The USCLVAR Drought Working Group: A Multi-Model Assessment of the Impact of SST Anomalies on Drought

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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

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The US CLIVAR Drought Working Group
http//www.usclivar.org/Organization/drought-wg.ht
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• U.S. Membership
• 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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• Other interested participants
• Lisa Goddard ltgoddard_at_iri.columbia.edugt
• Alex Hall ltalexhall_at_atmos.ucla.edugt
• Jerry Meehl ltmeehl_at_ucar.edugt
• Jin Huang ltJin.Huang_at_noaa.govgt
• John Marshall ltjmarsh_at_MIT.EDUgt
• Adam Sobel ltahs129_at_columbia.edugt
• Max Suarez ltMax.J.Suarez_at_nasa.govgt
• Phil Pegion ltpegion_at_gmao.gsfc.nasa.govgt
• Tim Palmer ltTim.Palmer_at_ecmwf.intgt
• Entin, Jared K. ltjared.k.entin_at_nasa.govgt
• Donald Anderson ltdonald.anderson-1_at_nasa.govgt
• Rong Fu ltrf66_at_mail.gatech.edugt
• Doug Lecomte ltDouglas.Lecomte_at_noaa.govgt
• Hailan Wang lthwang_at_climate.gsfc.nasa.govgt
• Junye Chen ltjchen_at_gmao.gsfc.nasa.govgt
• Eric Wood ltefwood_at_princeton.edugt
• Aiguo Dai ltadai_at_ucar.edugt
• Alfredo Ruiz-Barradas ltalfredo_at_atmos.umd.edugt

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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

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Model Experiments (SST and Soil Moisture Impacts)

• Force with the 3 leading REOFs of annual mean SST
  (/- 2 std)
• 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_drough
  t_wg/README/www/index.html
• (contact Hailan Wang)

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Leading Rotated EOFs of annual mean SST
(1901-2004)
Linear Trend Pattern
Pacific Pattern
Atlantic Pattern
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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.)
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Annual Mean 200mb Height Response (m)
Pacific Warm
Pacific Cold
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Annual Mean Tsfc Response (C)
Pacific Warm
Pacific Cold
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Annual Mean Tsfc Response (C)
Pacific Warm
Pacific Cold
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Annual Mean Tsfc Response (C)
Atlantic Warm
Atlantic Cold
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Annual Mean Tsfc Response (C)
Atlantic Warm
Atlantic Cold
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Annual Mean Tsfc Response (C)
Warm Trend (Cold Pacific
Warm Atlantic)
Warm Trend (Warm
Pacific Cold Atlantic)
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Annual Mean Tsfc Response (C)
Warm Trend
Spatially Uniform Warm Trend
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Annual Precipitation (mm/day)
Pacific Cold
Atlantic Warm
Tendency for US Drought!
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Annual Precipitation (mm/day)
Pacific ColdAtlantic Warm
Pacific WarmAtlantic Cold
US Drought!
US Pluvials!
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Annual Precipitation (mm/day)
Pacific ColdAtlantic Warm
Pacific WarmAtlantic Cold
US Drought!
US Pluvials!
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Seasonal Evolution of Response
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DJF - Cold
Contours 200mb height anomalies
Vectors 850mb wind anomalies
Colors precipitation anomalies
Weak and shifted anti-cyclonic anomalies
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MAM - Cold
General consistency in height anomalies but CFS
again shifted south
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JJA - Cold
Cyclonic anomalies in IAS
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SON - Cold
Cyclonic anomalies in IAS
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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)
• s2xy (S2XS2Y)/2
• S2X variance of seasonal mean from experiment
• S2Y variance of seasonal mean from control

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NW
GP
??
SW
SE
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Precipitation Response to Warm and Cold Pacific
(signal/noise)
R
R
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Tsfc Response to Warm/Cold Pacific (signal/noise)
R
R
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Uncertainties in Noise
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Noise (Z200mb) Unforced Interannual Variance in
Control Runs
DJF
MAM
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Noise (Z200mb) Unforced Interannual Variance in
Control Runs
JJA
SON
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Tsfc and Precip Noise

• Look at Pacific warm and cold SST cases

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MAM Pacific Great Plains
Cold
Warm
Precip mm/d
Precip mm/d
Tsfc C
Tsfc C
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JJA Pacific Great Plains
Warm
Cold
Precip mm/d
Precip mm/d
Tsfc C
Tsfc C
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What Are the Determining Factors for Noise
(unforced variability)?
Seasonal Dependence? Impact of SST (a signal in
the noise!)
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Noise in Great Plains in Spring/Summer driven
by land/atmosphere feedbacks but also depends on
SST Forcing!
Greater ?E for given change ?W
Schubert et al. 2008 JCLIM
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200mb ? Variability (10-30 days)
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
Schubert et al. 2001 JCLIM
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Some Basic Results Over US

• Mean Responses
• Models tend to agree that
• Cold PacificWarm Atlantic gt drought/warm
• Warm PacificCold Atlantic gt 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!

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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