Fines y objetivos en la enseñanza de la Geografía: los condicionantes

Chapter 3 represents the first systematic evaluation of the ability of the unified oscillator to realistically replicate key features of the ENSO cycle. The unified oscillator was found to poorly replicate the magnitude, phase, and structure of ENSO-related anomalies. The results suggest that in its current form, the unified oscillator is not an appropriate frame- work for diagnosing ENSO behaviours in CGCMs. However, this does not necessarily imply that there is no utility in conceptual models that combine multiple mechanisms to describe ENSO development and transition. Rather, further work on such unified oscillators should be undertaken before they are considered to be sufficiently versatile in capturing a full range of ENSO behaviours.

The accuracy of the unified oscillator in representing the main features of the ENSO cycle was first addressed via comparison of the unified oscillator equations for the sea surface temperature (SST) anomaly, thermocline depth anomaly, and wind stress anomaly ten- dencies with the corresponding simulated tendencies estimated directly from ACCESS- OM (objective 1). The parameter values in the unified oscillator equations were taken directly from Wang (2001) - the predicted values - and alternatively estimated via gener- alised least squares (GLS) regression analysis - the fitted values. In the case of the SST anomaly tendency equation, the predicted curve overestimated the magnitude of the corresponding simulated tendency, explaining only 14% of the variance in the simulated tendency, compared with 61% explained by the fitted curve. The predicted and fitted

6.2. KEY FINDINGS AND IMPLICATIONS

curves for the unified oscillator thermocline depth and zonal wind stress anomaly ten- dencies were unable to replicate the phase and variability of the corresponding simulated tendencies, explaining essentially none of their variances.

A further objective of chapter 3 was to compare the performance of the unified oscillator with those of the individual component models that it combines - the delayed, advective- reflective, western Pacific, and recharge oscillators (objective 2). Overall, the original delayed oscillator model provided the most accurate diagnostic of ENSO behaviour, predominantly due to the assumption of a diagnostic relation between the Ni˜no-4 zonal wind stress anomaly and Ni˜no-3 SST anomaly. The results from the unified oscillator formulations of the delayed and advective-reflective oscillators, which included prognostic zonal wind stress equations, were comparable to that of the unified oscillator.

The western Pacific oscillator equations - the original versions and those derived from the unified oscillator - were neither necessary nor sufficient in modelling the ENSO be- haviour simulated in ACCESS-OM. This was partly due to the western Pacific thermo- cline depth anomaly being averaged off- rather than on-equator. However, it is possible that the main negative feedback mechanism described by the western Pacific oscillator was inappropriately parameterised, or did not operate, either at all or in concert with other mechanisms, during every ENSO event in the ACCESS-OM simulation.

The original delayed oscillator equation for the SST anomaly tendency provided a closer fit to the simulated tendency than did the corresponding equation from the original recharge oscillator. In particular, the fitted recharge oscillator curve poorly emulated the magnitude, phase, and sign of the simulated tendency during periods marked by central Pacific El Ni˜nos. This result does not imply that the delayed oscillator was a better descriptor of ENSO than the recharge oscillator, particularly given the limitations of the delayed oscillator in inciting ENSO growth (Li and Clarke, 1994; Mantua and Battisti, 1994; Kessler and McPhaden, 1995). Rather, it may imply that the recharge oscillator equations are missing dynamics important to central Pacific El Ni˜no events (Kug et al., 2010).

Based on the findings of chapter 3, modifications to the unified oscillator model were proposed (objective 3). These included adopting a diagnostic, rather than prognostic,

6.2. KEY FINDINGS AND IMPLICATIONS

equation relating the Ni˜no-4 zonal wind stress and SST anomalies, as well as modifying the averaging region for the thermocline depth anomaly to be on- rather than off-equator in the western Pacific. Ultimately, however, due to the superior performance of the origi- nal delayed oscillator in capturing ENSO-related variability, this model was suggested as a most appropriate starting point from which to modify and improve conceptual models. While only a few modifications to the unified oscillator were considered in this thesis, recent studies have highlighted a number of additional considerations that may further enhance the ability of unified conceptual models to capture a range of ENSO behaviours. For example, asymmetries in the duration, magnitude, and timing of ENSO events have been effectively replicated through the inclusion of nonlinearities in the relationship be- tween SST and zonal wind stress anomalies (Frauen and Dommenget, 2010; Choi et al., 2013). Accounting for a southwards shift in the westerly wind stress anomalies may improve modelling of the transition from El Ni˜no to La Ni˜na (Harrison, 1987; Vecchi and Harrison, 2003). Explicitly including features such as the seasonal cycle (Tziper- man et al., 1995), stochastic forcings (e.g. westerly wind bursts or the Madden-Julian Oscillation; Vecchi et al., 2006), and the mean state of the tropical Pacific (Guilyardi, 2006) may aid in the ability of conceptual models to capture ENSO diversity.

Even with the incorporation of the additional mechanisms described above, further con- siderations should be taken into account when low-order unified conceptual models are used to evaluate ENSO behaviours in CGCMs. For example, variables included in con- ceptual models are averaged over fixed spatial regions that do not necessarily enclose the locations of maximum heating (or cooling) for both observed spatial flavours of ENSO. Given the diversity in ENSO types already observed, and the potential for more marked spatial variability on decadal to centennial timescales (Wittenberg, 2009; Wittenberg et al., 2014; Capotondi et al., 2015), spatial averaging regions should be mindfully cho- sen to ensure their relevance to the analysed data. On a similar note, the mechanisms leading to ENSO growth and decay vary from event to event, despite the fact that the parameter values weighting each of these mechanisms remain constant in time.

Finally, it is important to recognise that low-order conceptual models are derived based on observed features of the tropical Pacific that are not necessarily well replicated in

6.2. KEY FINDINGS AND IMPLICATIONS

CGCMs (Delecluse et al., 1998; Guilyardi et al., 2003; AchutaRao and Sperber, 2006; Belmadani et al., 2010; Collins et al., 2010; Capotondi and Wittenberg, 2013; Capotondi et al., 2015). For example, many CGCMs simulate net surface heat fluxes that are not phase locked to the seasonal cycle due to biases in the shortwave heat flux, which in turn alters the behaviour and variability of ENSO-related anomalies (Rashid and Hirst, 2015). Consequently, it is possible to imagine a scenario where the balance of parameter values obtained by the application of a unified conceptual model to a CGCM does not reflect the true dynamical behaviour of the CGCM (e.g. Mechoso et al., 2003). In fact, this highlights a significant challenge in the development of effective process- based diagnostics: while they are formulated to account for different behaviours and sensitivities of CGCMs, to be truly effective, their construction must be flexible enough to accurately distinguish between realistic and biased dynamics simulated by the CGCMs. It follows that the results of the application of conceptual models to CGCMs should be interpreted in light of the underlying dynamics of the dataset analysed, as it is possible to get the “right” result for the wrong reason.