extratropical air-sea interaction and patterns of climate variability michael alexander noaa/earth...
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Extratropical Air-Sea Interaction and
Patterns of Climate Variability
Michael Alexander
NOAA/Earth System Research Lab
Physical Science Division
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Overview • Processes that influence upper ocean temperature and
mixed layer depth
• Patterns of variability– Atmospheric e.g NAO – and their impact on the ocean NAO => SST tripole
• Process that impact ocean patterns – Nonlinear interactions & weather forcing of the ocean– Ocean Mixed layer dynamics: “Reemergence Mechanism”– ENSO teleconnections: “Atmospheric Bridge”– Changes in ocean gyres– MOC/THC (Delworth)
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SST Tendency Equation
Variablesv - velocity (current in ML) (vek, vg)Tm – mixed layer temp (SST)Tb – temp just beneath MLh – mixed layer depthw – mean vertical velocitywe – entrainment velocity Qnet – net surface heat fluxQswh – penetrating shortwave radiationA – horizontal eddy viscosity coefficient
2vm e net swhm b m m
Q QT w wT T T A T
t h ch
e.g. see Frankignoul (1985, Rev. Geophysics)
Integrated heat budget over the mixed layer:
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Extratropical Patterns of Variability
• Weather Maps - dominated by moving storms• On weekly and longer time scales larger stationary
atmospheric patterns emerge• These patterns are mainly due to internal
atmospheric variability (e.g. interactions with storm tracks, flow over mountains, etc).
• but can also be influenced by the ocean, sea ice, global warming, etc.
• The atmospheric patterns strongly influence the underlying ocean
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NH Teleconnection Patterns in upper atm
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NAO/AO/NAM
TNH
NPO/WPPNA
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North Atlantic Oscillation (NAO)
Deser et al. 2009
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NPO=> North Pacific Gyre Oscillation (NPGO)
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Di Lorenzo et al 2007, 2009
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Southern Annular Mode (SAM)
Nov-Mar May-Oct
Ciasto and Thompson, 2008, J. Climate
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What are the underlying characteristics of atmospheric
variability?
How do these patterns evolve with time?
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Linear, Nonlinear Interactions and Chaotic systems
Linear: y = ax, a is constant, x is variablee.g. dT/dt = U dT/dx; U mean wind
Time
Nonlinear: y = zx; z variablee.g dT/dx = u dT/dx; u actual wind
T
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Nonlinear Interactions Impact on Climate• Asymmetry: e.g El Niño different than La Niña • For atmosphere, coupled models or eddy resolving
ocean models,need an ensemble of simulations to isolate a forced signal
• A given year of a “20th century” simulation (with all observed forcings will not match the corresponding year in nature
• There is only very limited predictability (perhaps due to coupled air-sea interaction) of patterns like the NAO beyond a few weeks
• For air-sea interaction can consider nonlinear terms as “weather noise” and the ocean response as a slower and linear => “stochastic model”
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Southern Annular ModeSLP (65º-71ºS minus 37º-47ºS)
Linear Trend 2000-2061
Coupled Model (CCSM3) with A1B GHG forcingDots: Individual Ensemble MembersLine: 40-member Ensemble Mean
stro
nger
wea
ker
wes
terli
es
Member 11 Member 13
0hPa / 62yrs
5
-5
Nov-Mar
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Simple model for generating SST variability“stochastic model”
Heat fluxes associated with weather events,“random forcing”
Ocean response to flux back heatwhich slowly damps SST anomalies
SST anomalies formAir-sea interface
Fixed depth oceanNo currents
Bottom
d(SST´)/dt = F´ - SST´)
ch
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Stochastic Model Example: flux forcing and SST times series
SSTn+1 = *SSTn + =constant; = Random number
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Seasonal cycle of Temperature & MLD in N. PacificReemergence Mechanism
• 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
Qnet’
Alexander and Deser (1995, JPO), Alexander et al. (1999, J. Climate)
MLD
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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)
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Watanabe and Kimoto (2000); Timlin et al. 2002, Deser et al 2003 (J.Clim), De Coetlogon and Frankignoul 2003 : all J. Climate
Auto-correlation of EOF PC time series
Level ofsignificance
Degrees Celsius
Reemergence of theSST North Atlantic tripole
Leading EOF of March SST
ERSSTv2 Datasets [1950-2003]
Reemergence of SST Tripole
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“The Atmospheric Bridge”
Meridional cross section through the central Pacific
(Alexander 1992; Lau and Nath 1996; Alexander et al. 2002 all J. Climate)
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Global ENSO Signal(SST shaded, SLP contour, ci = 1mb)
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Leading Pattern (1st EOF) of North Pacific SST
+ Phase - PhaseK
Mantua et al. (BAMS 1997)
PC 1 SST North Pacific
The Pacific Decadal Oscillation (PDO)
H
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What Causes Midlatitude Anomalies SSTs? The PDO?
and Pacific Decadal Variability in General?
• Random forcing by the Atmosphere– Aleutian low => underlying ocean
• Signal from the Tropics?– Midlatitudes integrates ENSO interannual signal– decadal variability in the ENSO region
• Midlatitude Dynamics– Shifts in the strength/position of the ocean gyres– Could include feedbacks with the atmosphere
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Aleutian Low Impact on Fluxes & SSTs in (DJF)Leading Patterns of Variability AGCM-MLM
EOF 1 SLP (50%)
SLP PC1 - Qnet correlation
SLP PC1 - SST correlation
EOF 1 SST (34%)
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PDO or slab ocean forced by noise?
Pierce 2001, Progress in Oceanography
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SLP & SST Patterns of Pacific Variability
ENSO PDO
Regressions: SLP – Contour; SST Shaded
Mantua et al. 1997, BAMS
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El Niño – La Niña Composite:
Model
Obs
DJF SLP Contour (1 mb); FMA SST (shaded ºC)
reemergence
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Pacific Ocean Currents
Annual average ocean currents (m s-1) averaged over the upper 500 m from the Simple Ocean Data Assimilation (SODA, Carton and Giese, 2008) for the years 1958-2001. The current strength is indicated by the three tone gray scale with maximum values of ~0.7 m sec-1 in the Kuroshio.
Subtropical gyre
Subpolar gyre
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Wind Generated Rossby Waves
West East
Atmosphere
Ocean
Thermocline
ML
L
Rossby Waves
1) After waves pass ocean currents adjust2) Waves change thermocline depth, if mixed layer reaches that
depth, cold water can be mixed to the surface
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Rossby wave propagation
Qiu et al. 2007
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SST anomalies and spectra in KE region
Stochasticmodel
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Ocean Response to Change in Wind Stress
Contours: geostrophic flow from change in wind stress
Shading: vertically integrated temperature (0-450 m): 1982-90 – 1970-80
Deser, Alexander & Timlin 1999 J. Climate
SLP 1977-88 - 1968-76
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PDO Reconstruction
41%
38%
7%
85%
>8years
75%
20%
31%
24%
Schneider and Cornuelle 2005 J Climate
Forcings (F)
ENSO
AleutianLow
∆ in Gyres
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Prediction of the PDOMonthly values PDO Index
1998 Transition?
Curve Extrapolation
• Because of the multiple contributions to the PDO some of which are due to unpredictable atmospheric fluctuations PDO predictable out to 1-2 year
• Unclear if “regimes”, jumping between two different states (implies low order chaos). More likely impact of multiple processes that can add together to get rapid changes
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Hare and Mantua, 2000
Alaska Sockeye Salmon Catch
Western
Central
Southeast
1976 1988
1995198519751965
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Summary
• Climate noise– Expect decadal variability when looking at SST time series
• Atmospheric Bridge– Cause and effect well understood– Tropical Pacific => Global SSTs– Influence of air-sea feedback on extratropical atmosphere complex
• PDO (1st EOF of North Pacific SST)– Thermal response to random fluctuations in Aleutian Low– A significant fraction of the signal comes from the tropics
• Extratropical ocean integrates (reddens) ENSO signal• Decadal variability in tropics – impact atmosphere & ocean
– extratropical air-sea feedback modest amplitude (1/4 -1/3 of ENSO signal)
• Other Processes/modes of variability– Ocean currents & Rossby waves in N. Pacific and N. Atlantic– Changes in the Thermohaline Circulation =?=> AMO, AMM
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El Niño– La Niña composite average
a) surface wind stress vectors (N m-2, and SST
(b) net surface heat flux
c) Ekman transport in flux form during JF(1) as obtained from NCEP reanalysis for ENSO events during the period 1950-1999.
L represents the center of the anomalous negative SLP anomaly associated with a deeper Aleutian low and the red (blue) arrows indicate the direction of Ekman transport that warms (cools) the ocean.
Observed Atmos. Bridge Fluxes
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Midlatitude SST Variability
• The ocean primarily influences the atmosphere through changes in the SST
• There are many ways that SST anomalies form– We will explore just a few mechanisms – Ones that are part of larger climate signals
• Mechanisms for generating midlatitude SST anomalies– Climate Noise
• Random forcing of the ocean
– Upper Ocean mixing processes– “Atmospheric Bridge”: Teleconnections with ENSO
– Changes in ocean currents • Wind driven (through ocean Rossby waves)• Thermal/salt driven: Thermohaline
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Observed Standard Deviation of SST Anomalies (°C)
August
March
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Additional Slides
• More on the experiment of reemergence in the Atlantic
• Rossby waves that are
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Simple Ocean Model: correspondence to the real world?Observed and Theoretical Spectra for a location in the
North Atlantic Ocean
Theoretical spectra of Simple ocean model
Observed OWS
Tem
pera
ture
Var
ianc
e
1 year 1 month
(Hz) is the frequency
period:
Atmospheric forcing and ocean feedback estimated from data
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The Simple Ocean’s SST Anomaly Variability
Log plot ofSSTA Spectra
Period1yr10 yr
No damping
SS
TA
Var
ianc
e1 mo
Atm forcing
Frequency ()
SST()2 = |F|2 2 + 2
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Wet
Precipitation (land only)
Dry
180°
Warm
Cold
Surface Air Temperature
180°
Precipitation and Temperature Patterns Associated with NP (and PDO) Index
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Ocean Mixed layer• Turbulence creates a well mixed
surface layer where temperature (T), salinity (S) and density () 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
T s
∆T
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Seasonal Cycle of Temp & MLD the
Northeast Pacific (50ºN, 145ºW)
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Climatological Mixed Layer Depth (m)
QuickTime™ and a decompressor
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Do the reemerging SST anomalies impact the atmosphere?
• First examine relationship between atmospheric circulation and SSTs in the Atlantic to determine leading pattern of SSTs forced in winter and see if they reemerge
• Then use AGCM (NCAR CAM2) coupled to a mixed layer ocean model (predicts h)
• Cassou, Deser and Alexander (J Climate 2007)
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March SST EOF1 (shade)Regressed JFM SLP (contour)
PC time series: March SST (bars), JFM MSLP (line)
NCEP MSLP [1950-2003]
Correlation=0.63
e.g. Deser and Timlin (1997), J.Clim.
Atmosphere forcing the ocean in winter: NAO & the Atlantic SST tripole
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Summary
• Entainment & concept of MLD important for SST evolution– E.g. SST anomalies larger in summer than winter due to shallow MLD
• Reemergence– Adds predictability for SST and potentially for the atmosphere as well
– Extends the stocashtic model for SSTs
– Also occurs for salinity
– Reemergence extends oceanic impact of atmospheric teleconnections
• Other roles for mixing– Interaction with the deeper ocean
• Subduction (ML water leaves the surface)
• Rossby wave propagation to the Kuroshio region:– Remix temperature anomalies due to thermocline variability back to the
surface
– Biological• Bring nutrients to the surface (if not enough nutrient limited)
• Mix phytoplankton if too much (light limited)
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Nov-Feb Jul-OctMar-Jun
40-member CCSM3
10,000 year atmospheric model (CAM3)control integration
Standard Deviation of SLP Trends
Lack of stippling indicates standard deviations are not significantly different between CCSM3 and CAM3 control integration
(i.e., spread in CCSM3 trends is consistent with internal atmospheric variability or “weather noise”)
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1. What is the oceanic reemergence? 2. Surface signature of reemergence in the Labrador Sea
Sea SurfaceTemperature
e-folding = ~ 4 mths
Auto-correlation of the Labrador SST time series(all months considered), e.g. for lag=1, Jan50/Feb50/…/Dec00values are correlated with Feb50/Mar50/…/Jan01 values
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 Apriltime series are correlated, for lag =2 March and May etc.
Reemergence of the latewinter SST anomalies
a year after
Deser et al. 2003 (J.Clim)
ERSSTv2 Datasets [1950-2003]
Degrees Celcius
Level ofsignificance
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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
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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
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Observed Rossby Waves & SST
t o xP c F
Schneider and Miller 2001 (J. Climate)
March
KE Region: 40°N, 140°-170°E
SSTOBS
T400SSTfcst
Correlation Obs SST hindcast With thermocline depth anomaly
Forecast equation for SST based on integrating wind stress (curl) forcing and constant propagation speed of the (1st Baroclinic) Rossby wave
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Forecast Skill: Correlation with Obs SST Wave Model & Reemergence
Wave Model Reemergence
years
Schneider and Miller 2001 (J. Climate)
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Evolution of the leading pattern of SST variabilityas indicated by extended EOF analyses
Alexander et al. 2001, Prog. Ocean.
No ENSO;Reemergence
ENSO;No Reemergence
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Mechanism for Atmospheric Circulation Changes due to El Nino/Southern Oscillation
Horel and Wallace, Mon. Wea Rev. 1981
Latent heatrelease inthunderstorms
Atmospheric wave forced by tropical heating
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ConclusionsThe Atlantic Meridional Mode (AMM) is strongly related to Atlantic hurricane activity» The AMM is associated with a set of large-scale conditions that
all cooperate in their influence on hurricane activity» The AMM increases seasonal intensity because:
• There are more storms• They track through climatologically, and anomalously,
favorable environmental conditions• Their duration is increased, allowing more time to intensify
The AMM is predictable up to a year in advance.
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ConclusionsThe AMM provides a better framework for understanding existing hurricane / climate relationships in the Atlantic» The AMM represents an organizing “mode” of climate
variability in the tropical Atlantic» The strong relationship between SST and the AMM suggests that
SST is strongly related to hurricane activity because it is a good proxy for the AMM
» The AMM is related not only to intensity, but also to frequency and duration. As such, its influence on seasonal hurricane intensity metrics is larger than would be predicted by simple thermodynamic arguments
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PDO: Multiple Causes• Newman, Compo, Alexander 2003, Schneider and Miller 2005,
Newman 2006 (All in Journal of Climate)
• Interannual timescales:– Integration of noise (Fluctuations of the Aleutian Low)– Response to ENSO (Atmospheric bridge)– + Reemergence
• Decadal timescales (% of Variance)– Integration of noise (1/3)– Response to ENSO (1/3)– Ocean dynamics (1/3) – Predictable out to (but not beyond) 1-2 years
• Trend– Most Prominent in Indian Ocean and far western Pacific– A portion associated with Global warming