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Climate variability in wind waves from VOS visual observations. Vika Grigorieva & Sergey Gulev, IORAS, Moscow. OUTLINE:. Climatology of visually observed wind waves Errors and uncertainties Centennial-scale changes Decadal to interannual variability - PowerPoint PPT Presentation
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Climate variability in wind wavesClimate variability in wind wavesfrom VOS visual observationsfrom VOS visual observations
Vika Grigorieva & Sergey Gulev, IORAS, Moscow
Climatology of visually observed wind waves
Errors and uncertainties
Centennial-scale changes
Decadal to interannual variability
Changes in wave statistics derived from VOS
OUTLINE:
MARCDAT-II Workshop, 2005, Exeter
1 - 5 5 - 1 0 1 0 - 2 0 2 0 - 5 0 5 0 - 1 0 0 1 0 0 - 3 0 0 3 0 0 - 5 0 0 > 5 0 0
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Visual VOS observations: 2 streams (1784-1948) and (1948-2003)
1900 1920 1940 1960 1980 2000
Y E A R S
only SW Hmaxsea, swell, SW H
Global climatology of wind waves from VOS data:
http://www.sail.msk.ru/atlas
• monthly• 1958-2002 (updated)• 2-degree resolution• Separate estimates of sea, swell, SWH
Gulev and Grigorieva JGR, 2003
Random observational errors Sampling errors
All fields are accompanied by:
See poster of Grigorieva and Gulev for the error analysis
Very long-term changes along the major ship routes
- 1 0 0 - 8 0 - 6 0 - 4 0 - 2 0 0 2 0 4 0 6 0 8 0 1 0 0 1 2 0 1 4 0 1 6 0 1 8 0 2 0 0 2 2 0 2 4 0 2 6 0 2 8 0
- 1 0 0 - 8 0 - 6 0 - 4 0 - 2 0 0 2 0 4 0 6 0 8 0 1 0 0 1 2 0 1 4 0 1 6 0 1 8 0 2 0 0 2 2 0 2 4 0 2 6 0 2 8 0
- 8 0
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2 0
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0
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1900 1920 1940 1960 1980 2000years
1
10
100
1000
nu
mb
er
of
rep
ort
s
157
2550
65 regionswith high samplingduring 1885-2002
Homogenization:
sub-sampling for 7,15,25,50 reports per region per month
Homogenized time series
1900 1920 1940 1960 1980 2000years
1
2
3
4
SW
H, m
ete
rs
1900 1920 1940 1960 1980 2000years
1
2
3
4
SW
H, m
ete
rs
Buoys: Gower 2002:
OW S L
SSLV
6 8 7 0 7 2 7 4 7 6 7 8 8 0 8 2 8 4 8 6 8 8Y E A R S
1 . 6
1 . 8
2 . 0
2 . 2
2 . 4
2 . 6
2 . 8
3 . 0
3 . 2
3 . 4
3 . 6
3 . 8
4 . 0
4 . 2s
ign
ific
an
t w
av
e h
eig
ht,
m
Bacon and Carter 1991Gulev and Hasse 1999
Very long-term changes:
linear trends
Gulev andGrigorieva 2004
1900-2002
1958-2002
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70100 110 120 130 140 150 160 170 180 190 200 210 220 230 240 250 260 270 280
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(A)
(B)
(C)
(D)
(E)
(F)
-0.15-0.1-0.05
00.050.10.15
0.20.30.5
m/dec
-0.3
-0.2
Trends in sea, swell and SWH: 1958-2002
sea
swell
SWH
sea
swell
SWH
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70100 110 120 130 140 150 160 170 180 190 200 210 220 230 240 250 260 270 280
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70-100 -90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20
-100 -90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 200
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70100 110 120 130 140 150 160 170 180 190 200 210 220 230 240 250 260 270 280
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70-100 -90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20
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70100 110 120 130 140 150 160 170 180 190 200 210 220 230 240 250 260 270 280
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20
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-0.2-0.1-0.05
00.050.10.2
0.30.4
meters
-0.4
-0.3
(A)
(B)
(C)
(D)
(E)
(F)
Winter (JFM) 1st EOFs of sea, swell and SWH
sea
swell
SWH
sea
swell
SWH
Principal components
Atlantic
R(HW–NAO)=0.68R(HS–NAO)=0.48
R(SWH–NAO)=0.81
Pacific
R(HW–NPI)=0.72R(HS–NPI)=0.58
R(SWH–NPI)=0.61
1960 1970 1980 1990 2000
-4
-3
-2
-1
0
1
2
3
4n
orm
aliz
ed
PC
s, N
AO
ind
ex
1960 1970 1980 1990 2000
-3
-2
-1
0
1
2
3
no
rma
lize
d P
Cs
, NP
I
sea
swellSWH
NAO
seaSWH
swell NPI
Canonical patterns
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70-100 -90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20
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10
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70-100 -90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20
-100 -90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20
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70-100 -90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20
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(A)
(B)
(C)
(D)
(E)
(F)
-0.2-0.1-0.05
00.050.10.2
0.30.4
meters
-0.4
-0.3
-0.75-0.5-0.25
00.250.50.75
1
m/s
-1
Number of cyclones
swell
SWH
scalarwind
sea
SWH
IDM – initial distribution method – methodologically, most relevant for VOS, but does not allow for reliable estimation of extreme waves
POT – peak over threshold – sensitive to sampling inhomogeneity
Extreme waves from VOS: problem of estimation
-100 -80 -60 -40 -20 0 20 40 60 80 100 120 140 160 180 200 220 240 260 280
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1.79 2.17 1.05 1.52 1.15 1.15 1.28 2.67 1.06 1.40 1.77 2.84 0.70 1.79
1.07 1.29 1.25 1.20 1.17 1.51 1.32 3.18 1.06 1.50 1.40 1.17 1.20 1.21 1.07
1.47 1.38 1.23 1.12 1.08 1.20 0.77 1.00 1.16 1.33 1.46 1.07 1.23 1.20 1.16 1.19 1.12 1.47
1.12 1.16 0.91 0.77 1.16 1.17 0.76 1.30 1.26 1.27 1.22 1.18 1.27 1.14 0.86 1.70 1.52 1.18 1.12
0.91 0.63 2.58 0.78 0.88 1.23 0.87 1.11 1.04 1.22 1.16 0.94 1.04 0.92 0.80 1.15 0.56 0.82 0.91
0.67 0.88 1.25 1.52 0.98 1.43 1.63 1.44 1.31 1.15 1.59 1.48 1.81 2.06 1.54 1.34 0.97 1.54 0.67
0.57 1.81 1.50 1.08 1.72 2.24 3.54 0.68 1.45 2.19 1.14 1.92 1.77 1.73 1.07 2.25 0.57
7.02 9.63 14.98 12.56 12.01 11.54 7.25 9.97 9.56
13.18 10.51 14.02 17.03 14.81 10.82 8.17 9.02 9.47 11.92 14.81 13.43 13.61 12.40 13.18
8.66 10.49 12.20 12.08 10.15 9.41 8.68 6.44 5.78 10.67 9.85 13.23 13.90 13.79 11.65 9.83 6.59 8.66
8.08 8.84 8.19 7.97 6.77 4.10 6.84 6.98 6.46 6.37 9.97 9.47 8.12 9.29 9.11 9.14 7.97 6.19 8.08
5.74 5.40 6.88 5.89 5.67 7.13 7.99 7.20 8.12 7.60 9.09 8.03 7.25 9.02 7.99 7.06 6.19 6.84 5.74
7.92 7.67 7.31 7.16 7.79 9.88 10.31 9.85 9.88 8.91 9.94 9.88 9.47 9.74 9.90 9.49 7.43 8.19 7.92
12.13 9.18 11.57 12.94 12.58 11.14 12.65 11.59 12.87 13.27 10.21 10.69 10.04 10.55 12.58 9.95 16.24 14.51 12.13
6.79 8.26 7.42 8.01 7.25 4.88 7.31 9.85 8.64 7.76 8.95 7.47 10.17 9.00 10.93 11.03 9.58 6.79
-160 -140 -120 -100 -80
-160 -140 -120 -100 -80
100-yr returns in SWH - IDM
Estimation of extreme wave heights - POT
Changes in extreme SWH 100-yr returns
-100 -80 -60 -40 -20 0 20 40 60 80 100 120 140 160 180 200 220 240 260 280
-100 -80 -60 -40 -20 0 20 40 60 80 100 120 140 160 180 200 220 240 260 280
-80
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0
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-160 -140 -120 -100 -80
-160 -140 -120 -100 -80 -3.00 to -1 .00 -1.00 to -0 .50 -0.50 to -0 .20 -0.20 to 0 .00 0.00 to 0 .20 0.20 to 0 .50 0.50 to 1 .00 1.00 to 4 .00
-100 -80 -60 -40 -20 0 20 40 60 80 100 120 140 160 180 200 220 240 260 280
-100 -80 -60 -40 -20 0 20 40 60 80 100 120 140 160 180 200 220 240 260 280
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-160 -140 -120 -100 -80
-160 -140 -120 -100 -80
1980 - 1970 1990 - 1980
IDM
POT
+2 m - 1 m - 1 m + 2 m
+ 2 m
- 2 m
Conclusions:
Visual wave data allow for the analysis of centennial-scale variability of ocean wind wave characteristics: linear trends inthe North Pacific may amount to 1.2 m per century, being muchsmaller in the North Atlantic.
Interannual variability patterns are different for sea and swell, implying forcing frequency (e.g. cyclones) as a driving mechanism of swell changes with wind speed being responsible for the variations in sea.
Extreme wave statistics can be evaluated from VOS using IDM and POT methods. POT method shows the higher extreme waves, which are more close to those obtained from the model hindcasts.
However, estimation of decadal changes in extreme waves shows less skills of the POT method, largely influenced by sampling inhomogeneity
Sea, swell, SWH 100-years return
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