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An Analysis of the Nature of Short Term Droughts and Floods During Boreal Summer

An Analysis of the Nature of Short Term Droughts and Floods During Boreal Summer. Siegfried Schubert, Hailan Wang* and Max Suarez NASA/GSFC Global Modeling and Assimilation Office Workshop on Evaluation of Reanalyses – Developing an Integrated Earth System Analysis (IESA) Capability

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An Analysis of the Nature of Short Term Droughts and Floods During Boreal Summer

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  1. An Analysis of the Nature of Short Term Droughts and Floods During Boreal Summer Siegfried Schubert, Hailan Wang* and Max Suarez NASA/GSFC Global Modeling and Assimilation Office Workshop on Evaluation of Reanalyses – Developing an Integrated Earth System Analysis (IESA) Capability Baltimore, MD November 1-3, 2010   *also Goddard Earth Sciences and Technology Center University of Maryland at Baltimore County

  2. Role of Stationary Rossby Waves Use MERRA to: • Characterize the waves • Show their impacts on surface meteorology (including extremes) • Examine their forcing (together with stationary wave model) Builds on work by Lau and Peng 1992; Ambrizzi et al. 1995; Newman and Sardeshmukh, 1998; Chen and Newman 1998; Ding and Wang 2004; Wang et al. 2009

  3. Quality of Precipitation The time series of the spatial correlation of annual mean precipitation averaged over the globe from several reanalyses with that from GPCP. The comparison of CMAP against GPCP is also shown (black curve).

  4. One- point lead/lag Correlation (V250mb) (30-90 day filter,MERRA - JJA 1979-2008) Lag 0 Base point: US East Coast Lag +4 days Lag -4 days Lag -8 days Lag +8 days

  5. One- point lead/lag Correlation (V250mb) (30-90 day filter,MERRA - JJA 1979-2008) Lag 0 Base point: Northern Russia Lag +4 days Lag -4 days Lag -8 days Lag +8 days

  6. Leading Rotated EOFs of Intraseasonal (Monthly JJA) V250mb Based on MERRA: 1979- 2010

  7. Monthly JJA V250mb Anomalies Projected onto REOFs 2003 European Heat Wave 2010 Russian Heat Wave 1998 Texas, Florida heat waves, flooding in upper midwest 1988 US drought 2010 Pakistan floods

  8. Jun 79: Negative Jun 82: Negative Jun 87: Positive Summers with Large Amplitude REOF 1 Jun 89: Positive Jun 2003: Negative Jul 2010: Positive V 250mb: MERRA

  9. Jun 79: Negative Jun 82: Negative Jun 87: Positive Summers with Large Amplitude REOF 1 Jun 89: Positive Jul 2010: Positive Jun 2003: Negative T2m: MERRA

  10. Correlation Between V250 REOF 1 and T2m MERRA T2m HADCRU Gridded Station DataT2m Based on Monthly (subseasonal) data JJA (1979-2008)

  11. Correlation Between V250 REOF 1 and Precipitation MERRA Precipitation GPCP Precipitation Based on Monthly (subseasonal) data JJA (1979-2008)

  12. Fraction of Intraseasonal T2m (top panel) and Precipitation (bottom panel) Monthly Variance explained by the 10 leading v250mb REOFs

  13. Forcing Mechanisms • Stationary Wave Model (Ting and Yu 1998) • Idealized forcing • Forcing estimates from MERRA

  14. SWM: Response to localized heat sources Evolution of Eddy V-wind s=.257 North Pacific North Atlantic Day 1 Day 9 Day 1 Day 9 Day 3 Day 11 Day 3 Day 11 Day 5 Day 15 Day 5 Day 15 Day 30 Day 7 Day 30 Day 7 MERRA 1979-2008 JJA Base State

  15. “Optimal”Vorticity Forcing Pattern For REOF 1(Response to Idealized vorticity forcing in SWM with MERRA Basic State JJA 1979-2008 mean) Optimal pattern is computed by calculating the responses to forcings located at every 5° lat and 10°lon and taking the inner product between the response and REOF1 and plotting that at each forcing location REOF 1

  16. Example of optimal vorticity forcing pattern for REOF3 REOF 3 (250mb Vwnd) June 1988 Precip Anomaly JJA 1979-2008 Correlation (Precip, REOF3)

  17. Estimate 3-D Forcing Terms in SWM from MERRA(JJA 1979-2010, transient eddy fluxes and heating)

  18. Use Regression to Estimate Forcing for each REOF Estimated Vertically-Integrated Q Estimated TFvort RPC 1 RPC 2 RPC 3 RPC 4 °K/day S-2 RPC 5

  19. TFvort Comparison “Optimal” Idealized Forcing MERRA Estimate from Regression REOF 1 0° 0° REOF 3 180° 180°

  20. SWM Response to Forcing Estimated From MERRA (REOF 1) Q TF TFvort TFdiv REOF 1 TFtemp TF+Q

  21. SWM Response to Forcing Estimated From MERRA (REOF 3) Q TF TFvort TFdiv TFtemp REOF 3 TF+Q

  22. Conclusions/Summary • Stationary Rossby waves (the leading REOF’s of v250mb) account for a substantial fraction of summertime monthly mean surface temperature and precipitation variability over a number of regions of the Northern Hemisphere middle latitudes • They, at times, dominate the monthly circulation and surface meteorology: E.g., the leading wave pattern appears to have played an important role in the recent heat waves over Europe (2003) and Russia (2010) • We can reproduce the basic observed patterns of variability in a Stationary Wave Model using as a base state the JJA mean(1979-2008) flow and forcing (primarily vorticity) estimated from MERRA • We are continuing to investigate the nature of the forcing of these waves, and their predictability

  23. Issues for Reanalysis • How well can we estimate forcing (heating, vorticity sources)? • Predictability/initialization issues – likely sensitivity to small scales in forcing

  24. Extra

  25. Correlations: V250mb JJA 1979-2008 Base Point in Russia Base Point in US Great Plains 30-90 day filter 30-90 day filter 1-90 day filter 1-90 day filter

  26. REOF 2

  27. REOF 4

  28. REOF 5

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