A new tool for evaluating the physics of coupled atmosphere–ocean variability in nature and in general circulation models

Abstract

Intermediate models of the coupled tropical atmosphere–ocean system have been used to illuminate the physics of interannual climate phenomenon such as El Niño Southern Oscillation (ENSO) in the tropical Pacific and to explore how the tropics might respond to a forcing such as changing insolation (Milankovitch) or atmospheric carbon dioxide. Importantly, most of the intermediate models are constructed as anomaly models: models that evolve on a prescribed climatological mean state, which is typically prescribed and done so on a rather ad hoc basis. Here we show how the observed climatological mean state fields [ocean currents and upwelling, sea surface temperature (SST) and atmospheric surface winds] can be incorporated into a linearized intermediate model of the tropical coupled atmosphere–ocean system: called Linear Ocean–Atmosphere Model (LOAM), it is a linearized version of the Zebiak and Cane model. With realistic, seasonally varying mean state fields, we find that the essential physics of the ENSO mode is very similar to that in the original model and to that in the observations and that the observed mean fields support an ENSO mode that is stable to perturbations. Thus, our results provide further evidence that ENSO is generated and maintained by stochastic (uncoupled) perturbations. The method that we have outlined can be used to assimilate any set of ocean and atmosphere climatological data into the linearized atmosphere–ocean model. In a companion paper, we apply this same method to incorporate mean field output from two global climate models into the linearised model. We use the latter to diagnose the physics of the leading coupled mode (ENSO) that is supported by the climate models, and to illuminate why the structure and variance in the ENSO mode changes in the models when they are forced by early Holocene and Last Glacial Maximum boundary conditions.

Appendix: Comments on the empirically derived function \(\Upgamma\)

In Fig. 6 there is a spike in the function $\Upgamma$ (which maps T _s to $\widetilde{h}$ ; see Eq. 12) derived from the BOM temperature and FSU wind stress data at 260°E. This is explained by the shape of the function $cs(h^{*}_{20})$. In Eq. 12 we see that $\Upgamma$ is a function of the gradient of $cs(h^{*}_{20})$. As can be seen in Fig. 5, at thermocline depths of around 50 m, the gradient of $cs(h^{*}_{20})$ is at its steepest: deeper and shallower than 50 m, the gradient of $cs(h^{*}_{20})$ becomes less. When the FSU data is used to derive the values of $h^{*}_{20}=f(\overline{h})$ at which we evaluate $\frac{\partial cs}{\partial h^{*}_{20}}$, this function is evaluated very close to this inflection point—so close in fact that in the far east of the basin we cross the inflection point. Therefore, the gradient of $cs(h^{*}_{20})$ increases and then decreases as we traverse the basin. This behaviour is peculiar to $f(\overline{h})$ derived from the FSU windstress product becuase it alone produces very shallow thermocline depths in the far eastern Pacific. This can be seen in Fig. 14 which shows $h^{*}_{20}=f(\overline{h})$, derived using values of $\overline{h}$ calculated by forcing the 1.5 layer ocean with the observed seasonal cycle of wind stress from a number of combinations of temperature and wind stress data. Figure 14 demonstrates the general result that using the FSU wind stress results in a $h^{*}_{20}$ in the eastern Pacific that is 20 m shallower than that using due the NCEP dataset. This is due to the stronger equatorial easterlies in the FSU dataset (Wittenberg 2004).

Fig. 14

Mean thermocline depth, $\overline{h}_{20}$, using $f(\overline{h})$ derived from $\overline{h}$ and: the BOM subsurface temperature with FSU wind stress, solid line; the BOM subsurface temperature with NCEP wind stress, dash-dotted line; and the SODA subsurface data forced with NCEP wind anomalies, dotted line. Bold solid shows the ZC original