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

Siegfried Schubert, Hailan Wang* and Max Suarez

NASA/GSFCGlobal 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 CenterUniversity 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. 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 1979-2008)

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 1979-2008)

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: 1979- 2010

Monthly JJA V250mb Anomalies Projected onto REOFs2003 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: Negative Jun 87: Positive

Jun 2003: NegativeJun 89: PositiveJul 2010: Positive

V 250mb: MERRA

Summers with Large Amplitude REOF 1

Jun 79: Negative Jun 82: Negative Jun 87: Positive

Jun 2003: NegativeJun 89: Positive Jul 2010: Positive

T2m: MERRA

Correlation Between V250 REOF 1 and T2m

Based on Monthly (subseasonal) data JJA (1979-2008)

MERRA T2m

HADCRU Gridded Station DataT2m

Correlation Between V250 REOF 1 and Precipitation

Based on Monthly (subseasonal) data JJA (1979-2008)

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 1979-2008 JJA Base State

Evolution of Eddy V-wind s=.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 1979-2008 mean)

REOF 1Optimal 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 1979-2008 Correlation (Precip, REOF3)

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

Estimated Vertically-Integrated Q Estimated TFvort

°K/day S-2

€

Forcingrpcn = α RPCn+εUse 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° 0°

180° 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(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

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 1979-2008

Base Point in US Great Plains Base Point in Russia

30-90 day filter 30-90 day filter

1-90 day filter 1-90 day filter

REOF 2

REOF 4

REOF 5