ocean observation to increase predictability in climate...

Ocean observation to increase predictability in

climate change adaptation: status of scientific

studies and challenges in Asia and Pacific

Fangli Qiao, Zhenya Song, Jingsong Guo

First Institute of Oceanography, SOA, China

Nov 17, 2015 DaNang, Viet Nam

Outline

1. Where are we

2. Development of advanced models

3. Joint observation and DA

4. Summary

1. Where are we?

4

(1) Attacked by marine hazards such as Typhoon frequently

.

Haiyan attached the Philippines in Nov 2013, with 6201 dead and

11.8M affected

5

厄尔尼诺 季风

80 85 90 95 100 105 110 115 120 125 130

25

30

35

40

45

50

80 85 90 95 100 105 110 115 120 125 130

25

30

35

40

45

50

80 85 90 95 100 105 110 115 120 125 130

25

30

35

40

45

50

80 85 90 95 100 105 110 115 120 125 130

25

30

35

40

45

50

270

180

-90

-180

-270

90

Stong monsoon: More rain in south

and north China, while less rain along

Changjiang River

The relationship with Asian Monsoon

6

1999

2009

(2) Observation

7

Planning for the Indian Ocean Expedition 50th Anniversary

Initiative (IIOE–2, 2015-2020)

IIOE IIOE-2

8

ARGO

However, ocean monitoring network for marine hazards

and climate change is still urgently needed.

(3) Climate models of CMIP5: Double ITCZ and SST

MLD in CMIP5 models

Huang et al, 2014, JGR

MLD in OGCM

Observation Model from POM

12

For Asian Monsoon area

Prediction skills of 5 AGCMs

（Wang et al, 2004）

13

25 CMIP5 models

Rainfall

Too late for Monsoon onset

Asian summer monsoon too weak

while northwest Pacific monsoon too

strong

Sperber et al,2012, CD

Typhoon/Hurrican

14

Rappaport et al, 2012, BAMS

2. Development of advanced

numerical models

E(K) is the wave number spectrum which can be calculated

from a wave numerical model. It will change with (x, y, t), so

Bv is the function of (x, y, z, t). Qiao et al, 2004, 2010, 2015

If we regard surface wave as a monochramatic wave,

3 ( 3 ) ( 3 ) ,kz kz

v sB A k e A u e

Bv is wave motion related vertical mixing instead of wave breaking.

Stokes Drift

12

2exp 2 exp 2V

k k

B E k kz dk E k kz dkz

(1) Theory of surface wave-induced mixing

Laboratory experiments:

Wave tank: 5m in length with

height of 0.4m and width of 0.2m.

To generate temperature

gradient through bottom cooling

of refrigeration tubes, and

temperature sensors are self-

recorded with sampling

frequency of 1Hz.

Bottom of wave tank

Top of wave tank

Refrigeration tube

Temperature sensor

(1) Temperature evolution in natural condition

(2) Temperature evolution with wave

Dai and Qiao et al, JPO, 2010

Experiment results without and with waves

Evolution of water temperature without waves. (a) Observation; (b) simulation.

z

T Tk

t z z

kz=k0+Bv

Simulation results with waves

Evolution of water temperature with waves. Left: observation; right: simulation; (a,b) 1.0cm, 30cm; (c,d) 1.0cm, 52cm;

We apply Bv into:

Bohai Sea

Yellow Sea

East China Sea

And

South China Sea

Xia et al, JGR,2006

3-D coastal circulation model (Special Issue on JGR, 2006 at

http://www.agu.org/journals/ ss/CHINASEAS1/ )

(2) Improvement of ocean models

385 390 395 400

Latitude(1/10° N)

T_Bohai section

-25

-20

-15

-10

-5

0

H(m

)

17.5

18

18.5

19

19.5

20

20.5

21

21.5

22

22.5

23

23.5

24

24.5

25

25.5

26

38 38.5 39 39.5 40 40.5

-30

-25

-20

-15

-10

-5

0

18

18.5

19

19.5

20

20.5

21

21.5

22

22.5

23

23.5

24

24.5

25

25.5

26

Observation in summer

Lin et al, 2006 JGR

38.5 39 39.5 40-30

-25

-20

-15

-10

-5

0

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

(a)POM

POM+Bv

3-D coastal models

Along 35N transect in Aug.

World Ocean Atlas

With wave-

induce mixing

Without

wave effects

Pacific Atlantic Indian Pacific Atlantic

Along 35S transect in Feb.

35N

35S

3-D global ocean circulation models

Along 35N transect in Aug.

World Ocean Atlas

With wave-induce mixing

Pacific Atlantic Indian Pacific Atlantic

Along 35S transect in Feb.

Vertical Temperature Distributions

Without wave-induce mixing

FIO wave-tide-circulation coupled model 0.1X0.1

Yalin Fan, and Stephen M. Griffies, 2014, JC (Fig 3)

Summertime oceanic mixed layers

are biased shallow in both the

GFDL and NCAR climate models

(Bates et al. 2012;

Dunne et al. 2012, 2013).

This scheme (Qiao et al., 2004)

has most impact in our simulations

on deepening the summertime

mixed layers, yet it has minimal

impact on wintertime mixed layers.

(3) Improvement of climate models

50a averaged SST (251-300a).

Up: Exp1-Levitus, Down: Exp2-Exp1

Exp1: CCSM3 without Bv

Exp2: with Bv

Framework of FIO-ESM version1.0

Land carbon

CASA’

Atmosphere

CO2 transport

Ocean carbon

OCMIP-2

Wave-induced mixing,

Qiao et al., 2004

FIO-ESM for CMIP5

SST absolute mean errors for 45 CMIP5 models

Sea ice annual cycle in Arctic

1

1.2

1.4

1.6

1.8

2

2.2x 1013

January

RCP2.6

RCP4.5

RCP6.0

RCP8.5

1.2

1.4

1.6

1.8

2

2.2

2.4x 1013

February

1.2

1.4

1.6

1.8

2

2.2

2.4x 1013

March

1.2

1.4

1.6

1.8

2

2.2

2.4x 1013

April

1

1.2

1.4

1.6

1.8

2

2.2x 1013

May

0.8

1

1.2

1.4

1.6

1.8

2x 1013

June

4

6

8

10

12

14

16x 1012

July

0

5

10

15x 1012

August

2000 2020 2040 2060 2080 21000

2

4

6

8

10

12x 1012

September

2000 2020 2040 2060 2080 21000

5

10

15x 1012

October

2000 2020 2040 2060 2080 21000

0.5

1

1.5

2x 1013

November

2000 2020 2040 2060 2080 21000.5

1

1.5

2x 1013

December

Unit: m2 Time series of Arctic sea ice extent from RCP run.

Without spray

Without spray

With spray

With spray

Latent HF

Sensible HF

4-Nov 5-Nov 7-Nov 9-Nov 10-Nov

880

900

920

940

960

980

1000

Min

imu

mS

ea

Le

ve

lP

ressure

(hP

a)

Best track

WRF-POM

WRF-POM-MASNUM with Bv

WRF-POM-MASNUM with Spray

WRF-POM-MASNUM with Spray and Bv

3. Joint observation and DA

DA SST, SLA, Argo

AME

RMS

DA of FIO-ESM (1992-2014)

5.6 大气模式分量：

—降水

Double ITCZ

GPCP

0.0

0.2

0.4

0.6

0.8

1.0

1.2

SST /℃

60%

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

OHC /1019J

50%

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

SLP /hPa

37%

0.0

0.5

1.0

1.5

2.0

Rain /mm*day-1

35%

0.0

0.5

1.0

1.5

2.0

Hor. V/m*s-1

26%

0.0

1.0

2.0

3.0

4.0

5.0

6.0

Vertical V/10-3Pa*s-1

21% 25% 19%

0.0 2.0 4.0 6.0 8.0

10.0 12.0 14.0 16.0

Humidity /10-2g*kg-1

0.0

2.0

4.0

6.0

8.0

10.0

12.0

Cloud /%

RMS errors with and without DA

Indo-Pacific Ocean Environment

Variation and Air-sea Interaction IPOVAI Project Proposal

Dr. Prof. Fangli Qiao

First Institute of Oceanography, SOA, China 11-13 May 2015 , Phuket, Thailand

(3) IPOVAI 2015-2020

2015/11/16

Multi-scale

Interaction Predictability

Northern Indian Ocean Circulation Air-Sea Interaction

Typhoon

Mech

anism

An

alysis

Dyn

amic P

rocess

Mechanism and Model

Evaluation and Diagnosis

of Predictability

Dyn

amic P

rocess

Mech

anism

analy

sis

Propagation of climate signal

Inter-Ocean Exchange

Monsoon

Western Pacific Circulation, Warm Pool

4. Summary

We have identified the key role of surface

wave in ocean and climate models, and

coupled models with surface wave have

been successfully developed. We would like

to share the knowledge;

No one nation can afford ocean

observations. We need joint efforts, “Indo-

Pacific Ocean Environment Variation and Air-

sea Interaction” (IPOVAI) is open and

welcome participants.

Thanks for your attention