Ocean Mixed Layer Dynamics and its Impact on SST & Climate Variability Michael Alexander Earth System Research Lab michael.alexander@noaa.gov http://www.cdc.noaa.gov/people/ michael.alexander/presentations

Ocean Mixed layer T s ρ ∆T=Tb-Tm Turbulence creates a well mixed surface layer where temperature (T), salinity (S) and density (ρ) are nearly uniform with depth Primarily driven by vertical processes (assumed here) but can interact with 3-D circulation Density jump usually controlled by temperature but sometimes by salinity (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 surface T s ρ ∆T ∆T=Tb-Tm

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) Where M - Mechanical Turbulence (wind stirring) B - Buoyancy Forcing Net surface heating/cooling (Qnet) Precipitation – Evaporation (P-E) D - Dissipation (eh) Δρ - Density jump at base of the ML 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: Variables v – velocity (current in ML) Tm – mixed layer temp (SST) 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 ρ – density of sea water C – Specific heat of sea water Qnet Tm = SST

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 ΔSST = (Qnet/ρch) x Δt ρ = 1025 kg/m3; c = 3850 Joules/(kg °C) ΔSST = (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

Zonal Average Mean Standard Deviation Surface Heat Flux Entrainment Flux Zonal Average Mean The four terms net surface heat flux (Qnet), and heat flux through the base of the mixed layer due to entrainemnt (Qwe) and pentrating solar radiation at the base of the mixed layer Qswh) are obtained from an atmospheric general circulation model coupled to a mixed layer ocean model. The EkmEntrainment terms were multiplied by ch to get units of W m-2. The terms have been averaged over the North Pacific and Atlantic oceans For the 12 calendar months between 10 and 70N Standard Deviation

Observed Standard Deviation of SST Anomalies (°C) March August

Processes for Generating SST Anomalies

Simple model for generating SST variability “stochastic model” Heat fluxes associated with weather events, “random forcing” Ocean response to flux back heat which slowly damps SST anomalies SST anomalies form -λTm F Air-sea interface Fixed depth ocean No currents Bottom dTm/dt = Qnet ρch dTm/dt = F – λTm ρch

Stochastic SST Anomaly model II idealized forcing and time series Null Hypothesis for midlatitude SST variability

Stochastic Model: correspondence to the real world Stochastic Model: correspondence to the real world? Observed and Theoretical Spectra for SST anomalies (SSTA) at a location in the North Pacific Ocean Theoretical spectra (white line) of stochastic model Observed Spread (5%-95%) Period 1yr 10 yr No damping SSTA Variance 1 mo Temperature Variance Atm forcing Observed SST variance spectra (black line) in a 5°x5° box centered on 50°N, 145°W using 134 years of month anomalies from an SST data set. The gray and white cures are based on a stochastic model fit to the SST data (estimate F’ and lamda in stochastic model equation. The gray shading represents the 5th and 95th percentile bounds for 1200 134-yr simulated spectra; the white line is the average of simulated spectra and overlays the theoretical spectra on an AR(1) model, the discrete form of Equation 4. 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 Anomaly patterns associated with a +1 standard deviation departure of the North Atlantic Oscillation (NAO) Index during winter (December–March) defined using the station index of Hurrell et al. (2003). (a) Sea surface temperature (SST) (shading), sea-level pressure (SLP) (contours), and surface wind (arrows). (b) Sensible plus latent energy flux (Qsh + Qlh) (shading), SLP (contours), and surface wind (arrows). The SLP contour interval is 1 hPa, with negative values dashed. 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 Qnet’ MLD Winter Surface flux anomalies Create SST anomalies which spread over ML ML reforms close to surface in spring Summer SST anomalies strongly damped by air-sea interaction Temperature anomalies persist in summer thermocline 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 regions Regression between SST anomalies in April-May with monthly temperature anomalies as a function of depth. Regions 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 Auto-correlation of EOF PC time series Level of significance Degrees Celsius Reemergence of the SST North Atlantic tripole Leading EOF of March SST ERSSTv2 Datasets [1950-2003] 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 Deser et al. 2003 Heat content (EM) Obs (dashed) Entraining Model (EM) SST (EM) SST (OBS) Fig. 1. Conceptual ocean紡tmosphere systems considered in this study. (left) The original simple stochastic climate model of Frankignoul and Hasselmann (1977) and (right) the proposed extension. In both systems, temperature anomalies (T′) in the ocean mixed layer are assumed to result from atmospheric forcing (F′) only and damp back to the atmosphere at a rate λT′. In the original model, the mixed layer depth (H) is constant; in the extended model, H undergoes a strong seasonal cycle, with largest extent in winter and smallest in summer. In this configuration, T′ created in winter can persist beneath the summer mixed layer and become reentrained into the mixed layer the following winter, as indicated schematically by the thick black arrows. The effective thermal capacity of this system depends upon the depth of the winter mixed layer (Heff)

Deser et al. 2003 Heat content (EM) Obs (dashed) Entraining Model (EM) SST (EM) SST (OBS) North Atlantic Left. Correlation of February SST anomalies with monthly SST anomalies from the previous January through the following September for a region in the far North Atlantic (50｡N-65｡N, 60｡W-10｡W). Values are plotted for the obser- vations(solid circles) and the MLM(open circles). The other curves are computed using Eq.5: with =15, 17.5, and 20 Wm-2C and h in February from observations(456m; dashed line) and from the MLM (240m; solid line); with =17.5 and h=50 m (squares); and with =17.5 and monthly values of averaged over the domain from the MLM (triangles). Right: Mean seasonal cycle of mixed layer depth (m) for NPAC from observations (dashed) and that used in the entraining stochastic climate model (solid). (bottom) Monthly lag autocorrelation curves from Mar for heat content (solid step function like curve) and SST (solid) from the entraining model. The observed SST autocorrelation curve is dashed. The theoretical autocorrelation function for heat content based upon the extended simple stochastic climate model is shaded Heff = winter MLD for interannual variability in a stochastic model

Main Concepts Mechanisms for the behavior of SST anomalies Questions? 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?

Reemergence of the late 1. What is the oceanic reemergence? 2. Surface signature of reemergence in the Labrador Sea e-folding = ~ 36 mths e-folding = ~ 4 mths 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 Temperature Reemergence of the late winter SST anomalies a year after Deser et al. 2003 (J.Clim) e-folding = ~ 4 mths Auto-correlation of the Labrador SST time series (all months considered), e.g. for lag=1, Jan50/Feb50/…/Dec00 values are correlated with Feb50/Mar50/…/Jan01 values Level of significance ERSSTv2 Datasets [1950-2003] Degrees Celcius

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) Correlation=0.63 NCEP MSLP [1950-2003] e.g. Deser and Timlin (1997), J.Clim.

Summary Forcing of SST (mixed layer temperature Net heat flux key term, Ekman transport & entrainment also important 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 SSTs The Latif and Barnett mechanism for the PDO and “problems” with this mechanism

Atmosphere-Ocean Ice Model Atmospheric GCM NCAR CAM2–T42 resolution Ice Thermodynamic portion of NCAR CSIMv4 Ocean 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 h (MLD) Qwe Tm1 Tb1 Qswh CA

Mean ML Budget terms (Wm-2) in January From an AGCM couple to a mixed layer ocean model Surface Flux Qnet Ekman Qek = cvek(Tm) Entrainment Qwe = cwe(Tb-Tm)

Mean Mixed Layer Budget terms (Wm-2) in August

Standard Deviation of the Mixed Layer Budget Terms (Wm-2) in January Standard deviation is a frequently used measure of variability about the mean.

Standard Deviation of Fluxes in August Results from an AGCM- Ocean MLM Qnet W m-2 Qwe = cwe(Tb-Tm) W m-2 Qwe / Qnet

Fig11 JGR 2000 Fig.11. Composite of the six leading components (˚Cmon-1) of the SST tendency equation(3) as a function of calendar month. The composites are constructed at each MLM grid point based on when the local monthly SST tendency exceeds one standard deviation, the resulting values are then averaged between 20˚N-70˚N.

Wind Generated Rossby Waves L Atmosphere Ocean ML Rossby Waves Thermocline Wind sterss curl West East After waves pass ocean currents adjust 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 SSTOBS T400 SSTfcst March 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 years Schneider and Miller 2001 (J. Climate)

Climatological heat fluxes August

Zonal Average of the standard deviation of the mixed layer budget terms

Observed SST (C) / SLP (mb) Warm-Cold (50-03) DJF JAS

Evolution of the leading pattern of SST variability as indicated by extended EOF analyses No ENSO; Reemergence ENSO; No Reemergence Alexander et al. 2001, Prog. Ocean.

Upper Ocean: Temperature and mixed layer depth El Niño – La Niña model composite: Central North Pacific Alexander et al. 2002, J. Climate

ENSO SST & MLD in Western N. Pacific Region Niño – Niña: NCEP Ocean Temp & White MLD (1980-2001) La Niña MLD °C 20-30% increase in MLD in the Western North region Pacific As large as 50% in some locations during late summer-early fall El Niño MLD