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Chidong Zhang, Min Dong RSMAS, University of Miami

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1 Simulations of the Madden-Julian Oscillation by Global Models: Current Status
Chidong Zhang, Min Dong RSMAS, University of Miami Harry Hendon, Andrew Marshall BMRC Eric Maloney Oregon State University Kenneth Sperber PCMDI, Lawrence Livermore National Laboratory Wanqiu Wang CPC/NCEP

2 Objectives: (1). To evaluate how well we currently can simulate the
Objectives: (1) To evaluate how well we currently can simulate the MJO using global climate and weather forecast models (2) To gain insight into MJO dynamics from the success and failure of global model simulations Issues: - What is the improvement during the last decade? - What are the remaining common problems? - How does air-sea coupling affect MJO simulations? - How does the mean background state affect MJO simulations?

3 Models Uncoupled AGCM Coupled CGCM Institute BAM3 BAM3C BMRC GFS03
(20 yrs) BAM3C BMRC GFS03 GFS03C (CFS) NCEP CAM2R (16 yrs) CAM2RC (15 yrs) NCAR/OSU ECHAM4 ECHO-G MPI

4 Atmosphere Models BAM3 T47 17 GFS03 T62 64 CAM2R T42 26 ECHAM4 19
(Institute) Horizontal Resolution Vertical Levels (top level) Cumulus Parameterization Integration BAM3 (BMRC) T47 (2.5˚) 17 (10 hPa) Mass flux (Tiedtke 1989) Adjustment closure (Nordeng 1994) GFS03 (NCEP) T62 (1.8˚) 64 (0.2 hPa) Mass flux (Hong and Pan 1998) CAM2R (NCAR/OSU) T42 (2.8˚) 26 (3.5 hPa) Relaxed Arakawa-Schubert (Moorthi and Suarez 1992) 16 years ECHAM4 (MPI) 19 20 years

5 Meridional Resolution
Ocean Models Coupled Run Ocean Models Meridional Resolution Zonal Resolution Vertical Levels Flux Correction BAM3C ACOM2 0.5˚in 9˚N-9˚S 1.5˚ near the poles 2˚ 25 (12 in upper 185m) Yes GFS03C MOM3 1/3˚ in 10˚N-10˚S 1˚ beyond 30˚N/S 1˚ 40 (27 in upper 400m) No CAM2RC SOM 1 ECHO-G HOPE-G 0.5˚ in 10˚N-10˚S 2.8˚beyond 30˚N/S 2.8˚ 20 (8 in upper 200m)

6 Observations: (1) NCEP/NCAR reanalysis zonal wind at 850 hPa (U850) (Kalnay et al 1996) (2) CMAP precipitation (Xie and Arkin 1997) Analysis Method: (1) Time-space spectrum (Hayashi 1979) of unfiltered data (2) MJO reconstruction using Hilbert SVD (Zhang and Hendon 1997) applied to intraseasonally (20-90 day) band-passed data (3) Seasonal cycle and geographic distribution of the MJO (Zhang and Dong 2004)

7 U850 precipitation Time-space spectra 10˚N-10˚S/60-180˚E observations
BAM3 BAM3C GFS03 GFS03C CAM2R CAM2RC ECHAM4 ECHO-G Time-space spectra 10˚N-10˚S/60-180˚E Eastward power > westward power Wind signal stronger than precipitation Air-sea interaction enhance eastward power

8 PEastward/PWestward U850 P
Ratio of eastward vs. westward intraseasonal power for 850 hPa zonal wind (U850) and precipitation (P). Intraseasonal power is defined as within the window of days at zonal wavenumber 1 for U850 and zonal wavenumbers 1 and 2 for P. PEastward/PWestward OBS BAM3 BAM3C GFS03 GFS03C CAM2R CAM2RC ECHAM4 ECHO-G U850 3.5 2.2 2.8 2.7 4.6 2.0 3.2 P 2.4 1.2 1.3 1.7 1.5 1.9 Simulated signal in wind is more realistic than simulated signal in precipitation. Air-sea interaction helps strengthen the signals for all models except for precipitation in ECHO-G.

9 Isolation of the MJO signal
Number of Leading HSVD Modes for MJO Reconstruction and Accumulative Fractional Variance OBS BAM3 BAM3C GFS03 GFS03C CAM2R CAM2RC ECHAM4 ECHO-G U850 4 (49%) 10 (58%) (40%) (44%) (47%) 2 (22%) 6 (48%) (36%) (41%) P (20%) (30%) (23%) (14%) (25%) (31%) Only outstanding Modes are used (Based on the Rule of North et al 1982)

10 ECHO-G GFS03 CAM2R CAM2RC GFS03C BAM3 Obs BAM3C ECHAM4 Propagation of the MJO Lag-regression upon MJO of U850 at 160˚E and 0˚N Equatorial U850 (contours) Equatorial precipitation (colors).

11 (a) OBS (b) BAM3 (d) GFS03 (e) GFS03C (g) CAM2RC (f) CAM2R (c) BAM3C (i) ECHO-G (h) ECHAM4 Horizontal Structure Zero-lag regression upon MJO U850 at 160˚E and 0˚N. U850 (vectors) precipitation (colors)

12 U850 Geographic distribution
(a) OBS (b) BAM3 (d) GFS03 (e) GFS03C (f) CAM2R (c) BAM3C (i) ECHO-G (h) ECHAM4 (g) CAM2RC U850 Geographic distribution December -March Contours: MJO variance Colors: Mean

13 Precipitation Geographic distribution
(a) OBS (b) BAM3 (d) GFS03 (e) GFS03C (f) CAM2R (c) BAM3C (i) ECHO-G (h) ECHAM4 (g) CAM2RC Precipitation Geographic distribution December -March Contours: MJO variance Shadings: Mean

14 Modeled Variance / Observed Variance
December – March (15˚S- 15˚N, ˚E) BAM3 BAM3C GFS03 GFS03C CAM2R CAM2RC ECHAM4 ECHO-G U850 1.5 1.1 0.8 1.0 1.4 2.4 0.9 P 0.4 0.6 0.1 0.5

15 MJO Seasonal migration
U850 Precipitation OBS BAM3 GFS03 GFS03C CAM2R BAM3C ECHO-G ECHAM4 CAM2RC MJO Seasonal migration 60E - 180˚E average Contour: MJO Variance Color: Mean

16 MJO RMS error vs Mean state RMS error
(December - March) Effect of mean state Blue: Uncoupled Red: Coupled RMS MJO: 15˚S - 15˚N, ˚E RMS mean: 15˚S - 15˚N, ˚E

17 Effect of mean state Mean variance of MJO precipitation (contour) overlaid with mean moisture convergence December - March 850 hPa MC 925 hPa MC

18 Summary Improvement: (1)
Summary Improvement: (1) intraseasonal, planetary-scale, eastward propagating spectral power in winds stronger than westward propagating spectral power; (2) realistic eastward phase speed of the MJO in the western Pacific. Common problems: (1) weak MJO signal in precipitation, (2) unrealistic phase relation between precipitation and wind (maximum precipitation not in low-level westerlies in the western Pacific), (3) split of precipitation maxima in the western Pacific, (4) seasonal migration unrealistic in many models.

19 Summary (cont. ) Important issues: (1)
Summary (cont.) Important issues: (1) Effects of air-sea coupling on MJO simulation are highly model-dependent. (2) Biases in MJO simulations are related to biases in simulated mean low- level zonal wind and mean precipitation. (3) The MJO activity depend on mean boundary-layer (925 hPa) moisture convergence. (4) The incoherence between MJO wind and precipitation in the simulations raises questions regarding our understanding of the MJO dynamics.

20 Thank You!

21 Time - latitude plot of variance in MJO U850 (contour, interval of 2 m2 s-2) and precipitation (contour, interval of 2 mm2 day-2) averaged over ˚E. Mean U850 (color, m s-1, zero outlined by white contours) is overlaid with MJO U850 and mean precipitation (color, mm day-1) overlaid with MJO precipitation. Next we will discuss the seasonal migration.

22 (a) (b) (c) (d) Scatter diagrams of RMS differences between individual simulations and observations in (a) MJO U850 variance (m2 s-2) and mean U850 (m s-1), (b) MJO precipitation variance (mm2 d-2) and mean precipitation (mm d-1), (c) MJO precipitation variance (mm2 d-2) and mean 925 hPa moisture convergence (g kg-1 m-1),and (d) MJO precipitation and mean 850 hPa moisture convergence. Symbols represent: circles for BAM3/BAM3C, crosses for GFS03/GFS03C, plus signs for CAM2R/CAM2RC, and squares for ECHAM4/ECHO-G, with blue for uncoupled and red for coupled simulations. RMS differences were calculated over 15˚S - 15˚N, ˚E for the MJO variables and 15˚S - 15˚N, ˚E for the mean state variables during December - March. Arrows in (d) highlight changes from uncoupled to coupled simulations.

23 Mean variance of MJO precipitation (contour) overlaid with mean moisture convergence (g kg-1 s-1) at (a) 850 hPa and (b) 925 hPa for December - March. Contour intervals are 2 mm d-1 starting from 1. 850 hPa MC 925 hPa MC

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