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

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 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

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).

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

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

Leading Rotated EOFs of Intraseasonal (Monthly JJA) V250mb Based on MERRA:

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

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

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

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

Correlation Between V250 REOF 1 and Precipitation Based on Monthly (subseasonal) data JJA ( ) MERRA Precipitation GPCP Precipitation

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

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

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

“Optimal” Vorticity Forcing Pattern For REOF 1 (Response to Idealized vorticity forcing in SWM with MERRA Basic State JJA mean) REOF 1 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

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

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

Estimated Vertically-Integrated QEstimated TFvort °K/dayS -2 Use Regression to Estimate Forcing for each REOF RPC 1 RPC 2 RPC 3 RPC 4 RPC 5

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

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

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

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( ) flow and forcing (primarily vorticity) estimated from MERRA We are continuing to investigate the nature of the forcing of these waves, and their predictability

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

Extra

Correlations: V250mb JJA Base Point in US Great Plains Base Point in Russia day filter 1-90 day filter

REOF 2

REOF 4

REOF 5