ALBERT

Description: The El Niño-Southern Oscillation (ENSO) is a mode of interannual variability in the coupled equatorial Pacific coupled atmosphere/ocean system. El Niño describes a state in which sea surface temperatures in the eastern Pacific increase and upwelling of colder, deep waters diminishes. El Niño events typically peak in boreal winter, but their strength varies irregularly on decadal timescales. There were exceptionally strong El Niño events in 1982–83, 1997–98 and 2015–16 that affected weather on a global scale. Widely publicized forecasts in 2014 predicted that the 2015–16 event would occur a year earlier. Predicting the strength of El Niño is a matter of practical concern due to its effects on hydroclimate and agriculture around the world. This study discusses the frequency and regularity of strong El Niño events in the context of chaotic dynamical systems. We discover a mechanism that limits their predictability in a conceptual ‘recharge oscillator’ model of ENSO. Weak seasonal forcing or noise in this model can induce irregular switching between an oscillatory state that has strong El Niño events and a chaotic state that lacks strong events. In this regime, the timing of strong El Niño events on decadal timescales is unpredictable.Key words: Chaotic attractor, El Niño, mode switching, unpredictability.

Description: We present a 5-Myr probabilistic stack, which we name the Prob-stack. It is constructed from 180 globally distributed benthic δ 18 O records using a profile hidden Markov model (HMM). Benthic stacks have been extensively employed to estimate ages in marine sediment cores and lead–lag relationships between paleoclimate proxies. Because this stack is probabilistic, it incorporates the variability among multiple records used in its construction and the uncertainty in alignments of records to the stack that have not been included in previous studies. It represents the uncertainty stemming from these two sources using a normal distribution with mean and variance in benthic δ 18 O at each point in the stack. In doing so, Prob-stack captures the time-varying mean and variance of benthic δ 18 O in the global ocean, which allows for better assessment of the uncertainty in records’ relative ages, such as lead–lag relationships. Algorithms are included.

Description: Background It is a common practice to use a simple model to explain the mechanisms or processes that occur in a much more complex, complete and computationally expensive model. Many such examples can be found in climate change research. Objective This paper uses two illustrative examples to show how we can quantitatively relate the mechanisms or processes observed in a simple climate model to similar mechanisms in a more complex one. Method A simple model can only explain a more complex solution’s mechanisms if outcomes are tested over a broad range of inputs. By carefully sampling the full set of inputs for both the simple and complex models, we can robustly compare the process or mechanistic outcomes, statistically, between them. Thus, by examining the similarity or differences in the relationship between the inputs and outputs. The method can reject an incorrect simple model. Results The examples are, first, analytic and numerical solutions to the heat equation and, second, the 1948 Stommel model of horizontal ocean circulation and a more complex quasi-geostrophic ocean model. We quantitatively state how similar the simple model’s mechanisms are to the mechanisms in the more complex representation. In addition, when a simple solution may be correct, we give the percentage of the variance of the complex model’s outcomes that is explained by the simple response along with an uncertainty estimate. Conclusion We successfully tested a methodology for robustly quantifying how the physics encapsulated by a simple model of a process may exhibit itself in another, more complex formulation. Suggestions are given as a guide for use of the methodology with more complex and realistic models.