Download Effect of air-sea forcing on tropical ocean mixed layer depth (MLD

EFFECT OF AIR-­‐SEA FORCING ON TROPICAL OCEAN MIXED
LAYER DEPTH (MLD), INCLUDING ENSO
‡ Hela
‡ Chien
‡ Raju
‡ Suneet
‡ Ed
The Mixed Layer is the zone of relatively homogeneous water formed by the history of mixing (note that the mixing layer is the zone in which mixing is currently active)
MLD
Where: GLOBAL TROPICAL OCEANS (~ Lat -­‐25 to 25)
When: 1984(1955) ʹ 2006 DATA SETs (monthly)
USING GRADS
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
SST
Air Temperature at 2 m
Wind u at 10 m
Wind v at 10 m
Surface heat flux
Net long wave radiation
Net short wave radiation
Evaporation rate
Precipitation
Humidity at 2 m
Sea Level
OTHER SOURCES
‡ MEI
‡ NAO
‡ IMI & WNPMI
‡ Some Sea Level time series
¾ In order to check changes on the depth of the ocean mixed layer (MLD), and potential relationships with ENSO
SST
Air Temperature at 2 m
Wind Speed at 10 m
Net Heat Flux
PRECIPITATION
Evaporation
Sea Level
OCEAN MIXED LAYER DEPTHS (data source: DT0.2 Argo data)
Temperature Criterion :
depth where temperature change compared to temperature at 10 m depth equals + or -­‐ 0.2°C (allows to take into account T°C inversions)
Based on :
-­‐ profiles with temperature observations, from 1941 (first MBT data) until september 2008 (argo data)
-­‐ about 5 million profiles
-­‐ Regular 2° by 2° grid
-­‐ Annual Climatology (12 months)
-­‐ Global Ocean
MLD (Argo DB)
Monthly MEI & NAO indexes
4
3
MEI
NAO
2
Index
1
-­‐1
-­‐2
-­‐3
-­‐4
1955
1965
1975
1985
Year
1995
2005
Yearly MEI & NAO indexes
0.2
2
NAO
0.1
1
0
0
-­‐0.1
-­‐1
-­‐0.2
-­‐2
1955
1960
1965
1970
1975
1980
Year
1985
1990
1995
2000
2005
MEI
NAO
MEI
IMI & WNPMI indexes
3
3
IMI & WNPMI
Series1
Series2
IMI WNPMI
2
2
1
1
0
0
-­‐1
-­‐1
-­‐2
-­‐2
-­‐3
-­‐3
1955
1965
1975
1985
1995
2005
METHODS
OCEAN MIXED LAYER DEPTHS (GRADS data source)
MLD calculation is based on the shallowest extreme curvature of near surface temperature profiles
(Lorbacher, Katja, Dommenget, Dietmar, Niiler, P.P and Köhler, A (2006) Ocean mixedlayer depth: A subsurface proxy for ocean-­‐atmosphere variability Journal of Geophysical Research -­‐ Oceans, 111)
CORRELATIONS (TIME & FREQUENCY DOMAINS)
Linear correlations are calculated on time using classical linear correlation estimators.
Relevant data go to a data matrix with all variables (monthly means), and another with less
variables and yearly means for calculations. LINEAR Correlations are then calculated.
Hourly Sea Level data are filtered (MA S24S24S25) to remove the astronomical tide (>95%).
Then, hourly data are again filtered & averaged to get monthly and yearly means.
Also, some selected variables are analyzed for correlation in the frequency domain (cross
spectral analysis), in order to get spectral cross-­‐amplitudes and coherency (still linear). The
purpose of cross-­‐spectrum analysis is to uncover the correlations between two series at
different frequencies, i.e. a "coordinated" (i.e., correlated) cyclical behavior. The cross-­‐
amplitude can be interpreted as a measure of covariance between the respective frequency
components in the two series. The coherency value is the squared correlation between the
cyclical components in the two series at the respective frequency.
OCEAN MIXED LAYER DEPTHS (GRADS data source & LC)
ARGO
OUR
NORMAL YEAR MLD
EL NIÑO YEAR MLD
LA NIÑA YEAR MLD
FILTERED TIME SERIES
Monthly Mean Sea Level 1955-­‐2006
210
Mean Sea Level [cm]
200
190
TS
CA
180
170
160
150
140
130
120
1955
1965
1975
1985
1995
2005
1995
2005
MEI
Year
Yearly Mean Sea Level 1955-­‐2006
2
Mean Sea Level [cm]
1.5
195
190
1
185
0.5
180
TS
CA
0
175
-­‐0170
.5
165
-­‐1
160
-­‐1155
.5
-­‐2
1955
1955
1965
1960
1965
1975
1970
1975
1985
Year
1980
Year
1985
1990
1995
2000
2005
Monthly MLD at 100 W & 22 S (1959-­‐2010)
MonthlY MLD [1959-­‐2010]
140
120
MLD [m]
100
80
60
40
20
1959
1974
Year
2010
Yearly MLD at 100 W & 22 S (1959-­‐2010)
MLD [cm]
Yearly MLD & MEI
90
2
85
1.5
80
1
75
0.5
70
0
65
-­‐0.5
60
-­‐1
55
-­‐1.5
50
-­‐2
1959
1964
1969
1974
1979
1984
Year
1989
1994
1999
2004
LINEAR CORRELATIONS
Monthly Time Series
Mean Variance YMSLCA YMSLTS MEI NAO IMI MLD
WNPMI YMSLCA 162.1310 3.053067 1.000000 0.437866 0.110970 -­‐0.048420 -­‐0.274729 0.107144 0.02677
YMSLTS 174.2712 7.572974 0.437866 1.000000 0.360915 0.269858 -­‐0.109458 0.078028 -­‐0.0041
MEI 0.0980 0.790710 0.110970 0.360915 1.000000 0.178414 -­‐0.229575 0.126527 0.02126
NAO 0.0015 0.084717 -­‐0.048420 0.269858 0.178414 1.000000 -­‐0.134268 -­‐0.115135 0.00797
IMI 0.0201 1.013591 -­‐0.274729 -­‐0.109458 -­‐0.229575 -­‐0.134268 1.000000 -­‐0.180482 WNPMI -­‐0.0480 0.996278 0.107144 0.078028 0.126527 -­‐0.115135 -­‐0.180482 1.000000 MLD
70.1071 25.9182 0.02677 -­‐0.0041
1.000000
0.02123 0.00797
Yearly Time Series
Mean Variance MMSLCA MMSLTS MEI NAO MLD
MMSLCA 174.2694 10.22996 1.000000 0.049248 0.219690 0.055857 MMSLTS 162.1431 8.07724 0.049248 1.000000 0.060527 -­‐0.084956 0.259248
MEI 0.0980 0.99337 0.219690 0.060527 1.000000 0.012877 0.121621
NAO 0.0056 0.99834 0.055857 -­‐0.084956 0.012877 1.000000 0.097798
MLD
70.2954 5.297073 -­‐0.0322280.259248
0.121621
1.000000
0.097798
-­‐0.032228
CROSS SPECTRAL RESULTS
Selected variables are analyzed for correlation in the frequency
domain (cross spectral analysis), in order to get spectral cross-­‐
amplitudes and coherency (still linear). The purpose of cross-­‐
spectrum analysis is to uncover the correlations between two series
at different frequencies, i.e. a "coordinated" (i.e., correlated) cyclical
behavior. The cross-­‐amplitude can be interpreted as a measure of
covariance between the respective frequency components in the two
series. The coherency value is the squared correlation between the
cyclical components in the two series at the respective frequency.
Cross Am pl i tude
Cross Amplitude
X:M EI
Y:SLT S
4.0
4.0
3.5
3.5
3.0
3.0
2.5
2.5
2.0
2.0
1.5
1.5
1.0
1.0
0.5
0.5
0.0
0
1
2
3
4
5
6
7
8
9
0.0
10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
Peri od [years]
Squared Coherency
Squared Coherency
X:M EI
Y:SLT S
0.7
0.7
0.6
0.6
0.5
0.5
0.4
0.4
0.3
0.3
0.2
0.2
0.1
0.1
0.0
0
1
2
3
4
5
6
7
8
9
0.0
10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
Peri od [years]
Cross Am pl i tude
Cross Amplitude
X:M EI
Y:SLCA
8
8
7
7
6
6
5
5
4
4
3
3
2
2
1
1
0
0
1
2
3
4
5
6
7
8
9
0
10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
Peri od [years]
Squared Coherency
Squared Coherency
X:M EI
Y:SLCA
0.9
0.9
0.8
0.8
0.7
0.7
0.6
0.6
0.5
0.5
0.4
0.4
0.3
0.3
0.2
0.2
0.1
0.1
0.0
0
1
2
3
4
5
6
7
8
9
0.0
10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
Peri od [years]
Cross Am pl i tude
Cross Amplitude
X:M EI
Y:YM LD
12
12
10
10
8
8
6
6
4
4
2
2
0
0
1
2
3
4
5
6
7
8
9
0
10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Peri od [years]
Squared Coherency
Squared Coherency
X:M EI
Y:YM LD
0.9
0.9
0.8
0.8
0.7
0.7
0.6
0.6
0.5
0.5
0.4
0.4
0.3
0.3
0.2
0.2
0.1
0.1
0.0
0
1
2
3
4
5
6
7
8
9
0.0
10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Peri od [years]
Cross Am pl i tude
Cross Amplitude
X:M EI
Y:SLT S
60
60
50
50
40
40
30
30
20
20
10
10
0
0
24
48
72
96
120
144
168
192
216
240
264
288
0
312
Peri od [m onths]
Squared Coherency
Squared Coherency
X:M EI
Y:SLT S
1.0
1.0
0.8
0.8
0.6
0.6
0.4
0.4
0.2
0.2
0.0
0
24
48
72
96
120
144
168
Peri od [m onths]
192
216
240
264
288
0.0
312
Cross Am pl i tude
X:M EI
Y:SLCA
Cross Amplitude
100
100
80
80
60
60
40
40
20
20
0
0
24
48
72
96
120
144
168
192
216
240
264
0
288
Peri od [m onths]
Squared Coherency
Squared Coherency
X:M EI
Y:SLCA
1.0
1.0
0.8
0.8
0.6
0.6
0.4
0.4
0.2
0.2
0.0
0
24
48
72
96
120
144
168
Peri od [m onths]
192
216
240
264
0.0
288
Cross Am pl i tude
Cross Amplitude
X:M EI
Y:M LD
250
250
200
200
150
150
100
100
50
50
0
0
24
48
72
96
120
144
168
192
216
240
264
0
288
Peri od [m onths]
Squared Coherency
Squared Coherency
X:M EI
Y:M LD
1.0
1.0
0.8
0.8
0.6
0.6
0.4
0.4
0.2
0.2
0.0
0
48
96
144
Peri od
192
240
0.0
288
SOME FEW CONCLUSIONS
¾MLD CALCULATION USING GRADS COMPARE WITH OTHERS RESULTS, MAINLY IN THE SPATIAL PATERNS.
¾HOWEVER, OUR MLD RESULTS SHOW SHALLOWER VALUES.
¾MLD SHOWS DEEPER DEPTHS WHEN EL NIÑO OCCUR.
¾MLD SHOWS SHALLOWER DEPTHS WHEN LA NIÑA OCCUR.
¾MLD DURING A NORMAL YEAR DO NOT FOLLOW CLIMATOLOGICAL MEAN.
SOME FEW CONCLUSIONS
¾LINEAR CORRELATIONS DO NOT SHOW SIGNIFICANT COVARIANCES AMONG TESTED VARIABLES.
¾MONTHLY TIME SCALES SHOW GREATER CORRELATIONS COMPARED WITH YEARLY ONES.
¾HOWEVER, MANLY AT THE YEARLY TIME SCALES, VISUALLY COVARIANCES ARE CLEAR ENOUGH.
¾CROSS SPECTRAL ANALYSIS SHOWS SIGNIFICANT CORRELATIONS AT INTERANNUAL PERIODICITIES.
¾ MAXIMUM COHERENCY IS FOUND AT ~ 3.7; ~6 & ~18 YEARS.
¾ MAXIMUM COHERENCY IS FOUND AT LOW PERIODS AND ~700 MONTHS.
THE END
&
MANY THANKS
Squared coherency. One can standardize the cross-­‐amplitude values by
squaring them and dividing by the product of the spectrum density estimates
for each series. The result is called the squared coherency, which can be
interpreted similar to the squared correlation coefficient that is, the coherency
value is the squared correlation between the cyclical components in the two
series at the respective frequency. However, the coherency values should not
be interpreted by themselves; for example, when the spectral density
estimates in both series are very small, large coherency values may result (the
divisor in the computation of the coherency values will be very small), even
though there are no strong cyclical components in either series at the
respective frequencies.
Gain. The gain value is computed by dividing the cross-­‐amplitude value by the
spectrum density estimates for one of the two series in the analysis.
Consequently, two gain values are computed, which can be interpreted as the
standard least squares regression coefficients for the respective frequencies
Phase shift. Finally, the phase shift estimates are computed as tan-­‐1 of the ratio
of the quad density estimates over the cross-­‐density estimate. The phase shift
estimates (usually denoted by the Greek letter ʗͿ are measures of the extent
to which each frequency component of one series leads the other.
The purpose of this study was to examine the variation of ocean surface layer via the Mixed Layer Depth (MLD) dynamics on monthly and annual timescales in the tropical Global Ocean using temperature-­‐depth measurements from 1984 to 2006. We demonstrate that associated with interannual variations in atmospheric forcing, there were distinct changes in mixed layer depth between normal, El Niño, and La Niña time periods. During El Niño a warm, fresh mixed layer accompanied by an underlying barrier layer develops in the central and eastern Pacific in association with increased precipitation and reduced trade wind forcing. Conversely, during La Niña, when unusually cold conditions prevail in the central and eastern equatorial Pacific and the warm pool is confined to the far western Pacific, a thick barrier layer is found west of 160oE. The physical mechanism underlying this relationship is likely to be related to the reduced efficiency of vertical turbulent mixing in cooling the surface via entrainment of thermocline water when the barrier layer is thick. Changes in mixed layer temperature, on the other hand, can affect precipitation and therefore mixed-­‐
layer salinity, leading to the possibility of feedbacks on interannual timescales involving the mixed-­‐layer temperature balance and the hydrologic cycle over the ocean. The extent to which such feedbacks, if operative, may influence the detailed evolution of large-­‐scale, lower-­‐frequency variability in the tropical Pacific needs to be critically assessed in the context of coupled ocean-­‐atmosphere models.
Kentaro Ando & Michael J. McPhaden (1997). Variability of surface layer hydrography in the tropical Pacific Ocean. JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 102, NO. C10, PAGES 23,063-­‐23,078, OCTOBER 15, 1997
The main modes of interannal variabilities of thermocline and sea surface wind stress in the tropical Pacific and their interactions are investigated, which show the following results. (1) The thermocline anomalies in the tropical Pacific have a zonal dipole pattern with 160°W as its axis and a meridional seesaw pattern with 6‐8°N as its transverse axis. The meridional oscillation has a phase lag of about 90° to the zonal oscillation, both oscillations get together to form the El Niño/La Niña cycle, which Behaves as a mixed layer water oscillates anticlockwise within the tropical Pacific basin between equator and 12°N. (2) There are two main patterns of wind stress anomalies in the tropical Pacific, of which the first component caused by trade wind anomaly is characterized by the zonal wind stress anomalies and its corresponding divergences field in the equatorial Pacific, and the abnormal cross-­‐
equatorial flow wind stress and its corresponding divergence field, which has a sign opposite to that of the equatorial region, in the off-­‐equator of the tropical North Pacific, and the second component represents the wind stress anomalies and corresponding divergences caused by the ITCZ anomaly. (3) The trade winds anomaly plays a decisive role in the strength and phase transition of the ENSO cycle, which results in the sea level tilting, provides an initial potential energy to the mixed layer water oscillation, and causes the opposite thermocline displacement between the west side and east side of the equator and also between the equator and 12°N of the North Pacific basin, therefore determines the amplitude and route for ENSO cycle. The ITCZ anomaly has some effects on the phase transition. (4) The thermal anomaly of the tropical western Pacific causes the wind stress anomaly and extends eastward along the equator accompanied with the mixed layer water oscillation in the equatorial Pacific, which causes the trade winds anomaly and produces the anomalous wind stress and the corresponding divergence in favor to conduce the oscillation, which in turn intensifies the oscillation. The coupled system of ocean-­‐
atmosphere interactions and the inertia gravity of the mixed layer water oscillation provide together a phase-­‐
switching mechanism and interannual memory for the ENSO cycle. In conclusion, the ENSO cycle essentially is an inertial oscillation of the mixed layer water induced by both the trade winds anomaly and the coupled ocean-­‐
atmosphere interaction in the tropical Pacific basin between the equator and 12°N. When the force produced by the coupled ocean-­‐atmosphere interaction is larger than or equal to the resistance caused by the mixed layer water oscillation, the oscillation will be stronger or maintain as it is, while when the force is less than the resistance, the oscillation will be weaker, even break. ZHAO YongPing, CHEN YongLi, WANG Fan & WU AiMing (2007) Mixed-­‐layer water oscillations in tropical Pacific for ENSO cycle. Science in China Series D: Earth Sciences