Reanalysis data are extensively employed in investigation of the tropical large-scale climate variability. In this study, we attempt to use the Advanced Microwave Sounding unit-A (AMSU-A) window channel brightness temperature (TB) observations to examine their relationship with the sea surface temperature (SST). A close relationship is found between the AMSU-A window channel TB and SST in the tropical oceans. A TB Niño Index (TNI) of AMSU-A window (sounding) channels is found to reach its maximum about 2 months ahead of Oceanic Niño Index (ONI) for half of El Niño cases from 1998 to 2020. The AMSU-A sounding channels TNI had minimum values about 1–3 months behind ONI peak month. Spatial correlation maps of AMSU-A window channel TB anomalies with TNI are similar to those of SST anomalies with ONI, featuring a horseshoe-shaped pattern in the tropical Pacific and a dipole-shaped pattern in the tropical Indian Ocean. The AMSU-A retrieved liquid water path (LWP) is also highly correlated with the ONI, with larger values in El Niño years. An active development of tropical convective disturbances and their movement associated with westerly wind bursts may be the reason for the peak TNI to occur 1–3 months earlier than the peak ONI. The potential for AMSU-A TB observations to reveal El Niño signals adds additional satellite data source for El Niño research.
Research Article
El Niño signals revealed by AMSU-A brightness temperature observations
https://doi.org/10.21203/rs.3.rs-3295262/v1
This work is licensed under a CC BY 4.0 License
Journal Publication
published 27 Jan, 2024
You are reading this latest preprint version
AMSU-A
Brightness Temperature
Sea Surface Temperature
El Niño
The El Niño-Southern Oscillation (ENSO) is one of the most important climate variability on the interannual timescales with profound global impacts. It emerges from interactions between the large-scale ocean and atmosphere in the tropical Pacific. In normal years, easterly trade winds drive oceanic surface currents to the west, piling up warm seawater heated by the sun in the deep western Pacific warm pool (Bjerknes 1969). When El Niño event occurs, the trade winds systematically weaken, allowing warm seawater piled up in the west to migrate eastward, causing a rise of sea surface temperature (SST) in the eastern Pacific (Bjerknes 1966; Wyrtki 1975; Rasmusson and Carpenter 1982). The warm SST anomalies in the eastern tropical Pacific prompts changes in deep convection and air masses (Adler et al. 2003; Huang and Xie 2015). To quantitatively characterize the ENSO cycle, several SST indices have been proposed, and notably, the SST anomalies within the Niño 3.4 region (5°N-5°S, 120°W-170°W) demonstrate a significant capability in effectively capturing the evolution of ENSO (Trenberth, 1997; Hanley et al., 2003; Giese and Ray, 2011). The Oceanic Niño Index (ONI), defined by the Climate Prediction Center (CPC), employs a three-month running average of Niño 3.4 SST anomalies to monitor the development of ENSO. The utilization of a three‐month average could filter out sub-seasonal variations inherent to the tropical Pacific (McPhaden et al. 2020). The CPC identifies an El Niño (La Niña) event when the ONI is above 0.5°C (below − 0.5°C) for five consecutive months. Many SST data such as OISST (Reynolds et al. 2007), ERSST (Huang et al. 2017) and HadSST (Kennedy et al. 2019) are widely used in the study of ENSO associated variability. Differences between these various SST data sources are very small after the 1980s when satellite observations become increasingly prevalent (Cowtan et al. 2018; Yang and Huang 2021). Considering the high consistency of the spatio-temporal features, this study employs the OISST data for the ONI measurement in order to identify ENSO events from 1998 to 2020.
ENSO can exert significant impacts on a global scale through the oceanic and atmospheric teleconnection (e.g., Ropelewski and Halpert 1987; Klein et al., 1999; Trenberth and Caron 2000; Alexander et al. 2002; Zhang et al. 2019). Teleconnection denotes the linkage of oceanic and atmospheric conditions across great distances, and several teleconnection patterns have been proposed to be related to the ENSO (Trenberth et al. 1998)). The Pacific-North America (PNA) teleconnection pattern documents how ENSO affects the North American climate through forced atmospheric Rossby waves (Hoskins and Karoly 1981; Wallace and Gutzler 1981). The relationship between ENSO and the North Atlantic Oscillation (NAO) can provide potential seasonal predictability for the European climate (Brönnimann 2007). The development of El Niño can trigger a positive Indian Ocean dipole (IOD) event by altering the Walker Circulation (e.g., Webster et al. 1999). Moreover, investigations have demonstrated that preceding an El Niño event, the prevalence of westerly wind bursts in the western equatorial Pacific can weaken the zonal Pacific circulation (Tziperman and Yu 2007).
SST is a crucial and fundamental variable used to characterize ENSO events and their linkage with the regional climate anomalies. In the earlier stages, SST data were obtained from the ship observation (Wyrtki 1975), and later derived from thousands of buoys worldwide established by the Tropical Ocean-Global Atmosphere (TOGA) program (McPhaden et al. 2010). The continuous improvement of SST observation technologies allows people to have a deeper understanding of El Niño dynamics. Since the first Tiros-N/NOAA satellites, NOAA-6, was launched on 13 October 1978, satellite-derived data have finer spatial resolution and broader coverage compared to most in situ measurements (McPhaden et al. 2020). The satellite observations greatly improve the accuracy of SST, sea surface wind speed, precipitation, radiation and so on (Bonjean and Lagerloef 2002; Dohan 2017). Besides oceanic satellite observations, atmospheric satellite observations have also been used to examine the ENSO evolution. Yulaeva and Wallace (1994) displayed ENSO’s signature in global temperature and precipitation fields extracted from the Microwave Sounding Unit (MSU). Curtis and Adler (2000) constructed rainfall-based ENSO indices by using gridded satellite-derived precipitation datasets. Oman et al. (2013) discerned the lower tropospheric to lower stratospheric ozone response to ENSO based on observations from the Tropospheric Emission Spectrometer (TES) and the Microwave Limb Sounder (MLS). Recently, we extended a long-term dataset of AMSU-A brightness temperature (TB) from 28 February 2017 in the study by Xia and Zou (2020) to 30 December 2020, and constructed a corresponding long-term dataset of liquid water path (LWP) that is calculated from the AMSU-A TB observations at two window channels. AMSU-A succeeded MSU when the National Oceanic and Atmospheric Administration (NOAA)-15 was launched in 1998, which allows the measurement of atmospheric temperature from the surface to the lower stratosphere. In this study, we endeavor to explore a potential application of the TB and LWP observations within the context of ENSO evolution. Relationships between the AMSU-A observation and SST reanalysis are investigated during the El Niño events spanning from October 1998 to December 2020.
This paper is organized as follows. Section 2 describes satellite observations, SST and TB related Niño indices that are used in this study. Section 3 explores relationships between TBs at different AMSU-A channels and SST in Niño 3.4 region. Section 4 analyzes differences of LWP in El Niño and La Niña years. Section 5 compares two El Niño cases with and without a strong westerly wind burst. Summary and conclusions are presented in Section 6.
2.1 AMSU-A
Satellite observations used in this study are derived from microwave radiometer AMSU-A onboard polar-orbiting operational environmental satellites NOAA-15/18/19 and MetOp-A/B. The AMSU-A data period is available from October 1998 to December 2020. Data from different satellites can be merged together after an inter-calibration to eliminate biases among five AMSU-A instruments (Xia and Zou 2020). Since AMSU-A is a cross-scanning radiometer, a limb correction is applied to eliminate scan-dependent biases due to limb effect (Tian et al. 2018). A global AMSU-A TB dataset 0.5°x0.5° resolution is finally established by averaging daily observations at descending and ascending nodes (Qin and Zou 2023). The 22 years daily AMSU-A TB climate dataset is used for this study to examine the El Niño evolution.
Figure 1a shows the monthly mean local equator-crossing times of five polar-orbiting satellites, in which the data time period from each satellite used for the climate dataset is indicated. AMSU-A has 15 channels, among which channels 1–2 and 15 are window channels with their peak weighting functions (WFs) located at the earth surface and the atmospheric contribution is relatively small. These channels are sensitive to surface emissivity, surface skin temperature and water vapor near the surface. Channels 3–14 are temperature sounding channels with their central frequencies located at oxygen absorption band and with peak WFs located around 950, 850, 700, 400, 250, 200, 100, 50, 25, 10, 5, and 2 hPa, respectively. They can measure atmospheric temperature from the surface to lower stratosphere. The specific channels used in this study are three window channels and six tropospheric temperature-sounding channels 3–8. The WF profiles for these nine AMSU-A channels are shown in Fig. 1b. The solid curves represent the WF broadness of each channel amounting 3/4 of the total WF area (Xia and Zou 2021).
2.2 OISST
The NOAA International Comprehensive Ocean–Atmosphere Dataset (ICOADS), is a collection of surface marine observations, offering data for constructing gridded analyses of SST, estimates of air–sea interaction, and other meteorological variables. The data have been assimilated into nearly all major atmospheric, oceanic and coupled reanalysis (Freeman et al. 2017). The Optimum Interpolation Sea Surface Temperature (OISST) is the same as ICOADS except for adding sea ice data and satellite data (AVHRR and AMSR observation data). The incorporation of satellite observations greatly enhances spatial and temporal resolutions, as well as spatial coverage (Reynolds et al. 2007). In this study, we use the data from OISST version 2 (i.e., OISSTv2) in the period of 1998–2020, with a spatial resolution of 0.25o\(\times\)0.25o.
2.3 ONI and TNI indices
The ONI is calculated from the three-month running average of anomalous SST in the Niño3.4 region (Trenberth 1997):
$$ON{I_m}=\sum\limits_{{i=m - 1}}^{{m+1}} {\overline {{SST{a_i}}} /3}$$
1
where m represents the month, and SSTa represents monthly mean SST anomalies in the Niño 3.4 region. Following Trenberth (1997), an event is identified as an El Niño (La Niña) event when the ONI is above 0.5°C (below − 0.5°C) for five consecutive months. Similarly, we define a TB Niño Index (TNI) for each AMSU-A channel as:
$$TNI_{m}^{j}=\sum\limits_{{i=m - 1}}^{{m+1}} {\overline {{TBa_{i}^{j}}} /3}$$
2
where m represents the month, and 
represents monthly mean TB anomalies of AMSU-A channel j in the ith month over the Niño 3.4 region.
3.1 Index analysis
Utilizing the definition introduced in Section 2, we identify seven El Niño events (E1-E7) and five La Niña events (L1-L5), as outlined in Table 1. They comprise 66 El Niño months and 60 La Niña months. Figures 2a and 2b shows the composite maps of SST averaged from all El Niño and La Niña months, respectively. During El Niño months, an obvious weakening of the Pacific cold tongue is evident (Fig. 2a), while the La Niña months witness a further extension to the west (Fig. 2b). We also show the composite maps of AMSU-A channel-1 TB observations in Figs. 2c and 2d. The channel-1 refers to the surface in the troposphere. The spatial distributions of TB maps are basically similar to those of SST despite that the TB tends to be more concentrated on the equatorial region and the area of high TB values is generally more westward.
| E1 | June 2002 - February 2003 | |||||||||||||||||
| 0.52 | 0.66 | 0.75 | 0.89 | 1.11 | 1.29 | 1.29 | 1.11 | 0.82 | ||||||||||
| E2 | Aug 2004 to Feb 2005 | |||||||||||||||||
| 0.62 | 0.67 | 0.68 | 0.70 | 0.74 | 0.64 | 0.51 | ||||||||||||
| E3 | September 2006 - January 2007 | |||||||||||||||||
| 0.53 | 0.73 | 0.96 | 1.04 | 0.80 | ||||||||||||||
| E4 | Jul 2009 - Apr 2010 | |||||||||||||||||
| 0.58 | 0.68 | 0.80 | 1.44 | 1.71 | 1.64 | 1.44 | 1.09 | 0.63 | ||||||||||
| E5 | October 2014 - May 2016 | |||||||||||||||||
| 0.55 | 0.69 | 0.77 | 0.78 | 0.71 | 0.79 | 0.81 | 0.99 | 1.12 | 1.38 | |||||||||
| 1.63 | 1.95 | 2.33 | 2.57 | 2.71 | 2.56 | 2.28 | 1.83 | 1.03 | 0.50 | |||||||||
| E6 | September 2018 - June 2019 | |||||||||||||||||
| 0.52 | 0.75 | 0.97 | 0.96 | 0.92 | 0.94 | 0.92 | 0.92 | 0.76 | 0.64 | |||||||||
| E7 | November 2019 - March 2020 | |||||||||||||||||
| 0.55 | 0.61 | 0.58 | 0.59 | 0.52 | ||||||||||||||
| L1 | November 1998 - January 2001 | |||||||||||||||||
| -1.27 | -1.47 | -1.38 | -1.10 | -0.92 | -0.88 | -0.96 | -0.98 | -1.11 | ||||||||||
| -1.08 | -1.15 | -1.26 | -1.44 | -1.58 | -1.53 | -1.36 | -1.10 | -0.93 | ||||||||||
| -0.81 | -0.76 | -0.62 | -0.56 | -0.56 | -0.67 | -0.82 | -0.79 | -0.66 | ||||||||||
| L2 | August 2007 - June 2008 | |||||||||||||||||
| -0.63 | -0.90 | -1.15 | -1.22 | -1.30 | -1.30 | -1.22 | -1.00 | -0.74 | ||||||||||
| -0.67 | -0.50 | |||||||||||||||||
| L3 | June 2010 - April 2011 | |||||||||||||||||
| -0.51 | -0.90 | -1.15 | -1.32 | -1.36 | -1.29 | -1.25 | -1.15 | -1.03 | ||||||||||
| -0.82 | -0.57 | |||||||||||||||||
| L4 | September 2011 - January 2012 | |||||||||||||||||
| -0.59 | -0.75 | -0.83 | -0.77 | -0.61 | ||||||||||||||
| L5 | October 2017 - March 2018 | |||||||||||||||||
| -0.51 | -0.69 | -0.75 | -0.65 | -0.59 | -0.55 | |||||||||||||
To quantify the relationship between TB and SST, we compare ENSO related indices of TNI and ONI according to the method defined in section 2.3. Figure 3 shows the temporal evolutions of ONI (ONIm) and TNI indices for three window channels of AMSU-A channels 1–2 and 15 
The two types of indices based on SST and TB exhibit a high consistency on the interannual timescale, especially during strong El Niño and La Niña events (Fig. 3a). Interestingly, the TNI reaches its maximum a few months earlier than the ONI for most strong ENSO events. Considering that the AMSU-A window channels TB are sensitive to SST and the atmosphere in the lower troposphere, the change of TNI being ahead of ONI indicates that the near-surface atmosphere has changed in advance of ENSO-associated SST anomalies. For clarity, we zoomed in the time period of seven El Niño (Fig. 3b) and five La Niña events (Fig. 3c). It shows more clearly that when El Niño events E1, E3, E5, L1, L2 and L5 occurred, the change of AMSU-A channel-1 TNI reaches its peak about 2 months ahead of the ONI change. To explore whether the similar relationship exists between ONI and TNI of AMSU-A upper channels, we took AMSU-A channel 7 (~ 250hPa) as an example. Different from the lower level, the change of TNI of AMSU-A channel 7 (i.e., 
) generally lags behind the change of ONI by 1–3 months, as shown in Fig. 4. We also show the TNI changes of AMSU-A channels 3–8 during seven El Niño and five La Niña events, respectively (Figs. 5–6). Unlike window channels starting from AMSU-A channel 3, the changes of other tropospheric channels TNI almost all lag behind that of ONI. These results confirm that the response of upper-level atmospheric temperature lags behind SST in Niño 3.4 region.
One may ask why the TNI of an AMSU-A upper channel lags behind the change of ONI. The upper-level atmospheric response to ENSO is primarily driven by the SST anomalies that trigger convection and the subsequent release of latent heat. So, the TNI reaches its highest value a few months after the peak of ENSO-associated SST anomaly. In comparison, the relation of the window channel TNI and ONI exhibits a certain degree of uncertainty. For the window channel TNI, we noticed that the TNI is not ahead of the ONI for the E4, E6 and E7 events (see Fig. 4b). In particular, the TNI of AMSU-A channel 1 slightly lags behind ONI in E6 event. In addition, the TNI in the E2 El Niño event does not even have an obvious peak. One causation for those phenomena might be related to the westerly wind bursts that could occur before an El Niño event, which will be discussed in section 5.
3.2 Composite analysis
Based on the above analyses, TB of AMSU-A window channels frequently changes ahead of SST. Here, we selected three typical El Niño years (2002, 2006 and 2015) and three typical La Niña years (1999, 2007 and 2017) for a composite analysis of TB and SST. The composite diagram in Fig. 7 shows the superposition of AMSU-A channel-1 TB (shaded) and SST (isoline) during El Nino events (left column) and La Nina events (right column), at stages of ENSO maturation (November to January, Fig. 7a-b), 2 months before maturity (September to November, Fig. 7c-d), 4 months before maturity (July to September, Fig. 7e-f), and 6 months before maturity (May to July, Fig. 7g-h). The region of Niño 3.4 is represented by the dotted box. As depicted in the figure, noticeable alterations are observed in TB and SST in the eastern equatorial Pacific since spring. From September to November (c-d), the TB change is evident over the ocean during the positive (negative) phase of ENSO, showing an earlier “maturity period” for the TB evolution. We also find that the cold tongue of TB during La Niña events is largely located at the center of the Niño 3.4 region, consistent with the position of SST’s cold tongue. In the case of El Niño events, the cold tongue’s position of TB is approximately 5o southward compared to that of SST. This explains why TB curves for some El Niño events fluctuate before reaching its peak in Fig. 3. The cause of this phenomenon warrants further investigation.
To examine the similarity in the distribution of TB and SST, we next examine the correlation map of boreal autumn (September-November) SST anomalies upon the ensuing winter (December-February) ONI. It can be seen that ONI is positively correlated with SST anomalies in the equatorial central and eastern Pacific and the far western Indian Oceans, and negatively correlated with SST anomalies in the northwestern and southwestern Pacific oceans (Fig. 8a). The spatial pattern resembles the SST anomaly pattern during the ENSO mature phase. The correlation map of AMSU-A channel-1 TB anomalies in boreal autumn upon the ensuing winter TNI (Fig. 8b) is generally similar to SST correlation map in winter. As discussed above, the distribution of TB reaches the mature ENSO phase about 2 months earlier than that of SST, but the cold tongue of TB is approximately 5o southward of Niño 3.4 region during El Niño events. The correlation of autumn SST anomalies upon ensuing winter ONI is similar to that derived by AMSU-A window channels.
The variable LWP represents the total liquid water contained in the vertical atmospheric column. The clear-sky condition is defined as LWP ≤ 0.01 kg m− 2. Measurements of LWP can help us to know the occurrence and development of clouds within weather systems. At present, satellite retrieval is the most effective means to obtain LWP over global oceans. One previous study found out that the strength of the precipitation response increases monotonically with the local background SST, with a very sharp growth at high SST (He et al. 2018). To explore the LWP variations during El Niño, the monthly-mean global ocean LWP dataset at a spatial resolution of 0.25o×0.25o from October 1998 to December 2020 is built in this study. The LWP is retrieved from AMSU-A TB observations at channels 1–2 (Grody et al. 2001).
Figure 9 shows the composite maps of LWP averaged over all El Niño (Figure 9a) and La Niña (Figure 9b) months from 1998 to 2020, as well as their differences. The distribution of multi-year average LWP over global ocean is similar to climate precipitation (Adler et al. 2003). It can be seen that during the La Niña events, the cold tongue of SST in equatorial central Pacific extends westward, resulting in a significant decrease in precipitation in the equatorial western Pacific. Their difference shows remarkable positive LWP anomalies over the central Pacific, consistent with the ENSO-related convection anomaly pattern (Fig. 9c). To further explore the relationship between LWP and ONI, we show the climatology of annual cycle of LWP in Niño 3.4 region over normal, El Niño and La Niña months in Figure 10a. The LWP generally reaches the maximum in spring and minimum in autumn. The LWP during El Niño months is generally much higher than that during normal months, suggesting that the rising SST can enhance the evaporation and convection, and thus increase the liquid water content as clouds form in the atmosphere. In contrast, the LWP during La Niña months is almost same as that during normal months. We also examine the annual cycle of LWP during ENSO and normal years for comparison. Similar annual cycle can be derived by ERA5 reanalysis during ENSO conditions but with much less amplitude, especially during El Niño years (Fig. 11a). We further show the temporal variations of ONI and LWP anomalies in the Niño 3.4 region for AMSU-A retrievals and ERA5 reanalysis. These two data exhibit a high correlation between the ONI and LWP. In most El Niño events, high SST corresponds to high LWP and the variation of LWP is consistent with the variation of ONI especially during strong El Niño events. Therefore, the variation of LWP verifies that SST rising causes the content of liquid water in the atmosphere to increase from the brightness temperature point of view. According to the previous discussions, it is found that TB, LWP and SST are closely related.
The El Nino development is usually accompanied with west wind bursts in addition to the subsurface temperature anomalies. The westerly wind burst event can stimulate a series of Kelvin waves, making warm water move eastward and producing a warm sea temperature in the central to eastern Pacific Ocean. Therefore, the westerly wind bursts are often regarded as one potential precursor of El Nino. For half of the El Niño and La Niña cases, the AMSU-A window channel TB defined TNI reached its peak value before the SST defined ONI. A possible reason of this phenomenon may be related to the westerly wind burst that can occur before the appearance of El Niño-associated SST anomalies. Researches have shown that before an occurrence of El Niño, the burst and prevalence of westerly wind in the western equatorial Pacific may cause the weakening of Pacific circulation. Then, the water upwelling in the eastern equatorial Pacific will weaken and finally leads to abnormal rise of SST, resulting in El Niño condition (Seiki and Takayabu 2007). The enhancement of westerly wind will lead to the development and movement of convective clouds in the equatorial Pacific. The latent heat releases in cloud formation processes may be one of the reasons for an increase of AMSU-A observed TBs.
In the following, two El Niño events, E2 and E5, are selected for a comparison study. From Fig. 3 we know that there is no significant change in TNI in the E2 event, while the variation of TNI in the E5 event is significantly large. We will see that El Niño events E2 in 2004 and E5 in 2015 show weak and strong westerly wind burst, respectively. The temporal evolutions of latitudinal mean of 10-m zonal wind component anomaly in 2004 and 2015 within 5oS-5oN of the equatorial Pacific are calculated based on the ERA5 reanalysis data (Fig. 12a, b). The westerly wind burst is much stronger in 2015 than that in 2004. There is a strong positive anomaly of westerly wind in the central equatorial Pacific Ocean, especially in the second half of the year 2015 (Fig. 12b). The enhanced westerly wind will not only push the water of the western Pacific warm pool eastward, causing the SST rising in the equatorial middle east Pacific, but also favor the movement and development of convective clouds. In the development process of convective exuberance, an accompanied latent heat release will lead to an increase of AMSU-A TBs. Figures 12c and 12d present the results derived with TB observations of AMSU-A channel 1. Similar to zonal wind component, the TB anomaly in 2015 is significantly higher than that in 2004. In addition, it is noted that compared with U-wind, the high TB anomaly generally tends to move eastward. It is possible because that the westerly wind pushes the convective clouds to move eastward and develop vigorously. The latent heat release generated by these convective processes contribute to an increase of AMSU-A TB observations. The stronger westerly wind is, the higher TB rises. As shown in Fig. 3, the variation of TNI is weak in 2004 (E2) and strong in 2015 (E5). The influence of westerly events on LWP anomaly is presented in Fig. 13. Both AMSU-A retrievals (a-b) and ERA5 reanalysis (c-d) show that the higher liquid water content in clouds is consistent with the vigorous development of convection in the equatorial middle east Pacific, especially in 2015. Therefore, the impact of westerly wind burst on the movement and development of convective clouds may be the reason for an increase of AMSU-A TB observations. Here we only take these two typical cases to illustrate a possible relationship along TB, SST and LWP. More cases are needed to draw more general conclusions.
A relationship between satellite microwave TB observations and SST under El Niño episodes is explored in this study, suggesting a potential value of AMSU-A TB observations for a direct application in El Niño research. Our results show that TNI for AMSU-A upper sounding channels experience a rapid increase 1–3 months after the peak of ONI during most El Niño and La Niña events. In contrast, for AMSU-A window channels, TNI experiences a rapid increase and reached its maximum about 2 months ahead of that of ONI. It is found that the correlation of autumn AMSU-A window channel TB anomalies upon ensuing winter TNI is similar to that of SST versus ONI, both of which have a horseshoe-shaped pattern in the tropical Pacific and a dipole-shaped pattern over the Indian Ocean. Based on the monthly-mean LWP retrieved from AMSU-A window channels 1 and 2, we found that the temporal variation of LWP anomaly in Niño 3.4 region is consistent with that of ONI, especially during the strong El Niño events. The LWP in the atmosphere increases monotonically with SST, with a rapid growth at high SST. The enhancement of westerly wind bursts could lead to the movement and development of convective clouds in the equatorial Pacific and increase AMSU-A window channel TB observations due to latent heat releases.
In the future, we will expand the research area of TNI to Niño 4 region (5oS-5oN, 160oE-150oW) to explore relationships among TB, SST and westerly wind. The leading (lagging) relationship between TNI and ONI is still not conclusive due to limited El Niño and La Niña cases during 1998–2020. The TNI threshold for determining El Niño (La Niña) events with TNI also need more cases to warrant their validity. Since AMSU-A channel 3 is similar to MSU channel 1, we may use MSU channels 1–3 and MSU-like AMSU-A channels 3, 5 and 7 to conduct a similar study during a much longer, 44 years’ time period from 1978 to 2022.
Acknowledgments: This research was supported by the National Nature Science Foundation of China (42088101).
Ethics Declarations: The manuscript is original and only submitted to Climate Dynamics for now. We adhered to discipline-specific rules for acquiring, selecting and processing data, and the results are presented clearly and honestly.
Data availability statement: The AMSU-A data were collected from the NOAA’s Comprehensive Large Array-data Stewardship System (CLASS) (https://www.avl.class.noaa.gov/saa/products/). The OISST data were collected from https://psl.noaa.gov/data/gridded/data.noaa.oisst.v2.highres.html.
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