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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.
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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
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?
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)
U850 precipitation 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
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 30 - 90 days at zonal wavenumber 1 for U850 and zonal wavenumbers 1 and 2 for P. PEastward/PWestward • 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.
Isolation of the MJO signal Number of Leading HSVD Modes for MJO Reconstruction and Accumulative Fractional Variance Only outstanding Modes are used (Based on the Rule of North et al 1982)
Obs BAM3C BAM3 GFS03 GFS03C CAM2R CAM2RC ECHAM4 ECHO-G Propagation of the MJO Lag-regression upon MJO of U850 at 160˚E and 0˚N EquatorialU850 (contours) Equatorial precipitation (colors).
(a) OBS (b) BAM3 (c) BAM3C (d) GFS03 (e) GFS03C (f) CAM2R (g) CAM2RC (h) ECHAM4 (i) ECHO-G Horizontal Structure Zero-lag regression upon MJO U850 at 160˚E and 0˚N. U850 (vectors) precipitation (colors)
(a) OBS (b) BAM3 (c) BAM3C (d) GFS03 (e) GFS03C (f) CAM2R (g) CAM2RC (h) ECHAM4 (i) ECHO-G U850 Geographic distribution December -March Contours: MJO variance Colors: Mean
(a) OBS (b) BAM3 (c) BAM3C (d) GFS03 (e) GFS03C (f) CAM2R (g) CAM2RC (h) ECHAM4 (i) ECHO-G Precipitation Geographic distribution December -March Contours: MJO variance Shadings: Mean
Modeled Variance / Observed Variance December – March (15˚S- 15˚N, 50 - 180˚E)
U850 Precipitation U850 Precipitation OBS OBS BAM3C BAM3 GFS03C GFS03 CAM2RC CAM2R ECHAM4 ECHO-G MJO Seasonal migration 60E - 180˚E average Contour: MJO Variance Color: Mean
MJO RMS error vs Mean state RMS error (December - March) Effect of mean state Blue: Uncoupled Red: Coupled RMS MJO: 15˚S - 15˚N, 50 - 180˚E RMS mean: 15˚S - 15˚N, 50 - 270˚E
Effect of mean state Mean variance of MJO precipitation (contour) overlaid with mean moisture convergence December - March 850 hPa MC 925 hPa MC
SummaryImprovement:(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.
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.
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 60 - 180˚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.
(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, 50 - 180˚E for the MJO variables and 15˚S - 15˚N, 50 - 270˚E for the mean state variables during December - March. Arrows in (d) highlight changes from uncoupled to coupled simulations.
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