1
Coupled Model Simulations of Boreal Summer Intraseasonal (30-50 day) Variability: Validation and Caution on Use of Metrics Kenneth R. Sperber 1 and H. Annamalai 2 1 PCMDI, Lawrence Livermore National Laboratory, P.O. Box 808, L-103, Livermore, CA 94550 ([email protected]) 2 International Pacific Research Center/SOEST, University of Hawaii, Honolulu, HI 96822 ____________________________________________________________________________________________________________________________________________________________________ A nalyze borealsum m erA sian m onsoon intraseasonalvariability (B SISV)in generalcirculation m odels H ow w ell do the m odels representthe eastw ard and northw ard propagating com ponents ofthe convection? D oes the eastw ard propagating com ponentdevelop in the sam e m anneras during the borealw inter? H ow w ell do the m odels representthe interactive control thatthe w estern tropical Pacific rainfall exerts on the rainfall overIndia and vice-versa? W hatis the relationship betw een the m ean m onsoon vs.BSISV fidelity? B SISV in G CM s has received m uch less attention com pared to the sim ulation ofborealw interM JO AG CM s:Fennessy and Shukla (1994),Ferranti etal.(1997),Sperberetal. (2001),Waliseretal.(2003) CG CM s:Kem ball-C ook etal.(2002),Fu etal.(2003),Fu and W ang (2004) Goals and Background 1)O bservations (JJA S:1979-95) N C EP/N C AR R eanalysis AVHRR O LR CM AP R ainfall 2)C M IP2+ C ontrolIntegrations ECHAM 4/O PYC and ECHO -G (20 years) annual m ean flux adjustm entto help m aintain a realistic basic state have a very good borealw interM JO (Sperberetal.2005, Clim. Dynam.,25, 117-140) 3)C M IP3 IPC C A R 4 C oupled M odels (JJA S:1961-1999) 20c3m (Clim ate ofthe 20 th Century)simulations including greenhouse gases,O 3 ,and in som e cases volcanic aerosols A lldata are filtered w ith a 20-100 day Lanczos filter Data The CMIP3 and CMIP2+ (given in red) Models Yasunari(1979, J . Meteorol. S oc. J apan,57,227-242) Yasunari(1980, J . Meteorol. S oc. J apan,58,225-229) Sikka and G adgil(1980, MWR ,108,1840-1853) W ang and R ui(1990, Meteorol. Atmos. Phys .,44,43-61) Eastw ard propagation dom inates in borealw inter N orthw ard propagation presentin borealsum m er Discovery of Boreal Summer Intraseasonal Variability 78% of northward propagating events occurred in conjunction with eastward propagation (Lawrence and Webster 2002, JAS, 59, 1593-1606) BSISV northward and eastward propagation of AVHRR OLR (W m -2 ) using CsEOF analysis (Annamalai and Sperber 2005, JAS, 62, 2726-2748) Atmospheric GCMs do not properly represent the tilted rainband (mm day -1 ) that is characteristic of the BSISV (Waliser et al. 2003, Clim. Dynam., 21, 423-446) Simulation analysis methodology: all models are treated the same, their 20-100 day filtered OLR is projected onto the Day 0 pattern from (Annamalai and Sperber 2005, JAS, 62, 2726-2748) with the resulting PC time series used for lag regression analysis -12.5 -10 -7.5 -5 -2.5 0 2.5 5 7.5 10 12.5 PC Time Series 91250.0 234.6 - 84213.0 -50 -40 -30 -20 -10 0 10 20 30 40 50 -50 -40 -30 -20 -10 0 10 20 30 40 50 -50 -40 -30 -20 -10 0 10 20 30 40 50 OLR Anomalies t 0 t 1 t n 21 Jul 1981 30 Jul 1981 8 Aug 1981 Day 0 The ability of the models to simulate the tilted convection is demonstrated by calculating the Day 10 lag regression of the respective PC’s against 20-100 day filtered OLR (W m -2 ). The pattern correlation with the Day 10 CsEOF is also given. The largest pattern correlation for (g) ECHAM5/MPI-OM incorrectly indicates that this model gives the best agreement with observations in simulating the tilted convective anomalies. The same incorrect result holds true if one considers the pattern correlation for the full-space- time BSISV simulated by the models (not shown). Thus, caution is warranted in using the pattern correlation as a metric of model performance without a physical interpretation of the data a) AVHRR 0.9 0 b) CCSM3.0 0.4 2 -10 -5 0 5 10 c) CGCM3.1 (T47) 0.3 3 -10 -5 0 5 10 d) CGCM3.1 (T63) 0.3 0 e) CNRM-CM3 0.2 8 f) CSIRO Mk3.0 0.3 2 -10 -5 0 5 10 g) ECHAM5/MPI-OM 0.6 6 -10 -5 0 5 10 h) ECHAM4/OPYC 0.5 4 -10 -5 0 5 10 i) ECHO-G 0.3 6 j) ECHO-G (MIUB) 0.6 1 k) FGOALS- g1.0 0.2 6 -10 -5 0 5 10 l) GFDL-CM2.0 0.5 6 m) GFDL-CM2.1 0.6 4 n) GISS-AOM 0.2 8 o) IPSL-CM4 0.6 5 p) MIROC3.2 (hires) 0.2 8 q) MIROC3.2 (medres) 0.3 4 r) MRI-CGCM2.3.2 0.5 8 Caution is warranted in the use of latitude-time plots for isolating northward propagation. They must be used in conjunction with longitude- time plots to determine if the northward propagating component is associated with the near-equatorial eastward propagating convective anomalies, as observed. The ECHAM4/OPYC model has the most realistic BSISV. Also crucial to this finding was the examination of animations of the full BSISV life- cycle of the 20-100 day filtered OLR anomalies, snapshots of which are shown below The latitude-time plot of convective anomalies averaged over India and/or the Bay of Bengal longitudes has been an accepted diagnostic for demonstrating the presence of northward propagation (e.g., Sikka and Gadgil (1980, MWR, 108, 1840- 1853). For the observations and a subset of the models, shown below are latitude-time plots of the lag regression of the respective PC’s with 20-100 day filtered OLR (W m -2 ) that has been averaged between 71.25 o E-83.75 o E. -25 -20 -15 -10 -5 0 5 10 15 20 25 -12.5 -10 -7.5 -5 -2.5 -2.5 -2.5 -2.5 -2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 5 5 -25 -20 -15 -10 -5 0 5 10 15 20 25 -7.5 -5 -2.5 -2.5 -2.5 -2.5 -2.5 -2.5 -2.5 0 2.5 2.5 2.5 2.5 2.5 2.5 2.5 5 -25 -20 -15 -10 -5 0 5 10 15 20 25 -15 -10 -7.5 -5 -5 -5 -5 -2.5 -2.5 -2.5 -2.5 -2.5 -2.5 -2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 5 5 5 5 5 5 7.5 7.5 -25 -20 -15 -10 -5 0 5 10 15 20 25 -15 -10 -10 -7.5 -5 -5 -5 -5 -2.5 -2.5 -2.5 -2.5 -2.5 -2.5 -2.5 -2.5 -2.5 -2.5 -2.5 2.5 2.5 2.5 2.5 2.5 5 5 5 5 5 5 5 5 5 5 5 7.5 7.5 10 10 a) AVHRR b) CSIRO_Mk3.0 c) ECHAM4/OPYC d) GFDL_CM2.1 The results above suggest that the models exhibit northward propagation of convective anomalies that is essentially consistent with observations. However, the data above do not confirm that the anomalies that develops over the continental latitudes (poleward of 10 o N) arises as an extension of the eastward propagating MJO. This is investigated below by showing longitude-time lag plots of convective anomalies between 5 o N-5 o S and 15 o N-20 o N -15 -5 -5 -5 -2.5 -2.5 -2.5 -2.5 -2.5 -2.5 -2.5 -2.5 -2.5 -2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 5 5 5 5 5 7.5 7.5 10 -5 -5 -5 -5 -5 -5 -2.5 -2.5 -2.5 -2.5 -2.5 -2.5 -2.5 -2.5 -2.5 -2.5 -2.5 -2.5 -2.5 -2.5 -2.5 -2.5 -2.5 -2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 5 5 5 5 5 5 7.5 10 10 5 o N-5 o S 15 o N-20 o N AVHRR -12.5 -10 -5 -5 -2.5 -2.5 -2.5 -2.5 -2.5 -2.5 -2.5 -2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 5 5 5 -7.5 -5 -5 -5 -5 -2.5 -2.5 -2.5 -2.5 -2.5 -2.5 -2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 5 5 5 5 10 -10 -5 -5 -2.5 -2.5 -2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 5 5 -2.5 -2.5 -2.5 -2.5 -2.5 -2.5 -2.5 -2.5 -2.5 -2.5 -2.5 -2.5 -2.5 -2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 5 5 -15 -10 -7.5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -2.5 -2.5 -2.5 -2.5 -2.5 -2.5 -2.5 -2.5 -2.5 -2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 5 5 5 5 5 5 7.5 7.5 7.5 10 10 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -2.5 -2.5 -2.5 -2.5 -2.5 -2.5 -2.5 -2.5 -2.5 -2.5 -2.5 -2.5 -2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 5 5 5 5 5 5 7.5 7.5 7.5 7.5 7.5 CSIRO_Mk3.0 ECHAM4/OPYC GFDL_CM2.1 K. R. Sperber was supported under the auspices of the U.S. Department of Energy Office of Science, Climate Change Prediction Program by University of California Lawrence Livermore National Laboratory under contract No. W-7405-Eng-48. UCRL-POST-227735 Day -15 Day -10 Day -5 Day 0 Day 5 Day 10 Day 15 Day 20

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PC Time Series. OLR Anomalies. 91250.0. t 0. 234.6. t 1. -84213.0. t n. 21 Jul 1981. c) ECHAM4/OPYC. d) GFDL_CM2.1. a) AVHRR. b) CSIRO_Mk3.0. 30 Jul 1981. Day -15. Day -10. Day -5. Day 0. 8 Aug 1981. Day 10. Day 15. Day 20. Day 5. c) CGCM3.1 (T47). b) CCSM3.0. a) AVHRR. - PowerPoint PPT Presentation

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Page 1: Goals and Background

Coupled Model Simulations of Boreal Summer Intraseasonal (30-50 day) Variability: Validation and Caution on Use of Metrics

Kenneth R. Sperber1 and H. Annamalai2

1PCMDI, Lawrence Livermore National Laboratory, P.O. Box 808, L-103, Livermore, CA 94550 ([email protected])2International Pacific Research Center/SOEST, University of Hawaii, Honolulu, HI 96822

____________________________________________________________________________________________________________________________________________________________________

• Analyze boreal summer Asian monsoon intraseasonal variability (BSISV) in general circulation models– How well do the models represent the eastward and northward propagating

components of the convection?

– Does the eastward propagating component develop in the same manner as during the boreal winter?

– How well do the models represent the interactive control that the western tropical Pacific rainfall exerts on the rainfall over India and vice-versa?

– What is the relationship between the mean monsoon vs. BSISV fidelity?

• BSISV in GCMs has received much less attention compared to the simulation of boreal winter MJO– AGCMs: Fennessy and Shukla (1994), Ferranti et al. (1997), Sperber et al.

(2001), Waliser et al. (2003)

– CGCMs: Kemball-Cook et al. (2002), Fu et al. (2003), Fu and Wang (2004)

Goals and Background

• 1) Observations (JJAS: 1979-95)– NCEP/NCAR Reanalysis

– AVHRR OLR

– CMAP Rainfall

• 2) CMIP2+ Control Integrations– ECHAM4/OPYC and ECHO-G (20 years)

• annual mean flux adjustment to help maintain a realistic basic state

• have a very good boreal winter MJO (Sperber et al. 2005, Clim. Dynam., 25, 117-140)

• 3) CMIP3 IPCC AR4 Coupled Models (JJAS: 1961-1999)– 20c3m (Climate of the 20th Century) simulations

• including greenhouse gases, O3, and in some cases volcanic aerosols

• All data are filtered with a 20-100 day Lanczos filter

Data

The CMIP3 and CMIP2+ (given in red) Models

• Yasunari (1979, J. Meteorol. Soc. Japan, 57, 227-242)

• Yasunari (1980, J. Meteorol. Soc. Japan, 58, 225-229)

• Sikka and Gadgil (1980, MWR, 108, 1840-1853)

• Wang and Rui (1990, Meteorol. Atmos. Phys., 44, 43-61)– Eastward propagation dominates in boreal winter

– Northward propagation present in boreal summer

Discovery of Boreal Summer Intraseasonal Variability

78% of northward propagating events occurred in conjunction with eastward propagation (Lawrence and Webster 2002, JAS, 59, 1593-1606)

BSISV northward and eastward propagation of AVHRR OLR (W m-2) using CsEOF analysis (Annamalai and Sperber 2005, JAS, 62, 2726-2748)

Atmospheric GCMs do not properly represent the tilted rainband (mm day-1) that is characteristic of the BSISV (Waliser et al. 2003, Clim. Dynam., 21, 423-446)

Simulation analysis methodology: all models are treated the same, their 20-100 day filtered OLR is projected onto the Day 0 pattern from (Annamalai and Sperber 2005, JAS, 62, 2726-2748) with the resulting PC time series used for lag regression analysis

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21 Jul 198121 Jul 1981

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8 Aug 19818 Aug 1981

Day 0Day 0

The ability of the models to simulate the tilted convection is demonstrated by calculating the Day 10 lag regression of the respective PC’s against 20-100 day filtered OLR (W m-2). The pattern correlation with the Day 10 CsEOF is also given. The largest pattern correlation for (g) ECHAM5/MPI-OM incorrectly indicates that this model gives the best agreement with observations in simulating the tilted convective anomalies. The same incorrect result holds true if one considers the pattern correlation for the full-space-time BSISV simulated by the models (not shown). Thus, caution is warranted in using the pattern correlation as a metric of model performance without a physical interpretation of the data

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Caution is warranted in the use of latitude-time plots for isolating northward propagation. They must be used in conjunction with longitude-time plots to determine if the northward propagating component is associated with the near-equatorial eastward propagating convective anomalies, as observed. The ECHAM4/OPYC model has the most realistic BSISV. Also crucial to this finding was the examination of animations of the full BSISV life-cycle of the 20-100 day filtered OLR anomalies, snapshots of which are shown below

The latitude-time plot of convective anomalies averaged over India and/or the Bay of Bengal longitudes has been an accepted diagnostic for demonstrating the presence of northward propagation (e.g., Sikka and Gadgil (1980, MWR, 108, 1840-1853). For the observations and a subset of the models, shown below are latitude-time plots of the lag regression of the respective PC’s with 20-100 day filtered OLR (W m-2) that has been averaged between 71.25oE-83.75oE.

-25 -20 -15 -10 -5 0 5 10 15 20 25

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The results above suggest that the models exhibit northward propagation of convective anomalies that is essentially consistent with observations. However, the data above do not confirm that the anomalies that develops over the continental latitudes (poleward of 10oN) arises as an extension of the eastward propagating MJO. This is investigated below by showing longitude-time lag plots of convective anomalies between 5oN-5oS and 15oN-20oN

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10152025

-5

-5

-5

-5-5

-5

-2.5

-2.5 -2.5

-2.5

-2.5

-2.5-2.5 -2.5

-2.5

-2.5-2.5

-2.5

-2.5

-2.5-2.5

-2.5 -2.5-2.5

2.5

2.5

2.5

2.5 2.5

2.52.5

2.52.5

2.52.5

2.5

2.52.5

2.5 2.5

2.5

5

5

5

55

5

7.5

10

10

0 30E 60E 90E 120E 150E 180 150W 120W 90W 60W 30W 0-25-20-15-10

-505

10152025

5oN-5oS 15oN-20oN

AVHRR

-12.5 -10 -7.5 -5 -2.5 0 2.5 5 7.5 10 12.5

0 30E 60E 90E 120E 150E 180 150W 120W 90W 60W 30W-25-20-15-10

-505

10152025

-12.5

-10

-5

-5

-2.5

-2.5-2.5-2.5 -2.5

-2.5 -2.5

-2.5

2.5 2.52.5

2.52.5

2.5

2.5

2.52.5

2.5

5

5

5

0 30E 60E 90E 120E 150E 180 150W 120W 90W 60W 30W-25-20-15-10

-505

10152025

-12.5 -10 -7.5 -5 -2.5 0 2.5 5 7.5 10 12.5

0 30E 60E 90E 120E 150E 180 150W 120W 90W 60W 30W-25-20-15-10

-505

10152025

-7.5

-5

-5

-5 -5

-2.5

-2.5

-2.5

-2.5

-2.5

-2.5

-2.5

2.5

2.5

2.5

2.52.5

2.52.5

2.5 2.5 2.5

2.52.5

2.52.5

2.52.5

5

55

5

10

0 30E 60E 90E 120E 150E 180 150W 120W 90W 60W 30W-25-20-15-10

-505

10152025

-12.5 -10 -7.5 -5 -2.5 0 2.5 5 7.5 10 12.5

30E 60E 90E 120E 150E 180 150W 120W 90W 60W 30W-25-20-15-10

-505

10152025

-10

-5

-5

-2.5

-2.5

-2.5

2.5

2.5

2.5

2.52.5

2.52.5

5

5

30E 60E 90E 120E 150E 180 150W 120W 90W 60W 30W-25-20-15-10

-505

10152025

-12.5 -10 -7.5 -5 -2.5 0 2.5 5 7.5 10 12.5

30E 60E 90E 120E 150E 180 150W 120W 90W 60W 30W-25-20-15-10

-505

10152025

-2.5-2.5-2.5

-2.5

-2.5-2.5

-2.5-2.5 -2.5

-2.5

-2.5

-2.5-2.5 -2.5

2.5

2.5

2.52.5

2.5 2.5

2.5 2.5

2.5 2.5

5

5

30E 60E 90E 120E 150E 180 150W 120W 90W 60W 30W-25-20-15-10

-505

10152025-12.5 -10 -7.5 -5 -2.5 0 2.5 5 7.5 10 12.5

0 30E 60E 90E 120E 150E 180 150W 120W 90W 60W 30W 0-25-20-15-10

-505

10152025

-15

-10

-7.5

-5

-5 -5-5

-5

-5

-5

-5

-5

-5

-5

-2.5

-2.5

-2.5

-2.5-2.5

-2.5 -2.5-2.5

-2.5-2.5

2.52.5 2.5

2.5

2.5

2.52.5

2.52.52.5

2.52.5

2.52.5

2.52.52.5 2.5

2.52.52.5

2.5

2.5

2.52.5

55

55

5

5

7.5

7.5

7.5

10

10

0 30E 60E 90E 120E 150E 180 150W 120W 90W 60W 30W 0-25-20-15-10

-505

10152025

-12.5 -10 -7.5 -5 -2.5 0 2.5 5 7.5 10 12.5

0 30E 60E 90E 120E 150E 180 150W 120W 90W 60W 30W 0-25-20-15-10

-505

10152025

-5

-5-5

-5

-5

-5

-5

-5-5

-5

-5

-2.5-2.5

-2.5

-2.5-2.5

-2.5

-2.5 -2.5-2.5

-2.5

-2.5

-2.5

-2.5

2.5

2.52.5

2.52.52.5

2.5

2.52.5 2.5 2.5

2.52.5

2.52.5

2.5

5

55

5

5

5

7.5

7.5

7.5

7.5

7.5

0 30E 60E 90E 120E 150E 180 150W 120W 90W 60W 30W 0-25-20-15-10

-505

10152025

CSIRO_Mk3.0

ECHAM4/OPYC

GFDL_CM2.1

K. R. Sperber was supported under the auspices of the U.S. Department of Energy Office of Science, Climate Change Prediction Program by University of CaliforniaLawrence Livermore National Laboratory under contract No. W-7405-Eng-48. UCRL-POST-227735

-12.5

-10

-7.5

-5

-2.5

0

2.5

5

7.5

10

12.5

40E 60E 80E 100E 120E 140E 160E 18030S

10S

10N

30N-12.5

-10

-7.5

-5

-2.5

0

2.5

5

7.5

10

12.5

40E 60E 80E 100E 120E 140E 160E 18030S

10S

10N

30N

-12.5

-10

-7.5

-5

-2.5

0

2.5

5

7.5

10

12.5

40E 60E 80E 100E 120E 140E 160E 18030S

10S

10N

30N

-12.5

-10

-7.5

-5

-2.5

0

2.5

5

7.5

10

12.5

40E 60E 80E 100E 120E 140E 160E 18030S

10S

10N

30N

-12.5

-10

-7.5

-5

-2.5

0

2.5

5

7.5

10

12.5

40E 60E 80E 100E 120E 140E 160E 18030S

10S

10N

30N

-12.5

-10

-7.5

-5

-2.5

0

2.5

5

7.5

10

12.5

40E 60E 80E 100E 120E 140E 160E 18030S

10S

10N

30N

-12.5

-10

-7.5

-5

-2.5

0

2.5

5

7.5

10

12.5

40E 60E 80E 100E 120E 140E 160E 18030S

10S

10N

30N

-12.5

-10

-7.5

-5

-2.5

0

2.5

5

7.5

10

12.5

40E 60E 80E 100E 120E 140E 160E 18030S

10S

10N

30N

Day -15 Day -10 Day -5 Day 0

Day 5 Day 10 Day 15 Day 20