An Investigation of the Spring Predictability Barrier Yehui Chang, Siegfried Schubert, Max Suarez, M

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An Investigation of the Spring Predictability
BarrierYehui Chang, Siegfried Schubert, Max
Suarez, Michele Rienecker,Augustine Vintzileos
and Nicole Kurkowski Global Modeling and
Assimilation Office, NASA/GSFCGlobal Climate
and Weather Modeling Branch, Environmental
Modeling Center/NOAA

• NSIPP CGCM
• AGCM
• Finite-difference 2x2.5, dynamical core (Suarez
  and
• Takacs, 1995)
• PBL scheme (Louis et al., 1982)
• Radiative scheme (Chou and Suarez, 1996)
• Convection RAS (Moothi and Suarez, 1992)
• Gravity wave drag (Zhou et al., 1996)
• OGCM
• Poseidon V4 quasi-isopycnal 1/3x5/8 (Schopf and
  Lough, 1995)
• Embedded surface mixed layer Kraus-Turner
• Vertical mixing and diffusion (Pacanowski and
  Philander, 1981)
• Data Assimilation (Keppene and Rienecker, 2003)
• LSM
• Mosaic Land Surface Model (Koster and Suarez,
  1996)

• Data Sets
• 3-member ensemble 12-month coupled model
  forecasts.
• Forecasts initialed at 1st day of each month from
  1993-2005.
• The Reynolds SST for the same period.

• Introduction
• In this study, we use the NSIPP coupled model
  forecasting system to examine the nature of the
  spring predictability barrier. The focus is on
  the predictability of subsurface variability, and
  how that evolves to limit predictability in the
  sea surface temperatures and ultimately the
  atmosphere. The skill of both persistence and
  coupled model hindcasts of the Nino3.4 index show
  a clear signature of a spring barrier in
  predictability. The study demonstrates that there
  is no spring persistence barrier for upper ocean
  heat content consistent with McPhaden 2003.
  Lagged correlations show the anomalous warm water
  volume (WWV) leads Nino3.4 SST by 2-3 seasons.
• The results of this study agree with other ENSO
  forecast model studies indicating that accurate
  initialization of the equatorial upper ocean heat
  content reduces the spring prediction barrier for
  SST.

• Summary and Conclusions
• A boreal spring predictability barrier exists in
  SST. In contrast, WWV does not show a spring
  predictability barrier. In fact, February-March
  WWV anomalies have the greatest persistence.
• The correlations show that the WWV leads Nino3.4
  SST by 2-3 seasons.
• SST spring barrier and WWV persistence barrier
  developed in the boreal winter consistence with
  the observed results of McPhaden 2003.
• Initialization of the equatorial subsurface helps
  break through the spring barrier.

Skill of persistence forecast for Warm Water
Volume (WWV)
Nino3.4 vs WWV Crosscorrelations
Skill of persistence forecast for Nino3.4 index
(1993-2005)
Observed
Modeled
Crosscorrelation between monthly WWV and Nino3.4
SST anomalies as a function of start month and
lead-time. Positive (negative) leads imply WWV
leads (lags) SST.
Lag correlation of monthly anomalies in WWV as a
function of start month and lead-time.
Lag correlation of monthly anomalies in
Nino3.4 SST as a function of start month and
lead-time for observed results.
Lag correlation of monthly anomalies in
Nino3.4 SST as a function of start month and
lead-time for coupled model forecasts.
Skill of CGCM forecast for Nino3.4 index
(1993-2005)
Monthly anomalies of WWV and Nino3.4 SST

Monthly anomalies of WWV (5S-SN, 120E-90W above
20C isotherm) and Nino3.4 SST (5S-5N,170W-120W)
for observed (red) and model forecasts (green)
starting from November.
Monthly anomalies of WWV (5S-SN, 120E-90W above
20C isotherm) and Nino3.4 SST (5S-5N,170W-120W)
for observed (red) and model forecasts (green)
starting from February.
Difference of forecast skill between Baseline
forecasts and model persistence forecasts.
Forecasting skill of coupled model experiments.