICM from 1995 to 2019. The work consisted of three main parts. Part 1, comparing the results from EICM and CIEICM with OISST data, it was found that the Cressman method works properly because the Cressman Initialization method minimizes the error. From the statistical analysis, Root Mean Square Deviation decreased from 0.616 to 0.605, and the relationship between simulation and OISST increased from 0.535 to 0.548. Part 2 compares SST data between EICM that initializing with ICOADS and SST data from NOAA/PMEL TAO buoy network and compares SST data between CIEICM that initializing with Cressman using OISST and SST data from NOAA/PMEL TAO buoy network. It was found that the CIEICM was able to reduce the error each month. The
value decreased from 0.676 to 0.652, and the correlation coefficient increased from 0.600 to 0.616. The results showed that the Asymptotic Significance (1-tailed) value was less than the significance level of 0.05. This means that the test has a greater EICM error than the CIEICM error is shown in
Table 4. The last part compared the SSTA index at Niño 3.4 of the two simulations using HadISST data. It was found that the CIEICM was more accurate than the EICM. Moreover, the CIEICM simulation was more correlated with data from HadISST than EICM. Both simulations were able to predict the occurrence of ENSO phenomena well in the first six months, and had a strong correlation. This confirmed the reliability of the algorithm using OISST data in conjunction with the EICM to obtain the CIEICM, which yielded better prediction results.
Several studies have attempted to solve problems associated with the input data used in the model to increase ENSO prediction accuracy. The results of the study of Ji and Leetmaa indicated that adequate physical parameters of data assimilation can improve forecasting results and, thus, improve predictive skills [
42]. Chen demonstrated the effects and necessity of data assimilation on ICM and pointed out that assimilation is key in improving the prediction skills of the current ENSO model [
43]. Many assimilation techniques have been used for the initial simulation of the ocean atmosphere [
44]. A four-dimensional variational (4D-Var) data assimilation method has been implemented in an improved model of the tropical Pacific, in order to improve the accuracy of the model. The results showed that 4D-Var can effectively reduce the error in ENSO analysis [
45,
46,
47]. However, improving forecasting skills by assimilation is not the only method that can be achieved. The need for assimilation may create an imbalance between early ocean conditions and models. The results of the study of Zavala-Garay suggest that there is little room for improvement in predictive skills, as a result of the highly limited data assimilation method [
48]. These imbalances and errors in the model can be a significant limiting factor in forecasting skills, especially for predictions that occur in the northern winter. Most studies to date have focused on improving the model through data assimilation, while future studies are expected to focus on optimal error study efforts and actual forecast limit estimates [
49]. Some limiting factors cannot be avoided using data assimilation; they have to be addressed through modifications to the model.