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MJO Simulation Diagnostics An Activity of the (US) CLIVAR MJO Working Group

MJO Simulation Diagnostics An Activity of the (US) CLIVAR MJO Working Group. Matthew Wheeler Centre for Australia Weather and Climate Research A partnership between Bureau of Meteorology and CSIRO Melbourne, Australia.

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MJO Simulation Diagnostics An Activity of the (US) CLIVAR MJO Working Group

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  1. MJO Simulation DiagnosticsAn Activity of the (US) CLIVAR MJO Working Group Matthew Wheeler Centre for Australia Weather and Climate Research A partnership between Bureau of Meteorology and CSIRO Melbourne, Australia

  2. MJO Simulation Diagnostics – CMIP3 sub-meeting, CCSR, University of Tokyo – Nov 2008 • In this talk I aim to give you some additional understanding of the Madden-Julian oscillation and the steps being taken by an international working group to help guide its successful simulation by global circulation models (GCMs). • In particular, I will discuss a multi-authored paper by the working group on “MJO Simulation Diagnostics”. • Contents • Background on the CLIVAR MJO Working Group • Some background on the MJO • Motivation for having standardized diagnostics • The (observed) diagnostics • Summary • Brief comparison with models

  3. Background on the CLIVAR MJO Working Group Formed in June 2006 as the US-CLIVAR MJO Working Group with a life-time of two years. Now with a more international flavour, and continuing informally. See http://www.usclivar.org/mjo.php

  4. Membership of CAWCR/Bureau of Met CAWCR/Bureau of Met Note: Professor Nakazawa attended our November 2007 workshop. MJO WG - Terms of Reference 1. Develop a set of diagnostics to be used for assessing MJO simulation fidelity and forecast skill. 2. Develop and coordinate model simulation and prediction experiments, in conjunction with model-data comparisons, which are designed to better understand the MJO and improve our model representations and forecasts of the MJO. 3. Raise awareness of the potential utility of subseasonal and MJO forecasts in the context of the seamless suite of predictions. 4. Help to coordinate MJO-related activities between national and international agencies and associated programmatic activities. 5. Provide guidance to US CLIVAR and Interagency Group (IAG) on where additional modeling, analysis or observational resources are needed.

  5. Features: Has a 30 to 80-day period and slow eastward propagation. Also called the 40-50 day wave and the Intraseasonal Oscillation. First described by Madden and Julian in the early 1970s. Is the strongest mode of intraseasonal variability on earth. Generates many of the bursts and breaks of the monsoons. But also has far-reaching impact on other weather and climate phenomena. Some background on the MJO Approximate 1 month sequence Monsoon “break” TC formation Trade Wind surge Monsoon westerlies ACTIVE “BURST” OF INDONESIAN-AUSTRALIAN MONSOON Westerly Wind Burst

  6. Original schematic from Madden and Julian (1972) • Seen in upper-level zonal winds around the globe. • Strongest in Indo-Pacific domain where it is convectively-coupled. • Planetary scale: Zonal wavenumbers 1-3. • Baroclinic wind structure. Each panel separated by about 6 days.

  7. Convection has multi-scale structure. • Episodic and not always present.

  8. Strong seasonal dependence to off-equatorial behaviour (but still “MJO” in all seasons) Each phase is separated, on average, by about 6 days

  9. Motivation for diagnostics • Despite the important role for the MJO, GCMs still exhibit shortcomings in representing it, as has been documented by: • Slingo et al. (1996) AMIP 15 models • Waliser et al. (2003) CLIVAR 10 models • Sperber et al. (2005) 6 coupled/uncoupled models • Zhang et al. (2006) 8 coupled/uncoupled models • Lin et al. (2006) IPCC/AR4 14 coupled models • Due to the use of different diagnostics in these studies, however, progress has been difficult to track. • Thus there is a need for a standardized set of clearly-defined diagnostics for use by modelling groups. • These diagnostics should be able to succinctly capture the essential features of the MJO and its dynamics as just described. N.b. Due to interannual variability and the episodic nature of the MJO, it takes a simulation of at least 5 years to determine its simulation fidelity.

  10. 1st paper, in press 2nd paper, draft

  11. The journal papers provide only a sample of what is available on-line at http://climate.snu.ac.kr/mjo_diagnostics/index.htm Code is also available from this site

  12. Mean state diagnostics The ability of a model to simulate the MJO is intimately related to its ability to simulate the mean climate. Thus mean states of some relevant variables are also included. Nov-Apr contours of SST

  13. May-Oct contours of SST

  14. Level 1 diagnostics Maps of total intraseasonal (20-100 day) variance and variance fraction provide a lowest-level benchmark for GCMs. Of importance is the shift in variance north and south of the equator with season and the relative minimum over the Maritime Continent. Nov-Apr May-Oct Variance in the 20-100 day band for a) CMAP precipitation and b) NCEP1 850 hPa zonal wind (contours) for November-April (left) and May-October (right) seasons. The percent variance accounted for by the 20-100 day band band is shown in color. Precipitation variance contours are plotted every 6 mm2 day-2, starting at 3 mm2 day-2. Zonal wind variance contours are plotted every 3 m2 s-2, starting at 6 m2 s-2.

  15. The fundamental eastward propagating nature is best isolated with lag-longitude correlation analyses. “Predictor” is 10S-5N, 75-100E averaged precipitation. Nov-Apr 5 ms-1 Contour interval = 0.1 Qualitatively similar results are achieved in boreal summer!

  16. However, boreal summer intraseasonal variability is also characterised by distinct northward propagation. As before, “predictor” is 10S-5N, 75-100E averaged precipitation. May-Oct Contour interval = 0.1 Some southward propagation into the SH is also apparent.

  17. Level 2 diagnostics Wavenumber-frequency spectra show the dominance of eastward propagation. May-Oct Nov-Apr Data are averaged 10S-10N before computing spectra. Applied to 180-day segments from each year, then averaged for all years. WESTWARD EASTWARD

  18. Wavenumber-frequency cross-spectra quantify the coherence and phase between different variables. Using OLR and 850-hPa zonal wind is very useful for extracting the coherent modes of convectively-coupled behaviour in that exist, without the need for estimating a background spectrum. All seasons Applied to 256-day overlapping segments. Spectra computed for individual symmetric/antisymmetric latitudes first, then averaged 0-15. Upward-pointing vector is a phase of 0, to the right is OLR leading u850 by 90.

  19. An aside:New results from Hendon and Wheeler (J. Atmos. Sci.; 2008) Top figures: Contours for OLR power, shading for “signal strength” (like normalized power) Bottom figures: Cross-spectra between OLR and u850 (as on previous slide).

  20. Multivariate EOF analysis: Useful for extracting convetively-coupled structure, and for generating a MJO phase index. All seasons 15S-15N averaged data. These EOFs are virtually independent of season! Power spectrum of projection coefficients obtained by projecting unfiltered data on the EOFs. This analysis is the same as applied by Wheeler and Hendon (2004), except used 20-100d filtered data as input.

  21. For compositing, phases may be defined from the leading pair of principal component (PC) time series. Note the ‘smoothness’ of the phase-space trajectory because of the use of filtered data. “Weak MJO” defined to occur when PC12 + PC22 < 1.0.

  22. Nov-Apr Despite using an all-season EOF index, the seasonality of the MJO is still retained in season-specific composites. The weakening of the precipitation signal over the islands of the Maritime Continent is shown.

  23. May-Oct Northwest-southeast tilting and resulting northward propagation of the convective signal is shown.

  24. Nov-Apr Interaction with the ocean is also thought to provide at least a modifying effect on the MJO. Having the correct phase relationship of SST anomalies with the convection, however, is crucial for getting the impact of the ocean coupling correct.

  25. May-Oct

  26. Summary of paper 1 • The aim of the paper is to recommend a set of diagnostics that describe the essential features of the MJO, its dynamics, and aspects of the mean state important for its existence. • Further diagnostics, and computations with different observed datasets are available from the web-site. (as well as code and some data) • Level-1 diagnostics are meant to provide an initial indication of a model’s ability to reproduce the spatial extent and strength of intraseasonal variability. • Level-2 diagnostics provide a more comprehensive assessment of the propagating, convectively-coupled nature of the MJO, and its seasonal variation. • But how well do the models do? (i.e. paper 2)

  27. Wavenumber-frequency spectra of precipitation (shaded) and u850 (contours) WEST EAST wavenumber Observed MJO frequency Wavenumber-frequency spectra of 10N-10S averaged precipitation (shaded) and 850hPa zonal wind (contoured). Individual November-April spectra were calculated for each year, and then averaged over all years of data. Only the climatological seasonal cycle and time mean for each November-April segment were removed before calculation of the spectra. Units for the precipitation (zonal wind) spectrum are mm2 day-2 (m2 s-2) per frequency interval per wavenumber interval. The bandwidth is (180 d)-1. CLIVAR MJO Working Group (2008)

  28. Scatter plot of east/west ratio of power based on the data in previous figure. The east/west ratio is calculated by dividing the sum of eastward propagating power by westward propagating counterpart within wavenumber 1-6 (1-3 for zonal wind), period 30-80 days.

  29. Multivariate EOFs of 15S-15N averaged data.

  30. Power spectra of the unfiltered PCs derived by projecting unfiltered data onto the CEOFs.

  31. The End Thankyou for being wonderful hosts! m.wheeler@bom.gov.au

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