The year 2015 was special for climate scientists, particularly for the El Niño Southern Oscillation (ENSO) research community, as a major El Niño finally materialized after a long pause since the ...1997/1998 extreme El Niño. It was scientifically exciting since, due to the short observational record, our knowledge of an extreme El Niño has been based only on the 1982/1983 and 1997/1998 events. The 2015/2016 El Niño was marked by many environmental disasters that are consistent with what is expected for an extreme El Niño. Considering the dramatic impacts of extreme El Niño, and the risk of a potential increase in frequency of ENSO extremes under greenhouse warming, it is timely to evaluate how the recent event fits into our understanding of ENSO extremes. Here we provide a review of ENSO, its nature and dynamics, and through analysis of various observed key variables, we outline the processes that characterize its extremes. The 2015/2016 El Niño brings a useful perspective into the state of understanding of these events and highlights areas for future research. While the 2015/2016 El Niño is characteristically distinct from the 1982/1983 and 1997/1998 events, it still can be considered as the first extreme El Niño of the 21st century. Its extremity can be attributed in part to unusually warm condition in 2014 and to long‐term background warming. In effect, this study provides a list of physically meaningful indices that are straightforward to compute for identifying and tracking extreme ENSO events in observations and climate models.
Plain Language Summary
The El Niño Southern Oscillation (ENSO) continues to boast its prominence as Earth's strongest source of year‐to‐year climate variability with the appearance of a remarkable El Niño event in 2015–2016. The 2015/2016 El Niño was indeed a strong event with dramatic impact on a global scale. However, it exhibited distinct characteristics from those of past extreme El Niños in modern instrumental record. This challenges our previous understanding of an extreme El Niño which is important for ENSO prediction, monitoring, and future projections. The 2015/2016 El Niño has diversified the small sample of ENSO events in our short instrumental record. It has facilitated important discussions on our progress in understanding the nature of ENSO and its extremes, how they respond to greenhouse warming, and what the climate science community should do next in their quest to fully grasp the complexity of ENSO behavior. These are covered in this review paper which establishes the 2015/2016 El Niño as the first extreme El Niño of the 21st century.
Key Points
The 2015/2016 El Niño is the first extreme El Niño of the 21st century
The 2015/2016 El Niño contributes to a better understanding of ENSO extremes
Multiple simple indices can be used to monitor and identify ENSO extremes
The El Niño-Southern Oscillation (ENSO), which originates in the Pacific, is the strongest and most well-known mode of tropical climate variability. Its reach is global, and it can force climate ...variations of the tropical Atlantic and Indian Oceans by perturbing the global atmospheric circulation. Less appreciated is how the tropical Atlantic and Indian Oceans affect the Pacific. Especially noteworthy is the multidecadal Atlantic warming that began in the late 1990s, because recent research suggests that it has influenced Indo-Pacific climate, the character of the ENSO cycle, and the hiatus in global surface warming. Discovery of these pantropical interactions provides a pathway forward for improving predictions of climate variability in the current climate and for refining projections of future climate under different anthropogenic forcing scenarios.
During austral spring of 2019, an extreme positive Indian Ocean Dipole (pIOD) event occurred, with cold sea surface temperature (SST) anomalies over the eastern equatorial Indian Ocean (EEIO) and ...warming in the west. Although the growth of the EEIO cold anomalies involves forcing by equatorial nonlinear advection, unique to the 2019 pIOD is an air‐sea heat flux that was a forcing to the EEIO cold anomalies, rather than a damping as in previous extreme events. This unique thermodynamic forcing is due to a large latent cooling, which is supported by an unusually strong wind speed contributed by a large southerly anomaly as part of a long‐term trend. The wind trend is underpinned by a mean state SST change featuring slower warming off Sumatra‐Java. Given that a similar SST trend pattern is projected under greenhouse warming, the likelihood of such thermodynamical forcing operating more frequently in the future needs to be considered.
Plain Language Summary
The occurrence of 2019 extreme pIOD event features the strongest easterly wind anomalies and southerly wind anomalies on record, leading to the strongest wind speed that facilitates the latent cooling to overcome the increased radiative warming over the eastern equatorial Indian Ocean, leading to the unique thermodynamical forcing. This is the first time seen in such extreme pIOD event and is part of a long‐term increasing trend, supported by background changes in SST. As most climate models project a similar SST pattern over the Indian Ocean, it suggests that this unique thermodynamical forcing may operate more frequently under global warming.
Key Points
The 2019 positive Indian Ocean Dipole is a rare extreme event since 1900, second only to the strongest in 1997
Unique to the 2019 event is an air‐sea heat flux forcing the growth of cold anomalies in the eastern Indian Ocean, rather than damping
The unique feature is due to a windspeed‐induced increase in evaporation, supported by a warming pattern as projected for a warming climate
Abstract
Accurately representing the Indian Ocean Dipole (IOD) is crucial for reliable climate predictions and future projections. However, El Niño-Southern Oscillation (ENSO) and IOD interact, ...making it necessary to evaluate ENSO and IOD simultaneously. Using the historical simulation from 32 fifth phase of Coupled Model Intercomparison Project (CMIP5) models and 34 CMIP6 models, here we find that there are some modest changes in the basic characteristics of the IOD and ENSO from CMIP5 to CMIP6. Firstly, there is a slight shift in the seasonality of IOD toward an earlier peak in September in CMIP6, from November in CMIP5. Secondly, inter-model spread in the frequency of ENSO and the IOD has reduced in CMIP6 relative to CMIP5. ENSO asymmetry is still underestimated in CMIP6, based on the skewness of the Niño3 index, while the IOD skewness has degraded from CMIP5. Finally, mean state SST biases impact on the strength of the IOD; the Pacific cold tongue mean state is important in CMIP5, but in CMIP6 the Pacific warm pool mean state is more important.
The El Niño-Southern Oscillation (ENSO) is the dominant and most consequential climate variation on Earth, and is characterized by warming of equatorial Pacific sea surface temperatures (SSTs) during ...the El Niño phase and cooling during the La Niña phase. ENSO events tend to have a centre-corresponding to the location of the maximum SST anomaly-in either the central equatorial Pacific (5° S-5° N, 160° E-150° W) or the eastern equatorial Pacific (5° S-5° N, 150°-90° W); these two distinct types of ENSO event are referred to as the CP-ENSO and EP-ENSO regimes, respectively. How the ENSO may change under future greenhouse warming is unknown, owing to a lack of inter-model agreement over the response of SSTs in the eastern equatorial Pacific to such warming. Here we find a robust increase in future EP-ENSO SST variability among CMIP5 climate models that simulate the two distinct ENSO regimes. We show that the EP-ENSO SST anomaly pattern and its centre differ greatly from one model to another, and therefore cannot be well represented by a single SST 'index' at the observed centre. However, although the locations of the anomaly centres differ in each model, we find a robust increase in SST variability at each anomaly centre across the majority of models considered. This increase in variability is largely due to greenhouse-warming-induced intensification of upper-ocean stratification in the equatorial Pacific, which enhances ocean-atmosphere coupling. An increase in SST variance implies an increase in the number of 'strong' EP-El Niño events (corresponding to large SST anomalies) and associated extreme weather events.
The background of this research is the low learning motivation of students of SMK Negeri 1 Ngawen. This is indicated by the lack of ability to ask questions, thoroughness in doing questions, still ...cheating, the desire to find learning resources and so on. The purpose of this study was to determine the use of the scramble learning model to increase motivation to learn physics in students of SMK Negeri 1 Ngawen for the 2019/2020 academic year. This type of research is classroom action research. This research was conducted in two cycles. The subjects in this study were 31 students of class X TAB B of SMK Negeri 1 Ngawen in the 2019/2020 academic year, consisting of 16 female students and 15 male students. The data were collected using the observation method, the test method, the questionnaire method and the documentation method. After the data was obtained, it was analyzed using a percentage scale. The results of the study can be concluded that through the use of scramble learning models in learning physics can increase the learning motivation of students of SMK Negeri 1 Ngawen. This can be seen from the observation data of student learning motivation increased from 46.94% in the pre-cycle to 60.81% in the first cycle and increased again to 73.39% in the second cycle. The percentage of students' motivation questionnaire increased 58.06% in the pre-cycle to 72.90% in the first cycle and to 81.29% in the second cycle. This increase in learning motivation has an effect on improving learning outcomes. This is indicated by the increase in the average score of students. The mean score of students increased from 59.98 with completeness 38.71% in the pre-cycle to 77.66 with 80.69% completeness in cycle I and increased again to 85.97 with completeness of 93.97% in cycle II.
Abstract Previous examination of the Indian Ocean Dipole (IOD) response to greenhouse warming shows increased variability in the eastern pole but decreased variability in the western pole before ...2100. The opposing response is due to a shallowing equatorial thermocline promoting sea surface temperature (SST) variability in the east, but a more stable atmosphere decreasing variability in equatorial zonal winds that weakens SST variability in the west. Post-2100, how the IOD may change remains unknown. Here we show that IOD variability weakens post-2100 in majority of models under a long-term high emission scenario to 2300. Post-2100, the atmosphere stability increases further and persistent ocean warming arrests or even reverses the eastern Indian Ocean shallowing thermocline. These changes conspire to drive decreased variability in both poles, reducing amplitude of moderate, strong and early-maturing positive IOD events. Our result highlights a nonlinear response of the IOD to long-term greenhouse warming under the high emission scenario.
The Indian Ocean dipole is a prominent mode of coupled ocean-atmosphere variability, affecting the lives of millions of people in Indian Ocean rim countries. In its positive phase, sea surface ...temperatures are lower than normal off the Sumatra-Java coast, but higher in the western tropical Indian Ocean. During the extreme positive-IOD (pIOD) events of 1961, 1994 and 1997, the eastern cooling strengthened and extended westward along the equatorial Indian Ocean through strong reversal of both the mean westerly winds and the associated eastward-flowing upper ocean currents. This created anomalously dry conditions from the eastern to the central Indian Ocean along the Equator and atmospheric convergence farther west, leading to catastrophic floods in eastern tropical African countries but devastating droughts in eastern Indian Ocean rim countries. Despite these serious consequences, the response of pIOD events to greenhouse warming is unknown. Here, using an ensemble of climate models forced by a scenario of high greenhouse gas emissions (Representative Concentration Pathway 8.5), we project that the frequency of extreme pIOD events will increase by almost a factor of three, from one event every 17.3 years over the twentieth century to one event every 6.3 years over the twenty-first century. We find that a mean state change--with weakening of both equatorial westerly winds and eastward oceanic currents in association with a faster warming in the western than the eastern equatorial Indian Ocean--facilitates more frequent occurrences of wind and oceanic current reversal. This leads to more frequent extreme pIOD events, suggesting an increasing frequency of extreme climate and weather events in regions affected by the pIOD.
El Niño and La Niña, collectively referred to as the El Niño-Southern Oscillation (ENSO), are not only highly consequential
but also strongly nonlinear
. For example, the maximum warm anomalies of El ...Niño, which occur in the equatorial eastern Pacific Ocean, are larger than the maximum cold anomalies of La Niña, which are centred in the equatorial central Pacific Ocean
. The associated atmospheric nonlinear thermal damping cools the equatorial Pacific during El Niño but warms it during La Niña
. Under greenhouse warming, climate models project an increase in the frequency of strong El Niño and La Niña events, but the change differs vastly across models
, which is partially attributed to internal variability
. Here we show that like a butterfly effect
, an infinitesimal random perturbation to identical initial conditions induces vastly different initial ENSO variability, which systematically affects its response to greenhouse warming a century later. In experiments with higher initial variability, a greater cumulative oceanic heat loss from ENSO thermal damping reduces stratification of the upper equatorial Pacific Ocean, leading to a smaller increase in ENSO variability under subsquent greenhouse warming. This self-modulating mechanism operates in two large ensembles generated using two different models, each commencing from identical initial conditions but with a butterfly perturbation
; it also operates in a large ensemble generated with another model commencing from different initial conditions
and across climate models participating in the Coupled Model Intercomparison Project
. Thus, if the greenhouse-warming-induced increase in ENSO variability
is initially suppressed by internal variability, future ENSO variability is likely to be enhanced, and vice versa. This self-modulation linking ENSO variability across time presents a different perspective for understanding the dynamics of ENSO variability on multiple timescales in a changing climate.
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