PowerPoint Presentation - Ocean Mixed Layer Dynamics and its

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					Ocean Mixed Layer Dynamics and its
Impact on SST & Climate Variability

          Michael Alexander
     Earth System Research Lab
                       Ocean Mixed layer
•   Turbulence creates a well mixed
    surface layer where temperature (T),
    salinity (S) and density (ρ) are nearly
    uniform with depth                                       surface
•   Primarily driven by vertical processes
    (assumed here) but can interact with
    3-D circulation                            T     s   
•   Density jump usually controlled by
    temperature but sometimes by salinity       ∆T
    (especially in high latitudes)

•   Often “ measured” by the depth at
    which T is some value less than SST
    (e.g. ∆T = 0.5)

•   Under goes large seasonal cycle

•   This impacts the evolution of ocean
    temperature anomalies and has
    important biological consequences

    Vertical Flux: Entrainment and MLD (h)
Entrainment “To pull or draw along after itself”
MLD – Mixed Layer Depth or h

When deepening:
  dh/dt = we
  we = M + B – D / (Dr - S)

  M - Mechanical Turbulence (wind stirring)
  B - Buoyancy Forcing
      Net surface heating/cooling (Qnet)
      Precipitation – Evaporation (P-E)
  D - Dissipation (eh)
  -         Density jump at base of the
  S - Shear across ML (not in all models)

When Shoaling:
  we = 0 (no detrainment, h reforms closer to
  the surface)
  h = M /(B – D)
Seasonal Cycle of Temp & MLD
   Northeast Pacific (50ºN, 145ºW)

      MLD (h)
Climatological Mixed Layer Depth (m)
                 SST Tendency Equation
    Integrated heat budget over the mixed layer:

¶Tm Q net - Q swh æ w + we ö
    =            +ç        ÷ (Tb - Tm )- v ×ÑTm + AÑ 2Tm
 ¶t     r ch      è h ø
v –      velocity (current in ML)
Tm –     mixed layer temp (SST)                  Qnet
Tb –     temp just beneath ML
h –       mixed layer depth
w –      mean vertical velocity
we –     entrainment velocity
Qnet –   net surface heat flux
Qswh –   penetrating shortwave radiation
A –      horizontal eddy viscosity coefficient
 –      density of sea water
C –      Specific heat of sea water
          Temperature change due
           to the surface heat flux
• Over March through August a location in the North Pacific
  typically receives 150 Wm-2 flux through the surface. Assuming
  a constant mixed layer depth of 50 m, and no other changes in
  the ocean how much will the SST change over that time?

• dTm/dt = d(SST)/dt = Qnet/ρch
 SST = (Qnet/ρch) x t
• ρ = 1025 kg/m3; c = 3850 Joules/(kg °C)
 SST = (150 Wm-2 / (1025 kg m-3 x 3850 Joules kg-1 °C-1 x 50
  m)) * (184 days * 86400 s day-1)
• SST = 12.1°C
• => Check units (W = J s-1)
• => reasonable value for winter to summer change in SST
                Surface Heat Flux   Entrainment Flux

Zonal Average


Observed Standard Deviation
  of SST Anomalies (°C)

Processes for Generating
    SST Anomalies
Simple model for generating SST variability
          “stochastic model”

Heat fluxes associated   Ocean response to flux back heat
with weather events,     which slowly damps SST anomalies
“random forcing”

      F                    -Tm
                                  Air-sea interface
      SST anomalies form             dTm/dt = Qnet
       Fixed depth ocean
                                     dTm/dt = F – Tm
       No currents                             ρch
Stochastic SST Anomaly model II
idealized forcing and time series

    Null Hypothesis for midlatitude SST variability
Stochastic Model: correspondence to the real world?
Observed and Theoretical Spectra for SST anomalies (SSTA)
          at a location in the North Pacific Ocean

                        No damping
SSTA Variance

                                                     Temperature Variance
                                                                                Spread (5%-95%)


                                                                            Theoretical spectra
                                                                            (white line) of
                      Atm forcing                                           stochastic model

                     10 yr     1yr         1 mo

                Atmospheric forcing and ocean feedback can be estimated from data. Then can
                then develop stochastic model and generate multiple time series to look at spread
      Patterns of Surface Fluxes and SSTs:
       example North Atlantic Oscillation

Contours are sea level pressure (SLP); vectors - winds
Shading left is SST anomalies, on right is the Flux anomalies
NAO north-south SLP anomaly pattern over the Atlantic
         The Reemergence Mechanism

                                    •   Winter Surface flux anomalies
                                    •   Create SST anomalies which
                                        spread over ML
                                    •   ML reforms close to surface in
                                    •   Summer SST anomalies
                                        strongly damped by air-sea
                                    •   Temperature anomalies persist
                                        in summer thermocline
       MLD                          •   Re-entrained into the ML in the
                                        following fall and winter

Namias and Born 1970, 1974;
Alexander and Deser (1995, JPO); Alexander et al. 1999
         Reemergence in three North Pacific
   Regression between SST
   anomalies in April-May
   with monthly temperature
   anomalies as a function of


Alexander et al. (1999, J. Climate)
          Reemergence in the North Atlantic

                                     Reg 2 - Northeast Atlantic (47%)

Reg 1 - Subtropical Atlantic (48%)

                                     Timlin, Alexander, Deser,
                                     2002, J.Climate
                    Reemergence of SST Tripole

   Leading EOF of March SST              Auto-correlation of EOF PC time series

                                                                 Reemergence of the
                                                               SST North Atlantic tripole

                                                                                Level of

ERSSTv2 Datasets [1950-2003]

                Degrees Celsius

      Watanabe and Kimoto (2000); Timlin et al. 2002, Deser et al 2003,
      De Coetlogon and Frankignoul 2003 : all in J. Climate
Impact of reemergence on SST Persistence:
    Augmenting the Stochastic SST model

                                      Model (EM)
                                   Obs (dashed)

                                    Heat content (EM)

                               SST (EM)
                                   SST (OBS)
     r(t ) = exp [ -lt rch ]
                                  Deser et al. 2003
     North Atlantic                                   Entraining
                                                      Model (EM)
                                                   Obs (dashed)

                                                    Heat content (EM)

                                              SST (EM)
                                                   SST (OBS)

                                                  Deser et al. 2003
Heff = winter MLD for interannual variability in a stochastic model
                     Main Concepts

• Mixed Layers
   – Processes that control its depth
   – Wind stirring buoyancy forcing, density jump at base of ML
   – Processes that control its temperature (SST)
       • Surface heat flux
       • Entrainment heat flux

• Mechanisms for the behavior of SST anomalies
       • Stochastic model
       • Reemergence
       • Large scale patterns of atmospheric forcing organizes fluxes, shapes
         SST Anomaly and reemergence patterns

• Questions?
1. What is the oceanic reemergence?                 2. Surface signature of reemergence in the Labrador Sea

                                                         Auto-correlation of the Labrador SST time series
                                                         (Starting from March), e.g. for lag=1, March and April
                                                         time series are correlated, for lag =2 March and May etc.
                                          Sea Surface

                                                                                  e-folding = ~ 36 mths

                                                                                                            Level of

        ERSSTv2 Datasets [1950-2003]
                                                                      e-folding = ~ 4 mths
                                                                     e-folding = ~ 4 mths
                        Degrees Celcius

                                                                                      Deser et al. 2003 (J.Clim)

     Reemergence of the late                            Auto-correlation of the Labrador SST time series
                                                        (all months considered), e.g. for lag=1, Jan50/Feb50/…/Dec00
      winter SST anomalies                              values are correlated with Feb50/Mar50/…/Jan01 values
           a year after
   Atmosphere forcing the ocean in winter:
      NAO & the Atlantic SST tripole
   March SST EOF1 (shade)
  Regressed JFM SLP (contour)

                                        PC time series: March SST (bars),
                                                JFM MSLP (line)


   NCEP MSLP [1950-2003]

e.g. Deser and Timlin (1997), J.Clim.
• Forcing of SST (mixed layer temperature
   – Net heat flux key term, Ekman transport & entrainment also
   – SST anomalies larger in summer than winer due to shallow MLD

• Processes that impact extratropical SST variability
   – Stochastic atmospheric forcing
   – Reemergence

• Atmospheric Bridge
   – Tropical Pacific => Global SSTs
   – Impacts in both winter and summer
   – Influence of air-sea feedback on extratropical atmosphere complex

• Other Processes that influence SST variability
   – Cloud - SST feedbacks
   – Ocean currents & Rossby waves in western N. Pacific
   – Changes in the Thermohaline Circulation
              Additional Topics
• The flux components and their variability
• Schematic of the mixed layer model
• Pattern of atmospheric circulation (SLP) and the
  underlying fluxes)
• Basin-wide reemergence
• The Pacific Decadal Oscillation
• Wind generated Rossby waves and its relation to
• The Latif and Barnett mechanism for the PDO and
  “problems” with this mechanism
                    Atmosphere-Ocean Ice Model
Atmospheric GCM
    – NCAR CAM2–T42 resolution

      Thermodynamic portion of NCAR CSIMv4

   Mixed layer Model (MLM)
•  An individual column model with a uniform mixed layer
•  Atop a layered model that represents conditions in the pycnocline
•  Prognostic ML depth
•  Same grids as the atmosphere (128 lon x 64 lat)
•  36 vertical levels (from 0m to 1500m depth)
       •   higher resolution close to surface and a realistic bathymetry
•     Flux correction needed to get reasonable climate
•     Cassou et al. 2007 J Clim; Alexander et al. 2000 JGR, Alexander et al 2002 – J.Clim ;
      Gaspar 1988 – JPO
Mixed Layer Ocean Model

          Qnet Qcor

        Qwe     Tm1    (MLD)



        Mean ML Budget terms (Wm-2) in January
         From an AGCM couple to a mixed layer ocean model

Surface Flux

Qek =

Qwe =
 Mean Mixed
Layer Budget
terms (Wm-2)
  in August
 Deviation of
  the Mixed
Layer Budget
Terms (Wm-2)
 in January
Standard Deviation of Fluxes in August
          Results from an AGCM- Ocean MLM

 W m-2

Qwe =

W m-2

  Qwe /
       Wind Generated Rossby Waves

                             L                      Atmosphere

                                                    ML Ocean


West                                                     East
1) After waves pass ocean currents adjust
2) Waves change thermocline depth, if mixed layer reaches that
   depth, cold water can be mixed to the surface
      Observed Rossby Waves & SST
   Correlation Obs SST hindcast
   With thermocline depth anomaly     KE Region: 40°N, 140°-170°E

                      March                    SSTfcst

   Forecast equation for SST based on integrating wind stress
   (curl) forcing and constant propagation speed of the
   (1st Baroclinic) Rossby wave

Schneider and Miller 2001 (J. Climate)
   Forecast Skill: Correlation with Obs
   SST Wave Model & Reemergence
         Wave Model                              Reemergence


Schneider and Miller 2001 (J. Climate)
Climatological heat fluxes August
Average of
deviation of
 the mixed
Observed SST (C) / SLP (mb) Warm-Cold (50-

Evolution of the leading pattern of SST variability
    as indicated by extended EOF analyses

                              No ENSO;      ENSO;
                              Reemergence   No Reemergence
Alexander et al. 2001, Prog. Ocean.
Upper Ocean: Temperature and mixed layer
 El Niño – La Niña model composite: Central North Pacific

  Alexander et al. 2002, J. Climate
ENSO SST & MLD in Western N. Pacific
Niño – Niña: NCEP Ocean Temp & White MLD (1980-2001)

La Niña MLD

                                El Niño MLD

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