The Atlantic Meridional Mode: Observations, Modeling & Predictability

1. The Atlantic Meridional Mode: Observations, Modeling & Predictability Dima Smirnov April 1, 2009 Advisor: Professor Vimont

2. Outline • Tropical Atlantic Climatology 101 • The Atlantic Meridional Mode: what is it? • “Sensible impacts” • Observational analysis • Modeling results • Review of “Slab-” vs. “Data-” Ocean models • Predictability of AMM • Linear Inverse Model (LIM): statistical • Global climate model: physical (mostly)

3. Tropical Atlantic Climatology • ITCZ: the source of precipitation over SSTs > 27° C • Convection provides rainfall of up to 10 mm/day (0.4 in) Source: Xie & Carton (2004)

4. Tropical Atlantic Climatology • Cold tongue exists, but why is there no Atlantic ENSO? • ITCZ is mostly north of the equator due to land geometry Source: Xie & Carton (2004)

5. AMM: What is it? The “Nordeste” seasonal rainfall • Explains variations in rainfall in Brazil’s Nordeste region • Early work (1970’s) showed changes in the interannual ITCZ position relating to rainfall in the Nordeste Source: Namias (1972)

6. AMM: defined SST & Surface wind Precipitation • Basic definition: changes in the tropical Atlantic ocean-atmosphere circulation associated with an anomalous ITCZ • Technically: leading maximum covariance analysis mode of SST & wind covariance after ENSO is removed Source: Chiang & Vimont (2004)

7. Influences of AMM • Recently, Kossin & Vimont (2007) have shown strong correlations between hurricane activity & AMM • SST is not the only factor! Sea-level pressure (and wind), SST and wind shear all act in tandem Source: Kossin & Vimont (2004)

8. Observation Analysis I • Goal: Investigate the vertical structure of the AMM • Result: AMM structure is limited to boundary layer SLP & Sfc Temp 500-hPa Temp & Moisture

9. Observation Analysis II 925- & 250-hPa Height • Lower-level cyclonic anomalies lie under upper-level anticyclonic flow • This is consistent with findings of shear reduction during (+) AMM • Climatological shear during ASO is 8 to 10 m/s. Shear anomalies from this analysis are ~1.5 m/s • Shear reductions come mainly from upper-level contributions (250hPa height anomalies are 5X that of 925hPa anomalies)

10. Modeling the AMM • Model: NCAR-CCSM3 Global Climate Model (GCM) System • Atmospheric model CAM3.0 coupled to an ocean model • CAM3.0: • horizontal resolution: 2.5° by 2.5° • 26 vertical levels: hybrid sigma-pressure & pressure coordinates • Two choices for ocean model: “Data-ocean” or “slab-ocean” • DOM uses prescribed SSTs - one way forcing! • SOM contains a 50m mixed layer - allows for coupling

11. Choosing an ocean model • Data ocean model (DOM): • Forced with seasonally-varying climatological SSTs • Ocean does not respond to atmospheric forcing • Useful for investigating causal relationships • Slab ocean model (SOM): • Ocean model can store heat • Allows for oceanic-atmospheric coupling • BUT! Ocean is static, so oceanic phenomena like ENSO are removed

12. The Slab Ocean Model Q: oceanic heat flux, Fnet: net surface heat flux, T: water temperature SW: shortwave; LW: longwave; SH: sensible heat; LH: latent heat fluxes • Q flux term is derived through a DOM simulation • Gulf Stream: large latent heat loss, large positive q-flux • Cold tongue: high insolation, negative q-flux

13. AMM modeling results • Compare SOM and DOM SST, wind and shear in Aug-Oct Data-Ocean Model Slab-Ocean Model Contour: 1 m/s; max vector: 1 m/s • Both models successfully depict the AMM • AMM SST anomalies are responsible for reduced shear

14. Predictability of AMM • Kossin & Vimont (2007) showed that prediction of the AMM is possible with 1 year of lead time • KV07 use a statistical “linear inverse model” that is centered on September 1 • Note long persistence times in the tropics • Physically, predictability has been shown to originate from ENSO, the NAO and the Atlantic Multidecadal Oscillation • My main research goal: to initialize a GCM with LIM results Source: Kossin & Vimont (2007)

15. The Linear Inverse Model • LIM is “the extraction of dynamical properties of a system from its observed statistics” [Penland & Sardeshmukh 1995] : • X is the SST field, B is the linear deterministic matrix, N is the sum of the nonlinear terms and P is the white noise • Simplification occurs by neglecting non-linear terms and setting P to zero; thus, main goal is to find B • Thus far, LIM has been successfully used for ENSO

16. The LIM setup Nov 0 Oct 1 • Domain of SST data was 30S to 75N & 120E to 15W • Sea ice and land points were removed from data set • Used Hadley & NCEP data • Mid-latitude Atlantic SSTs are essential to forecast skill • The LIM is seasonally-independent and incorporates data from every month • By coincidence, it turns out that maximum forecast skill for Oct 1st occurs with a ~11 month lead time Source: Professor Vimont

17. The GCM setup • Initialize 13-month long GCM simulations using LIM-derived “optimal initial condition” SST anomaly for November 1st Hadley NCEP • 30 ensemble members with “climatological” initial conditions • In addition to “total forcing” case, low- and mid-latitude only cases were done demarked at 23° N Source: Professor Vimont

18. Modeling Results I “TOTAL” FORCING SST (NCEP) NDJ FMA MJJ ASO • Values represent the average SST difference between the warm and cold ensemble members • Null hypothesis: No difference; contour shows significance at the 95% confidence level using a 2-tailed T-test • Although weak, the AMM structure emerges in Aug - Oct

19. Modeling Results II “MID-LATITUDE” FORCING SST (NCEP) NDJ FMA MJJ ASO • “Mid-latitude” forcing case shows a much more robust AMM signal than “total” forcing case • There is a long persistence of low-latitude SST anomalies starting in FMA - does this suggest a feedback mechanism? • Initial tropical anomalies interfere destructively (not shown)

20. Interpreting results I • Inspect results of the model output by averaging over two regions: the mid-latitudes and the low-latitudes • Low-latitude box is the “center of action” of the AMM as well as the main development region for tropical cyclones

21. Interpreting results II • Consider an net surface energy budget for each simulation “Total” Forcing: (+) Upwards “Mid-Latitude Only” Forcing: (+) Upwards

22. Interpreting results III • Latent heat flux dominates the surface heat budget • Proposed wind-evaporation-SST feedback may operate in the following manner: L See: Hoskins & Karoly (1981)

23. Interpreting results III • Latent heat flux dominates the surface heat budget • Proposed wind-evaporation-SST feedback may operate in the following manner: L See: Hoskins & Karoly (1981)

24. Interpreting results III • Latent heat flux dominates the surface heat budget • Proposed wind-evaporation-SST feedback may operate in the following manner: SST & SLP • An SST anomaly (north of the ITCZ) reduces SLP, which causes a low-level wind flow across the initial SST anomaly. The wind slackens easterlies, reduces LH flux and further warms SST. • Unanswered question: How did the SST warm to begin with?? H L

25. Decomposing LH Flux I • The Latent Heat flux, from the GCM source code, is: “K” • K is a constant comprised of air density, an exchange coefficient and the latent heat required for evaporation • To find the contribution of moisture or wind, decompose: • Clausius eqn: 1°C increase in SST and air will increase LH flux due to the exponential q(T) relationship

26. Decomposing LH flux II “Total” Forcing Simulation - Nov to Jan Total U Q 95% confidence contour • Wind contribution seems more important to LH flux • Moisture contribution seems to oppose the wind contribution although no physical connection has been identified

27. Decomposing LH flux III “Total” Forcing Simulation - Feb to Apr Total U Q 95% confidence contour • Strong downward latent heat flux in the tropics is caused mainly by a slackening of trade winds, rather than moisture • Moisture contribution to LH flux related to Clausius equation

28. Sensitivity Study DOM forcing study: initialize GCM with the same SST anomaly for two different seasons NDJ SST & LH Flux FMA SST & LH Flux Contour: 5 W/m2 • Importance of seasonality: ITCZ position, storm track, ENSO!

29. Interpreting results IV • Analyze latitude-pressure cross-sections of temperature & geootential height to investigate the atmospheric response

30. Interpreting results IV • Analyze latitude-pressure cross-sections of temperature & geootential height to investigate the atmospheric response NDJ MJJ ASO Contour: 1 m • Equivalent barotropic response is very different from observational analysis, BUT maintains importance of BL

31. Conclusions • The AMM relates the effects of an anomalous ITCZ position in terms of the tropical air-sea circulation • The PBL structure is out of phase with the upper-levels • Hurricane activity is highly correlated to the AMM due to SLP, SST and shear considerations in tandem • Using a LIM, the AMM can be predicted with a 1 yr. lead • The LIM suggests mid-latitude Atlantic SST anomalies are responsible for AMM predictability • GCM simulations confirm the LIM results and emphasize the importance of latent heat flux in generating the AMM

32. References • Penland, C. E., Sardeshmukh, P. D., 1995: The Optimal Growth of Tropical Sea Surface Temperature Anomalies. J. Climate, 8, 1999-2024. • Czaja, A., P. van der Vaart, and J. Marshall, 2002b: A diagnostic study of the role of remote forcing in tropical Atlantic variability. J. Climate., 15, 3280-3290. • Kossin, J.P., Vimont, D.J., 2007: A More General Framework for Understanding Atlantic Hurricane Variability and Trends. Bull. Amer. Met. Soc., 88, 1767-1781. • Chiang, J. C. H., Vimont, D. J., 2004: Analogous Pacific and Atlantic meridional modes of tropical atmosphere-ocean variability, J. Clim., 17, 4143-4158. • Vimont, D.J., Kossin, J.P., 2007: The Atlantic meridional mode and hurricane activity. Geophys. Res. Lett., 34, L07709, doi:10.1029/2006GL029683. • Namias, J., 1972: Influence of Northern hemisphere general circulation on drought in Northeast Brazil. Tellus, 24, 336-342.