[PPT] - Twitter: @coralsncaves PP029: Paleoclimatic history of the El PowerPoint Presentation

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Twitter: @coralsncaves

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PP029: Paleoclimatic history of the El Niño-Southern Oscillation

bservations, theory, modeling

Tom Marchitto (CU-Boulder) Kim Cobb (Georgia Tech) Diane Thompson (Boston University)

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Orbital controls on Western Pacific hydrology

Kim Cobb (Georgia Tech)

@coralsncaves

Stacy Carolin (Oxford) Shelby Ellis (Georgia Tech) Sang Chen (Caltech) David Lund (U. Connecticut) Nele Meckler (U. Bergen) Ian Orland (U. Wisconsin) Jess F. Adkins (Caltech) Jud W. Partin (UT-Austin) Sharon Hoffmann (UNC-Wilmington) Julien Emile-Geay (U. Southern California) Andrew A. Tuen (U. Sans Malaysia) Brian Clark, Syria Lejau, Jenny Malang (Gunung Mulu National Park) Jean Lynch-Stieglitz (Georgia Tech) Jessica Moerman (Smithsonian)

#fiercefemalefieldphoto

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Orbital controls on Western Pacific hydrology

Kim Cobb (Georgia Tech)

@coralsncaves

Stacy Carolin (Oxford) Shelby Ellis (Georgia Tech) Sang Chen (Caltech) David Lund (U. Connecticut) Nele Meckler (U. Bergen) Ian Orland (U. Wisconsin) Jess F. Adkins (Caltech) Jud W. Partin (UT-Austin) Sharon Hoffmann (UNC-Wilmington) Julien Emile-Geay (U. Southern California) Andrew A. Tuen (U. Sans Malaysia) Brian Clark, Syria Lejau, Jenny Malang (Gunung Mulu National Park) Jean Lynch-Stieglitz (Georgia Tech) Jessica Moerman (Smithsonian)

#fiercefemalefieldphoto

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“the ice cores

f the tropics”

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2015/16 El Niño event Outgoing Longwave Radiation (OLR)

Gunung Mulu National Park

wet dry

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40N 20S 40S 40N 20N 20S 40S 60E 120E 180 120W 60W 40N 20N 20S 40S Correlation (R) Correlation between TRMM and Mulu 18O 0.2 0.4 0.6

0.6
0.4
0.2

60E 120E 180 120W 60W 40N 20N 20S 40S Correlation (R) Correlation between TRMM and Mulu precip 0.2 0.4 0.6

0.6
0.4
0.2

A C

x x

Rainfall oxygen isotopes at Mulu are better than a rain gauge!

Moerman et al., 2013

R(Mulu d18O,TRMM) R(Mulu precip,TRMM)

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40N 20S 40S 40N 20N 20S 40S 60E 120E 180 120W 60W 40N 20N 20S 40S Correlation (R) Correlation between TRMM and Mulu 18O 0.2 0.4 0.6

0.6
0.4
0.2

60E 120E 180 120W 60W 40N 20N 20S 40S Correlation (R) Correlation between TRMM and Mulu precip 0.2 0.4 0.6

0.6
0.4
0.2

A C

x x

Why? Rainfall isotopes integrate through space and time.

Moerman et al., 2013

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40N 20S 40S 40N 20N 20S 40S 60E 120E 180 120W 60W 40N 20N 20S 40S Correlation (R) Correlation between TRMM and Mulu 18O 0.2 0.4 0.6

0.6
0.4
0.2

60E 120E 180 120W 60W 40N 20N 20S 40S Correlation (R) Correlation between TRMM and Mulu precip 0.2 0.4 0.6

0.6
0.4
0.2

A C

x x

40% of Mulu d18OR variance controlled by ENSO 20% by seasonal variability

Moerman et al., 2013

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12
10
8
6
4
2

WF 18O (%o)

10
9
8
7
6
5

L2 18O (%o)

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

12
10
8
6
4
2

18

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

10
9
8
7
6
5

18

Mulu d18OR variations reflected in cave dripwater d18O variations 5-10 month residence times

Moerman et al., 2014; Ellis et al., in prep measured drip d18O rainfall d18O average

ver last 5 months

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12
10
8
6
4
2

WF 18O (%o)

10
9
8
7
6
5

L2 18O (%o)

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

12
10
8
6
4
2

18

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

10
9
8
7
6
5

18

Moerman et al., 2014; Ellis et al., in prep measured drip d18O rainfall d18O average

ver last 5 months
(NIÑO3.4 SST)

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Working group members: Kim Cobb (Co-Chair) – Georgia Tech David Noone (Co-Chair) – U. Oregon Samantha Stevenson – UCSB Gabe Bowen – U. Utah Jess Conroy – U. Illinois C-U Alyssa Atwood – UC-Berkeley, GA Tech Bronwen Konecky – Washington Univ. Allegra Legrande – NASA-GISS Adrianna Bailey – NCAR Jesse Nusbaumer – NASA-GISS Natalie Burls – George Mason

Key goals water isotopes as essential

cean and climate variable?

design 21st century obs network data archive for all water isotope obs & model data coordinating modeling efforts à CMIP7?

Observations and Modeling of Water Isotopes in the Climate System (funded 2018-2021)

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“the ice cores

f the tropics”

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What is the sensitivity of western tropical Pacific hydroclimate to

rbital forcing?

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Age (kyr BP) T-I T-VII T-VI T-V T-IV T-III T-II δ18O (VPDB, ‰) Insolation (W m–2) 5 cycles ~115 kyr T-IIIa 100 200 300 400 500 600 T-VIIa 5 cycles ~113 kyr 4 cycles ~92 kyr 5 cycles ~92 kyr 5 cycles ~105 kyr 4 cycles ~93 kyr 4 cycles ~92 kyr 2 cycles ~47 kyr 2 cycles ~46 kyr 1 cycle ~21 kyr Precession 0.01 0.03 0.05 Eccentricity 22 23 24 25 Obliquity (°)

a c b f d

MIS 11 MBE MIS 4/3 MIS 5.2/5.1 –0.04 0.00 0.04 –11 –9 –7 –5 380 420 460 500

e

Sea level (m) –120 –80 –40 MIS 15.2 MIS 15.1 MIS 7.4 MIS 7.3

Hulu/Sanbao, Cheng et al., Nature 2016

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(W/m2)

strong precessional signal tied to boreal fall insolation

more convective activity

Mulu stalagmite d18O

Partin et al., 2007 (0-30kybp) Meckler et al., 2012 (200-550kybp) Carolin et al., 2013 (30-100kybp) Carolin et al., 2016 (100-160kybp)

Carolin et al., 2016

boreal fall insolation at 0o

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(W/m2) more convective activity

Partin et al., 2007 (0-30kybp) Meckler et al., 2012 (200-550kybp) Carolin et al., 2013 (30-100kybp) Carolin et al., 2016 (100-160kybp)

Carolin et al., 2016

Mulu stalagmite d18O

clear influence of glacial boundary conditions

boreal fall insolation at 0o

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(W/m2)

clear influence of glacial boundary conditions (d18Osw +/- Sunda Shelf +/- temp +/- DENSO, ENSO-like)

more convective activity

Partin et al., 2007 (0-30kybp) Meckler et al., 2012 (200-550kybp) Carolin et al., 2013 (30-100kybp) Carolin et al., 2016 (100-160kybp)

Carolin et al., 2016

Mulu stalagmite d18O

boreal fall insolation at 0o

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What dynamical processes underlie the strong response of Borneo stalagmite d18O to boreal fall insolation?

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9.5

485

δ18

10.5
10.0
9.5
9.0
8.5
8.0
7.5
7.0
6.5

Equator SON insolation (W/m2) 405 410 415 420 425 430 435 440 445

(a)

Age (yr BP)

2000 4000 6000 8000 10000 12000

6.0

450

Stalagmite BA03

70-150μm/yr 30-60 μm sampling = 2-5 samples/yr

Our Holocene Playground

Chen et al., EPSL 2016 high-resolution sampling intervals

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Age (yr BP)

2390 2410 2430 2450 2470 2490 2510 2530 2550 2570 2590

δ18

10.0
9.8
9.6
9.4
9.2
9.0
8.8
8.6
8.4

Age (yr BP)

3240 3260 3280 3300 3320 3340 3360 3380

Age (yr BP)

5170 5190 5210

Age (yr BP)

2390 2410 2430 2450 2470 2490 2510 2530 2550 2570 2590

2-7yr filtered δ18

0.20
0.15
0.10
0.05

0.05 0.10 0.15 0.20

Age (yr BP)

3240 3260 3280 3300 3320 3340 3360 3380

Age (yr BP)

5170 5190 5210

Age (yr BP)

5640 5660 5680 5700

Age (yr BP)

6590 6610 6630 6650 6670 6690 6710 6730

Age (yr BP)

8140 8160 8180 8200 8220 8240 8260 8280 8300

Age (yr BP)

5640 5660 5680 5700

Age (yr BP)

6590 6610 6630 6650 6670 6690 6710 6730

Age (yr BP)

8140 8160 8180 8200 8220 8240 8260 8280 8300 2390

δ18

10.0
9.8
9.6
9.4
9.2
9.0
8.8
8.6
8.4

2390

2-7yr filtered δ18

0.20
0.15
0.10
0.05

0.05 0.10 0.15 0.20

2.5kybp 3.3kybp 5.2kybp 5.7kybp 6.7kybp 8.2kybp

sub-annually resolved stalagmite d18O records, 50-200yrs long

Chen et al,. 2016

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Age (yr BP)

2390 2410 2430 2450 2470 2490 2510 2530 2550 2570 2590

δ18

10.0
9.8
9.6
9.4
9.2
9.0
8.8
8.6
8.4

Age (yr BP)

3240 3260 3280 3300 3320 3340 3360 3380

Age (yr BP)

5170 5190 5210

Age (yr BP)

2390 2410 2430 2450 2470 2490 2510 2530 2550 2570 2590

2-7yr filtered δ18

0.20
0.15
0.10
0.05

0.05 0.10 0.15 0.20

Age (yr BP)

3240 3260 3280 3300 3320 3340 3360 3380

Age (yr BP)

5170 5190 5210

Age (yr BP)

5640 5660 5680 5700

Age (yr BP)

6590 6610 6630 6650 6670 6690 6710 6730

Age (yr BP)

8140 8160 8180 8200 8220 8240 8260 8280 8300

Age (yr BP)

5640 5660 5680 5700

Age (yr BP)

6590 6610 6630 6650 6670 6690 6710 6730

Age (yr BP)

8140 8160 8180 8200 8220 8240 8260 8280 8300 2390

δ18

10.0
9.8
9.6
9.4
9.2
9.0
8.8
8.6
8.4

2390

2-7yr filtered δ18

0.20
0.15
0.10
0.05

0.05 0.10 0.15 0.20

2.5kybp 3.3kybp 5.2kybp 5.7kybp 6.7kybp 8.2kybp

sub-annually resolved stalagmite d18O records, 50-200yrs long

Chen et al,. 2016

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Age (yr BP)

2390 2410 2430 2450 2470 2490 2510 2530 2550 2570 2590

δ18

10.0
9.8
9.6
9.4
9.2
9.0
8.8
8.6
8.4

Age (yr BP)

3240 3260 3280 3300 3320 3340 3360 3380

Age (yr BP)

5170 5190 5210

Age (yr BP)

2390 2410 2430 2450 2470 2490 2510 2530 2550 2570 2590

2-7yr filtered δ18

0.20
0.15
0.10
0.05

0.05 0.10 0.15 0.20

Age (yr BP)

3240 3260 3280 3300 3320 3340 3360 3380

Age (yr BP)

5170 5190 5210

Age (yr BP)

5640 5660 5680 5700

Age (yr BP)

6590 6610 6630 6650 6670 6690 6710 6730

Age (yr BP)

8140 8160 8180 8200 8220 8240 8260 8280 8300

Age (yr BP)

5640 5660 5680 5700

Age (yr BP)

6590 6610 6630 6650 6670 6690 6710 6730

Age (yr BP)

8140 8160 8180 8200 8220 8240 8260 8280 8300 2390

δ18

10.0
9.8
9.6
9.4
9.2
9.0
8.8
8.6
8.4

2390

2-7yr filtered δ18

0.20
0.15
0.10
0.05

0.05 0.10 0.15 0.20

2.5kybp 3.3kybp 5.2kybp 5.7kybp 6.7kybp 8.2kybp

sub-annually resolved stalagmite d18O records, 50-200yrs long

Chen et al,. 2016

tracking the variance of ENSO over the Holocene

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BA03 δ18

0.02 0.03 0.04 0.05 0.06 0.07

Total Variance of Foraminiferal δ18O

0.1 0.15 0.2 0.25 0.3 0.35

Figur Holocene estimates foraminiferal, and Estimates from and Koutavas

n

2-7 windows

(a)

BA03 V21-30

Age (yr BP)

1000 2000 3000 4000 5000 6000 7000 8000 9000 10000

Red intensity stdev of 2-7yr band

(c)

Pallcacocha Moy et al., 2002) and El Junco in the Galapagas Islands (red, Conroy et al., 2008).

stronger ENSO

Borneo stalagmite interannual d18O variance

Chen et al., EPSL 2016 Joanides & Koutavas, 2012 variance of single foram d18O from EEP

Borneo time slices

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BA03 δ18

0.02 0.03 0.04 0.05 0.06 0.07

Total Variance of Foraminiferal δ18O

0.1 0.15 0.2 0.25 0.3 0.35

Figur Holocene estimates foraminiferal, and Estimates from and Koutavas

n

2-7 windows

(a)

BA03 V21-30

Age (yr BP)

1000 2000 3000 4000 5000 6000 7000 8000 9000 10000

Red intensity stdev of 2-7yr band

(c)

Pallcacocha Moy et al., 2002) and El Junco in the Galapagas Islands (red, Conroy et al., 2008).

stronger ENSO

Borneo stalagmite interannual d18O variance

Chen et al., EPSL 2016 Joanides & Koutavas, 2012

weaker ENSO

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weaker ENSO

Cobb et al., 2013; Carre et al., 2014; McGregor et al., 2013

Borneo data consistent with other ENSO proxies

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330

1000 2000 3000 4000 5000 6000 7000 Year (BP)

80
60
40
20

20 40 60 80 Change in stdev of ENSO (%)

A

Palmyra Fanning Christmas stronger ENSO weaker ENSO

Modern Coral

Change in stdev of ENSO (%)

80
60
40
20

20 40 60 80

B

1987-2007 ENSO variance

Grothe et al., under review

Cobb et al, 2013 Cobb et al., 2003 McGregor et al., 2013 Grothe et al., in prep Woodroffe et al., 2003

Year-to-year variability over 7,000yrs

Most data fall below the late 20th century benchmark. weaker ENSO

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9.5

485

δ18

10.5
10.0
9.5
9.0
8.5
8.0
7.5
7.0
6.5

Equator SON insolation (W/m2) 405 410 415 420 425 430 435 440 445

(a)

Age (yr BP)

2000 4000 6000 8000 10000 12000

6.0

450

weaker ENSO

period during peak boreal fall insolation

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9.5

485

δ18

10.5
10.0
9.5
9.0
8.5
8.0
7.5
7.0
6.5

Equator SON insolation (W/m2) 405 410 415 420 425 430 435 440 445

(a)

Age (yr BP)

2000 4000 6000 8000 10000 12000

6.0

450

weaker ENSO

period during peak boreal fall insolation

Why does boreal fall insolation drive ENSO reduction?

SLIDE 30

Karamperidou & DiNezio, in prep

ENSO growth rate as a function of season

El Niño events grow during boreal fall

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Karamperidou & DiNezio, in prep

ENSO growth rate as a function of season

El Niño events grow during boreal fall

But models and theory and some

bs support link between summer

insolation and weakened ENSO… (e.g. Chiang et al., 2009; Liu et al., 2014; White et al., 2017)

SLIDE 32

Tropical Pacific coupled system sensitive to external radiative forcing, especially to changes in the seasonal cycle, but full dynamical picture still unclear.

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Tropical Pacific coupled system sensitive to external radiative forcing, especially to changes in the seasonal cycle, but full dynamical picture still unclear. Key roles for modeling community: 1) explore isotope-enabled simulations to provide dynamical context for key reconstruction sites (Hulu, Mulu, central Pacific Galapagos, East Africa)

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Tropical Pacific coupled system sensitive to external radiative forcing, especially to changes in the seasonal cycle, but full dynamical picture still unclear. Key roles for modeling community: 1) explore isotope-enabled simulations to provide dynamical context for key reconstruction sites (Hulu, Mulu, central Pacific Galapagos, East Africa) 2) combine a hierarchy of models in novel ways to explore interactions between mean state, seasonal cycle, & ENSO (Atwood et al., in prep)