The Role of Ocean Dynamics in North Atlantic Tripole Variability

Transcription

1 The Role of Ocean Dynamics in North Atlantic Tripole Variability Edwin K. Schneider George Mason University and COLA C20C Workshop, Beijing October 2010

2 Collaborators Meizhu Fan - many of the results come from her PhD thesis. Ben Kirtman - developer of the Interactive Ensemble

3 Motivation Diagnose and understand the mechanisms of observed low frequency observed ( ) North Atlantic SST variability. In particular, what were the roles of weather noise forcing, coupled feedbacks, and ocean dynamics? How could a decadal spectral peak be explained? What are the implications for predictability?

4 Tripole Index: area average SST difference. Northern box minus Southern. (Czaja and Marshall 2001, QJRMS) JFM detrended JFM detrended, 7 year running mean

5 Tripole Index 1880 to Present (ERSST) What is the power spectrum?

6 Approach Simulate the observed tripole index using a CGCM-class model forced by observed weather noise. Try to understand the results in the framework of the simple model of Frankignoul and Hasselmann (1976) as extended by Marshall et al. (2001), Czaja and Marshall (2001): Weather noise Atmospheric feedback to SSTA Gyre circulation AMOC

7 Data and Models NCEP reanalysis , monthly means COLA CGCM COLA V2 AGCM (T42, L18) MOM3 OGCM (1.5º, finer meridional near equator) non-polar domain Anomaly coupled

8 Tripole Mechanism Issues External forcing or internal variability? Remote or local origin? Why decadal time scale? Delayed oscillator? Role of coupled feedbacks? Red noise (no decadal peak)? Implications for predictability?

9 Elements of Tripole SST Variability Weather noise (NAO variability) Feedback of SST NAO In terms of heat flux is this positive? negative? Ocean dynamics heat flux Gyre circulations Modulations of mean gyre Intergyre gyre AMOC

10 Weather Noise and Feedbacks regression 7 year running mean onto tripole Noise Feedback Heat Flux Wind Stress Curl Fresh Water Flux

11 What is the SST Response to Weather Noise? Force the Interactive Ensemble CGCM (IE CGCM) developed by Kirtman with observed weather noise surface fluxes. IE Filters out the random part of weather noise surface fluxes in forcing of ocean Similar to forcing an OGCM with observed surface fluxes (heat, wind stress), except atmospheric feedbacks to SST are calculated by IE CGCM (so double counting, like in OGCM, is eliminated).

12 Noise Forced Interactive Ensemble Response 1 Response 2 Response N AGCM 1 AGCM 2 AGCM Weather Noise Surface Fluxes SST Ensemble Mean Surface Fluxes OGCM

13 Experiments to Diagnose Observed Variability Forcing Data: NCEP reanalysis monthly surface fluxes and SST Experiment Forcing Noise Forcing Region NActl heat, wind stress, fresh water North Atlantic 15 0 N~65 0 N NAh heat NAm momentum

14 Earlier Work Kushnir 1994; Deser and Blackmon 1993 Observational Seager et al Tripole forced by surface heat flux, no role for the ocean Marshall et al. 2001; Czaja and Marshall 2001 Observational diagnosis. Simple model of tripole variability Eden and Willibrand 2001 Force OGCM in NA with NCEP reanalysis surface fluxes Eden and Greatbatch 2003 Force OGCM in NA with simple stochastic atmosphere Bellucchi et al Analysis of tripole simulated in SINTEX-G CGCM

15 Tripole Index (Detrended) Observed NAm NActl Gctl NAh

16 Gyre Circulation and Variability in NActl Mean Gyre EOF 1 (31%) ( Intergyre Gyre ) PC1 of EOF1 (gyre index); NAO Index (observed)

17 IGG Lags Tripole by ~3 years in NActl Intergyre Gyre index: area avg. streamfn., 60 W-40 W, N

18 AMOC Lags Tripole x (-1) by 1 Year NActl NAh IE Unforced

19 Summary of IE Results The observed tripole variability is locally forced by the weather noise heat flux. Wind stress weather noise forces a tripole response that damps the full response (ocean dynamics response).

20 Simple Model Czaja and Marshall 2001 Interpretation: Parameterized ocean heat budget. Heat storage parameterized as proportional to ΔT ΔT ψ g dδt dt = λδt + αn + gψ g Tripole temperature difference, north minus south Intergyre gyre strength (IGG, positive clockwise) N Tripole surface heat flux noise difference, north minus south λ Damping parameter g IGG heat storage tendency parameter (CM01 assume >0) α = 1/(ρcH) with heat budget interpretation, effective depth H (1)

21 τ Simple Model II τ = γn f 'ΔT Tripole wind stress difference, north minus south γ Relates tripole wind stress to surface heat flux (<0) f ' Feedback factor for tripole on wind stress, >0 when the feedback heat flux is >0. ψ g = a t t t d τdt fδt(t t d 2 ) t d Delay time for wind stress to set up the IGG, related to Rossby wave propagation

22 Simple Model III dδt dt = λδt fgδt(t t d 2 ) + αn (2) Stochastically forced delayed oscillator equation. If N=0, properties governed by the parameter R = fg/λ R < 0 solutions are damped, non-oscillatory R > 0 solutions are oscillatory o R < R 0 decaying (1<R 0 <π) o R > R 0 growing

23 Czaja and Marshall (2001) Take g>0, f>0 Estimate of parameters gives damped delayed oscillator regime, R 0.4. IGG due to feedback wind stress produces decadal peak in power spectrum of the response to white noise. Peak is enhanced by IGG response to weather noise wind stress.

24 Heat Budget Analysis Vertical integral over full depth of the ocean d dt 0 z b ΔTdz = Tripole index = d dt ΔT ΔT = ΔF net ρc + d dt Net surface heat flux index = ΔT ΔF net dyn Heat storage tendency, surface flux found from OGCM output. Ocean dynamics tendency obtained as a residual.

25 Heat Budget Tendencies 7 year running annual means Ocean dynamics, surface heat flux, and heat storage are all equally important in the ocean s heat budget in NAh and NActl NAh NActl NAm Net surface heat flux weather noise heat flux, so the dominant balance is not net heat flux 0 Heat storage tendency Net surface heat flux Ocean Dynamics tendency

26 Regression of Barotropic Streamfunctions Against Ocean Dynamics Tendencies Intergyre gyre Modulation of mean gyres Counterclockwise increases tripole Δ heat (!) NAh NAm Reduction of mean gyres increases ΔT NActl

27 Regression of Heat Storage against Tripole Gives Effective Depth H 500m NAh NAm This is vertical structure of T regressed agains tripole (no signal below 1200m). NActl

28 Estimate of Parameters for Simple Model H = 500m (regression vertically integrated T vs. tripole) t d /2 = 3-4 years (lag regression gyre index against tripole index) f = -3 K Sv -1 (lag regression gyre/tripole) g = K Sv -1 yr -1 (fit to NAh heat budget) λ= 1/3 yr -1 (fit to NAh heat budget)

29 Simple Model of NAh Verification Force with observed heat flux noise Use NAh initial conditions ( ) NAh simulation Simple model solution

30 R = 0.48 Most Important Features 0<R<1 implies the unforced solutions are damped oscillatory (CM01 estimate is R=.4). g < 0, f<0 g<0, while CM01 assert g>0 is a given. Therefore a clockwise IGG increases tripole ΔT f<0, while CM01 use f>0. This is model dependent. Delayed oscillator dynamics can be found for both f>0 and f<0(!)

31 Tripole Power Spectrum Czaja and Marshall 2001 This Study Passive ocean No ocean dynamics Oscillator model Passive ocean + oscillator No ocean dynamics is g=0 (Hasselmann mechanism, red noise) Passive ocean is noise heat flux and wind stress but no feedback wind stress (f=0) Oscillator model is noise heat flux and feedback wind stress, but no noise wind stress

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35 Retrospective Predictions with Simple Model (Hindcasts) dδt dt = λδt fgδt(t t d 2 ) + αn Set heat flux and wind stress noise to zero (??) NAh initial conditions Need ΔT for 3, 2, and 1 years before initial time 12 year predictions starting each year Verified against NAh ΔT

36 Hindcast Verification Simple Model Hindcasts Persistence Simulation

37 Hindcasts from ERSST Initial Conditions

38 Tripole ΔT Predictions from 2008 and 2009 ICs

39 COLA Model Diagnosis of the Observed North Atlantic SST Variability The reconstructed later 20th century North Atlantic tripole SST variability is predominantly forced by the local weather noise. In the context of the simple model of Czaja and Marshall (2001), the tripole is in a damped oscillatory regime, even though the atmospheric heat flux feedback to the tripole is negative, because the intergyre gyre carries heat in the opposite direction from that found/assumed in other studies. A decadal peak in the spectrum should result from the simple model with R>0 forced by white noise (Czaja and Marshall 2001). The peak will be more pronounced if the atmospheric feedback to the tripole is positive. The simple model indicates no decadal predictability of the tripole variability.

40 For Additional Details Wu, Z., E. K. Schneider, and B. P. Kirtman, 2004: Causes of low frequency North Atlantic SST variability in a coupled GCM. Geophys. Res. Lett., 31, L09210, doi: /2004gl Schneider, E. K., 2006: Stochastic forcing of surface climate. COLA Technical Report 224, 34 pp. ftp://grads.iges.org/pub/ctr/ctr_224.pdf Schneider, E. K. and M. Fan, 2007: Weather noise forcing of surface climate variability. J. Atmos. Sci., 64, Fan, M., 2009: Low frequency North Atlantic SST variability: Weather noise forcing and coupled response. PhD thesis, George Mason University. Fan, M. and E. K. Schneider, 2010: Low frequency North Atlantic SST variability: Weather noise forcing and coupled response (in preparation).