The statistics and structure of subseasonal variability in NASAGSFC GCMs.

1
The statistics and structure of subseasonal
variability in NASA/GSFC GCMs.

• Dennis P. Robinson
• Robert X. Black
• November 2004

2
What are GCMs?

• General Circulation Models computer simulations
  of large-scale motions in the Earths atmosphere
• Examine 2 GCMs from NASA/GSFC NASCAR (NASA/NCAR)
  and NSIPP models
• 17 calendar years 1979-1995
• Grid
• Forced w/ similar boundary conditions (e.g.)
• ocean temperatures
• solar forcing and orbital parameters
• sea ice and snow cover

3
Comparison Study of GCMs

• NCEP/NCAR Reanalysis Dataset
• Observed data surface, ship, weather balloons,
  aircraft, satellite observations, etc.
• Assimilated onto 3-D global grid
• Output of GCMs compared with the NCEP/NCAR
  Reanalysis dataset
• Use same time period
• Focus on North Hemisphere winter (DJF)
• Examine midlatitude subseasonal variability
• Key Variables
• Temperature (T), zonal wind (u), meridional wind
  (v), and geopotential height (Z)

4
Geopotential Height
5
Partitioning subseasonal data
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Climatology Geopotential Height

• Stationary waves (focus on Pacific ridge)
• NASCAR anomalously weaker, shifted relatively
  westward
• NSIPP anomalously stronger, shifted westward

7
Climatology Zonal Wind

• Over the western Pacific basin
• NASCAR model compares well w/ observations
• NSIPP model anomalously strong jet core,
  terminates upstream relative to observations
• Both models have anomalously strong jets over the
  Atlantic region

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Eddy Kinetic Energy

• Eddy activity in the GCMs
• Relatively weak in upper-troposphere (especially
  in the HP band)
• Greater than observed levels near the surface
• NSIPP is superior model at mid-tropospheric levels

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Climatological Storm Tracks

• Both models produce anomalously weak storm tracks
• The GCMs adequately simulate the 3-D structure
  (not shown) and dynamical properties of HP eddies
  (e.g. E-vectors)
• Discrepancies in the simulated storm tracks are
  most likely due to differences in the
  climatological flow

10
Midwinter Suppression

• Level of instability for HP eddy growth (Eady
  parameter) reaches maximum in midwinter for PAC
  and ATL (left figure)
• HP eddy activity (envelope function) directly
  correlates with the Eady parameter in ATL but not
  PAC (right figure)
• Nakamura (1992) HP eddy activity correlates with
  jet core speeds up to 45 m/sec (reverses beyond
  this threshold)

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Midwinter Suppression

• Both GCMs simulate a Pacific midwinter
  suppression
• NSIPP model produces an Atlantic midwinter
  suppression
• Only dataset where wind speeds in the Atlantic
  jet core exceed 45 m/sec

12
Low Frequency Variability

• Composite case study of Persistent Flow Anomalies
  (PFAs)
• A positive (negative) case is selected when the
  value of
• is above 100 m (below -100 m) for greater than
  10 days
• The total number of cases is tallied at each
  point for the Northern Hemisphere during the cool
  season

Negative Case
Positive Case
13
PFAs Case Frequency

• Key regions found near exit regions of
  climatological jet streams
• Well represented by the GCMs

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PFAs Pacific Composite Average

• Horizontal structure anomalous composites in the
  GCMs are more isotropic (especially NSIPP)
• Vertical structure greater westward tilt with
  height found in the GCMs
• Suggests differences in relative roles of
  different dynamical mechanisms in maintaining
  model events (future work)

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Summary of Results

• Discrepancies exist in magnitude and location of
  stationary waves and climatological jet streams
• NSIPP stronger PAC ridge, stronger jet cores
• Key impact on discrepancies in the simulated
  storm tracks
• Both models produce PAC midwinter suppression
• NSIPP models creates an ATL midwinter suppression
• Distribution of PFAs are well-represented in the
  GCMs
• 3-D composite PFA structures (for PAC region)
• More isotropic horizontal structure and greater
  westward tilt with height
• Suggests the roles physical mechanisms play in
  the maintenance PFAs are different in the GCMs