ENSO Flashcards

CCV

1
Q

Pacific Ocean -> lots of CAPE as oceans are warm

A

ENSO events -> 2 years and then a return to normal conditions

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

ENSO (Ropelewski and Halpert, 1987).

A

is a coupled oceanic-atmospheric climate phenomenon which has widespread teleconnections influencing global precipitation

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

Southern Oscillation Index

A

Pressure differences between Darwin and Tahiti.
Pressure difference more strongly associated through DJF -> when ENSO events take places, less strongly associated through AMJ e.g. April -> monsoon forms as the SOI is weakest.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

Walker -> analysed when there were pressure differences across the I.O and Pacific Ocean when the monsoon formed

A

Bjerknes -> identified Walker Circulation

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

Wyrtki -> identified that El Niño events are produced prior to the transfer of warm water from the western Pacific Ocean to the eastern Pacific Ocean -> analysed in more detail the role of kelvin and Rossby waves

A

Philander used the term La Niña in the 1980s

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

Walker Circulation

A
  1. Trade winds (easterlies) converge producing a water deficit at the eastern Pacific Ocean = cold tongue, upwelling -> in contrast warm water accumulates in the western Pacific Ocean
  2. Thermocline produced by the 4-10C temp gradient –> deeper in eastern Pacific Ocean than western -> enhances p.g. and easterlies -> Bjerknes feedback
  3. leads to convection, instability, and precipitation over the warmer waters of the equatorial western Pacific Ocean and divergence, subsidence over the eastern equatorial Pacific Ocean
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

El Niño (McPhaden, 2004)

A
  1. Trade winds weaken -> production of westerly wind events -> zone of convection shifts east -> thermocline shallows and temperature gradient decreases as Humboldt Current replaced by warm water
  2. wetting across the equatorial Pacific, drying in the western Pacific -> Bjerknes feedback as WWBs reinforced by SST alterations (Rasmusson and Carpenter, 1982)
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

cause of El Niño

A

Madden-Julian Oscillation -> intraseasonal convective system causes downwelling Kelvin waves through WWB, but was strong in the 1990s than 19980s (McPhaden, 1999; Tang and Yu, 2008)
stochastic forcing (Hu and Fedorov, 2014) or recharge oscillator theory (McPhaden, 2004)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

La Niña (McPhaden, 2004)

A
  1. Trade winds intensify -> warmer water pushed further W -> increase western equatorial convection -> strong p.g. and steeper thermocline in e. pacific and shoaling in w. pacific
    -> intensification of Walker Circulation
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

Recharge Oscillator Theory (Jin, 1997)

A

ENSO states reverse due to delayed responses through the production of equatorial waves which lead to sustaining oscillation -> based on heat content build up and the position of heat build up
- A La Niña is viewed as a mechanism which recharges the build-up = released during an El Niño

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

Delayed Oscillator Theory (1. Battisti and Hirst, 1989; Suarez and Schopf, 1988):

A

idea that delayed equatorial waves drive the ENSO system -> produced by wind stresses, density contrasts in the thermocline in the form or Rossby and Kelvin waves + Bjerknes (1969) feedback as it couples the strength of anomalies in the ocean with the atmosphere leading to a “lock” on the state

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
12
Q

Outline of the delayed oscillator theory

A
  1. An initial wind stress anomaly in the central Pacific leads to the generation of an eastward propagating downwelling Kelvin wave (2.9m/s) at the equator and upwelling Rossby wave propagating westwards at ~5° N and S of the equator
  2. It reaches the coast the coast in 2-3 months and ‘flicks’ the thermocline, leading to strong local SST warm anomaly by reducing upwelling cooling off the coast of S. America
  3. Weakens pressure gradient across basin, reduces wind stress, warm pool expands, etc. -> strengthened by Bjerknes feedback
  4. Kelvin waves reflect off S. America and travel back across the basin as downwelling Rossby waves increasing the thermocline again via Sverdrup transport (Timmerman et al., 2018)
  5. Rossby wave travels slowly (0.93 m/s) towards the west after ~210 days they reache the W and are reflected back as upwelling Kelvin wave
  6. Therefore 375 days later -> initial WWB reversed -> termination based on wave propagation speeds
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
13
Q

Role of Ekman Transport in DOT

A
  1. WWBs cause localised warm anomaly on the equator as Ekman transport moves warm water towards the equator via upwelling = additional mass = depresses the thermocline and produces the downwelling kelvin waves
  2. Upwelling Rossby waves are induced by localised cooling either side of the equator where heat is transported towards the equator by WWB-induced Ekman transport -> mass deficit as water converges at the equator = production of cyclonic vortices = Rossby waves which propagate poleward
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
14
Q

Alternative ENSO mechanisms (Wang, 2018)

A

Capacitator theory (Webster and Lucas, 1992), Advective-Reflective Oscillator (Picaut et al., 1997), Unified Oscillator (Wang, 2001)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
15
Q

El Niño events vary in where and how they occur/propogate

A

anomalies typically occur within the NINO3.4 region
EP = most common -> formation in eastern pacific = flattening of the thermocline -> often lead to La Niña events (Timmerman et al., 2018)
CP = more recently identified -> warming in central pacific -> impacts not the same and thermocline does not flatten (Timmerman et al., 2018)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
16
Q

2002/2003 El Niño (McPhaden, 2004)

A

o WWBs from May-June 2002 high -> MJO enhanced these.
o SSTs underestimated -> different sections of the Pacific Ocean warmed.
o Delayed Oscillator Theory -> occurred = return to normal conditions by May 2003.

17
Q

1997/1998 El Niño (McPhaden, 1999)

A

o Initiated by 2 WWBs -> downwelling Kelvin waves
o Strongest expression in the E: thermocline became 90m deeper, anomalies +4C, were 20-40m in W
o Abrupt end, where SSTs dropped by 8°C in 30 days in the E due to strong upwelling -> swing to La Niña conditions
o Conformed to recharge oscillator model development and termination -> often referred to as a canonical event

18
Q

2014/15 El Niño (L’Heureux et al., 2017)
o Produced by WWB from the cyclones located either side of the equator -> stochastic forcing
o Strong warming in NINO3.4 region (more central than normal for EP), so had many global impacts

A

Did not form until 2016 (Hu and Fedorov, 2014)
o Heat content matched that of the 1997/98 El Niño and WWBs from Jan-Feb -> no El Niño though -> recharge oscillator?
was cancelled due to the presence of stochastic easterly wind bursts which halted the El Niño forming in 2014 -> prevented the warm pools in the equatorial Pacific Ocean joining from central to eastern so could not enhance Bjerknes feedback (Hu and Fedorov, 2014)

19
Q

1991-1994 El Niño -> did not adhere to the delayed oscillator theory

A

1982 El Niño -> occurred when the MJO was not active (Tang and Yu, 2008).

20
Q

EP El Niño = easier to predict while CP El Niño = harder to predict (Zheng & Yu, 2017)

A

ENSO -> weaker in the early-mid Holocene
ENSO -> stronger during Eocene (earth’s warmest temp period) -> meaning for the future (McPhaden et al., 2006)

21
Q

Spring Predictability Barrier (Webster and Yang, 1992)

A

harder to predict during spring as oceanic-atmospheric changes are more variable + Indian ocean monsoon takes place
La Niña had a 4-5month predictability (Timmerman et al., 2018)

22
Q

MJO -> stochastic (Tang and Yu, 2008)

A

WWB formation as a result is therefore hard to predict with a max of 26 days (Luo et al., 2016)

23
Q

main ways to approach modelling

A

statistical models -> analyse atmospheric and oceanic precursors to predict future evolution (Latif et al., 1998)
dynamical models -> mechanistic e.g. GCMs with observations (Tang et al., 2018)

24
Q

models are bad at simulating the amplitude and phase lock of ENSO -> CMIP3-5 only change was in ability to meet amplitude (Bellenger et al., 2014)

A

Wind-SST feedback = underestimation by 20-50% in CMIP5 meaning resultant impact on SSTs not anticipated (Bellenger et al., 2014)
double ITCZ

25
Q

models for enso struggle to depict the thermocline

A

because their vertical mixing is parameterised = poor Kelvin and Rossby wave simulation (Tang et al., 2018)

26
Q

El Niño teleconnections = signals propagating through the atmosphere

A

alters global precipitation rates (Ropelewski and Halpert, 1987)
e.g. wet conditions in Ecuador and Peru in El Niño but reverse in La Niña (Mason and Goddard, 2001)
drying in SE Africa and the Sahel
wetting in E. Africa

27
Q

gill type teleconnection -> weakened warm pool convection = waves propagating (Gill, 1980)

A

atmospheric bridge -> changes in walker circulation cause changes in Hadley circulation resulting in changes in standing waves and thus surface heat flux in oceans (Alexander et al., 2002)

28
Q

ENSO on the Asian monsoon

A

severe droughts = not necessarily accompanied by El Niño events
the changes in oceanic SSTs lead to supression in convection and moisture flux into the region = drought (Fan et al., 2021)

29
Q

ENSO and tropical storms

A

La Niña = increased hurricanes over the Atlantic Ocean while El Niño = decreased hurricanes over the Atlantic Ocean for the USA (McPhaden et al., 2006).

30
Q

C.C. and ENSO

A

increase in precipitation e.g. Clausius Clapeyron (Huang, 2017) -> leaning more towards El Niño in CMIP modelling.