Transcript Chapter 4 - UCLA: Atmospheric and Oceanic Sciences

Chapter 4
El Niño and Year-to-Year Climate Prediction
4.1 Recap of El Niño basics
4.2 Tropical Pacific climatology
4.3 ENSO mechanisms I: Extreme phases
4.4 Pressure gradients in an idealized upper layer
4.5 Transitions into the 1997-98 El Niño
4.6 El Niño mechanisms II: Dynamics of transition phases
4.7 El Niño prediction
4.8 El Niño remote impacts: teleconnections
4.9 Other interannual climate phenomena and prospects …
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
4.1 Recap of El Niño basics
Supplementary Fig.:
Reynolds SST data set
[From chapter 1]
Climatology
1982-2001 (C)
Sea Surface Temp.
Dec. 1997
Anomaly
(Dec.97 SST-Clim.)
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December 1997 Anomalies of precipitation
during the fully developed warm phase of ENSO
Recap Figure 1.8
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DJF Low-level wind anomalies during the 1997-98 El Niño
relative to the 1958-98 climatology
Recap Figure 1.9
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December 1997 Anomalies of sea level height
during the fully developed warm phase of ENSO
Recap Figure 1.10
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4.2 Tropical Pacific climatology
Sea surface temperature
climatology - January
Sea surface temperature
climatology - July
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Recap Figure 2.16
Recap Figure 2.13
Precipitation
climatology - January
Precipitation
climatology - July
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Equatorial Walker circulation
Recap Figure 2.14
Adapted from Madden and Julian, 1972, J. Atmos. Sci., and Webster, 1983, Large-Scale Dynamical Processes in the Atmosphere
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Pacific in three-dimensions under "Normal" conditions
10 km
15000 km
~28 C
~24 C
~20 C
15 C or colder
Figure 4.1
Atmosphere:
•Trade winds blow across Pacific
air rises in convergence zone
over the warm SSTs in the west.
Ocean:
•Thermocline ~100m deeper in west [sea level 40cm higher; see 4.4]
• Pressure gradient in ocn. (eastward) balances wind stress in vert avg
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Pacific in three-dimensions under "Normal" conditions
Figure 4.1
Recall equatorial upwelling: wind stress & Coriolis force either side
of equator give surface divergence
•Shallow thermocline in east
upwelling brings up colder water
• [Equatorial undercurrent above thermocline flows eastward]
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4.3 ENSO mechanisms I: Extreme phases
Pacific basin under El Niño conditions
Figure 4.2a
• Warmer SST in east; rainfall tends to spread east
• Trade winds weaken
• Unbalanced eastward PGF in ocean
anomalous currents (in vert
avg through layer above thermocline)
thermocline deepens in east
• Upwelling brings up water less cold than normal
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Pacific basin under La Niña conditions
Figure 4.2b
• Cooler SST in east; rainfall concentrated in west
• Trade winds strengthen
• Westward wind stress exceeds eastward PGF in ocean
anomalous currents along Eq.
thermocline shallows in east
• Upwelling brings up water colder than normal
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Jakob Bjerknes
"It is the gradient of SST along the
equator which is the cause of [...] the
Walker circulation. An increase in
equatorial easterly winds [is associated
with] an increase in upwelling and an
increase in the east-west temperature
contrast that is the cause of the Walker
circulation in the first place. [...] On the
other hand, a case can also be made for a
trend of decreasing speed [...] There is
thus ample reason for a never-ending
succession of alternating trends by air-sea
interaction in the equatorial belt, but just
how the turnabout between trends takes
place is not yet clear.” 1969
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The Bjerknes feedbacks (warm phase)
Figure 4.3
• Positive feedback loop reinforces initial anomaly
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The Bjerknes feedbacks (cold phase)
Figure 4.3
[Supplemental]
• Positive feedback loop reinforces initial anomaly
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The El Niño Pumpkin
Neelin, 2011.
4.4 Pressure gradients in an idealized upper layer
Figure 4.4
Idealized upper
ocean layer
• Pressure mass above
• At A ocean surface high so PGF from A toward B in upper ocean
• Deeper thermocline balances higher sea surface
PGF small
below thermocline
• Sea surface height  thermocline depth [cm vs. m]
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Two positions of the thermocline, indicating
region of thermocline anomalies
Figure 4.5
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4.5 Transitions into the 1997-98 El Niño
Buoy from the TAO array
Figure 4.6
Courtesy of the Pacific Marine Environmental Laboratory
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Global tropical moored buoy array
(the original TAO array in the Pacific augmented by subsequent programs)
Figure 4.7
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The transition into the 1997-98 El Niño warm phase (Jan. 1997)
Figure 4.8
After figures courtesy of David Pierce, Scripps Institute of Oceanography.
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
The transition into the 1997-98 El Niño warm phase (Apr. 1997)
Figure 4.8 cont.
After figures courtesy of David Pierce, Scripps Institute of Oceanography.
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
The transition into the 1997-98 El Niño warm phase (Sep. 1997)
Figure 4.8 cont.
After figures courtesy of David Pierce, Scripps Institute of Oceanography.
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
The transition into the 1997-98 El Niño warm phase (Jan. 1998)
Figure 4.8 cont.
After figures courtesy of David Pierce, Scripps Institute of Oceanography.
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
The transition into the 1998-98 La Niña cold phase (May 1998)
Figure 4.9
After figures courtesy of David Pierce, Scripps Institute of Oceanography.
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
The transition into the 1998-98 La Niña warm phase (Sep. 1998)
Figure 4.9 cont.
After figures courtesy of David Pierce, Scripps Institute of Oceanography.
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
The transition into the 1998-98 La Niña phase (Jan. 1999)
Figure 4.9 cont.
After figures courtesy of David Pierce, Scripps Institute of Oceanography.
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
4.6 El Niño mechanisms II: Dynamics of transition phases
Schematic of an equatorial jet
Figure 4.10
• deep thermocline = high pressure in upper ocean, H
• current can flow along Eq. (Coriolis=0)
• equatorial jet: balance of deep thermocline and current anomalies
near equator (with PGF= Coriolis; note change in CF with latitude crucial)
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Schematic of an equatorial jet showing that it can
extend itself eastward but not westward
Figure 4.11
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• Currents carry mass
affects pressure
• Deep thermocline extends eastward where mass added (edge
moving eastward = “Kelvin wave”)
• NB: something has to continually supply mass in the west for the
jet to persist
• Shallow thermocline and westward currents also give equatorial jet
( just switch sign of anoms)
• Low is extended by removing water ( in the ocean upper layer), so also
extends eastward
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Kelvin wave front at the eastern edge of an equatorial jet
Figure 4.12
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Response of the ocean to a westerly wind anomaly
Re: onset and demise of El Niño warm phase
•To east of the wind anomalies,
equatorial jet (Kelvin wave) extends
east, deepening thermocline (H)
• (recall: warms SST…)
•To west, inflow of water to jet (in
oc. upper layer) comes from off the
equator (but little effect on SST)
•shallow thermocline in west extends
westward (Rossby wave), as mass
transferred to east by jet
• when reaches western boundary,
can no longer supply mass by
extending shallow region
•Weakening of jet extends eastward,
ending warm phase
•As wind anomalies weaken, shallow
thermocline extends eastward:
transition to cold phase
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Wind anoms
Figure 4.13
currents
Deep Thermocline anoms
Response of the ocean to a easterly wind anomaly
Re: Onset and demise of La Niña cold phase (supplementary Fig.)
• Same as Figure 4.13 but anomalies of opposite sign
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ENSO transitions
Meantime, in the West
Pacific (subsurface)
Recall: feedbacks that
strengthen El Niño
Delay: no
surface effect
until…
And vice versa…
Figure 4.14
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Onset of La Niña
cold phase
4.7 El Niño prediction
Forecast of SST anomalies
(as three month averages)
•Forecast of the onset of
the 1997-98 El Niño
•From National Center for
Environmental Prediction
climate model
•Data through March 1997
(previous wind stress anomalies,
ocean subsurface temperatures,
SSTs,…)
•[“Data assimilation” process
includes interpolation of sparse
observations to all model grid
points, balancing terms in model
equations,…]
• climate model runs
forward in prediction mode
(from April)
Figure 4.15
Courtesy of the National Center for Environmental Prediction
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Courtesy of the National Center for Environmental Prediction
Supplementary Figure: NCEP Forecast vs. Observation
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Commonly used index regions for ENSO SST anomalies
Recall: Figure 1.5
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Series of forecasts of SST anomalies averaged
over the Niño-3 region of the equatorial Pacific
Figure 4.16
forecast had gone three months
into the future), 6-month lead,
9-month lead, …
•E.g. March 1997 forecast shown
•Compare each forecast to
the SST anomaly that was
later observed (solid line)
•skill decreases with longer
lead; still useful at 9 months
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Courtesy of the National Center for Environmental Prediction
Track record of forecasts:
•From forecasts made each
month, collect all the
forecast SST anomalies at 3month lead (i.e. after each
4.7.1 Limits to skill in ENSO forecasts
Loss of skill in ENSO forecasts:
(i) Imperfections in the forecast system
-e.g., model errors, scarcity of input data (can be improved, if $)
(ii) Fundamental limits to predictability
-weather unpredictable beyond two weeks (chaos theory):
slightly different initial conditions lead to later weather patterns as
dissimilar as weather maps chosen at random (except for aspects determined
by sea surface temperature…)
- “weather noise”: acts like a random forcing on slow oceanatmosphere interaction
e.g. in the Bjerknes hypothesis, SST gradient determines average
strength of Tradewinds. But in a particular month, storms or
other transient weather events can cause equatorial Easterlies
to differ from this, causing a greater or lesser change of
currents than you would expect from the SST anomalies
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Effects of weather noise on the ENSO cycle
Figure 4.17
• Schematically, random weather events cause cycle to depart
from the evolution it would otherwise have had
• Cumulative effects cause departure from prediction
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An ensemble of forecasts during
the onset of the 1998-99 La Niña
•Start coupled model
Figure 4.18
from different ocean
initial conditions (leading
also to changes in atm. )
•Initial differences grow
 ensemble of prediction
runs
•Ensemble spread gives
estimate of uncertainty
• Spread tends to grow
with time (due to weather
noise & coupled
feedbacks)
• Ensemble mean gives
Courtesy of the European Centre for Medium-range Weather Forecasting.
best estimate
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Supplementary Figure
ECMWF forecast of the 09/10 El Nino from May 2009
with overlaid observations
Courtesy of the European Centre for Medium-range Weather Forecasting.
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Supplementary Figure
Courtesy of the European Centre for Medium-range Weather Forecasting.
ECMWF forecast from March 2010
predicting transition to La Niña of 2010
Courtesy of the European Centre for Medium-range Weather Forecasting.
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Supplementary Figure
Courtesy of the European Centre for Medium-range Weather Forecasting.
ECMWF forecast from March 2010:
with overlaid observations for verification
Courtesy of the European Centre for Medium-range Weather Forecasting.
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Supplementary Figure
Courtesy of the European Centre for Medium-range Weather Forecasting.
ECMWF forecast from Sept. 2010:
with overlaid observations for verification
Courtesy of the European Centre for Medium-range Weather Forecasting.
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Supplementary Figure
Courtesy of the European Centre for Medium-range Weather Forecasting.
ECMWF forecast from March 2011
Courtesy of the European Centre for Medium-range Weather Forecasting.
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4.8 El Niño remote impacts: teleconnections
Regions with statistically reliable relation of precipitation and
surface air temperature to El Niño and La Niña
Figure 4.19
• Impact
regions
change
with
seasonal
climatology
• La Niña
similar but
opposite
sign
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Patterns of typical response to El Niño observed
for northern hemisphere winter
Figure 4.20
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Jet stream and storm track changes
associated with El Niño or La Niña
Figure 4.21
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Schematic of shift of probability distribution of precipitation, e.g.
in Southern California, during El Niño
•E.g., find value of precip which only 1/3 of winters exceed, and ask what
fraction of El Niño winters exceed it
•Probability of rainy winter enhanced (but far from certain)
Figure 4.22
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4.9.1 Hurricane season forecasts
Factors that affect tropical cyclone development
Figure 4.23
NOAA GOES-9 satellite photo of hurricane Linda. NASA Goddard Space Flight Center. Initial rendering by Marit Jentoft-Nilsen.
Cross-section follows Emanuel, K. A., 1988, Am. Sci.
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Effect of ENSO on number of Atlantic “named storms”
(tropical storms and hurricanes) in July-Oct. each year
Figure 4.24
• avg 8-9
•Regression:
La Niña ~10
El Niño ~6
•But large scatter (&
increases w earlier SST)
Tang and Neelin, Geophys. Res. Lett., 2004.
Tropical storm: sustained winds > 18 m/s; hurricane: winds > 33m/s (74 mph);
Category 5 > 69 m/s
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4.9.2 Sahel drought
Recap Figure 2.13, zoomed on
Africa: Precipitation
climatology – January & July
Box shows averaging region for
Fig. 4.25 (next slide)
• Sahel:
• region at margin of African
monsoon (seasonal movement of
convection zones)
• On border with arid regions,
just south of Sahara desert
• Receives all its rainfall in JuneSept. when convection zone
moves north
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Sahel drought: annual rainfall anomalies (13-20N, 15W-20E)
Figure 4.25
Data from Hulme, Global Precipitation and Climate Change, 1994.
•Sahel region has experienced decades of drought from 1970s to
present, compared to 1950s and 1960s
•Also has year to year variation
•Three hypotheses:
(i) Land surface change increases albedo. More sunlight reflected
gives less energy transferred from surface to atmosphere to drive
convection
(ii) (Most likely) SST anomalies in Atlantic and Indian oceans cause
the drought by teleconnections
(iii) Possible anthropogenic contribution by aerosols/warming
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Supplementary Fig.: Lake Chad (Africa) shrinking due to drought
a.) 12/08/72
b.) 12/14/87
LandSat images (US Geological Survey)
c.) 12/18/2002
4.9.3 The North Atlantic Oscillation (NAO) and annular modes
•Northern and Southern
Annular Modes: low
pressure near pole, high at
mid-latitudes (positive
phase) or vice versa
•NAO roughly the Atlantic
sector of N. Annular Mode
• surface pressure shown;
extends to stratosphere
•Winds enhance/reduce jet
(in pos./neg. phase), shifting
end of storm track
north/south
impacts
European precipitation
•atmospheric origin but
includes decadal vbty/trend
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP