Evaluating ENSO Spatiotemporal Complexity in Climate Model Hindcasts

doi:10.21203/rs.3.rs-1534335/v1

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Evaluating ENSO Spatiotemporal Complexity in Climate Model Hindcasts

https://doi.org/10.21203/rs.3.rs-1534335/v1

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Using hindcasts produced by a coupled climate model, this study evaluates whether the model can simulate the observed spatiotemporal complexity in El Niño − Southern Oscillation (ENSO) during 1982 − 2011: the eastern Pacific (EP), central Pacific-I (CP-I) and -II (CP-II) types of El Niño, and the atypical multi-year evolution events of El Niño occurred in 1986‒1988 (i.e., 1986/87/88 El Niño) and La Niña occurred in 1998‒2000 (i.e., 1998/99/00 La Niña). In regards to spatial complexity, it is uncovered that the CP-I type of El Niño is most easily to be hindcasted, CP-II is second, and the EP El Niño type is most difficult to be hindcasted as its amplitude is significantly underestimated. The model deficiency in hindcasting the EP El Niño is related to a warm bias of climatological sea surface temperatures (SSTs) in the tropical eastern Pacific, which is mainly caused by the strengths of the Pacific Walker circulation and South Pacific high that are notably weakened in the model simulation. As for the temporal complexity, the model successfully hindcasts the multi-year evolution of the 1998/99/00 La Niña but fails to hindcast that of the 1986/87/88 El Niño. This contrasting model performance between the ENSO’s multi-year evolution events is related to a cold bias in the model simulation of tropical central Pacific climatological SSTs, which makes La Niña event more easily activate the Pacific basinwide interactions than El Niño event and result in a multi-year evolution pattern.

ENSO spatiotemporal complexity

Three types of El Niño

Multi-year ENSO evolution

Pacific basinwide interactions

It has been increasingly recognized that the El Niño − Southern Oscillation (ENSO) has considerable complexity in its properties (e.g., Timmermann et al. 2018). The complexity appears in both the spatial structure and temporal evolution of El Niño and La Niña (Kao and Yu 2009; Yeh et al. 2014; Capotondi et al. 2015; Wang et al. 2017; Yu et al. 2017; Timmermann et al. 2018; An et al. 2020). In terms of the spatial complexity, some El Niños, such as the 1982/83 and 1997/98 events, exhibited their largest sea surface temperature (SST) anomalies in the tropical eastern Pacific, while some others, like the 2002/03 and 2009/10 events, had their largest SST anomalies primarily confined to the tropical central Pacific. Such structural diversity among El Niño events, first referred to as “ENSO diversity” (sometimes also called ENSO flavors), has separated El Niño into two different types: the conventional Eastern Pacific (EP) El Niño and the newly-emerging non-conventional Central Pacific (CP) El Niño (Yu and Kao 2007; Kao and Yu 2009), which is also known as the Dateline El Niño by Larkin and Harrison (2005), El Niño Modoki by Ashok et al. (2007), or Warm Pool El Niño by Kug et al. (2009). During its onset, development, and mature stages, the EP El Niño generally follows the conventional view of El Niño with its maximum SST anomalies mostly located in the tropical eastern Pacific, closer to the South American coast (Fig. 1a). The CP El Niño, however, has its maximum SST anomalies mostly in the tropical central Pacific, near the International Dateline (Fig. 1b, c). Considering that the CP El Niño has occurred more frequently than the EP El Niño since the 1990s (Lee and McPhaden, 2010; Yu et al. 2012) and is also known to have its own generation mechanism (Kao and Yu 2009; Kug et al. 2009; Yu et al. 2010; Yu and Kim 2011; Yeh et al. 2015) and global climate impacts (H. M. Kim et al. 2009; Weng et al. 2009; Yu et al. 2012; Yu and Zou 2013) that are profoundly distinguishable from the EP El Niño, our conventional views on ENSO dynamics, impacts, and predictions have been challenged and largely modified.

More recent studies have even suggested that the CP El Niño can be further divided into two subtypes based on their different generation mechanisms and distinct climate impacts (Wang and Wang 2013), which are referred to as CP-I and CP-II El Niños following Chen et al. (2019) in this study. The CP-I El Niño has its maximum SST anomalies that originate directly from the tropical central Pacific and develop locally with a structure symmetric to the equator (Fig. 1b). The CP-II El Niño, besides having its maximum SST anomalies in the tropical central Pacific, is characterized by its warm anomalies originating from the subtropical northeastern Pacific which develop southwestward with a structure asymmetric to the equator (Fig. 1c). Although the warming center of CP-II El Niño is within the tropical central Pacific, the location is further west compared to that of CP-I El Niño (cf. Figure 1b, c; see also Fig. 4 in Wang and Wang 2013). These structural differences between CP-I and CP-II El Niños, despite their subtlety, can exert a variety of distinct climate impacts, such as affecting rainfall amounts in southern China, typhoon tracks over the western North Pacific (Wang and Wang 2013), SST variations in the tropical Indian Ocean (Wang and Wang 2014) and the South China Sea (Tan et al. 2016), strengths and locations of the western Pacific subtropical high (Chen et al. 2019, 2021; Tan et al. 2020), and responses of the East Asian winter monsoon (J. W. Kim et al. 2021).

Meanwhile, researchers have also recognized that there is another aspect of complexity in the temporal evolution of ENSO (e.g., Yu and Fang 2018; X. Wu et al. 2019; Fang and Yu 2020a, b; Kim and Yu 2020, 2021). Some ENSO events, after reaching their mature stage, transition rapidly into a neutral or opposite ENSO phase during the second year, resulting in a single-year evolution pattern. Two examples of such single-year ENSO events are the 1997/98 El Niño and 2005/06 La Niña (Fig. 2a). Some other ENSO events, however, do not transition to the opposite phase after their mature stage. They, instead, linger in the same ENSO phase and decay slowly during the second year, producing a multi-year evolution pattern. Some of these events can re-intensify during the second year to develop into re-intensified multi-year ENSO events, such as the 1986/87/88 El Niño and 1998/99/00 La Niña (Fig. 2b). Recent studies have started to look into the physical mechanisms behind ENSO’s multi-year evolution pattern with more focus on multi-year La Niña events (e.g., Ohba and Ueda 2009; Okumura et al. 2011; McGregor et al. 2012; Choi et al. 2013; Dommenget et al. 2013; DiNezio and Deser 2014; An and Kim 2017, 2018) and less on multi-year El Niño events (Fang and Yu 2020a, b; Kim and Yu 2020, 2021). This is mainly because La Nine events last longer than El Niño events, on average, and more often evolve into a multi-year evolution event. However, it has been observed that multi-year El Niño events occurred quite frequently after the 21st century. While the two super El Niños in the 20th century (i.e., 1982/83 and 1997/98 events) were both single-year evolution events, the super El Niño in the 21st century (i.e., 2015/16 event) was a multi-year evolution event preceded by a weak El Niño in 2014/15 (cf. Kim and Yu 2020). Even more surprisingly, the next El Niño after the 2015/16 super event was another multi-year El Niño that occurred between 2018‒2020. Therefore, it is increasingly important to have a better understanding of ENSO’s temporal complexity as it may enable us to reduce the source of uncertainties in ENSO’s evolution pattern and ultimately help improve ENSO predictions which are now limited to lead times of up to approximately one year (Barnston et al. 2019; X. Wu et al. 2021). Enhancing the predictability of ENSO’s temporal complexity would also aid in preparation for ENSO-driven climate impacts that can vary highly depending upon whether an ENSO event becomes a single- or multi-year evolution event (e.g., Paek et al. 2017; Okumura et al. 2017; Iwakiri and Watanabe 2020).

Given the great importance of ENSO complexity in terms of spatial structure and temporal evolution (hereafter, collectively referred to as ENSO spatiotemporal complexity), we in this study examine the observational data and climate model hindcast simulations to extend our understanding of how and why climate models can or cannot properly simulate ENSO spatiotemporal complexity. To achieve this purpose, we specifically choose to analyze the (i) three types of El Niño (relating to ENSO spatial complexity) and (ii) two multi-year evolution events (relating to ENSO temporal complexity). The remainder of the paper is organized as follows. A detailed description of the observational/reanalysis datasets, climate model hindcast outputs, and analysis methodologies used in this study is provided in Section 2. In Section 3, by comparing the observational data and model hindcast outputs, we evaluate the model’s ability to simulate the observed ENSO spatiotemporal complexity into two subsections: Section 3.1 and 3.2. The model deficiencies that cause it to simulate ENSO spatiotemporal complexity differently from the observations are also identified with reasonable explanations. A summary and discussion of the main findings in this study is given in Section 4.

Two types of monthly mean observational/reanalysis datasets were utilized in this study. Monthly means of SST were obtained from the Hadley Centre Sea Ice and Sea Surface Temperature, version 1.1 (HadISSTv1.1; Rayner et al. 2003). The HadISSTv1.1 data has a horizontal resolution of 1.0° × 1.0° and covers the period from 1871 to the present. Monthly means of atmospheric variables including surface pressure (P_s) and zonal (U) and meridional (V) wind components at 850-hPa level were provided by the National Centers for Environmental Prediction/U. S. Department of Energy Global Reanalysis 2 (NCEP/DOE R2; Kanamitsu et al. 2002). The NCEP/DOE R2 data, which encompasses the period from 1979 to the present, has a horizontal resolution of 2.5° × 2.5° with 17 pressure levels from 1000 to 10 hPa. For the purpose of the model evaluation, we analyzed the data period from January 1982 to December 2011. All data were interpolated to the grid points of the model hindcasts (i.e., 1.0° × 1.0° global grids) to reduce uncertainties associated with different spatial resolutions. Anomalies were defined as departures from the climatological seasonal cycle throughout the analysis period.

In this study, we chose to analyze hindcast outputs produced by the Taiwan Central Weather Bureau Climate Forecast System 1-tiered model (TCWB1T model; cf. T. Y. Wu et al. 2019) due to it availability to us. The TCWB1T model is a coupled atmosphere-ocean model that consists of the Global Forecast System in Central Weather Bureau as its atmospheric model component (Paek et al. 2015) and the third version of Modular Ocean Model version as its oceanic model component (Pacanowski and Griffies 1998). The model is proven to have reasonable skills in sub-seasonal to seasonal forecasts (cf. T. Y. Wu et al. 2019) and has been broadly used for evaluating the observed ENSO properties and climate impacts (e.g., Paek et al. 2015; Chen et al. 2020; Kim et al. 2021). The model hindcasts have a 1.0° × 1.0° horizontal resolution and cover the period from January 1982 to December 2011. For each month, hindcasts were launched at days 1, 3, 6, 8, 11, 13, 16, 18, 21, and 23 to result in 10-member ensembles. The lead-0 to lead-8 month hindcasts were obtained from the monthly averages of these daily predictions (i.e., the 10-member ensembles). Thus, we have model hindcasts with lead time from 0 to 8 months from January 1982 to December 2011. Similar to the observational analysis, anomalies used were defined as departures from the climatological seasonal cycle in the model hindcasts.

To examine the spatial complexity of ENSO, we analyzed the three types of El Niño (i.e., EP, CP-I, and CP-II El Niños) using the event years provided in Chen et al. (2019). During the analysis period of 1982 − 2011, there were two EP El Niños (i.e., 1982/83 and 1997/98 events), three CP-I El Niños (i.e., 1987/88, 1990/91, and 2002/03 events), and four CP-II El Niños (i.e., 1991/92, 1992/93, 2004/05, and 2009/10 events). To examine the temporal complexity of ENSO, we specifically focused on the hindcasts of the two multi-year evolution events during 1982 − 2011: the multi-year El Niño occurred in 1986‒1988 and the multi-year La Niña occurred in 1998‒2000. The calendar months of the year when an ENSO event develops (i.e., 1st year) were denoted as January⁰, February⁰, …, and December⁰. The calendar months of the year following when an ENSO event decays (i.e., 2nd year) were denoted as January¹, February¹, …, and December¹, and so on for the next year (i.e., 3rd year) as January², February², …, and December². All seasons used in this study were for the Northern Hemisphere.

3.1 ENSO complexity in spatial structure: Three types of El Niño

Figure 3 shows the longitude-time evolutions of equatorial Pacific SST anomalies during the three types of El Niño in the observations and the TCWB1T model hindcasts at different lead times. In the observations, the EP El Niño (Fig. 3a) has warm SST anomalies originating off the South American coast during the spring of the first year, developing westward during the summer and fall, reaching its mature stage during the winter, and then retreating back toward the coast in the spring and summer of the second year. It is also noticeable that the largest EP El Niño SST anomalies of its lifecycle, from the developing to decaying years (i.e., 1st - and 2nd -years), are mostly confined to the tropical eastern Pacific. In contrast, from the developing to decaying years of the observed CP-I and CP-II El Niños (Figs. 3g, m), the largest SST anomalies are concentrated in the tropical central Pacific and very weak warm SST anomalies are in the tropical eastern Pacific.

The TCWB1T model hindcasts at lead-0 month (Fig. 3b, h, n) accurately reproduce the observed longitude-time evolution features of the three types of El Niño, as expected, but begin to produce discrepancies from the observations as the hindcast lead time increases. The EP El Niño hindcasts, with lead months increasing from lead-2 to lead-8 (Fig. 3c − f), show a strong tendency to underestimate the El Niño intensity. It is further noticed that the underestimation of the El Niño intensity is most severe in the eastern and far-eastern tropical Pacific regions, shortening the duration of EP El Niño in those regions. Therefore, the hindcasted EP El Niño exhibits a faster termination and phase transition to La Niña in the decaying year, with the model error intensifying as the hindcast lead time increases. This model deficiency is also clearly detected in Fig. 4 when we compare the temporal evolutions of the observed and hindcasted Niño indices, which include the Niño4, Niño3.4, Niño3, and Niño1 + 2 indices (cf. black rectangles in Fig. 1a). During the EP El Niño (Fig. 4a − d), it is evident from the figure that the hindcasted Niño indices largely underestimate the observed ones during the developing and decaying years (note. the hindcasted Niño indices shown are the mean index values averaged from the lead-0 to lead-8 month hindcasts). The underestimation from the EP El Niño hindcasts becomes more severe as the region quantifying Niño index moves from the tropical central Pacific to tropical far-eastern Pacific. For both the CP-I and CP-II El Niños, their longitude-time evolutions (Fig. 3i − l and 3o − r) show that the model hindcasts generally reproduce the major features of the two CP El Niño types from all lead times in terms of El Niño’s intensity and spatial structure. Despite this, some minor deficiencies exist. Firstly, the model hindcasts tend to slightly underestimate (overestimate) El Niño intensity during the CP-I (CP-II) El Niño, particularly in its development and mature stages. Secondly, the model hindcasts tend to simulate the decay of CP-I and CP-II El Niños slower than the observed, causing their phase transition to be delayed by 2–3 months. These minor model deficiencies for the CP-I and CP-II El Niño simulations are also well detected in the temporal evolutions of various Niño indices (Fig. 4e‒l; except for the Niño1 + 2 index whose values indicate the SST anomaly in the far-eastern equatorial Pacific).

The results above indicate that the TCWB1T model has a noticeable deficiency in the hindcasts of EP El Niño as it largely underestimates El Niño intensity during both the developing and decaying years. Meanwhile, in spite of some minor deficiencies, the model is able to realistically hindcast the observed evolution features of CP-I and CP-II El Niños with a lead time up to eight (8) months. To examine how the model error grows in the hindcasts, we compare in Fig. 5a‒c the hindcasted and observed Niño3.4 index values for all the three types of El Niño, from lead-0 to lead-8 month hindcasts. It is evident from the figure that, as the lead time increases, the hindcasted evolution of the index begins to deviate from the observed evolution for all three types of El Niño. However, when taking a closer look, the overall deviations are revealed to be the smallest for the CP-I El Niño, the second-smallest for the CP-II El Niño, and the largest for the EP El Niño. This result suggests that, for this particular climate model, the CP-I El Niño is the easiest El Niño type to be hindcasted, followed by the CP-II El Niño, while the EP El Niño is the most difficult type to be hindcasted. We then examine the growth of these hindcast errors by integrating the deviations between the hindcasted and observed Niño3.4 index values throughout the El Niño lifecycle (i.e., 1st -to-2nd years) for each lead time, which can be simply formulated as

$$\int \left|{\text{N}\text{i}\text{ñ}\text{o}3.4}_{\text{T}\text{C}\text{W}\text{B}1\text{T}}\left(\text{t}\right)-{\text{N}\text{i}\text{ñ}\text{o}3.4}_{\text{O}\text{B}\text{S}}\left(\text{t}\right)\right|\text{d}\text{t}$$

The bar-charts in Fig. 5d − f, which display the error growth in the hindcasts of the three types of El Niño from lead-0 to lead-8, further support the previous suggestion that the EP El Niño has the largest simulation errors while the CP-II El Niño has the second largest and the CP-I El Niño has the smallest. It is also found from Fig. 5d − f that the hindcast errors for the EP and CP-II El Niños grow continually exceeding 6°C as the lead month increases, with the former being relatively faster than the latter after the 3-month lead. However, the hindcast errors for the CP-I El Niño grow very slowly and reach a plateau without exceeding 6°C until the 8-month lead. Collectively, it can be concluded that the TCWB1T model has the best performance when hindcasting the CP-I El Niño, the second-best performance for the CP-II El Niño, and the worst performance for the EP El Niño.

Then, what is responsible for the model’s worst performance on the EP El Niño hindcasts? The annual cycle plots of tropical Pacific climatological SSTs in the observations (Fig. 6a) and the TCWB1T model hindcast mean (averaged from lead-0 to lead-8 month; Fig. 6b) give us a clue as to why the model largely underestimates the EP El Niño intensity. Compared to the observations (Fig. 6a), the model largely overestimates the climatological SSTs over the tropical eastern Pacific (i.e., east of 120°W) (Fig. 6b). This warm bias of the model is well revealed in the difference between the hindcasted and observed climatological SSTs (Fig. 6c). It is also evident from the annual cycles of the tropical eastern Pacific climatological SSTs (Fig. 6d) that the warm bias tends to be stronger during the second half of the calendar year (i.e., July to December) than the first half (i.e., January to June). This overall strong warm bias in the model indicates that the simulated mean thermocline in the tropical eastern Pacific is too deep. The model’s deeper mean thermocline in the tropical eastern Pacific can weaken the sensitivity of the overlying SSTs to wind variations due to the weakening of the so-called thermocline feedback in that region (An and Jin 2001). When the mean thermocline is deeper (as in the model hindcasts), the strength of the surface wind stress-forced ocean wave that induces thermocline variation should also become weaker since the downward transfer of surface wind stress momentum for the geostrophic adjustment is mostly consumed above the deeper thermocline (cf. An and Kim 2017)―a weakening of the thermocline feedback. It is well known that the SSTs in the tropical eastern Pacific are primarily influenced by the subsurface ocean dynamics related to the thermocline variation and its coupling with surface winds (cf. Philander et al. 1996; Yu and Mechoso 1999). As a consequence of the abovementioned processes, the model tends to largely underestimate El Niño intensity over the tropical eastern Pacific (Figs. 3 and 4) and has the worst simulation performance for EP El Niño among the three types of El Niño (Fig. 5).

Furthermore, we investigate to understand why the model has deficiency in simulating climatological SSTs over the tropical eastern Pacific. Climatologically, the tropical Pacific Ocean exhibits zonal contrast in SSTs between the western Pacific warm pool and the eastern Pacific cold tongue. A colder cold tongue SST can be established for at least two reasons. One is related to the strength of the Pacific Walker circulation, whose surface easterly winds can cause the cooling of the cold tongue SST by upwelling cold water from deeper layers. The other is related to the strength of the South Pacific high, a semi-permanent anticyclone located in the southeast Pacific near the west coast of Peru and northern Chile (see the letter ‘H’ in Fig. 7a), which can drive cross-equatorial surface winds over the tropical eastern Pacific to then upwell cold subsurface ocean water which cools down the overlying SSTs. Figure 7a, which displays the observed climatological atmosphere and ocean structures over the Pacific basin during fall (i.e., August to October, when the cold tongue SST climatologically marks its lowest value), clearly illustrates the abovementioned Pacific conditions, including the strengths of the Pacific Walker circulation (manifested by the easterly trade winds and the zonal SST contrast) and South Pacific high (manifested by the southeastern Pacific anticyclone and the northward component of surface winds). From the model hindcast mean (Fig. 7b) and its difference from the observations (Fig. 7c), it is evident that the simulated strengths of the Pacific Walker circulation and South Pacific high are weaker than the observations. As shown in Fig. 7c, the difference is characterized by surface westerlies along the equatorial Pacific (i.e., an indication of a weaker Pacific Walker circulation) and a cyclonic circulation over the southeastern Pacific (i.e., an indication of a weaker South Pacific high). These weaker-than-observed Pacific Walker circulation and South Pacific high in the model can both cause weaker cooling of the cold tongue SST, thus producing a strong warm bias over the tropical eastern Pacific.

Further attributing these model deficiencies to a particular cause is difficult due to the strong atmosphere‒ocean coupling nature of the tropical eastern Pacific (Mechoso et al. 1995). Small model deficiencies in the atmospheric or oceanic model component can be largely amplified by this coupling, thus easily affecting the coupled climate model performance. Yet previous studies have pointed out that climate models’ inability to properly simulate the marine stratus clouds over the southeast Pacific can influence the strength of the South Pacific high through radiative cooling (cf. Mechoso et al. 1995; Philander et al. 1996). A majority of contemporary climate models still have difficulty properly simulating marine stratus clouds over the southeastern Pacific as well as the resultant equatorial upwelling processes over the tropical Pacific (e.g., Gordon et al. 2000; Large and Danabasoglu 2006).

3.2 ENSO complexity in temporal evolution: Multi-year evolution events

To evaluate whether the TCWB1T model can or cannot hindcast the temporal complexity of ENSO, we choose to focus on analyzing the model hindcasts of the atypical multi-year evolution events. Those are the multi-year El Niño which occurred in 1986‒1988 (simply, the 1986/87/88 El Niño) and the multi-year La Niña which occurred in 1998‒2000 (simply, the 1988/99/00 La Niña) (cf. Figure 2b). Both are re-intensified events whose 2nd -year SST anomaly peak intensity is larger than their 1st -year peak intensity. We firstly compare in Fig. 8 the observed and hindcasted longitude-time evolutions of equatorial Pacific SST anomalies during the life cycle (from 1st to 3rd years) of these multi-year evolution events. For the 1986/87/88 El Niño in the observations (Fig. 8a), its warm SST anomalies emerge and develop mostly in the tropical central Pacific during the summer and fall of 1986 and then peak in the following winter to materialize the 1st -year of its multi-year evolution. While the 1st -year El Niño persists into the spring of 1987 to start the 2nd -year evolution, warm anomalies also emerge separately from the tropical eastern Pacific. The latter processes become dominant for the development of the 2nd -year evolution of this event, forming the largest warm anomalies in the tropical eastern Pacific. This re-intensified multi-year El Niño in 1986/87/88 can, therefore, be assumed to be a CP El Niño pattern during its 1st -year evolution and an EP El Niño pattern in its 2nd -year evolution. This sequence of SST evolution is similar to that observed during the 2014/15/16 multi-year El Niño (Paek et al. 2017; Kim and Yu 2021). For the model hindcasts, despite the slightly underestimated El Niño intensity, the lead-0 month hindcast (Fig. 8b) adequately reproduces the observed multi-year evolution of the 1986/87/88 El Niño by capturing its 1st - and 2nd -year El Niño peaks. However, with the lead months increasing from lead-2 to lead-8 (Fig. 8c − f), it is found that the model fails to develop the 2nd -year El Niño during 1987‒1988. The hindcasted event, instead, tends to become a single-year El Niño with its peak in the winter of 1986/87 before transitioning into a La Niña afterwards. Figure 9a, which displays the Niño3.4 index evolutions during 1986/87/88 El Niño in the observations and the individual model hindcasts, also supports this model’s failure in hindcasting the multi-year evolution of the 1986/87/88 El Niño.

Next, we extend our analysis to evaluate whether the model hindcasts have an ability to forecast the multi-year evolution of 1986/87/88 El Niño. To achieve this, we plot the same Niño3.4 index evolutions as in Fig. 9a but using the individual model hindcasts that are launched at different starting months from December⁰ to December¹ (note. the lead-0 month hindcast values were utilized as starting months). As depicted by thin curves with various colors and markers in Fig. 9b, the results clearly indicate that the model has no chance to forecast the 1986/87/88 El Niño’s multi-year evolution. Once they are launched at any starting month, the initial Niño3.4 index values, with the lead months increasing, have an immediate downward slope toward a neutral or negative ENSO phase, failing to capture the re-intensification of the 1986/87/88 El Niño. As shown in Fig. 10, the horizontal maps of seasonally-averaged SST and wind anomalies during the 1st -year winter, following spring, and summer for the 1986/87/88 El Niño allow us to find why the model hindcasts fail to capture the re-intensification of the 1986/87/88 El Niño. For the observations (Fig. 10a − c), it is easily noticed that the El Niño, after its 1st -year peak in winter, persists and re-intensifies during the following spring and summer to give rise to its 2nd -year peak. Meanwhile, during the 1st -year winter (Fig. 10a), we also notice that there is a meridional band of warm SST anomalies extending from the subtropical northeastern Pacific to the tropical central Pacific accompanied by southwesterly wind anomalies in between (see black parallelograms in Fig. 10). This meridional band of anomalous SST warming in the subtropical northeastern Pacific is also known as a positive Pacific Meridional Mode (PMM; Chiang and Vimont 2004). The positive PMM, which is often induced during El Niño as a result of El Niño’s teleconnection to the extratropics (Stuecker 2018; Fang and Yu 2020a), is able to maintain for several seasons and spread its warm anomalies into the central equatorial Pacific via a wind − evaporation − SST feedback mechanism (Xie and Philander 1994), enabling to develop another El Niño (Yu et al. 2010; Yu and Kim 2011; Amaya 2019; Kim and Yu 2020, 2021). As shown in Fig. 10a − c, the abovementioned tropical‒subtropical climate interactions within the Pacific basin (simply, the Pacific basinwide interactions) are indeed at work in the re-intensification of the 1986/87/88 El Niño. For the model hindcasts (Fig. 10d − f), differently from the observations, the positive PMM during the 1st -year winter, which is represented by the anomalous SST warming and southwesterly wind in the subtropical northeastern Pacific, suddenly disappears and is replaced by anomalous SST cooling and northeasterly wind during the following spring and summer. This indicates that the Pacific basinwide interactions are absent in the hindcasts; hence, the model hindcasts fail to capture the re-intensification of the 1986/87/88 El Niño.

When analyzing the model’s ability to hindcast the observed multi-year La Niña event that occurred from 1998 − 2000 (i.e., the 1998/99/00 La Niña; Fig. 8g), we find that the hindcasts generally reproduce the multi-year evolution of 1998/99/00 La Niña, not only for the lead-0 month hindcast (Fig. 8h), but also for hindcasts at lead-2, 4, 6, and 8 months (Fig. 8i − l). The Niño3.4 index evolutions shown in Fig. 9c, d confirm that all individual model hindcasts successfully simulate the 1998/99/00 La Niña’s multi-year, re-intensification evolution feature (Fig. 9c) regardless of the launching months (Fig. 9d). Why, then, is the model able to successfully simulate/forecast the 1998/99/00 La Niña evolution in stark contrast to the model’s failure to simulate the 1986/87/88 El Niño evolution? We find in Fig. 11, which uses the same horizontal maps as in Fig. 10 but for the 1998/99/00 La Niña, that the model reproduces the observed Pacific basinwide interactions in the La Niña hindcasts. During the 1st -year winter, following spring, and summer of the 1998/99/00 La Niña, the model properly simulates the observed negative PMM with anomalous SST cooling and northeasterly wind in the subtropical northeastern Pacific (Fig. 11a − c vs d − f; see black parallelograms in Fig. 11). This indicates that, contrary to the El Niño simulations, the TCWB1T model is able to capture the Pacific basinwide interactions activated by the 1st -year La Niña during 1998‒1999 to successfully simulate the re-intensification of the 2nd -year La Niña during 1999‒2000.

One question remains as to why the La Niña event can activate the Pacific basinwide interactions in the model to produce a multi-year evolution pattern but the El Niño event cannot. In this regard, we find in Fig. 12a, b that the simulated climatological SSTs in the tropical central Pacific are too cold compared to the observations during spring, which is the season when the PMM are most active (Chiang and Vimont 2004). The cold bias can provide more favorable conditions for La Niña to activate the Pacific basinwide interactions but not for El Niño. This is because, as the cold bias lowers climatological SSTs closer to the convective threshold temperature (~ 28°C, Sud et al. 1999; see magenta contours in Fig. 12a, b), the La Niña-related cold anomalies can easily bring down SSTs to below the threshold value over the tropical central Pacific and switch off the deep convection. The induced large anomalous cooling can then excite an anomalous anticyclone over the subtropical Pacific, including the PMM region, activating a negative phase of the Pacific basinwide interactions, whose cold anomalies can later intrude into the equatorial Pacific to start the 2nd -year La Niña in subsequent seasons. The bar-charts representing the climatological spring SSTs averaged over the tropical central Pacific (cf. black rectangles in Fig. 12a, b) in the observations and the model hindcast mean (Fig. 12c) further confirm that the model’s cold bias is statistically significant. Although the El Niño-related warm anomalies can also strengthen deep convection over the tropical central Pacific to produce an anomalous heating in that region, the heating is relatively small compared to the cooling induced by a La Niña event that completely turns off the climatological deep convection. Also, since most CP El Niño events have weak intensities (e.g., Kao and Yu 2009; Lee and McPhaden 2010), the anomalous heating they induce is weak and provides less favorable conditions for El Niño to activate the Pacific basinwide interactions. The physical mechanism explaining the La Niña-favored activation of the Pacific basinwide interactions has been well documented in Fang and Yu (2020a).

In order to evaluate how and why climate models can or cannot properly simulate ENSO complexity, we examined in this study the hindcasts of the ENSO’s spatial (Section 3.1) and temporal (Section 3.2) complexities observed during 1982‒2011 by the TCWB1T model. The main findings of this study are summarized as follows:

The CP-I El Niño is the easiest to be hindcasted in this particular climate model, followed by the CP-II El Niño, while the EP El Niño is the most difficult El Niño type to be hindcasted. The model significantly underestimated the observed EP El Niño intensity during both the developing and decaying years, while the model slightly underestimated (overestimate) the CP-I (CP-II) El Niño intensity and produced 2‒3 months slower decays of the El Niños. The significant underestimation for the EP El Niño was related to a model deficiency in which it produces a warm bias in the tropical eastern Pacific, thus weakening the thermocline feedback in that region. The strengths of the Pacific Walker circulation and South Pacific high that were largely weakened in the model climatology can be the cause for the model to produce the warm bias in the tropical eastern Pacific.
The model is able to hindcast multi-year La Niña evolution pattern but not multi-year El Niño evolution pattern. It failed to produce a 2nd -year El Niño after the 1st -year El Niño matures and decays. These contrasting results were mostly related to the absence of the PMM-related Pacific basinwide interactions during the El Niño hindcasts in the 1986/87/88 El Niño simulations, while the Pacific basinwide interactions indeed existed in the 1998/99/00 La Niña simulations. Additional analysis suggested that the model deficiency in producing too-cold tropical central Pacific climatological SSTs can be the cause of the selective existence of the Pacific basinwide interactions between the multi-year El Niño and La Niña simulations as it provides more (less) favorable conditions for La Niña (El Niño) to activate the Pacific basinwide interactions.
While the model deficiency in the EP El Niño hindcasts is related to a warm bias in the model SST climatology in the tropical eastern Pacific, the model deficiency in the multi-year El Niño hindcasts is related to a cold bias in the tropical central Pacific. The former warm bias weakens the atmosphere‒ocean coupling in the tropical eastern Pacific to affect the intensity of the hindcasted EP El Niño. The latter cold bias causes the model to enable the La Niña but not El Niño to activate basinwide interactions to produce multi-year evolution pattern.

Since this study only examined multi-year ENSO evolution events (i.e., 1986/87/88 El Niño and 1998/99/00 La Niña), there may also be a need to evaluate whether climate models can or cannot properly simulate single-year ENSO evolution events. To do so, we chose to further analyze two single-year ENSO events as the El Niño occurred from 1997 to 1998 (i.e., 1997/98 El Niño) and the La Niña occurred from 2005 and 2006 (i.e., 2005/06 La Niña) (cf. Figure 2a). The results revealed that the TCWB1T model (this study) successfully hindcasted the observed single-year evolutions of the 1997/98 El Niño and 2005/06 La Niña, including their phase transition in the second-year evolution (figures not shown). It may be concluded that the model in general has a better simulation performance for single-year ENSO evolution events than multi-year ones. This conclusion can also be supported by the fact that many contemporary climate models, despite progress in simulating basic ENSO features, are still struggling with the tendency to produce a sharp, biennial 2-yr ENSO oscillation rather than a broad spectral peak in the 3 − 7-yr band (e.g., Guilyardi et al. 2009; Bellenger et al. 2014).

Acknowledgements

This research was supported by a grant from Central Weather Bureau of Taiwan under Grant IISI-202924. J.-W. Kim and J.-Y. Yu were also supported by NSF Climate and Large‐Scale Dynamics Program under grants AGS-1833075 and 2109539.

Data availability

Authors appreciate all data providers, which can be found in the following addresses: HadISSTv1.1 at https://www.metoffice.gov.uk/hadobs/ and NCEP/DOE R2 at https://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis2.html

Declarations

Conflict of interest: The authors declare that they have no conflict of interests

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Evaluating ENSO Spatiotemporal Complexity in Climate Model Hindcasts

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Abstract

Figures

1 Introduction

2 Data And Methods

3 Results

3.1 ENSO complexity in spatial structure: Three types of El Niño

3.2 ENSO complexity in temporal evolution: Multi-year evolution events

4 Summary And Discussion

Declarations

References

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