1 / 21

MJO simulations under a dry environment

MJO simulations under a dry environment. (… in a Nudging World). Marcela Ulate M Advisor: Chidong Zhang. Motivation.

chana
Télécharger la présentation

MJO simulations under a dry environment

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. MJO simulations under a dry environment (… in a Nudging World) Marcela Ulate M Advisor: Chidong Zhang

  2. Motivation • The Madden Julian Oscillation (MJO) is a convective coupled wave that dominates the intraseasonal variability in the Tropics. It is characterized by westerly wind anomalies preceding the convection and easterly wind anomalies ahead of the convective center in lower levels (Madden and Julian 1994; Zhang 2005). • We want to study the roles of convective cloud populations at initiation stages of MJO events, BUT Global Climate Models (GCM) and Regional Numerical Models (RNM) fail to reproduce the MJO (Slingo 1996; Zhang 2005; Zhang et al. 2006; Gustafson and Weare 2004, Monier et al. 2010). • Some numerical models are not able to reproduce the MJO at all, and some others present decoupling between the wind and the precipitation.

  3. The case of study U850 Anom. ERA-Interim TRMM TIME m/s mm/day LON

  4. Model Description • WRF v3.2 • 1º x 1º horizontal resolution. • 28 vertical levels. • Tropical channel domain: Periodic Boundary conditions in the east-west direction. • Boundary Conditions form ERA-Interim data

  5. Wind and Precipitation decoupling TIME LON This was the best of our model simulations ….

  6. WRFRH – ERAIRH The dry bias P (hPa) Days P (hPa) P (hPa) Lat Lon

  7. Hypothesis The model has a dry bias in the mid troposphere i.e. it dries the atmosphere faster than the adjustment time between the wind convergence at lower levels and the rain production by the cumulus scheme. The net effect is to inhibit convection. Therefore, the convective component of the MJO cannot fully develop in the model environment

  8. Approach to the problem: Humidity Nudging • Four-Dimensional Data Assimilation (FDDA) or nudging is the process where the model is set to converge at a desired rate to the analysis or observations. • The process adds an extra tendency term to the model equations proportional to the difference between the model simulation and the analysis value at every grid point, forcing the simulation closer to the analysis value.

  9. Humidity Nudging Model forcing terms Nudging Tendency term :analysis field value : nudging factor , : four dimensional weight function, From Skamarock et al. 2008.

  10. Spectral Nudging of Humidity Analysis data (ERA-Interim) Spatially filter the data (minimunx,ywavelengh) Nudging Tendency

  11. Grid Nudging vs Spectral Nudging Dependency of the small scales (~100Km) BUT, Do we need the large scales ….? (See Future Work)

  12. x100y500 TIME LON LON

  13. WRFRH – ERAIRH Reduction of the dry bias P (hPa) Days P (hPa) P (hPa) Lat Lon

  14. Humidity Tendency due to nudging Humidity Tendency due to cumulus scheme - CONTROL g/Kg day-1 P (hPa) Humidity Tendency due to cumulus scheme MJO-like precipitation simulation g/Kg day-1 P (hPa)

  15. Heating Tendency - Control K/day P (hPa) Heating Tendency – MJO-like precipitation simulation K/day P (hPa)

  16. Super MJO … ?? How much nudging is too much nudging? What if Ga=1 ?

  17. Conclusions Spectral and grid Nudging of water vapor mixing ratio reduces the model dry-bias and allows the model to produce an improved MJO-like precipitation pattern and wind signal. The moisture at mid levels of the troposphere is crucial in order to reproduce the convective signal associated with the MJO. The dependencies of the meridional and zonal scales close to ~100Km are important in order to obtain a better MJO-like simulations.

  18. More Conclusions The prediction of the first MJO event improves when nudging is preformed,whilethe initiation of the second event is not. This suggests that improving the humidity field is one component of the problem, and we need to investigate further on this matter. Without nudging, the cumulus scheme remains relative inactive i.e. lack of precipitation during the MJO event. This translates to aweak heating profile. When the MJO precipitation pattern improves, the heating profile resembles the results of other studies more closely.

  19. Future work Study more in depth the relationship between the large scale’s moisture role in MJO simulations. Investigate the general characteristics of the transition between 2 MJO events and the inability of the model to capture abrupt changes in precipitation patterns. Simulate the MJO using a cloud- resolving version of the WRF (25Km,8Km,3Km resolution) with YOTC data as boundary conditions for the same case of study and the addition of the second MJO event.

  20. Thanks

More Related