Relationships BETWEEN INDO-PACIFIC SSTs AND …...WY (large-scale Asian monsoon, Webster and Yang...
1
0.47 0.67 0.86 (a) DJF EOF1 (28.72% ) 0.87 SON NINO3 0.65 (b) MAM EOF1 (28.68% ) DJF NINO3 0.65 (c)JJA EOF1 (25.45%) MAM NINO3 0.62 (d) SON EOF1 (24.19% ) JJA NINO3 0.58 DJF NINO3 0.65 Regression of Grid-SST Against IO SST PC1 Objective To assess the model performance in generating the characteristics of the mean climatology, seasonal cycle, and interannual variability of IO SST and the relationship between Indo-Pacific SSTs and Asian-Australian monsoon system in the NCEP CFS and GFS models. Relationships BETWEEN INDO-PACIFIC SSTs AND Asian-Austrailian monsoon in ncep models 1, 2 Soo-Hyun Yoo , 2 Song Yang, and 1 Chang-Hoi Ho 1 School of Earth and Environmental Sciences, Seoul National University, Seoul 151‐742, Korea 2 Climate Prediction Center, NCEP/NWS/NOAA, Camp Springs, Maryland 20746, USA Data and Models SST: Reynolds/NCEP version 2 monthly 1°x1° optimally interpolated SST (OISST) product from 1982 to 2004 (Reynolds and Smith 1994) Winds: NCEP/NCAR reanalysis data (Kalnay et al. 1996) Precipitation: pentad and monthly CPC Merged Analysis of Precipitation (CMAP) data on a global 2.5 °x2.5° grid (Xie and Arkin 1997) Indices: NINO3 SST index (5°S-5°N, 90°-150°W; Bjerkness 1969) IOD (Indian Ocean Dipole, Saji et. al 1999) SDP (Subtropical Dipole Pattern, Behera and Yamagata 2001) AIMR (All-India monsoon rainfall index; Parthasarathy et al. 1995) WY (large-scale Asian monsoon, Webster and Yang 1992) SEAM (southeast Asian monsoon; DU2, Wang and Fan 1999) SAM & EAM (south & east Asian monsoon; RM1 & RM2, Lau et al. 2000) AuM (Austrailan monsoon, Meehl and Arblaster 2002) Models: - NCEP Atmospheric Global Forecast system (GFS): T62L64 - NCEP Coupled atmosphere-ocean Forecast system (CFS): a. Atmospheric component - NCEP GFS03, T62L64 b. Oceanic component - GFDL MOM3, 1/3˚x1° in tropics; 1°x1° in extratropics; 40 layers c. Coupled model – Once a day coupling, Observed climatological sea-ice data Table 1. Simultaneous correlation between the principle components of the first EOF modes of IO SST and climate indices including Nino3 SST, WY monsoon, AIMR, SAM, SEAM, EAM, AuM, IOD, and SDP. Only the values exceeding the 95% confidence level are shown. Nino3 SST (0.54 ) IOD ( 0.65 ) SDP ( -0.43 ) IOD ( -0.51 ) SDP ( -0.73 ) Nino3 SST ( 0.50 ) SDP ( -0.65 ) GFS EOF PC1 IOD ( 0.96 ) SDP ( -0.53 ) Nino3 SST ( 0.70 ) IOD ( 0.78 ) SON Nino3 SST ( 0.56 ) IOD ( 0.87 ) WY ( 0.50 ) SEAM ( -0.42 ) EAM ( -0.43) WY ( -0.52 ) SEAM ( -0.57 ) JJA Nino3 SST ( 0.60 ) SDP ( -0.42 ) SDP ( -0.60 ) MAM Nino3 SST ( 0.69 ) IOD ( 0.86 ) SDP ( -0.83 ) AuM ( -0.53 ) Nino3 SST ( 0.55 ) IOD ( 0.41 ) SDP ( -0.59 ) AuM ( -0.40 ) DJF CFS Obs Summary The CFS performs better than the GFS in simulating the variability of the monsoons, implying the importance of ocean-atmosphere coupling. Both models produce weaker climatological anticyclonic circulation over the southern IO and summer monsoon flow over the northern IO compared to observations. 0.60 0.87 (a) DJF EOF1 (35.41% ) 0.69 SON NINO3 0.60 (b) MAM EOF1 (30.37% ) DJF NINO3 0.79 (c) JJA EOF1 (26.91% ) MAM NINO3 0.64 (d) SON EOF1 (51.78% ) DJF NINO3 0.55 Regression of SST Against IO SST PC1 (CFS) MAM NINO3 0.44 SON NINO3 0.52 Table 2. Lagged correlation between the JJA Asian monsoon and the EOF of previous MAM IO SST, and between the DJF AuM and the EOF of previous SON IO SST. Values exceeding significantly the 95% confidence level are highlighted. SEAM EAM SAM WY AIMR MAM PC1 (CFS) MAM PC1 (GFS) MAM PC1 (Obs) 0.40 JJA 0.01 -0.09 -0.34 -0.29 -0.36 0.47 0.05 0.06 0.02 -0.46 0.06 -0.64 AuM 0.46 -0.12 -0.49 DJF SON PC1 (CFS) SON PC1 (GFS) SON PC1 (Obs) In CFS, the annual evolution of IO SST, the feedback of monsoon flow, and the expratropical southern IO SST have not been simulated realistically. The relationship between IO SST and the Asian-Australian monsoons appear relatively more realistic in the CFS than in the GFS.
Transcript of Relationships BETWEEN INDO-PACIFIC SSTs AND …...WY (large-scale Asian monsoon, Webster and Yang...
0.470.67
0.86
(a) DJF EOF1 (28.72% )
0.87
SON NINO3
0.65
(b) M AM EOF1 (28.68% )
DJF NINO3
0.65
(c) JJA EOF1 (25.45% )
MAM NINO3
0.62
(d) SON EOF1 (24.19% )
JJA NINO3
0.58
DJFNINO3
0.65
Regression of Grid-SST Against IO SST PC1
Objective
To assess the model performance in generating the characteristics of the mean climatology, seasonal cycle, and interannual variability of IO SST and the relationship between Indo-Pacific SSTs and Asian-Australian monsoon system in the NCEP CFS and GFS models.
Relationships BETWEEN INDO-PACIFIC SSTs AND Asian-Austrailian monsoon in ncep models1, 2Soo-Hyun Yoo , 2Song Yang, and 1Chang-Hoi Ho
1School of Earth and Environmental Sciences, Seoul National University, Seoul 151‐742, Korea2Climate Prediction Center, NCEP/NWS/NOAA, Camp Springs, Maryland 20746, USA
Data and ModelsSST: Reynolds/NCEP version 2 monthly 1°x1° optimally interpolated SST (OISST) product from 1982 to 2004 (Reynolds and Smith 1994)Winds: NCEP/NCAR reanalysis data (Kalnay et al. 1996)Precipitation: pentad and monthly CPC Merged Analysis of Precipitation (CMAP) data on a global 2.5 °x2.5° grid (Xie and Arkin 1997) Indices:
NINO3 SST index (5°S-5°N, 90°-150°W; Bjerkness 1969) IOD (Indian Ocean Dipole, Saji et. al 1999)SDP (Subtropical Dipole Pattern, Behera and Yamagata 2001)AIMR (All-India monsoon rainfall index; Parthasarathy et al. 1995)WY (large-scale Asian monsoon, Webster and Yang 1992)SEAM (southeast Asian monsoon; DU2, Wang and Fan 1999)SAM & EAM (south & east Asian monsoon; RM1 & RM2, Lau et al. 2000) AuM (Austrailan monsoon, Meehl and Arblaster 2002)Models:
- NCEP Atmospheric Global Forecast system (GFS): T62L64 - NCEP Coupled atmosphere-ocean Forecast system (CFS):
a. Atmospheric component - NCEP GFS03, T62L64b. Oceanic component - GFDL MOM3, 1/3̊ x1° in tropics; 1°x1° in extratropics; 40 layersc. Coupled model – Once a day coupling, Observed climatological sea-ice data
Table 1. Simultaneous correlation between the principle components of the first EOF modes of IO SST and climate indices including Nino3 SST, WY monsoon, AIMR, SAM, SEAM, EAM, AuM, IOD, and SDP. Only the values exceeding the 95% confidence level are shown.
Nino3 SST (0.54 )IOD ( 0.65 )SDP ( -0.43 )
IOD ( -0.51 )SDP ( -0.73 )
Nino3 SST ( 0.50 )
SDP ( -0.65 )
GFSEOF PC1
IOD ( 0.96 ) SDP ( -0.53 )
Nino3 SST ( 0.70 )IOD ( 0.78 )SON
Nino3 SST ( 0.56 )IOD ( 0.87 )WY ( 0.50 )
SEAM ( -0.42 )EAM ( -0.43)
WY ( -0.52 )SEAM ( -0.57 )
JJA
Nino3 SST ( 0.60 )
SDP ( -0.42 )SDP ( -0.60 )MAM
Nino3 SST ( 0.69 )IOD ( 0.86 )SDP ( -0.83 )AuM ( -0.53 )
Nino3 SST ( 0.55 )IOD ( 0.41 )SDP ( -0.59 )AuM ( -0.40 )
DJF
CFSObs
Summary
The CFS performs better than the GFS in simulating the variability of the monsoons, implying the importance of ocean-atmosphere coupling.Both models produce weaker climatological anticyclonic circulation over the southern IO and summer monsoon flow over the northern IO compared to observations.
0.60
0.87
(a) DJF EOF1 (35.41% )
0.69
SON NINO3
0.60
(b) M AM EOF1 (30.37% )
DJF NINO3
0.79
(c) JJA EOF1 (26.91% )
MAM NINO3
0.64
(d) SON EOF1 (51.78% ) DJFNINO3
0.55
Regression of SST Against IO SST PC1 (CFS)
MAMNINO3
0.44
SONNINO3
0.52
Table 2. Lagged correlation between the JJA Asian monsoon and the EOF of previous MAM IO SST, and between the DJF AuM and the EOF of previous SON IO SST. Values exceeding significantly the 95% confidence level are highlighted.
SEAMEAMSAMWYAIMR
MAM PC1(CFS)
MAM PC1(GFS)
MAM PC1(Obs) 0.40
JJA
0.01
-0.09
-0.34
-0.29
-0.36
0.47
0.05
0.06
0.02
-0.46
0.06
-0.64
AuM
0.46
-0.12
-0.49
DJF
SON PC1(CFS)
SON PC1(GFS)
SON PC1(Obs)
In CFS, the annual evolution of IO SST, the feedback of monsoon flow, and the expratropical southern IO SST have not been simulated realistically.The relationship between IO SST and the Asian-Australian monsoons appear relatively more realistic in the CFS than in the GFS.
