1. Korea Ocean Research & Development Institute, Ansan, Republic of Korea
Recent warming in the Yellow and East China Sea during the boreal winter
Sang-Wook Yeh
Korea Ocean Research & Development Institute, Ansan, Republic of Korea

2. Global Surface Temperature Variations
I would like to start to show these two popular figures which were taken from the IPCC AR4. The left figure shows the global surface temperature variations since the 1850s and the right figure shows the time series of global ocean heat content for the period of 1955 to Both figures show that warming is evident in particular during recent decades.
IPCC AR4

3. Linear trend (1950.1-2009.2) Warming in the Yellow and East China Sea
I also could reach a similar conclusion through a linear trend map of SST for the period of 1950 to The SST data is taken from the hadley centre. A warming trend is everywhere except but some limited region. If you look at this figure closely, You may find that the Yellow and East China Sea is one of the regions where a warming trend is large. So, it is useful to examine the variability of sea surface temperature in the Yellow and East China sea.
Unit: C/month
Warming in the Yellow and East China Sea

4.  Data
Hadley Centre SST dataset (HadISST)
NCEP/NCAR reanalysis data (Sea level pressure, Winds)
Period:
Season: December-January-February (DJF)

5.  Yellow and East China Sea – Time series
First of all, I show the time series of SST averaged in the Yellow and East china Sea during winter for the last 60 years. You can see the averaged region in this map. In the bottom figure the solid line shows a year-to-year variability of SST averaged in the Yellow and East China sea for the period of during winter. The red line shows an 8-year running mean. First of all, you may find that the interannual SST variability is quite dominant around the period of 2-4 years timescales. In addition, it is evident that the YES experiences a significant warming during recent decades, in particular after the mid 1980s.

6.  Yellow and East China Sea – Linear trend
So, I calculated a linear trend of SST in the YES. Shading ~. you can see a warming occurs at the basin scale in the YES. A maximum warming is observed in the northeast-to-southwest direction in the YES.
Shading denotes a statistical significance at 95% confidence level.

7. Yellow and East China Sea
- Empirical Orthogonal Function (EOF) analysis
The mean state of SST in the Yellow and East China Sea can be characterized by two distinct periods around the mid 1980s, i.e., one is a cold period before the mid 1980s and the other is a warm period after the mid 1980s.
There exist a significant increase of mean SST in the Yellow and East China Sea.
To examine details of SST variability in the YES, I perform an EOF analysis to the SST variability in the YES. This figure is the first EOF mode of SST variability in the YES. The bottom figure shows the time series of the first EOF principal component. The principal component time series indicates that ~. Also you may find that the spatial pattern of linear trend map of SST and the first EOF SST is quite similar. This indicates that ~

8.  Analysis
Regressed sea level pressure & low level winds (1000hPa) against with the EOF1 PC in the Yellow and East China Sea.
In order to find a corresponding atmospheric forcing, I regressed the principal component time series to the sea level pressure and low level winds. The regressed SLP is characterized by a dipole like structure in the meridional direction over the North Pacific. Due to such atmospheric forcing, the anomalous southerly winds are dominated over the Yellow and East china Sea. We expect that the anomalous southerly winds are associated with a weakening of climatological winds over the same region during winter.

9.  Analysis Clim. Wind 1986-2008 minus 1950-1985
The upper figure shows the climatological low level winds over the Yellow and East china sea during winter. As we know very well, the northerly or northwesterly winds are dominant, which is associated with an east asia monsoonal flow during winter. The bottom figure shows the difference of low level winds before and after the mid 1980s. Consistent with a previous slide, the southerly winds are enhanced after the mid 1980s, which indicating that the climatological winter monsoon flows are reduced after the mid 1980s.

10. Regressed latent heat flux against the low level wind speed anomaly
 Analysis
Low level (1000hPa)
Wind speed anomaly
During winter
Regressed latent heat flux against the low level wind speed anomaly
A reduced monsoon flow is associated with a weakening of wind speed anomaly over the YES. The upper figure shows the time series of low level wind speed anomaly averaged over the Yellow and East China Sea during winter since You can see that the wind speed anomaly gradually decreases over the Yellow and East China Sea, which is consistent with the result in previous slide. The bottom figure shows the regressed latent heat flux against with the time series of low level wind speed anomaly, because of reduced wind speed anomaly, the latent heat flux comes into the ocean, which plays a role to increase the SST.

11.  Analysis Corr. Coeff. : 0.79 North Pacific Oscillation (NPO)
Now I show the second EOF mode of slp variability over the North Pacific, which is so called the North Pacific Oscillation, (NPO). The spatial pattern of NPO is characterized by a dipole-like structure in the meridional direction over the North Pacific. If you compare with the spatial patterns between the North Pacific oscillation and the regressed slp I showed earlier, it is very similar.. The black bar in the bottom figure shows the time series of NPO and the red line indicates an 8-year running mean. I calculate a simultaneous correlation coefficient between the principal component time series and the SST variability in the YES on the low-frequency timesales, a correlation coefficient is 0.79, which is quite high. This indicates that warming in the YES is associated with a large scale forcing of atmosphere over the North Pacific, that is the North Pacific Oscillation
Corr. Coeff.
: 0.79

12.  Summary I
The Yellow and East China Sea experiences a significant warming after the mid 1980s
Recent warming in the Yellow and East China Sea is associated with a large scale of atmospheric variability over the North Pacific basin.
The circulation anomalies and thermal advection associated with the NPO-like SLP of variability play a role to warm the mean SST in the Yellow and East China Sea during recent decades.

13.  Yellow and East China Sea – EOF2
From now on, I will show the result that the SST variability in the YES seems to be a part of basin scale SST variability in the North Pacific. The upper figure is the second EOF mode of sst variability in the YES. The bottom figure is the time series of the second EOF mode of SST variability. The spatial pattern of second EOF is characterized by a dipole like structure in the YES.

14.  Yellow and East China Sea – EOF2
In order to find the associated sst pattern in relation to the second EOF mode of sst variability in the YES, I regressed the second eof principal component time series to the global SST. The bottom figure shows the regressed SST in the North Pacific. The spatial pattern is characterized by a cool sst with an ellipitcal shape in the North Pacific and an opposite sign in the north, east and south. You may notice that this spatial pattern is similar to the Pacific decadal oscillation which is the most dominant sst varibility in the North Pacific.

15.  Yellow and East China Sea versus the North Pacific
Pacific Decadal Oscillation (Mantua et al. 1997)
So, the left two figures represent the first two EOF modes of sst variability in the North Pacific, one is the PDO and the other is NPGO. On the other hand, the right two figures show that the regressed SST variability against with the frist and second EOF principal component time series in the YES. ~~~ simply, this result suggests that the SST variability in the YES is associated with the two dominant SST variability in the North Pacific.
North Pacific Gyre Oscillation (Di Lorenzo et al. 2008)

16.  Analysis
The left upper figure shows the first EOF mode of slp variability over the NP and the bottom figure shows the principal component time series. The first Eof mode of slp variability over the North pacific represents the variability of aleutian low over the NP. The black bar represent a year-to-year variability and the red line indicates an 8-year running mean. A correlation coefficient between the time series of frist EOF principal compoent and the second Eof mode of sst variability in the YES is .87, which is significanlty high.
Corr. Coeff.
: 0.87

17.  Analysis
The SST variability in the Yellow and East China Sea is closely associated with a large scale forcing of atmosphere over the North Pacific.
Aleutian low variability
NPO-like SLP variability

18.  Summary II
While the first EOF mode in the Yellow and East China Sea seems to be related with North Pacific Gyre Oscillation, the second EOF mode is characterized by a dipole-like structure of SST anomalies and it is associated with Pacific Decadal Oscillation.
The SST variability in the Yellow and East China Sea is closely associated with the two dominant large scale variabilities of atmosphere over the North Pacific.

19. Thank you for your attention!

20.  Time series of EAWMI
(Jhun and Lee 2004 J.Climate)

21. Warming in the Yellow and East China Sea – North Pacific Ocean?
The basin warming mode in the Yellow and East China Sea is closely associated with a linear trend in the North Pacific.

22. Warming in the Yellow and East China Sea – Global Ocean?
A spatial pattern associated with warming in the Yellow and East China sea is similar to a linear trend over the globe.

23.  Detrended SSTA

25.  Yellow and East China Sea - Variability
Unit: C. Contour Interval: 0.05C