1. Introduction
El Niño events are characterized by anomalous warm sea surface temperature (SST) in the central-eastern equatorial Pacific, which have severe impacts on global climate and human society (Barsugli et al. 1999; Wu et al. 2004; McPhaden et al. 2006; Cai et al. 2015; Timmermann et al. 2018; Wei et al. 2020). In recent decades, extensive studies have revealed that El Niño events differ in terms of temporal evolution (Lengaigne and Vecchi 2010; Xie et al. 2018), amplitude (Chen et al. 2016; Cai et al. 2017), and spatial pattern (Ashok et al. 2007; Kao and Yu 2009; Kug et al. 2009; Chen et al. 2015). These differences give El Niño its different “flavors” and lead to different climate impacts (Alexander et al. 2002; An et al. 2007; Kim et al. 2009; Yuan and Yang 2012). In particular, the different spatial patterns of El Niño, generally measured by the different zonal locations of the largest SST anomalies (SSTAs), can induce distinct climate anomalies worldwide through air–sea interaction processes and atmospheric teleconnections (Horel and Wallace 1981; Larkin and Harrison 2005; Taschetto and England 2009; Taschetto et al. 2016; Xu et al. 2019). Understanding the diversity of spatial pattern of El Niño and its formation mechanisms are crucial for a reliable prediction of El Niño, as well as the associated climate and socioeconomic impacts (Capotondi et al. 2015; Yang and Huang 2021).
One notable manifestation of the diversity of spatial pattern of El Niño is that most El Niño events present moderately warm SSTAs with the largest magnitude in the central Pacific, while a few extreme El Niños have extraordinarily warm SSTAs that are centered in the equatorial eastern Pacific close to the South American coast (Takahashi et al. 2011). Much attention has been paid to the differences in the formation mechanisms between extreme and moderate El Niños (Jin et al. 2003; Chen et al. 2015; Chen et al. 2016). For instance, oceanic nonlinear dynamic heating was revealed to be an essential role for developing extreme El Niños (Jin et al. 2003); oceanic vertical advection anomalies caused by thermocline deepening are believed to be the dominant contributor for extreme El Niños, but not for moderate ones (Kug et al. 2009; Chen et al. 2015); and zonal advection anomalies caused by anomalous zonal currents appear to be the most important factor contributing to the discrepant magnitudes of SSTAs in the eastern Pacific between extreme and nonextreme El Niños (Chen et al. 2016). However, these studies mainly concentrated on the role of dynamic ocean heat transport, with little attention on the discrepant effects of atmospheric adjustments on the development of SSTAs between extreme and moderate El Niños.
In general, atmospheric adjustments during the development of El Niño SSTAs are always treated as damping roles to balance the positive effects from dynamic ocean heat transport anomalies, as they produce negative surface heat flux anomalies (Jin et al. 2006; Zhang and McPhaden 2008; Chen et al. 2015; Chen et al. 2016; Lian et al. 2017). However, it has been revealed that the spatial patterns of surface heat flux anomalies do not always exhibit a straightforward reversed relationship with the pattern of SSTAs (Wang and McPhaden 2000; Pavlakis et al. 2008). For example, the surface latent heat flux anomalies near and to the west of the date line were revealed to play a positive role in the development of locally warm SSTAs owing to reduced surface wind speed (Wang and McPhaden 2000), and the largest negative shortwave radiation anomalies during El Niño events are usually found to be located to the west of the positive SSTA center as a result of more convective activities locally (Pavlakis et al. 2008; Pinker et al. 2017). These findings imply that atmospheric adjustments may not only act in damping roles, but could also impact the spatial pattern of El Niño SSTAs.
There are considerable differences in the atmospheric responses to warm SSTAs between extreme and moderate El Niños. For example, the intertropical convergence zone, whose climatological position is north of the equator, migrates toward the eastern equatorial Pacific and turns the normally dry cold tongue condition into heavy rainfall under an extreme El Niño, but maintains north of the equator under a moderate El Niño and keeps the rainfall anomalies in the eastern equatorial Pacific small (Cai et al. 2014, 2017; Hu and Fedorov 2018); also, the westerly anomalies induced by convective heating intrude into the eastern Pacific during an extreme El Niño, but are confined to the central-western Pacific during a moderate one (Lengaigne and Vecchi 2010; Xie et al. 2018; Peng et al. 2020). These different responses imply discrepant atmospheric adjustments between extreme and moderate El Niños, which may in turn lead to discrepant effects on the further development of SSTAs through coupled ocean–atmosphere interaction processes (Bjerknes 1969; Xie and Philander 1994). However, it is still unclear whether atmospheric adjustments play different roles in the developing phase of SSTAs between extreme and moderate El Niños. Moreover, whether atmospheric adjustments impact the formation of the spatial pattern of El Niño SSTAs, rather than merely acting in damping roles, also needs to be further explored.
In this study, we investigate the discrepant effects of atmospheric adjustments on the spatial pattern formations of SSTAs during the developing phase of extreme and moderate El Niños, as well as the underlying mechanisms. We find that surface net heat flux anomalies in extreme El Niños, generally displaying a “larger warming gets more damping” zonal paradigm, have little impact on the formation of the zonal pattern of SSTAs, while those in moderate El Niños can help shape the zonal pattern of SSTAs by producing more damping effects in the eastern than central equatorial Pacific, thus favoring larger SSTAs being located in the central equatorial Pacific.
The rest of the paper is organized as follows: section 2 describes the data and methods used in the study. Section 3 presents the main results, including the objective separation of extreme El Niños from other moderate ones, the discrepant effects of surface net heat flux anomalies during the developing phase between extreme and moderate El Niños, and the associated formation mechanisms. Conclusions and discussion are given in section 4.
2. Data and methods
a. Datasets
The monthly SST data are from the National Oceanic and Atmospheric Administration (NOAA) Optimum Interpolation SST, version 2, with a horizontal grid resolution of 1° × 1°, which is provided by the NOAA Earth Research Laboratory Physical Science Division (http://www.esrl.noaa.gov/psd/data). The monthly atmospheric data are from the fifth major global reanalysis developed by the European Centre for Medium-Range Weather Forecasts (ERA5; https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels-monthly-means?tab=form), with a horizontal resolution of 0.25° × 0.25°, including the surface latent heat flux, sensible heat flux, net shortwave radiation, net longwave radiation, precipitation, boundary layer height, surface zonal and meridional winds, surface wind speed, air temperature, and three-dimensional relative humidity. Besides, the monthly SST from ERA5 is chosen only for computing the regressions between SSTAs and relative humidity anomalies, and between SSTAs and boundary layer height anomalies. The monthly oceanic three-dimensional data are from the National Centers for Environmental Prediction (NCEP) Global Ocean Data Assimilation System (GODAS; https://www.esrl.noaa.gov/psd/data/gridded/data.godas.html), with a horizontal resolution of 1/3° longitude × 1° latitude. In addition, we also use surface net heat fluxes from GODAS and the NCEP–National Center for Atmospheric Research (NCAR) reanalysis (https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.html) to confirm the results derived from ERA5. All the datasets are chosen for the period 1982–2018 during which all variables are available. The monthly anomalies are obtained by removing the long-term trend as well as the climatological annual cycle of the chosen time period, and then a 3-month running mean is applied to reduce the intraseasonal variability.
b. Fuzzy clustering method
The members applied to the FCM here are a subset of the monthly SSTAs in the tropical Pacific (20°S–20°N, 150°E−90°W) during El Niño events. We first use a 40° × 10° window zonally sliding by 2.5° along the equator (5°S–5°N), starting from 150°E to 90°W, in order to obtain a set of regional mean SSTAs and the corresponding standard deviations (STDs). The month in which any regional-mean SSTA is greater than the corresponding positive STD and 0.5°C is then regarded as a warm record. When all the warm records are extracted, those segments with less than five successive months in the set of warm records are deleted. Moreover, as the peak time of El Niño tends to be phase locked in boreal winter (Tziperman et al. 1998), the warm segments that do not contain boreal wintertime (November–January) are also discarded. The remaining warm months are then used for our classification of different El Niño types. In addition, the type of a specific El Niño event is based on the type into which its DOM in boreal winter falls. Details regarding the application of the FCM technique in El Niño classification can also be found in Chen et al. (2015).
c. Ocean mixed layer heat budget analysis
d. Decomposition of the surface latent heat flux anomaly
3. Results
a. Classification of El Niños based on the FCM
The FCM is applied to classify El Niño events during 1982–2018 into two types. As shown in Fig. 1, the first warm pattern displays robust positive SSTAs in the central and eastern Pacific and has its largest warming in the eastern equatorial Pacific near the South American coast (Fig. 1a), which is a typical feature of extreme El Niños (Takahashi et al. 2011; Chen et al. 2015; Xie et al. 2018). Three historical El Niños, commonly known as the extreme El Niño events of 1982/83, 1997/98, and 2015/16 (Cai et al. 2017; Lian et al. 2017), fall into the first pattern classification (Fig. 1c, red curve). The second warm pattern exhibits moderately positive SSTAs centered in the central equatorial Pacific east of the date line around 170°W (Fig. 1b). Nine historical El Niños other than the three aforementioned extreme ones—in 1986/87, 1987/88, 1991/92, 1994/95, 2002/03, 2004/05, 2006/07, 2009/10, and 2014/15—are all classified as the second warm pattern (Fig. 1c, blue curve). Thus, the FCM naturally separates the extreme El Niños from other moderate El Niños when two clusters are set. Moreover, the classified result by the FCM indicates that the pattern differences between extreme and moderate El Niños appear to be the most robust among different El Niño types.
The two El Niño clusters identified by the FCM and the associated DOMs: (a) the extreme El Niño cluster, which involves three historical extreme El Niño events; (b) the moderate extreme El Niño cluster, which includes nine historical moderate El Niño events; and (c) the DOM for extreme El Niño (red curve), moderate El Niño (blue curve), and neither (black curve). Stippling in (a) and (b) indicates that the compositions are significant at the 95% confidence level based on the Student’s t test.
Citation: Journal of Climate 34, 13; 10.1175/JCLI-D-20-0757.1
b. Discrepant roles of for the development of SSTA patterns between extreme and moderate El Niños
Figure 2 presents the spatial patterns of SSTAs, SSTA tendencies, and
Spatial patterns of (a) SSTAs and (b)
Citation: Journal of Climate 34, 13; 10.1175/JCLI-D-20-0757.1
As in Figs. 2b and 2d, but for
Citation: Journal of Climate 34, 13; 10.1175/JCLI-D-20-0757.1
With regard to each individual El Niño event, it is shown that all the three extreme El Niños exhibit larger positive (negative) SSTAs (
Scatterplot of difference of SSTAs vs that of
Citation: Journal of Climate 34, 13; 10.1175/JCLI-D-20-0757.1
Figure 5 displays a Hovmöller diagram (averaged over 2.5°S–2.5°N) that compares the temporal evolutions of equatorial SSTAs as well as
Hovmöller diagram for equatorial (2.5°S–2.5°N) (a),(c) SSTAs and (b),(d) surface net heat flux anomalies during the developing year in (a),(b) extreme and (c),(d) moderate El Niños. Contours in (a) and (c) denote the tendency of SSTAs (units: °C month−1, with an interval of 0.05°C month−1; zero contour thickened and negative dashed), and in (b) and (d) denote the SSTAs (units: °C, with an interval of 0.25°C; zero contour thickened and negative dashed). Stippling indicates that the compositions of shaded values are significant at the 95% confidence level based on the Student’s t test.
Citation: Journal of Climate 34, 13; 10.1175/JCLI-D-20-0757.1
To quantify the discrepant effects of
The ocean mixed layer heat budget during the developing phase of (a) extreme and (b) moderate El Niños based on GODAS. The red, blue, and black bars denote the regional-mean values in the eastern equatorial Pacific (EEP; 2.5°S–2.5°N, 140°–90°W), the central equatorial Pacific (CEP; 2.5°S–2.5°N, 180°–140°W), and their differences (EEP minus CEP). The
Citation: Journal of Climate 34, 13; 10.1175/JCLI-D-20-0757.1
By contrast, the
The
c. Discrepant atmospheric adjustments involved in between extreme and moderate El Niños
Figure 7 displays the spatial patterns of
Spatial patterns of (a) surface net shortwave radiation anomalies, (b) surface latent heat flux anomalies, and (c) the sum of the two in extreme El Niños. The black contours in (a) and (c) are the spatial patterns of precipitation anomalies (units: °C, with an interval of 0.5 mm day−1; zero contour thickened and negative dashed) and surface net heat flux anomalies (units: W m−2, with an interval of 7.5 W m−2; zero contour thickened and negative dashed), respectively. (d)–(f) As in (a)–(c), but for moderate El Niños. Stippling indicates that the compositions of shaded values are significant at the 95% confidence level based on the Student’s t test.
Citation: Journal of Climate 34, 13; 10.1175/JCLI-D-20-0757.1
The factors contributing to
Spatial patterns of the (a) reconstructed surface latent heat flux anomalies based on Eq. (6) and (b)–(e) each factor involved in the surface latent heat flux anomalies in extreme El Niños based on Eqs. (7)–(10): (b) the Newtonian cooling effect, and the atmospheric forcing effect due to anomalies in (c) surface wind speed, (d) relative humidity, and (e) surface stability. Contours in (a)–(e) are the spatial patterns of the original surface latent heat flux anomalies (units: W m−2, with an interval of 7.5 W m−2; zero contour thickened and negative dashed), the SSTAs (units: °C, with an interval of 0.2°C; zero contour thickened and negative dashed), the surface wind speed anomalies (units: m s−1, with an interval of 0.15 m s−1; zero contour thickened and negative dashed), the relative humidity anomalies (with an interval of 7.5 × 10−3; zero contour thickened and negative dashed), and the surface stability anomalies (units: °C, with an interval of 0.15°C; zero contour thickened and negative dashed), respectively. (f)–(j) As in (a)–(e), but for moderate El Niños. Stippling indicates that the compositions of shaded values are significant at the 95% confidence level based on the Student’s t test.
Citation: Journal of Climate 34, 13; 10.1175/JCLI-D-20-0757.1
The discrepant effects of
Spatial patterns of the atmospheric forcing effect due to anomalies in (a) surface zonal wind speed and (b) meridional wind speed in extreme El Niños. Contours in (a) and (b) are the surface zonal wind anomalies and meridional wind anomalies (units: m s−1, with an interval of 0.4 m s−1; zero contour thickened and negative dashed), respectively. Vectors in (a) and (b) are the surface wind vector anomalies (units: m s−1). (c),(d) As in (a) and (b), but for moderate El Niños. Note that the interval of contours in (c) and (d) is 0.2 m s−1, which is different from that in (a) and (b). Stippling indicates that the compositions of shaded values are significant at the 95% confidence level based on the Student’s t test.
Citation: Journal of Climate 34, 13; 10.1175/JCLI-D-20-0757.1
The damping effects of
Spatial patterns of (a) relative humidity–SST feedback index (RSFI) and (b) boundary layer height–SST feedback index (BHFI). (c) Vertical distribution of equatorial (2.5°S–2.5°N) RSFI in the eastern Pacific. Contours in (c) denote the climatological relative humidity. Stippling indicates that the regressions are significant at the 95% confidence level based on the Student’s t test.
Citation: Journal of Climate 34, 13; 10.1175/JCLI-D-20-0757.1
Figure 11 quantifies the major flux anomalies during the developing phase of El Niño both in the eastern (2.5°S–2.5°N, 140°–90°W) and central (2.5°S–2.5°N, 180°–140°W) equatorial Pacific, as well as their differences. In extreme El Niños with the larger SSTAs in the eastern equatorial Pacific,
The major heat flux anomalies during the developing phase of (a) extreme and (b) moderate El Niños. The red, blue, and black bars denote the regional-mean values in the eastern equatorial Pacific (2.5°S–2.5°N, 140°–90°W), the central equatorial Pacific (2.5°S–2.5°N, 180°–140°W), and their differences (eastern Pacific minus central Pacific). The
Citation: Journal of Climate 34, 13; 10.1175/JCLI-D-20-0757.1
4. Conclusions and discussion
In this study, we reveal that the surface net heat flux anomalies (
The
In moderate El Niños, the negative relative humidity–SST feedback also appears to be the most dominant atmospheric adjustments for the damping effects of
The classification of El Niño diversity has been always a heated debate in climate research community (Kao and Yu 2009; Takahashi et al. 2011; Karnauskas 2013; Chen et al. 2015). In a pioneering application of the FCM to the classification of El Niño by Chen et al. (2015), three warm patterns are classified—the extreme El Niños, which are identical to the current first warm pattern; the warm-pool El Niños, which has weak positive SSTAs centered near the date line; and the canonical El Niños with moderate positive SSTAs along the central-eastern equatorial Pacific. In this study, however, we do not try to clarify different types of El Niño, but to explore different atmospheric adjustments specifically between extreme and other nonextreme El Niños. Therefore, the number of cluster set chosen here is two [i.e., M = 2 in (1)] to highlight the different warm patterns between extreme El Niños and other moderate ones. The main conclusions in this study do not change essentially between the extreme El Niños and the other two nonextreme El Niños if three types of El Niño are classified as in Chen et al. (2015).
The present study focuses on the discrepant effects of atmospheric adjustments on the formation of zonal SSTA patterns in different El Niño types, with a particular focus on contributions of atmospheric adjustments in the formation of SSTA patterns in moderate El Niños, while the effects of ocean heat transport anomalies have not been explored extensively. In fact, many studies have revealed that some specific ocean dynamical processes play key roles in the development of SSTAs in specific El Niño types (Kug et al. 2009; Chen et al. 2015; Lian et al. 2017). For instance, ocean thermocline feedback was revealed to play the dominant role in the development of extreme El Niños (Chen et al. 2015), while zonal advective feedback plays a crucial role during warm pool El Niños (which essentially can be classified into moderate El Niños in the current study) (Kug et al. 2009; Takahashi et al. 2011). Thus, the atmospheric adjustment processes, especially for the relative humidity–SST feedback and the WES feedback in the eastern equatorial Pacific, could be supplementary mechanisms in modulating the zonal pattern formation of SSTAs in moderate El Niños, and do not conflict with previous ocean origin mechanisms. Moreover, these atmospheric adjustments may play potential roles in predicting the SSTA pattern of El Niño during the peak phase. For example, if the SSTA-induced deep convections do not move to the eastern Pacific to trigger the conventional Bjerknes feedback during the developing phase of an El Niño (Karnauskas 2013; Lian et al. 2017), the positive SSTA center in the peak phase is likely to be closer to the central equatorial Pacific, as the damping effects from atmospheric adjustments will further suppress the growth of SSTAs in the eastern equatorial Pacific. They may also explain, to some extent, why there are only few cases that have the spatial patterns similar to extreme El Niño but with their magnitudes similar to moderate El Niño (McPhaden et al. 2011; Zhang et al. 2015), although more details need to be provided to verify such interpretation. We highlight that atmospheric adjustments should be considered during the development of moderate El Niños in order to obtain a comprehensive understanding of the formation of El Niño diversity.
Acknowledgments
This work was supported by the Scientific Research Fund of the Second Institute of Oceanography, Ministry of Natural Resources (Grant QNYC2001), the National Natural Science Foundation of China (Grants 41690121, 41690120, 41706024, 41621064, 41831175), the Indo-Pacific Ocean Variability and Air–Sea Interaction (IPOVAI; Grant GASI-01-WPAC-STspr), the Youth Innovation Promotion Association of the Chinese Academy of Sciences, and the Key Deployment Project of Centre for Ocean Mega-Research of Science, Chinese Academy of Sciences (Grant COMS2019Q03). We thank Prof. Jian Ma and Dr. Qun Liu for their helpful discussions.
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