Download Christmas Island Coral Demonstrates Tropical Pacific ENSO Variability

Christmas Island Coral Demonstrates
Tropical Pacific ENSO Variability
Pamela Grothe
EAS 4480 Class Project
April 25th, 2012
REVIEW ARTICLE
NATURE GEOSCIENCE DOI: 10.1038/ NGEO868
MOTIVATION FOR PALEO-ENSO
RECONSTRUCTION
Collins et al., 2010
Upwelling
Change in s.d. (s.d.)
past millennium49,50. However, there is no direct palaeo-analogue
Increased variabilit y
0.4
of the rapid greenhouse-gas-induced climate change that we are
NATURE GEOSCIENCE DOI: 10.1038/ NGEO868
REVIEW
ARTICLE
currently experiencing.
0.2
Detecting externally forced
changes in
the
characteristics of
Normal
conditions
a
ENSO using observational and climate change simulations is dif0
Walker
cult because of the large intrinsic variations
in ENSO behaviour,
circulation
which can occur on multidecadal and centennial timescales, even
–0.2
in the absence of external changes52–54. is problem can be partially overcome in CGCMs by performing multiple runs with the
Decreased variability
Warm pool
Cold tongue
–0.4
same model and measuring forced changes against natural variCC CG CN CS EC FG GF GF GI IN IPS M M M PC UK UK
ability from long, unforced control experiments.
However, in the
SM CM RM IRO HA OA DL DL SS– M – L– IRO IRO RI– M M M
Thermocline
O O
3 3. –C –M M LS –CM –CM EH CM CM C3 C3 CGC
–H –H
real world this is not possible, and naturally occurring variability
.2 .2 M
1( M k 5/ –g 2 2
4
3
ad ad
T4 3 3 M 1.0 .0 .1
(h (m 2
.0
could be masking changes driven by global warming.
CM GE
.0 P
ire e .3
7)
I
d
s) re .2
Upwelling
O
3 M1
ENSO processes and feedbacks may be a ected by greenhouseM
s)
gas-induced
changesElin
climate or by direct changes to
some
~ conditions (SST anomalies)
~mean
El Nino
Nino
conditions
b
of those physical feedbacks and this could, in turn, lead to changes Figure 3 | Projected
Motivational
Questions:
changes in the amplitude
of ENSO variability,
in the characteristic amplitude or frequency of ENSO events. As as a response to global warming, from the CMIP3 models8,9. The
How will ENSO variability be
illustrated in Fig. 3, some CGCMs show an increase in the ampli- measure is derived1)
from the interannual standard deviation (s.d.)
tude of ENSO variability in the future, others show a decrease, and of a mean sea-level-pressure
index, which
related to
the strength
affected
by isglobal
warming?
some show no statistically signi cant changes. Figure 3 is based of the Southern Oscillation variations. Positive changes indicate a
2) and
What
climate models do best
on just one of many studies that have come to the same conclu- strengthening of ENSO,
negative changes indicate a weakening.
sions9,10,55–60. Based on
the assessment of the current generationThermocline
of Statistical significance isat
Thermocline
assessed
by the sizeENSO
of the blue
bars, and the
predicting
variability
CGCMs, there is no consistent picture of changes in ENSO ampli- bars indicated in bold colours are from those CMIP3 CGCMs that are
tude or frequency in the future.
However, by assessing individual judged
Upwelling
Upwelling
to have the best simulation of present-day ENSO characteristics
feedback processes16 separately in CGCMs, we can shed some light and feedbacks. Problems:
on chow ENSO might be
a change
ected by climate change:
Climate
Climate change (SST anomalies)
1)El Niño
Modern
of SST
inphase.
the As
gradient ampli es
eventsrecord
during their
growth
Mean upwelling and advection. Both the mean upwelling of cold there is little change in
the meanPacific
zonal SSTisgradient
in CGCMs
Tropical
not long
water in the eastern equatorial Paci c and the mean subsurface (Fig. 2c), it is unlikely that this feedback would change signi to test
validity
of
advection act to strengthen the climatological temperature gradi- cantly under climate enough
change. However,
it might
be important
if
ents in the horizontal and the vertical. If a positive thermal anomaly the relative frequencyfuture
of occurrence
di erent types of ENSO
ENSOof predictions
occurs in the east Paci c, then these processes damp that anom- modes changes31. e zonal advective feedback is more promi2) cNeed
longer
proxy
records
for
Thermocline
aly. Mean upwellingThermocline
and mean advection in CGCMs are reduced
nent in central Paci
El Niño,
or ‘Modoki’
, variability
in which
under climate change due to the general weakening of the equa- SST anomalies occur principally
in the central Paci c without the
model calibration
25
torial
trade
winds
.
is
would
lead
to
a
tendency
for
enhanced
warm
anomalies
in
the
east.
Collins et al., 2010
Upwelling
Research Question:
Can we extend SST
records, and thus ENSO
records, using coral from
the Central Tropical
Pacific?
Before you can use fossil coral for paleo
ENSO-reconstruction, you must calibrate the
modern coral to the instrumental record
• Can I use the δ18O of modern
coral on Christmas Island as a
proxy for SST?
• If so, how well does the δ18O,
and thus SST, at Christmas
Island record ENSO variability?
• These are fundamental
questions that must be
answered before I can apply my
δ18O work on fossil coral at
Christmas Island as a
climate/ENSO proxy
Nurhati et al., 2009
2012
Geochemistry and Coral Background
Coral’s build aragonite skeleton
During growth of their skeleton, they take up oxygen from the
water
CaCO3
We can measure the ratio of oxygen 18 to oxygen 16 atoms
in their skeleton
δ18O
δ18O of the sea water is a function of SST and Salinity
Lighter (more negative) = warmer and wetter
Heavier (more positive) = cooler and dried
1998
Christmas Island Porites corals grow on average 15 mm a year
- Band count to get accurate age model
We sample every 1 mm – obtaining a sub-monthly resolution
40 cm
Methods
• Data
– Coral δ18O from my own work and from Cobb et al. (2013)
– SST from IGOSS dataset from Reynolds et al. (2002)
– NINO3.4 Index
• Data Processing
– Splice coral δ18O records
– Interpolation methods for δ18O
• Correlation between δ18O and SST at Christmas Island
– Pearson’s correlation coefficient and its significance at the 95%
confidence interval
• Correlation between δ18O and Nino3.4 Index
– Individual periodograms
– Cross spectral analysis and coherence
– Band-pass filter to the most coherent frequencies
– Pearson’s correlation coefficient and its significance at the 95%
confidence interval
Data Processing
Splice my record to Cobb et al. 2013 record
Correct for offsets – 0.1 per mil offset
between overlapping values
Data Processing
Interp1 to interpolate the δ18O to the monthly SST
and Nino3.4 Index
-
Confidence Interval = [-0.7786 to -0.8229]
RMA Slope = -5.13
RMA Intercept = 2.77
PC Slope = -6.32
PC Intercept = -2.92
Coral δ18O and Nino3.4 Index
Confidence Interval = [-0.9185 to -0.9291]
Future Work – Advanced
Environmental Data Analysis!
• Add a third component – Precipitation?
• Reconstruct ENSO using fossil coral δ18O –
look at a change in the standard deviation
compared to the modern?
• It’s only my first year – I have four more years
to do all this!