ocean observation to increase predictability in climate...
TRANSCRIPT
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
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80 85 90 95 100 105 110 115 120 125 130
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80 85 90 95 100 105 110 115 120 125 130
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80 85 90 95 100 105 110 115 120 125 130
25
30
35
40
45
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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