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AbruptSLR

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Mega ENSO
« on: March 06, 2016, 09:52:26 PM »
I know nothing about Mega ENSOs, so I will begin learning by compiling related articles:


Yefan Zhou andZhiwei Wu (2016), "Possible impacts of mega-El Niño/Southern Oscillation and Atlantic multidecadal oscillation on Eurasian heat wave frequency variability", Quarterly Journal of the Royal Meteorological Society, DOI: 10.1002/qj.2759

http://onlinelibrary.wiley.com/doi/10.1002/qj.2759/abstract

Abstract: "Identifying predictability sources of heat wave variations is a scientific challenge and of practical importance. This study investigates the summertime heat wave frequency (HWF) over Eurasia for 1950–2014. The Eurasian HWF is dominated by two distinct modes: the interdecadal (ID) mode featured by an increasing pattern overall, centered around eastern Europe-central Asia and Mongolia-southwestern China; the interannual (IA) mode resembling a tri-pole anomaly pattern with three centers over western-northern Europe, northeastern Asia and East Asia. The ID mode is found to be influenced by mega-El Niño/Southern Oscillation (mega-ENSO) and the Atlantic multidecadal oscillation (AMO), and the latter has far more effect, whereas the IA mode is connected with mega-ENSO.

Further analysis suggests that mega-ENSO variations can incite a Gill-type response spreading to Eurasia, while the AMO changes cause eastward-propagating Rossby wave trains toward Eurasia. These two teleconnection patterns together contribute to the large-scale circulation anomalies of the ID mode, and those related to the IA mode arise from the teleconnection pattern excited by mega-ENSO. A strong mega-ENSO triggers subsidence with high pressure anomalies, warms the surface and increases the HWF significantly over northeastern Asia particularly. Likewise, the warm AMO-induced circulation anomalies engender surface radiative heating and HWF growth in most of Eurasian continent except some localized Siberian and Asian regions. The situation is opposite for a weak mega-ENSO and AMO. Those models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) which realistically capture the features of the ID mode can reproduce the AMO-like sea surface temperature anomalies (SSTAs), while signals resembling mega-ENSO are found in those with favorable capability of simulating the IA mode. On the contrary, these relevant SSTAs linked to the respective modes vanish in the models with little skills. Thus, mega-ENSO and the AMO might provide two critical predictability sources for heat waves over Eurasia."
“It is not the strongest or the most intelligent who will survive but those who can best manage change.”
― Leon C. Megginson

AbruptSLR

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Re: Mega ENSO
« Reply #1 on: March 06, 2016, 09:58:48 PM »
Here is another Mega ENSO article:


Zhiwei Wu , Peng Zhang (August 2015, First online: 11 October 2014), "Interdecadal variability of the mega-ENSO–NAO synchronization in winter", Climate Dynamics, Volume 45, Issue 3, pp 1117-1128, DOI: 10.1007/s00382-014-2361-8


http://link.springer.com/article/10.1007%2Fs00382-014-2361-8

Abstract: "Mega-El Niño-Southern Oscillation (ENSO) and the North Atlantic Oscillation (NAO), as two principal components of the global air-sea coupling system, may have synchronous or out-synchronous fluctuations during different epochs. Understanding such connection change is instrumental for climate prediction, particularly the decadal prediction. Results in this study show that mega-ENSO has experienced a notable inter-decadal change in its linkage with the winter NAO during the past 56 years: mega-ENSO was significantly correlated with the NAO during 1957–1981 (or synchronous epoch), while such correlation has broken down since 1982 (or out-synchronous epoch). This marked change might be attributed to a sea surface temperature (SST) forcing change in the North Atlantic, based on the observational and numerical evidences in this study. The synchronous epoch is concurrent with the anomalous tropical North Atlantic (TNA) SST forcing, whereas the out-synchronous epoch is associated with the anomalous extra-tropical North Atlantic (XNA) SST forcing. Two possible reasons may explain how the synchronous behaviors between mega-ENSO and the NAO were tied to the TNA SST anomaly (SSTA). There is a positive feedback between the TNA SSTA and the NAO-like atmosphere anomalies, which helps to “prolong” the NAO impacts from the developing phase through mature phase of mega-ENSO. Additionally, the TNA SSTA itself may induce a NAO-like atmosphere anomaly. Since 1982, the TNA SSTA has been replaced by the XNA SSTA and the latter primarily favors a NAO-neutral state in the atmosphere, which ends the synchronous epoch."
“It is not the strongest or the most intelligent who will survive but those who can best manage change.”
― Leon C. Megginson

AbruptSLR

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Re: Mega ENSO
« Reply #2 on: March 06, 2016, 10:07:01 PM »
Here is another Mega ENSO related reference:

Bin Wang, Jian Liuc, Hyung-Jin Kime, Peter J. Webster, So-Young Yim, and Baoqiang Xian (April 2013), "Northern Hemisphere summer monsoon intensified by mega-El Niño/southern oscillation and Atlantic multidecadal oscillation", PNAS, vol. 110, no. 14, 5349

http://www.pnas.org/content/110/14/5347.full.pdf

Abstract: "Prediction of monsoon changes in the coming decades is important for infrastructure planning and sustainable economic development.  The decadal prediction involves both natural decadal variability and anthropogenic forcing. Hitherto, the causes of the decadal variability of Northern Hemisphere summer monsoon (NHSM) are largely unknown because the monsoons over Asia, West Africa, and North America have been studied primarily on a regional basis, which is unable to identify coherent decadal changes and the overriding controls on planetary scales. Here, we show that, during the recent global warming of about 0.4 °C since the late 1970s, a coherent decadal change of precipitation and circulation emerges in the entirety of the NHSM system. Surprisingly, the NHSM as well as the Hadley and Walker circulations have all shown substantial intensification, with a striking increase of NHSM rainfall by 9.5% per degree of global warming. This is unexpected from recent theoretical prediction and model projections of the 21st century. The intensification is primarily attributed to a mega-El Niño/Southern Oscillation (a leading mode of interannual-to-interdecadal variation of global sea surface temperature) and the Atlantic Multidecadal Oscillation, and further influenced by hemispherical asymmetric global warming. These factors driving the present changes of the NHSM system are instrumental for understanding and predicting future decadal changes and determining the proportions of climate change that are attributable to anthropogenic effects and long-term internal variability in the complex climate system."
“It is not the strongest or the most intelligent who will survive but those who can best manage change.”
― Leon C. Megginson

AbruptSLR

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Re: Mega ENSO
« Reply #3 on: March 06, 2016, 10:19:01 PM »
The linked (open access article) both provides a definition of the Mega ENSO (a type of combined PDO/IPO/ENSO phenomenon) and links it to the Indian summertime monsoon rainfall (which I am concerned might fail if the current strong El Nino transitions to a weak El Nino by this boreal summer):

Bin Wang, Baoqiang Xiang, Juan Li,   Peter J. Webster, Madhavan N. Rajeevan, Jian Liu & Kyung-Ja Ha (2015), "Rethinking Indian monsoon rainfall prediction in the context of recent global warming", Nature Communications 6, Article number: 7154 doi:10.1038/ncomms8154

http://www.nature.com/ncomms/2015/150518/ncomms8154/full/ncomms8154.html

Abstract: "Prediction of Indian summer monsoon rainfall (ISMR) is at the heart of tropical climate prediction. Despite enormous progress having been made in predicting ISMR since 1886, the operational forecasts during recent decades (1989–2012) have little skill. Here we show, with both dynamical and physical–empirical models, that this recent failure is largely due to the models’ inability to capture new predictability sources emerging during recent global warming, that is, the development of the central-Pacific El Nino-Southern Oscillation (CP–ENSO), the rapid deepening of the Asian Low and the strengthening of North and South Pacific Highs during boreal spring. A physical–empirical model that captures these new predictors can produce an independent forecast skill of 0.51 for 1989–2012 and a 92-year retrospective forecast skill of 0.64 for 1921–2012. The recent low skills of the dynamical models are attributed to deficiencies in capturing the developing CP–ENSO and anomalous Asian Low. The results reveal a considerable gap between ISMR prediction skill and predictability."


Extract: "The mega-ENSO, which has a pattern similar to Interdecadal Pacific Oscillation but involves both interannual and multi-decadal variations of the Pacific basin-wide SST variability, has been recently identified as a major driver for the northern hemisphere monsoon rainfall variations including the ISMR. The mega-ENSO involves an off-equatorial atmosphere–ocean thermodynamic feedback between the two Pacific subtropical highs (PSHs)/trades and basin-wide SST anomalies."

See also:
http://www.nature.com/ncomms/2015/150518/ncomms8154/fig_tab/ncomms8154_F3.html
« Last Edit: March 06, 2016, 10:26:21 PM by AbruptSLR »
“It is not the strongest or the most intelligent who will survive but those who can best manage change.”
― Leon C. Megginson

AbruptSLR

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Re: Mega ENSO
« Reply #4 on: March 07, 2016, 10:30:02 AM »
While the linked article does not use the concept of a Mega ENSO index, it does correlate ENSO behavior with the predictability of Indian Summer Monsoon Rainfall (ISMR), and Bin Wang is a co-author in this and the previously posted paper correlating the Mega ENSO & the ISMR:

Li, Juan; Wang, Bin (2015), "How predictable is the anomaly pattern of the Indian summer rainfall?", Climate Dynamics, pp 1-15, DOI: 10.1007/s00382-015-2735-6

http://link.springer.com/article/10.1007%2Fs00382-015-2735-6

Abstract: "Century-long efforts have been devoted to seasonal forecast of Indian summer monsoon rainfall (ISMR). Most studies of seasonal forecast so far have focused on predicting the total amount of summer rainfall averaged over the entire India (i.e., all Indian rainfall index-AIRI). However, it is practically more useful to forecast anomalous seasonal rainfall distribution (anomaly pattern) across India. The unknown science question is to what extent the anomalous rainfall pattern is predictable. This study attempted to address this question. Assessment of the 46-year (1960-2005) hindcast made by the five state-of-the-art ENSEMBLE coupled dynamic models' multi-model ensemble (MME) prediction reveals that the temporal correlation coefficient (TCC) skill for prediction of AIRI is 0.43, while the area averaged TCC skill for prediction of anomalous rainfall pattern is only 0.16. The present study aims to estimate the predictability of ISMR on regional scales by using Predictable Mode Analysis method and to develop a set of physics-based empirical (P-E) models for prediction of ISMR anomaly pattern. We show that the first three observed empirical orthogonal function (EOF) patterns of the ISMR have their distinct dynamical origins rooted in an eastern Pacific-type La Nina, a central Pacific-type La Nina, and a cooling center near dateline, respectively. These equatorial Pacific sea surface temperature anomalies, while located in different longitudes, can all set up a specific teleconnection pattern that affects Indian monsoon and results in different rainfall EOF patterns. Furthermore, the dynamical models' skill for predicting ISMR distribution primarily comes primarily from these three modes. Therefore, these modes can be regarded as potentially predictable modes. If these modes are perfectly predicted, about 51 % of the total observed variability is potentially predictable. Based on understanding the lead-lag relationships between the lower boundary anomalies and the predictable modes, a set of P-E models is established to predict the principal component of each predictable mode, so that the ISMR anomaly pattern can be predicted by using the sum of the predictable modes. Three validation schemes are used to assess the performance of the P-E models' hindcast and independent forecast. The validated TCC skills of the P-E model here are more than doubled that of dynamical models' MME hindcast, suggesting a large room for improvement of the current dynamical prediction. The methodology proposed here can be applied to a wide range of climate prediction and predictability studies. The limitation and future improvement are also discussed."
“It is not the strongest or the most intelligent who will survive but those who can best manage change.”
― Leon C. Megginson

AbruptSLR

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Re: Mega ENSO
« Reply #5 on: March 07, 2016, 10:54:40 AM »
The linked two part paper also has Bin Wang as a co-author and uses concepts related to the Mega ENSO to improve the predictability of peak summer rainfall in both Southeast Asia and extratropical East Asia:

So-Young Yim, Bin Wang & Wen Xing (2014), "Peak-summer East Asian rainfall predictability and prediction part I: Southeast Asia", Climate Dynamics, pp 1-13, DOI: 10.1007/s00382-014-2385-0

http://link.springer.com/article/10.1007/s00382-014-2385-0

Abstract: "The interannual variation of East Asia summer monsoon (EASM) rainfall exhibits considerable differences between early summer [May–June (MJ)] and peak summer [July–August (JA)]. The present study focuses on peak summer. During JA, the mean ridge line of the western Pacific subtropical High (WPSH) divides EASM domain into two sub-domains: the tropical EA (5°N–26.5°N) and subtropical-extratropical EA (26.5°N–50°N). Since the major variability patterns in the two sub-domains and their origins are substantially different, the Part I of this study concentrates on the tropical EA or Southeast Asia (SEA). We apply the predictable mode analysis approach to explore the predictability and prediction of the SEA peak summer rainfall. Four principal modes of interannual rainfall variability during 1979–2013 are identified by EOF analysis: (1) the WPSH-dipole sea surface temperature (SST) feedback mode in the Northern Indo-western Pacific warm pool associated with the decay of eastern Pacific El Niño/Southern Oscillation (ENSO), (2) the central Pacific-ENSO mode, (3) the Maritime continent SST-Australian High coupled mode, which is sustained by a positive feedback between anomalous Australian high and sea surface temperature anomalies (SSTA) over Indian Ocean, and (4) the ENSO developing mode. Based on understanding of the sources of the predictability for each mode, a set of physics-based empirical (P-E) models is established for prediction of the first four leading principal components (PCs). All predictors are selected from either persistent atmospheric lower boundary anomalies from March to June or the tendency from spring to early summer. We show that these four modes can be predicted reasonably well by the P-E models, thus they are identified as the predictable modes. Using the predicted PCs and the corresponding observed spatial patterns, we have made a 35-year cross-validated hindcast, setting up a bench mark for dynamic models’ predictions. The P-E hindcast prediction skill represented by domain-averaged temporal correlation coefficient is 0.44, which is twice higher than the skill of the current dynamical hindcast, suggesting that the dynamical models have large rooms to improve. The maximum potential attainable prediction skills for the peak summer SEA rainfall is also estimated and discussed by using the PMA. High predictability regions are found over several climatological rainfall centers like Indo-China peninsula, southern coast of China, southeastern SCS, and Philippine Sea."

So-Young Yim, Bin Wang & Wen Xing (2015), "Peak-summer East Asian rainfall predictability and prediction part II: extratropical East Asia", Climate Dynamics, pp 1-16, DOI: 10.1007/s00382-015-2849-x

http://link.springer.com/article/10.1007%2Fs00382-015-2849-x

Abstract: "The part II of the present study focuses on northern East Asia (NEA: 26°N–50°N, 100°–140°E), exploring the source and limit of the predictability of the peak summer (July–August) rainfall. Prediction of NEA peak summer rainfall is extremely challenging because of the exposure of the NEA to midlatitude influence. By examining four coupled climate models’ multi-model ensemble (MME) hindcast during 1979–2010, we found that the domain-averaged MME temporal correlation coefficient (TCC) skill is only 0.13. It is unclear whether the dynamical models’ poor skills are due to limited predictability of the peak-summer NEA rainfall. In the present study we attempted to address this issue by applying predictable mode analysis method using 35-year observations (1979–2013). Four empirical orthogonal modes of variability and associated major potential sources of variability are identified: (a) an equatorial western Pacific (EWP)-NEA teleconnection driven by EWP sea surface temperature (SST) anomalies, (b) a western Pacific subtropical high and Indo-Pacific dipole SST feedback mode, (c) a central Pacific-El Nino-Southern Oscillation mode, and (d) a Eurasian wave train pattern. Physically meaningful predictors for each principal component (PC) were selected based on analysis of the lead–lag correlations with the persistent and tendency fields of SST and sea-level pressure from March to June. A suite of physical–empirical (P–E) models is established to predict the four leading PCs. The peak summer rainfall anomaly pattern is then objectively predicted by using the predicted PCs and the corresponding observed spatial patterns. A 35-year cross-validated hindcast over the NEA yields a domain-averaged TCC skill of 0.36, which is significantly higher than the MME dynamical hindcast (0.13). The estimated maximum potential attainable TCC skill averaged over the entire domain is around 0.61, suggesting that the current dynamical prediction models may have large rooms to improve. Limitations and future work are also discussed."
“It is not the strongest or the most intelligent who will survive but those who can best manage change.”
― Leon C. Megginson