Forced or unforced? New views of ENSO from 6,000yrs ago to the present

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FORCED OR UNFORCED? NEW VIEWS OF ENSO FROM 6,000 YEAR AGO TO PRESENT

Kim M. Cobb Pamela Grothe, Hussein Sayani, Intan Nurhati Earth and Atmospheric Sciences Georgia Tech Chris Charles, SIO, UCSD Larry Edwards, Hai Cheng, UMN

• Pa

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Cobb Lab

IS ENSO CHANGING?

DID ENSO CHANGE IN THE PAST? (AND IF SO, WHY?) ROLE OF FORCED VS INTERNAL VARIABILITY?

AN 18YR OBSESSION

FIRST, A FIELDTRIP

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Coral-to-coral offsets in δ18O and Sr/Ca The legend for Figure 2 reveals that the overlapping sections of fossil coral have

markedly different mean δ18O values – differences far larger than the ±0.07‰ (1σ) analytical uncertainty. Linsley et al., 1999 noted similar offsets while comparing mean δ18O values from modern corals growing on the same eastern Pacific reef. Indeed, we observe a similar spread of mean coral offsets in an ensemble of late 20th century modern coral δ18O records from Christmas Island (Figure 3), but the origin of such offsets remains unknown. Given the empirical coral δ18O-SST relationship of roughly -0.2‰/ºC, such offsets (±0.09‰, 1σ) translate into uncertainties of ~±0.5ºC, 1σ.

Figure 3. Plot of 4 different modern coral δ18O records from Christmas Island, with coral-to-coral offsets reported in legend, shown with satellite-blended SST from Christmas Island (Reynolds and Smith, 2002). Unpublished data, P. Grothe.

Perhaps not surprisingly, recent work in our lab has revealed ±0.1mmol/mol (1σ) coral-to-coral offsets in Sr/Ca from Palmyra modern corals (Figure 4), roughly equivalent to ±1ºC (1σ) using the Nurhati et al., 2011 Sr/Ca-SST relationship. Our approach seeks to minimize the uncertainties associated with coral-to-coral δ18O and Sr/Ca offsets by analyzing hundreds of individual fossil corals covering the last 6,000 years.

Figure 4. Plot of 3 different modern coral Sr/Ca timeseries from Palmyra Island, with coral-to-coral offsets corrected and reported in legend, shown with satellite-blended SST from Palmyra Island (grey; Reynolds and Smith, 2002). Unpublished data, H. Sayani. The existing Line Islands coral reconstruction

Amassed over the course of six field expeditions over the last 15 years by the PI, the northern Line Islands coral δ18O dataset is comprised of ~1000 year’s worth of monthly-resolved records. It presents a detailed view of central tropical Pacific climate over the last 7,000 years – one that raises as many questions as it provides answers.

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a “Mickey Mouse” technique to compare coral to regional SST

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Figure S2. Plots of the raw coral δ18O sequences from Fanning (this page) and Christmas (next page), shown with their respective U-series dates (see Table S1).

Line Island coral δ18O records of ENSO: FOSSIL

New fossil coral data: # sequences: 22 dating: U/Th (±0.5% error) resolution: monthly record lengths: 19-81yrs

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How to measure ENSO strength through time?

metric of ENSO strength: standard deviation of 2-7yr filtered records in 30yr windows

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Compilation of new and published coral-based paleo-ENSO reconstructions

MODERN FOSSIL

Key points: 1)  large range in paleo-ENSO variations implied by available data

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Key points: 1)  large range in paleo-ENSO variations implied by available data 2)  almost all coral data fall below late 20th century benchmark



1968K1998&

Li&et&al.,&2013&

“Our data indicate that ENSO activity in the late 20th century was anomalously high over the past seven centuries . . .”

ENSO reconstruction from 2,222 tree ring records

! 3!

evolution of interannual variance in 14 different ENSO reconstructions over the last 600 years, compiling both single proxy records as well as multi-proxy reconstructions. They conclude that ENSO variance over the period 1979-2009 was significantly higher than ENSO variance for the last 400 years (Figure 2). Taken at their face value, such studies suggest that ENSO may be strengthening in response to anthropogenic greenhouse forcing. However, both of these studies are associated with different types of uncertainties. In the case of Cobb et al., 2013, it is possible that the corals’ original climate signals have been subtly altered by geochemical alteration over the last several millennia of exposure. In the case of McGregor et al., 2013, many of the multi-proxy reconstructions that they use to track ENSO variance may have accumulated dating errors in the older portions that essentially lead to a smoothing of interannual variance.

The above discussion illustrates the profound uncertainties inherent in quantifying paleo-ENSO variance from the existing set of proxy data. First, as discussed in the previous section, multi-century records of ENSO are required to yield robust estimates of pre-industrial ENSO variance, given the high level of natural variability in ENSO. Second, proxy records often reflect a complex mixture of local environmental parameters that is often obscured by noise in the proxy recorder itself (see Cobb et al., 2008 and Jones et al., 2009 for reviews on high-resolution proxy uncertainties). Second, the spatial footprints of central Pacific vs eastern Pacific ENSO extremes differ appreciably (e.g. Ashok et al., 2007), even in the core of the ENSO region. Therefore, ENSO reconstructions that rely on a single proxy record may under- or over-estimate ENSO variance during a given interval, depending on the frequency of central vs. eastern Pacific extremes. But the existing set of multi-proxy reconstructions of ENSO are not immune, given

Figure 2. Evolution of ENSO variance in 14 different proxy reconstructions of ENSO over the last 600 years, plotted as the running variance of 10-yr highpass filtered individual reconstructions (grey circles) and as their mean running variance (black line with error envelope). The number of proxy ENSO reconstructions available through time is show in pink. Also plotted is the evolution of the 10-yr highpass filtered NIÑO3.4 SST variance derived from four different instrumental products (cyan circles), with their mean running variance (blue line). The red star indicates the variance of the 1979-2009 NIÑO3.4 index. Modified after Fig 7 of McGregor et al., 2013.

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1! 2!Figure 6: a) the 5% (dashed line plus +’s), 50% (solid line) and 95% (dashed lines plus x’s) of 3!the correlation coefficients calculated between the CM2.1 rainfall MRV and ENSO running 4!variance, while b) displays the percentiles of correlation coefficients calculated between CM2.1 5!rainfall RVM and ENSO running variance. The black lines indicate those percentiles using data 6!with no introduced temporal shifts, while the red, yellow, green and blue line respectively 7!represent those percentiles using data with 1/5, 1/4, 1/3, 1/2 of the time series including an 8!introduced temporal shift. 9! 10!!11!!12!!13! !14!

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Fig. 7. The 30 yr running variance (grey dots) of each of the 14 high-pass filtered (HPF, 10 yrcuto�) ENSO reconstructions, overlaid with the MRV (thick black line). At any point in time priorto the observation period, the thin black linesrepresent the widest median running variancesignal error bars of the two types of error analysis detailed in the Appendix A. Inside the windowof instrumental data these error bars change to thin black dash-dot lines, as we have a directmeasurement of ENSOs variance. The width of each of these two error bar estimates variesdepending on the number of ENSO variance proxies available (see purple line at top of panel).Cyan dots indicate the 30 yr running variance of the 4 observed HPF Niño 3.4 SST anomalies,while the blue line represents the observed ensemble median running variance. The red starindicates the most recen value of the ensemble median 30 yr running variance of the 4 observedHPF Niño 3.4 SST anomalies (1979–2009), while the thin red line just extends this most recentvalue back through time, for comparison with the ensemble median proxy running variance andits error bars.

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McGregor&et&al.,&2013&

Recent variance changes in 14 paleo-ENSO reconstructions

“. . . we find that the common ENSO variance over the period 1600-1900 was considerably lower than during 1979–2009.”

Fig. 3. Our NIÑO4 ENSO reconstruction. (A) The reconstruction of NIÑO4 SSTs from

the prior December to current October over 1190–2007 AD. SST anomalies (SSTAs) are

relative to the mean of observed SSTs during 1971–2007 AD. The red bold line denotes a

30-yr low-pass filter. The gray area is ±1σ error bars. The arrow is the 1651 AD very

strong El Niño event. (B) The 31-yr and (C) the 100-yr running variances of the each

individual series and the reconstruction (thick red line). (D) Comparison of our NIÑO4

SSTs reconstruction (red) with the ring-width based NIÑO3.4 SST (blue, Prior November

to current January) reconstruction for the past six centuries (Li et al., 2013), and (E) 50-yr

low-pass filter to both series.



















100yr'windows'

14 replicated Taiwan tree δ18O records, 1190-2007AD

Yu&et&al.,&submiOed&

Fig. 1 Map of study site plotted with the spatial correlation field of composite tree-ring δ18O: (A) 1950–2007 AD; (B) Our NIÑO4 SSTs reconstruction and Kaplan

global SSTs (Kaplan et al., 1998) records from December of the previous year to October

of the current year. Analyses are confined to post-1970, when the availability of

high-quality SST estimates increased greatly with the advent of satellite observations (Xie

et al., 2010). The black rectangle denotes the NIÑO4 region. The shaded letters denote

the locations where δ18O are sensitive to CP ENSO: 1–Taiwan (This paper); 2–Fujian,

China (Xu et al., 2013a); 3–Mu Cang Chai, Laos (Xu et al., 2013b); 4–Phu Leuy

Mountain, Vietnam (Sano et al., 2012); 5–Maiana (Urban et al., 2000); 6–Palmyra (Cobb

et al., 2003); 7–Quelccaya (Thompson et al., 2013) (table S2).

Several lines of paleo-evidence suggest that 20th century ENSO is stronger than in recent past.

NOTE: all records in question rely (to varying extents) on hydrological response to ENSO SST anomalies

Is SST variance higher? or Are we already seeing “more rainfall bang for SST buck”? (Power et al., 2013; Cai et al., 2014)

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MODERN FOSSIL

Key points: 1)  large range in paleo-ENSO variations implied by available data 2)  almost all coral data fall below late 20th century benchmark 3)  prolonged reduction 3-5kybp (NEW)



d13C, Mg/Ca SST, d18O of seawater (d18Osw), d18O ofindividual G. ruber (N = 2071), and C-14 ages used in theage model (Table 1). Stratigraphic relationships are consistentwith other open-ocean cores from the EEP [Lea et al., 2006;Pena et al., 2008] establishing the site as representative. TheLGM is defined by maximum d18O enrichment (Figure 2a)terminating !19 ka with a warming step in Mg/Ca SST(Figure 2b) and a correlative negative d13C shift (Figure 2d)previously observed in other sites from the EEP [Spero andLea, 2002; Pena et al., 2008]. The SST amplitude fromLGM to Late Holocene averages 2.0"C (Figure 2b), similar tothe !1.8"C change in nearby core TR163–22 [Lea et al.,2006]. V21–30 also shows a !1.5"C deglacial reversalduring the Younger Dryas, a previously ambiguous signal inMg/Ca but more clear in alkenone records [Kienast et al.,2006]. The d18Osw calculated from G. ruber d18O andMg/Ca shows a mean LGM-Holocene shift of 1‰ consistentwith the mean ocean shift due to ice volume [Waelbroecket al., 2002] but with a lag during deglaciation evident alsoin nearby TR163–22 [Lea et al., 2006]. A key advantage ofV21–30 is its shallow depth, which minimizes carbonatedissolution and contributes to superior preservation and highabundance of G. ruber. Our main objective here is to exploitthese attributes for single-specimen d18O analysis to recon-struct shifts in variance.

[10] The full ensemble of individual G. ruber d18O isshown in Figure 2a, and the corresponding estimates ofvariance are shown in Figure 3. As a test of our method’sability to reproduce modern conditions we compared themean and variance of d18O in core top samples withexpected values from instrumental data. Two core tops withages of 0.96 and 1.06 ka from V21–30 (617 m water depth)and nearby V21–29 (712 m water depth) [Koutavas andLynch-Stieglitz, 2003] produced identical d18O distributionswith means of #1.72 and #1.78‰ and standard deviationsof 0.507 and 0.510‰. By comparison expected values frominstrumental (1958–2007) monthly SST and salinity [Cartonand Giese, 2008] average #1.55 to #1.88‰ (bracketed bythe equations of Bemis et al. [1998]) and standard deviationof 0.514‰. The core top and late twentieth century valuesare indistinguishable indicating the method faithfully capturesboth mean and variance. We proceed to examine recon-structed monthly variance in the past from G. ruber d18O.

4. Holocene

[11] The complete sequence of reconstructed d18O variance(s2) in our samples is shown in Figure 3. For reference thefigure also shows the late twentieth century variance calcu-lated from instrumental data [Carton and Giese, 2008] (green

Figure 3. d18O variance of individual G. ruber from the Holocene and LGM. The variance is calculatedas the squared standard deviation (s2) of individual d18O in each sample. Two bars are shown for eachsample: the high bar includes all the data while the low bar excludes the heaviest and lightest value fromeach distribution, to test for spurious outlier effects. Both approaches yield similar shifts in variancedowncore. The age of each sample is indicated at the top of each bar (rounded to 0.1 ka). The 1.1 ka sampleis the core top of V21–29. The 7.0 ka sample marked with a star appears anomalous as its variance is drivenby two positive outliers (>3-sigma from the mean) and may be spurious. This sample was excluded fromfurther analysis. Green bar on left and dashed horizontal line indicate the 1958–2007 monthly d18Ovariance from instrumental SST and salinity [Carton and Giese, 2008]. Orange bar marked “No ENSO”is the monthly variance due to the annual cycle only [Locarnini et al., 2006]. Dashed arrows suggest broadtrends in the data. Hatched bars mark data breaks.

Figure 2. Stratigraphic and paleoclimatic proxies from core V21–30. (a) d18O of bulk G. ruber (red line), d18O of G. ruberindividuals (open circles) and their mean values (red circles). (b) Mg/Ca SST of G. ruber. (c) d18O of seawater calculatedfrom G. ruber Mg/Ca and d18O, compared to global ocean d18O [Waelbroeck et al., 2002]. (d) d13C of G. ruber. Trianglesat bottom indicate C-14 ages used in the age model (Table 1). The Holocene and LGM are shaded in gray.

KOUTAVAS AND JOANIDES: ENSO EXTREMA IN THE HOLOCENE AND LGM PA4208PA4208

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Variance of single foraminifera δ18O near Galapagos suggests EP ENSO disappeared ~4-6ky

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Data-model synthesis of Holocene ENSO Emile-Geay et al., submitted (PMIP paleovar group)

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EAST

reduced ENSO

Problem: If ENSO proxies reflect ENSO’s sensitivity to spring/fall precessional forcing, then GCM simulations of ENSO responding to summer/winter forcing are inaccurate.

!0.5 !0.4 !0.3 !0.2 !0.1 0 0.1 0.2 0.3 0.4 0.50

1000

2000

3000

4000

5000

6000

7000

mean=0.138mean=0

x95%=0.106 x95%=0.036

x95%=0.052 x95%=0.088

x95%=0.024 x95%=0.116

Null TRACE

Linear regression coefficient (oC/10000yr)N

umbe

r of o

ccur

ence

s

Nino3.4 pseudocoral PDFsets:100000 win:30yrs

nsample=50

nsample=200

nsample=1000

!60

!40

!20

0

% c

hang

e of

TRA

CE

ENSO

benc

hmar

k la

st 1

ka

20 18 16 14 12 10 8 6 4 2 0

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

0.55

Age (ka)

ENSO

var

iabi

lity

(1.5

!7

yr)

a

b

e.g.&CCSM4&Liu&et&al.,&2014&

seasonality&de

creasing&

ENSO&increas

ing&

Opportunity: How to explain a large, prolonged reduction in ENSO activity? Forced by spring/fall precessional insolation? Or was it unforced?

0 2 4 6 8 10 12 14 16 18 20

-3

-2

-1

0

1

2

3

4

5

6

7

8Proxy Observations

Scal

ed E

NSO

var

ianc

e

Scaled Seasonality

TLS fit, ̀o = +0.1±0.1

TLS fit, 95% CI

0 5 10 15 20 25 30 35 40 45 50

1

1.2

1.4

1.6

1.8

2

2.2

2.4

2.6

2.8

3General Circulation Models

Scal

ed E

NSO

var

ianc

e

Scaled seasonality

HadGEM2 PI

GISS PI

KCM PI

CCSM4 PI

MIROC PI

MPI PI

CNRM PI

IPSL PI

CSIRO PI

TLS fit, ̀m

= -0.012±0.0042

TLS fit, 95% CI

ref. 13, 14

this study

ref. 11, 12

ref. 18, 19

ref. 15, 16, 20, 21

ref. 17

ref. 22, 23

ref. 24, 25

Emile-Geay et al., submitted (PMIP paleovar group)

0 2 4 6 8 10 12 14 16 18 20

-3

-2

-1

0

1

2

3

4

5

6

7

8Proxy Observations

Scal

ed E

NSO

var

ianc

eScaled Seasonality

TLS fit, ̀o = +0.1±0.1

TLS fit, 95% CI

0 5 10 15 20 25 30 35 40 45 50

1

1.2

1.4

1.6

1.8

2

2.2

2.4

2.6

2.8

3General Circulation Models

Scal

ed E

NSO

var

ianc

e

Scaled seasonality

HadGEM2 PI

GISS PI

KCM PI

CCSM4 PI

MIROC PI

MPI PI

CNRM PI

IPSL PI

CSIRO PI

TLS fit, ̀m

= -0.012±0.0042

TLS fit, 95% CI

ref. 13, 14

this study

ref. 11, 12

ref. 18, 19

ref. 15, 16, 20, 21

ref. 17

ref. 22, 23

ref. 24, 25

Emile-Geay et al., submitted (PMIP paleovar group)

A)  Proxies Need more data. Separate SST and hydrological components? How to combine proxies to isolate signal (mean state, decadal, and ENSO)? B)  Models Need to revisit annual cycle-ENSO relationship. ENSO teleconnections – sensitivity to mean state?

NEAR-TERM CHALLENGES

A)  Proxies Need more data. Separate SST and hydrological components? How to combine proxies to isolate signal (mean state, decadal, ENSO)? B)  Models Need to revisit annual cycle-ENSO relationship. ENSO teleconnections – sensitivity to mean state?

NEAR-TERM CHALLENGES

water&isotopes&(salinity)&

forward&models&of&proxies&(‘pseudoproxies’)&

THE&GLUE&

PMIP&

Line&Islands&Project&2014K2016&

Forced or unforced? New views of ENSO from 6,000yrs ago to the present

Science

Transcript of Forced or unforced? New views of ENSO from 6,000yrs ago to the present