Breeding With The Nsipp Global

449 views

Published on

Published in: Technology
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
449
On SlideShare
0
From Embeds
0
Number of Embeds
3
Actions
Shares
0
Downloads
4
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide
  • Breeding With The Nsipp Global

    1. 1. Breeding with the NSIPP global coupled model: applications to ENSO prediction and data assimilation Shu-Chih Yang Advisors: Profs. Eugenia Kalnay and Ming Cai
    2. 2. Outline <ul><li>Introduction </li></ul><ul><li>Objectives </li></ul><ul><li>NASA/NSIPP CGCM </li></ul><ul><li>Breeding method </li></ul><ul><li>Results from a 10-year perfect model experiment </li></ul><ul><ul><li>Comparison with breeding in NCEP CGCM </li></ul></ul><ul><li>Summary </li></ul><ul><li>NSIPP operational system: preliminary results </li></ul>
    3. 3. Introduction <ul><li>ENSO simulation </li></ul><ul><ul><li>Because the coupled nature of ENSO phenomenon, the key factor to simulate and predict ENSO lies in the correct depiction of SST. </li></ul></ul><ul><li>ENSO prediction skill </li></ul><ul><ul><li>The prediction skill of a coupled model can be significantly improved through more refined initialization procedures (ex: Chen et al.,1995 and Rosati et al, 1997) </li></ul></ul><ul><ul><li>Initialization of operational ensemble forecast for CGCMs </li></ul></ul><ul><ul><ul><li>Two-tier (Bengtsson et al., 1993) </li></ul></ul></ul><ul><ul><ul><ul><li>An ensemble of atmospheric forecast generated by a forecasted SST </li></ul></ul></ul></ul><ul><ul><ul><li>One-tier (Stockdale et al., 1998, adopted in ECMWF) </li></ul></ul></ul><ul><ul><ul><ul><li>Generate all the ensemble members via CGCM </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Initial perturbations are introduced in atmosphere components only </li></ul></ul></ul></ul>
    4. 4. <ul><ul><li>How to construct effective ensemble members? </li></ul></ul><ul><ul><li>2 methods have been considered to construct initial perturbations: </li></ul></ul><ul><ul><ul><li>Singular vectors have been used for ENSO prediction with the Cane and Zebiak model </li></ul></ul></ul><ul><ul><ul><li>Limitations </li></ul></ul></ul><ul><ul><ul><ul><li>Strong dependence on the choice of norm and optimization time </li></ul></ul></ul></ul><ul><ul><ul><ul><li>High computational cost makes it impractical for CGCMs </li></ul></ul></ul></ul>
    5. 5. <ul><ul><li>Breeding method </li></ul></ul><ul><ul><ul><li>Toth and Kalnay (1996) </li></ul></ul></ul><ul><ul><ul><li>Cai et al. (2002) with CZ model </li></ul></ul></ul><ul><ul><ul><ul><li>Bred vectors are sensitive to the background ENSO, showing that the growth rate is weakest at the peak time of the ENSO states and strongest between the events . </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Bred vectors can be applied to improve the forecast skill and reduce the impact of the “spring-barrier” . </li></ul></ul></ul></ul><ul><ul><ul><ul><li>The results show the potential impact for ensemble forecast and data assimilation </li></ul></ul></ul></ul>
    6. 6. “ Spring Barrier”: The “dip” in the error growth chart indicates a large error growth for forecasts that begin in the spring and pass through the summer. Removing the BV from the initial errors reduces the spring barrier Monthly Amplification Factor of Bred Vector Background ENSO El Ni ño La Ni ña La Ni ña
    7. 7. Improvement on ensemble forecasts FCT error with  BV FCT error with  RDM
    8. 8. Objectives of this research <ul><li>Implement the breeding method with the NASA/NSIPP CGCM </li></ul><ul><li>Construct effective perturbations for initial conditions of ENSO ensemble forecasts </li></ul><ul><li>Test methods first with a “perfect model” simulation to develop understanding </li></ul><ul><li>Apply methodology to NSIPP operational system, which is more complex (e.g. model errors) </li></ul><ul><li>The ultimate goals is to improve seasonal and interannual forecasts through ensemble forecasting and data assimilation using coupled breeding </li></ul>
    9. 9. NASA Seasonal-to Interannual Prediction (NSIPP) coupled GCM <ul><li>AGCM (Suarez, 1996) </li></ul><ul><li>Finite-difference C grid in horizontal </li></ul><ul><li>Generalized sigma coordinate </li></ul>Coordinates Model features <ul><li>Primitive equations </li></ul><ul><li>Empirical cloud diagnostic model </li></ul><ul><li>4th-order version of the enstrophy conserving scheme </li></ul><ul><li>4th-order horizontal advection schemes for potential temperature, moisture </li></ul><ul><li>Penetrative convection parameterized with Relaxed Arakawa-Schubert scheme </li></ul>2  2.5  34 levels Resolution
    10. 10. <ul><li>OGCM </li></ul><ul><ul><li>Poseidon V4, (Schopf and Loughe,1995) </li></ul></ul>generalized horizontal and vertical coordinates Coordinates 1  3  5  8  27 layers Resolution <ul><li>Quasi-isopycnal model </li></ul><ul><li>Reduced-gravity formulation </li></ul><ul><li>Turbulent well-mixed layer with entrainment parameterized according to a Kraus-Turner bulk mixed layer model </li></ul><ul><li>Vertical mixing and diffusion are parameterized using a Richardson number dependent scheme </li></ul><ul><li>Horizontal mixing is implemented with high order Shapiro filtering </li></ul>Model features
    11. 11. Current prediction skill from NSIPP CGCM h indcasts Niño-3 Forecast SST anomalies up to 9-month lead April 1 starts September 1 starts Observations Ensemble member Ensemble mean
    12. 12. Breeding method <ul><li>Bred vectors : </li></ul><ul><li>The differences between the control forecast and perturbed runs </li></ul><ul><li>Tuning parameters </li></ul><ul><ul><li>Size of perturbation </li></ul></ul><ul><ul><li>Rescaling period (important for coupled system) </li></ul></ul><ul><li>Advantages </li></ul><ul><ul><li>Low computational cost </li></ul></ul><ul><ul><li>Easy to apply to CGCM </li></ul></ul>
    13. 13. Size of perturbation: 10% of the RMS of the SSTA (0.085  C) Rescaling period: one month Breeding OGCM AGCM Poseidon V4: 1/2° X 1.25° X 27 (90S - 72N) 10 years breeding “perfect model” experiment NSIPP-1: 3° X 3.75° X 34 (global) CGCM NINO3 INDEX(ºC) SOI INDEX
    14. 14. Snapshot of background SST (color) and bred vector SST (contour) Instabilities associated with the equatorial waves in the NSIPP coupled model are naturally captured by the breeding method model year JUN2024
    15. 15. Lead/lag correlation between BV growth rate and absolute value of background NINO3 index  BV grows before the background event Peak of the background event
    16. 16. Lead/lag correlation between BV growth rate and absolute value of BV NINO3 index
    17. 17. EOF analysis of SST Background SST anomaly EOF1 (46%) BV SST EOF1 (11%)
    18. 18. EOF analysis of thermocline (Z20) Background Z20 EOF1 (22%) Background Z20 EOF2 (16%) BV Z20 EOF1 (10%) BV Z20 EOF2 (7%) Z20 EOF2, SST EOF1 represent the mature phase of ENSO
    19. 19. Oceanic maps regressed with PCs Background: regressed with SST PC1 BV: regressed with Z20 PC1 SST Thermocline (Z20) Surface zonal current
    20. 20. Atmospheric maps regressed with PCs Tropical Pacific domain Wind at 850mb Surface pressure Geopotential at 500mb OLR Background BV
    21. 21. Atmospheric maps regressed with PCs Northern Hemisphere BV Background S ea-level pressure G eopotential at 200mb Even though the breeding rescaling is in the Nino3 region, the atmospheric response is global
    22. 22. Southern Hemisphere BV Background S ea-level pressure G eopotential at 200mb Atmospheric maps regressed with PCs
    23. 23. Lead/lag regression maps BV SST vs. | CNT NINO3 | BV zonal wind stress vs. | CNT NINO3 | BV surface height vs. | CNT NINO3 | Bred vector leads ENSO episode in the Eastern Pacific Bred vector lags ENSO episode in the Central Pacific 
    24. 24. NASA/NSIPP BV vs. NCEP/CFS BV NSIPP NCEP SST Z20  x Tropical Pacific domain
    25. 25. <ul><li>Results obtained with a 4-year NCEP run are extremely similar to ours </li></ul>NASA/NSIPP BV vs. NCEP/CFS BV Z20 EOF2 Z20 EOF1 SST EOF1 NCEP NSIPP
    26. 26. NASA/NSIPP BV vs. NCEP/CFS BV Northern Hemisphere NSIPP geopotential height at 500mb NCEP geopotential height at 500mb
    27. 27. Summary of “perfect model” results <ul><li>Larger BV growth rate leads the warm/cold events by about 3 months. </li></ul><ul><li>The amplitude of BV in the eastern tropical Pacific increases before the development of the warm/cold events. </li></ul><ul><ul><li>The ENSO related coupled instability exhibits large amplitude in the eastern tropical Pacific. </li></ul></ul><ul><ul><li>In N.H, BV teleconnection pattern reflect their sensitivity associated with background ENSO. Rossby wave-train atmospheric anomalies over both Hemispheres. </li></ul></ul><ul><ul><li>Breeding method is able to isolate the slowly growing coupled ENSO instability from weather noise </li></ul></ul><ul><ul><li>Bred vectors can capture the tropical instability waves </li></ul></ul><ul><li>Results of a “perfect model” experiment with the NCEP CGCM are very similar </li></ul>
    28. 28. <ul><li>Develop breeding strategy for the NASA/NSIPP coupled operational forecasting system </li></ul><ul><ul><li>Perform breeding runs with different rescaling norms </li></ul></ul><ul><ul><li>Perform experiments with a modified breeding cycle to reduce spin-up: </li></ul></ul><ul><ul><li> </li></ul></ul><ul><ul><li>Replace the restart file from an AMIP run to NCEP atmospheric re-analysis data </li></ul></ul>Current work t=1 t=2 t=3 t=4 t=5   A F 1month B 2month  B’ B’     
    29. 29. Relationship between bred vectors and background errors <ul><ul><li>This case was chosen because the BV growth rate was large . The excellent agreement suggests that the operational OI could be improved by augmenting the background error covariance with the BV as in Corazza et al, 2002 </li></ul></ul>BV Temp (contour) vs. analysis increment (color) at OCT1996
    30. 30. SST: Analysis - Control forecast Analysis – BV ensemble ave fcst For this case, we performed the first ensemble forecast: [(+BV fcst)+(-BV fcst)]/2 OCT1996 OCT1996
    31. 31. <ul><li>Summary of plans for application to the operational NSIPP system </li></ul><ul><ul><li>Develop a strategy to include the coupled growing modes extracted from coupled bred vectors in the initial condition of the ensemble system: For example, use perturbations +BV and –BV with an appropriate amplitude in the ensemble forecast system </li></ul></ul><ul><ul><li>Develop a methodology for using advantage of the ENSO BVs within the operational NSIPP ocean ensemble data assimilation: For example, augment the OI background error covariance with BVs . </li></ul></ul>
    32. 33. BV Geopotential at 500mb NCEP NSIPP
    33. 34. From 10 year perfect model simulation
    34. 35. Joint EOF map of BV SST
    35. 36. BV1 Z20PC1 vs. BV1 growth rate BV2 Z20PC1 vs. BV2 growth rate Growth rate Z20 PC1 Growth rate Z20 PC1
    36. 37. CNT Background Z20 EOF1 Background Z20 PC1 Background Z20 EOF2 Background Z20 PC2
    37. 38. Background ENSO vs. ENSO embryo CNT EOF1 BV1 EOF1 BV2 EOF1 CNT EOF2 BV1 EOF2 BV2 EOF2
    38. 39. BV growth rate BV SST vs. (SST fcst -SST a ) MAR1996
    39. 40. BV regression maps constructed with Z 20 PC1
    40. 41. Color: T fcst -T a Contour: BV (SST norm) Vertical cross-section along the Equator Color: T fcst -T a Contour: BV (Z20 norm) JAN2000
    41. 42. Color: T fcst -T a Contour: BV (SST norm) Color: T fcst -T a Contour: BV (Z20 norm) MAR1996 Vertical cross-section along the Equator

    ×