So please feel free to enlighten me.
ENSO is but one of several different interacting positive reinforcements of various Earth Systems (Arctic Amplification, Bipolar Seesaw, Permafrost degradation, PDO/ENSO, Ice-Climate Feedback, Hadley Cell expansion, etc.); which Chaos Theory calls Strange (or Lorenz) Attractors. I believe that such strange attractors can progressively/interactively ratchet-up different Earth System States (see the first attached image) so as to increase the effective climate sensitivity so that some "slow-response" feedbacks (see the second figure from Andrew – Ringberg 2015, where the middle panel indicates an effective climate sensitivity of about 5C) occur within decades rather than millennia. This potential acceleration of the rate of activation of "slow-response" feedbacks close to what happened during the PETM, is supported by such considerations as:
(a) We are radiatively forcing the Earth at well over 10 times the rate experienced during the PETM;
(b) The Antarctic anthropogenically induced ozone hole accelerated the westerly winds over the Southern Ocean; which induced the conveyance of warm Circumpolar Deep Water, CDW, over portions of the Antarctic continental shelves where the CDW has been melting glacial ice at the grounding lines of key marine glaciers, thus initiating Hansen's ice-climate feedback.
(c) Anthropogenic aerosols have been temporarily masking the impacts of anthropogenic radiative forcing; much as dust in paleo times resulted in negative forcing that caused cooling. However, reticent science has discounted the efficiency of both of these mechanisms leaving the modern world subject to unexpectedly high rates of GMST increases due to the GHGs that accumulated in the atmosphere during the recent faux hiatus.
(d) The ENSO cycle appears to be increasing the frequency of large El Ninos.
Indeed the first linked reference indicates that when analyzing modern day observations: "Severe testing is applied to observed global and regional surface and satellite temperatures and modelled surface temperatures to determine whether these interactions are independent, as in the traditional signal-to-noise model, or whether they interact, resulting in steplike warming." The reference concludes that indeed steplike warming occurs due to "… a store-and-release mechanism from the ocean to the atmosphere…" like the classical Lorenzian attractor case of ENSO decadal cycles. Such steplike behavior confirms the mechanism that I call "Ratcheting of Quasi-static Equilibrium States" (see the first attachment). As the authors point-out reticent science likely missed this behavior because: "This may be due in part to science asking the wrong questions."; and they advise that such reticent AR5/CMIP5 researchers should change how they view the output from their models. For example, the third attached image (see panel "e" of that Figure 6) from the reference shows global warming increasing much faster for a steplike response if ECS is 4.5 than for a the traditional AR5/CMIP5 interpretation; which means that ESLD researchers are exposing society to far more risk of the consequences of high ECS values than AR5/CMIP5 are leading us to believe:
Jones, R. N. and Ricketts, J. H.: Reconciling the signal and noise of atmospheric warming on decadal timescales, Earth Syst. Dynam. Discuss., doi:10.5194/esd-2016-35, in review, 2016.
http://www.earth-syst-dynam-discuss.net/esd-2016-35/&
http://www.earth-syst-dynam-discuss.net/esd-2016-35/esd-2016-35.pdfExtract: "This finding does not invalidate the huge literature that assesses long-term (>50 years) climate change as a relatively linear process, and the warming response as being broadly additive with respect to forcing (e.g., Lucarini et al., 2010; Marvel et al., 2015). However, on decadal scales, this is not the case – warming appears to be largely governed by a storage and release process, where heat is stored in the ocean and released in bursts projecting onto modes of climate variability as suggested by Corti et al. (1999). We discuss this further in another paper (Jones and Ricketts, 2016).
This has serious implications for how climate change is understood and applied in a whole range of decision-making contexts. The characterisation of changing climate risk as a smooth process will leave climate risk as being seriously underdetermined, affecting how adaptation is perceived, planned and undertaken (Jones et al., 2013).
The interaction of change and variability is typical of a complex, rather than mechanistic, system. The possibility of Lorenzian attractors in the ocean-atmosphere acting on decadal time scales was raised by Palmer (1993) and, despite later discussions about the potential for nonlinear responses on those timescales (e.g., Lucarini and Ragone, 2011;Tsonis and Swanson, 2012), very little progress has been made in translating this into applied research that can portray a better understanding of changing climate risk. This may be due in part to science asking the wrong questions.
The signal to noise model of a gradually changing mean surrounded by random climate variability poorly represents warming on decadal timescales. The separation of signal and noise into ‘good’ and ‘bad, likewise, is poor framing for the purposes of understanding and managing risk in fundamentally nonlinear systems (Koutsoyiannis, 2010; Jones, 2015b). However, as we show, the presence of such changes within climate models shows their current potential for investigating nonlinearly changing climate risks. Investigating step changes in temperature and related variables does not indicate a need to fundamentally change how climate modelling is carried out. It does, however, indicate a need to change how the results are analysed."
Furthermore, the second linked (open access) research indicates that the traditional model approach consistently underestimates values of climate sensitivity based on experiments (& paleo data) with dynamic changes in atmospheric CO2 concentrations:
Anna S. von der Heydt, Peter Ashwin (Submitted on 12 Apr 2016), "State-dependence of climate sensitivity: attractor constraints and palaeoclimate regimes", arXiv:1604.03311
http://arxiv.org/abs/1604.03311&
http://arxiv.org/pdf/1604.03311v1.pdfAbstract: "Equilibrium climate sensitivity is a frequently used measure to predict long-term climate change. However, both climate models and observational data suggest a rather large uncertainty on climate sensitivity (CS). The reasons for this include: the climate has a strong internal variability on many time scales, it is subject to a non-stationary forcing and it is, on many timescales, out of equilibrium with the changes in the radiative forcing. Palaeo records of past climate variations give insight into how the climate system responds to various forcings although care must be taken of the slow feedback processes before comparing palaeo CS estimates with model estimates. In addition, the fast feedback processes can change their relative strength and time scales over time. Consequently, another reason for the large uncertainty on palaeo climate sensitivity may be the fact that it is strongly state-dependent. Using a conceptual climate model, we explore how CS can be estimated from unperturbed and perturbed model time series. Even in this rather simple model we find a wide range of estimates of the distribution of CS, depending on climate state and variability within the unperturbed attractor. For climate states perturbed by instantaneous doubling of CO2, the sensitivity estimates agree with those for the unperturbed model after transient decay back the attractor. In this sense, climate sensitivity can be seen as a distribution that is a local property of the climate attractor. We also follow the classical climate model approach to sensitivity, where CO2 is prescribed and non-dynamic, leading to CS values consistently smaller than those derived from the experiments with dynamic CO2. This suggests that climate sensitivity estimates from climate models may depend significantly on future dynamics, and not just the level of CO2."
Extract: “... the presence of variability on the attractor on a number of timescales means there are clear and non-trivial distributions of sensitivities, even for unperturbed climates. The distribution of sensitivities depends strongly on the background state as well as on the timescale considered. This suggests that it could be useful to think of the unperturbed climate sensitivity as a local property of the “climate attractor”. For a perturbed system (we have considered instantaneously doubled CO2) this is still useful once an initial transient has decayed. This transient will depend in particular on ocean heat uptake, though also on carbon cycle and biosphere processes that act on time scales roughly equivalent with the forcing time scale. If the climate system has more than one attractor, the perturbed system may clearly evolve to a completely different set of states than the original attractor – a situation that did not occur in the climate model used here. In less extreme cases, there may still be very long transients for some perturbations associated parts of the climate system that are associated with slow feedbacks.
Such perturbations (illustrated in Fig. 1b,d) are not normally applied in climate models used for climate predictions [IPCC, 2013], where climate sensitivity is derived from model simulations considering prescribed, non-dynamic atmospheric CO2. In our conceptual model, we have derived climate sensitivities from both types of perturbations and find that the classical climate model approach (section 2.2, Fig. 4f) leads to significantly lower values of the climate sensitivity than the perturbations away from the attractor with dynamic CO2 (section 2.3, Fig. 11a). This emphasises the importance of including dynamic carbon cycle processes into climate prediction models. Moreover, it supports the idea that the real observed climate response may indeed be larger than the model predicted one, because those models never will include all feedback processes in the climate system.“
