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PCMDI > WCRP CMIP3 Model Output > Diagnostic Subprojects Printer Friendly Version
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Structure of inherent variability modes of coupled ocean-atmosphere models

PI: Fabian Mager
Institution: University of Cambridge
Additional Investigators: Hans Graf
Project overview
The objectives of the project are (i) to better understand the dynamics of atmospheric variability in the North Atlantic region, (ii) to define the influence of the stratosphere on the structure of these variability modes, (iii) to analyse in detail the ability of coupled OAGCMs to reproduce these atmospheric variability modes and their sensitivity to external forcing, and (iv) to begin to investigate possible causes of multidecadal regime transitions of tropospheric teleconnectivity.

The North Atlantic Oscillation (NAO) is the dominant atmospheric variability mode in the North Atlantic Region. It has a large impact on the weather and climate of the North Atlantic ocean and the surrounding landmasses by influencing temperature, wind and precipitation patterns (e.g. Hurrell, 1995), and as such controls drought or flood conditions, the trajectory and strength of storms, and heat waves and cold spells, especially over Europe. It additionally interacts with the ocean and may influence the Thermohaline Circulation (THC) in the North Atlantic (Hurrel et al., 2003) as well as the position of the Gulf Stream. NAO has a life cycle of approximately two weeks and is driven by the interaction of baroclinic synoptic and planetary waves (Feldstein, 2003). Several studies (e. g. Baldwin and Dunkerton, 2001) show that the polarity of the NAO index in winter depends on the state of the stratospheric polar vortex. Wind anomalies associated with the strength of the polar vortex extend to the upper troposphere (Christiansen, 2003). Upper tropospheric wind shear influences the preferred area of growth of baroclinic eddies and, therefore, of storm tracks. Hence, changes in polar vortex regimes over time will lead to changes in the variability structure of the NAO (Perlwitz et al., 2000). Walter and Graf (2005) made clear that these structural changes are due to shifts in the weights of contributing dynamical processes in the atmosphere and that the changes involve storm tracks and precipitation and their correlation with the NAO index. Potentially this will also involve changes in the effectiveness and patterns of ocean-atmosphere interaction on a basin wide scale (Graf and Walter, 2005).

It is therefore of crucial importance for the scientific community to be able to predict the short- and long-term variability of the NAO. However, the Earth's atmosphere and oceans constitute a coupled system with a large number of interactions and feedbacks. The underlying processes are portrayed with different accuracy and weight in different atmospheric, oceanic and coupled circulation models that would allow for the prediction of future climate conditions. This leads to differences in the forecasts of ocean-atmosphere general circulation models (OAGCMs) for an increasing greenhouse gas burden. Many of these differences may be related to differences in the atmospheric part of the coupled models, others may be due to biases at the interface between atmosphere and ocean. Very recently, Osborn (2004) showed that the current coupled OAGCMs exhibit only weak similarity with observed NAO/AO patterns. Only if the mean of the models' first EOFs is computed, a structure similar to the observed NAO evolves. However, the amplitudes then are much smaller than observed. Reasons for this unsatisfying behaviour remain unknown.

With this project I propose to study in detail the ability of coupled OAGCMs to reproduce the dynamics of atmospheric internal variability structures and their sensitivities to external forcing (e.g. Increasing GHG burden or volcanic eruptions). By investigating the mechanisms behind the dominant atmospheric variability mode in the North Atlantic area (the NAO) both in observations (reanalysis data for the years 1958-2003) and in models (control runs and GHG forced runs from CMIP, see http://www-pcmdi.llnl.gov/projects/cmip) I want to uncover basic controlling processes for ocean-atmosphere coupling and their changing weight in the past and future. By comparison of these processes between the "real world" and the "model world(s)" I shall
be able to assess the level of trustworthiness of different OAGCMs and their potential to forecast future climates. At the same time I may be able to interpret the causes of failure of some of the models to reproduce the observed past. I finally will investigate processes that lead to transitions of regimes of the stratospheric polar vortex on a seasonal to (multi)decadal time scale, which might have contributed to (multi) decadal variability in the efficiency of ocean atmosphere coupling and teleconnectivity.

Methodology/work plan
The project is broken into 3 work packages. Atmospheric data consisting of approximately four decades of daily 3-D grid values of geopotential heights, wind vectors, temperatures etc. will be obtained from the British Atmospheric Data Centre (ERA40) and from the NOAA web page (NCEP reanalysis). Analysis tools will be developed with these data and can then be applied to the model data. I will study in detail the ability of coupled OAGCMs from CMIP as well as the Hamburg ECHAM5 model to reproduce the dynamics of internal patterns of variability and their sensitivities to external forcing. Since differences in the probability density functions of the index time series of the fundamental atmospheric variability modes (between different models and between models and reanalysis data) can be expected, I will document these and derive respective wind stress fields, freshwater and heat fluxes. All investigations will be based on daily data, if necessary after application of numerical filters. I will start with reanalysis data and apply the developed methodology therein to model data as these are received. Finally I will investigate if I can detect regimes of variability of the polar vortex strength (Feser et al., 2000) in the control runs of the coupled models considered, and study the cause of transitions between different multi-decadal regimes and their impact on global teleconnections (Walter and Graf, 2002).

1st year:
Acquire reanalysis data, develop of dynamic analysis tools, analyse polar vortex regimes, define new NAOlike indices, analyse time series (reanalysis data)

2nd year:
Determine mechanisms of build-up and decay of NAO-like variability in reanalysis data for different stratospheric regimes (reanalysis data)
Analyse polar vortex regimes, define new NAO-like indices, and produce associated patterns, time series analysis of model data, start to search for causes of stratospheric regime transitions (intra-seasonal) and forced by GHG in reanalysis and model data (model data)

3rd year:
Determine mechanisms of build-up and decay of NAO-like variability in models, model-model and modelreanalysis intercomparison (model data) Study mechanisms of global/regional regime transitions (multi-decadal) in control and GHG forced model run (model data)

Baldwin, M.P., and T.J. Dunkerton, 2001: Stratospheric harbingers of anomalous weather regimes. Science, 294,581-584.
Christiansen, B., 2003: Evidence for non-linear climate change: Two stratospheric regimes and a regime shift.J. Climate, Vol. 16, No. 22, pp. 3681-3690. doi: 10.1175/1520-0442(2003)016<3681:EFNCCT>2.0.CO;2
Feldstein, S.B., 2003:The dynamics of NAO teleconnection pattern growth and decay. Quart. J. Roy. Met. Soc., 129, 901--924.
Feser, F., H.-F. Graf, and J. Perlwitz, 2000: Secular variability of the coupled tropospheric and stratospheric circulation in the GCM ECHAM 3/LSG.Theoret. Appl. Meteor.,65, 1--15.
Graf, H.-F., and K. Walter, 2005: Polar vortex controls coupling of North Atlantic ocean and atmosphere. Geophys. Res. Lett. , VOL.32, L01704, doi:10.1029/2004GL020664.
Hurrell, J.W., 1995: Decadal trends in the North Atlantic Oscillation and relationships to regional temperature and precipitation. Science, 269, 676-679.
Hurrell, J.W., Y. Kushnir, G. Ottersen, and M. Visbeck, Eds. The North Atlantic Oscillation: Climate Significance and Environmental Impact, 2003 , Geophysical Monograph Series, 134, 279pp.
Osborn TJ (2004) Simulating the winter North Atlantic Oscillation: the roles of internal variability and greenhouse gas forcing. Climate Dynamics (in press).
Perlwitz, J., H.-F. Graf and R. Voss, 2000: The leading variability mode of the coupled tropospherestratosphere winter circulation in different climate regimes. J. Geophys. Res., 105, 6915-6926.
Walter, K., and Graf, H.-F., 2002: On the changing nature of the regional connection between the North Atlantic Oscillation and sea surface temperature, J. Geophys. Res., 107 (D17), 4338, doi:
10.1029/2001JD000850, 2002
Walter, K., and Graf, H.-F., 2005: North Atlantic variability structure, storm tracks and precipitation. ACP, 239-248.

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