The USCLVAR Drought Working Group: A Multi-Model Assessment

The USCLVAR Drought Working Group: A
Multi-Model Assessment
of the Impact of SST Anomalies on Drought
NOAA's 33rd Climate Diagnostics and Prediction Workshop/
CLIVAR Drought Workshop
Lincoln, NE 20-24 October 2008
By
The USCLIVAR Drought Working Group
Presented by S. Schubert
NASA/GSFC
Global Modeling and Assimilation Office
The US CLIVAR Drought Working Group
http://www.usclivar.org/Organization/drought-wg.html
U.S. Membership
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Tom Delworth
NOAA GFDL
Rong Fu Georgia Institute of Technology
Dave Gutzler (co-chair)
University of New Mexico
Wayne Higgins
NOAA/CPC
Marty Hoerling
NOAA/CDC
Randy Koster
NASA/GSFC
Arun Kumar
NOAA/CPC
Dennis Lettenmaier
University of Washington
Kingtse Mo
NOAA CPC
Sumant Nigam
University of Maryland
Roger Pulwarty
NOAA- NIDIS Director
David Rind
NASA - GISS
Siegfried Schubert (co-chair)
NASA GSFC
Richard Seager
Columbia University/LDEO
Mingfang Ting
Columbia University/LDEO
Ning Zeng
University of Maryland
International Membership: Ex Officio
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Bradfield Lyon
Victor O. Magana
Tim Palmer
Ronald Stewart
Jozef Syktus
International Research Institute for Climate
Mexico
ECMWF
Canada
Australia
Other interested participants
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Lisa Goddard <[email protected]>
Alex Hall <[email protected]>
Jerry Meehl <[email protected]>
Jin Huang <[email protected]>
John Marshall <[email protected]>
Adam Sobel <[email protected]>
Max Suarez <[email protected]>
Phil Pegion <[email protected]>
Tim Palmer <[email protected]>
Entin, Jared K. <[email protected]>
Donald Anderson <[email protected]>
Rong Fu <[email protected]>
Doug Lecomte <[email protected]>
Hailan Wang <[email protected]>
Junye Chen <[email protected]>
Eric Wood <[email protected]>
Aiguo Dai <[email protected]>
Alfredo Ruiz-Barradas <[email protected]>
Jae Kyung E Schemm <[email protected]>
Clara Deser [email protected]
Kirsten Findell <[email protected]>
Mark Helfand [email protected]
Scott J. Weaver <[email protected]>
Kit K. Szeto <[email protected]>
Chunzai Wang <[email protected]>
Adam Phillips <[email protected]>
Matias Mendez <[email protected]>
Hugo Berbery <[email protected]>
Terms of Reference
• propose a working definition of drought and related model predictands
of drought
• coordinate evaluations of existing relevant model simulations
• suggest new model experiments designed to address
some of the outstanding uncertainties concerning the
roles of the ocean and land in long term drought
• coordinate and encourage the analysis of observational data sets to
reveal antecedent linkages of multi-year drought
• organize a community workshop in 2008 to present and discuss results
Model Experiments (SST and Soil
Moisture Impacts)
• Force with the 3 leading REOFs of annual mean SST (+/- 2 std)
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Also fixed soil moisture experiments
Also “tropics only” versions of some patterns
Also high and low frequency Pacific SST patterns (separating ENSO, PDO)
Also AMIP simulations
• Participating groups/models: NASA (NSIPP1), Lamont(CCM3),
NCEP(GFS), GFDL (AM2.1), NCAR (CAM3.5), and COLA/Univ.
of Miami/ (CCSM3.0)
• Web site with subset of monthly data
ftp://gmaoftp.gsfc.nasa.gov/pub/data/clivar_drought_wg/README/www/index.html
(contact: Hailan Wang)
Leading Rotated EOFs
of annual mean SST
(1901-2004)
Linear
Trend
Pattern
Pacific
Pattern
Atlantic
Pattern
Annual Mean Response
-all runs 50 years (35 for GFS)
-force with each sign of the leading patterns and combinations of
the patterns (e.g., cold Pacific, warm Pacific,
warm
Pacific + cold Atlantic, etc.)
Annual Mean 200mb Height Response (m)
Pacific Warm
Pacific Cold
Annual Mean Tsfc Response (°C)
Pacific Warm
Pacific Cold
Annual Mean Tsfc Response (°C)
Pacific Warm
Pacific Cold
Annual Mean Tsfc Response (°C)
Atlantic Warm
Atlantic Cold
Annual Mean Tsfc Response (°C)
Atlantic Warm
Atlantic Cold
Annual Mean Tsfc Response (°C)
Warm Trend+
(Warm Pacific +Cold Atlantic)
Warm Trend+
(Cold Pacific +Warm Atlantic)
Annual Mean Tsfc Response (°C)
Warm Trend
Spatially Uniform Warm Trend
Annual Precipitation (mm/day)
Pacific Cold
Atlantic Warm
Tendency for US Drought!
Annual Precipitation (mm/day)
Pacific Cold+Atlantic Warm
US Drought!
Pacific Warm+Atlantic Cold
US Pluvials!
Annual Precipitation (mm/day)
Pacific Cold+Atlantic Warm
US Drought!
Pacific Warm+Atlantic Cold
US Pluvials!
Seasonal Evolution of Response
DJF Cold
Contours:
200mb height
anomalies
Vectors:
850mb wind
anomalies
Colors:
precipitation
anomalies
Weak and shifted
anti-cyclonic
anomalies
MAM
- Cold
General consistency in
height anomalies but
CFS again shifted south
JJA Cold
Cyclonic anomalies in IAS
SON Cold
Cyclonic anomalies in IAS
Signal to Noise Ratio ( R)
R = (x-y)/sxy
( ¯ ): 50 yr mean
X: seasonal mean from experiment
Y: seasonal mean from control (forced with
climatological SST)
s
2
2
xy =
(S
2
2
X+S Y)/2
S X variance of seasonal mean from experiment
2
S Y : variance of seasonal mean from control
NW
GP
SW

SE
Precipitation Response to Warm and Cold Pacific (signal/noise)
R
R
Tsfc Response to Warm/Cold Pacific (signal/noise)
R
R
Uncertainties in Noise
Noise (Z200mb): Unforced Interannual Variance in Control Runs
DJF
MAM
Noise (Z200mb): Unforced Interannual Variance in Control Runs
JJA
SON
Tsfc and Precip Noise
• Look at Pacific warm and cold SST cases
MAM Pacific: Great Plains
Cold
Warm
Precip
mm/d
Precip
mm/d
Tsfc °C
Tsfc °C
JJA Pacific: Great Plains
Warm
Precip
mm/d
Cold
Precip
mm/d
Tsfc °C
Tsfc °C
What Are the Determining
Factors for Noise (unforced
variability)?
Seasonal Dependence?
Impact of SST (a signal in the
noise!)
“Noise” in Great Plains in Spring/Summer driven by
land/atmosphere feedbacks but also depends on SST Forcing!
Pacific Cold
Pacific Warm
DE
Greater ∆E
for given
change ∆W
DE
DW
DW
W (soil moisture)
Schubert et al. 2008 JCLIM
Subseasonal Noise is the result of barotropic
instability of the jet - depends on SST!
JFM 98 (El Nino)
JFM 99 (La Nina)
Model
120 ensemble
members
Obs
200mb  Variability
(10-30 days)
Schubert et al. 2001 JCLIM
Some Basic Results: Over US
• Mean Responses
– Models tend to agree that
• Cold Pacific+Warm Atlantic => drought/warm
• Warm Pacific+Cold Atlantic => pluvial conditions/cold
– There are substantial differences in details of anomaly patterns
– There is a large seasonality in responses
• Potential Predictability (Pacific signal to noise)
– Largest in spring
– Models appear to agree more on precipitation than surface
temperature responses!
Some Basic Results-2
• Models show substantial differences in seasonally dependent
controls
– Cold season (planetary waves/storm tracks)
– Warm season (land surface memory and feedbacks)
– Summer/fall (Low level response: LLJ/IAS)
• Models show considerable differences in basic noise levels
– For the upper level circulation this is likely tied to differences in
climatological jet structures and related instabilities (weather,
PNA, etc)
– For Precip and Tsfc over the Great Plains, land surface
interactions may play a role during warm seasons