The challenge of non-Markovian energy balance models in climate.

We first review the way in which Hasselmann's paradigm, introduced in 1976 and recently honored with the Nobel Prize, can, like many key innovations in complexity science, be understood on several different levels. It can be seen as a way to add variability into the pioneering energy balance models (EBMs) of Budyko and Sellers. On a more abstract level, however, it used the original stochastic mathematical model of Brownian motion to provide a conceptual superstructure to link slow climate variability to fast weather fluctuations, in a context broader than EBMs, and led Hasselmann to posit a need for negative feedback in climate modeling.

Temperature variability implies greater economic damages from climate change.

A number of influential assessments of the economic cost of climate change rely on just a small number of coupled climate-economy models. A central feature of these assessments is their accounting of the economic cost of epistemic uncertainty-that part of our uncertainty stemming from our inability to precisely estimate key model parameters, such as the Equilibrium Climate Sensitivity. However, these models fail to account for the cost of aleatory uncertainty-the irreducible uncertainty that remains even when the true parameter values are known.

Storylines: an alternative approach to representing uncertainty in physical aspects of climate change.

As climate change research becomes increasingly applied, the need for actionable information is growing rapidly. A key aspect of this requirement is the representation of uncertainties. The conventional approach to representing uncertainty in physical aspects of climate change is probabilistic, based on ensembles of climate model simulations.

Dissipation and heating in solar wind turbulence: from the macro to the micro and back again.

The past decade has seen a flurry of research activity focused on discerning the physics of kinetic scale turbulence in high-speed astrophysical plasma flows. By 'kinetic' we mean spatial scales on the order of or, in particular, smaller than the ion inertial length or the ion gyro-radius--the spatial scales at which the ion and electron bulk velocities decouple and considerable change can be seen in the ion distribution functions. The motivation behind most of these studies is to find the ultimate fate of the energy cascade of plasma turbulence, and thereby the channels by which the energy in the system is dissipated.

Fast reconfiguration of high-frequency brain networks in response to surprising changes in auditory input.

How do human brain networks react to dynamic changes in the sensory environment? We measured rapid changes in brain network organization in response to brief, discrete, salient auditory stimuli. We estimated network topology and distance parameters in the immediate central response period, <1 s following auditory presentation of standard tones interspersed with occasional deviant tones in a mismatch-negativity (MMN) paradigm, using magnetoencephalography (MEG) to measure synchronization of high-frequency (gamma band; 33-64 Hz) oscillations in healthy volunteers. We found that global small-world parameters of the networks were conserved between the standard and deviant stimuli.

Topological isomorphisms of human brain and financial market networks.

Although metaphorical and conceptual connections between the human brain and the financial markets have often been drawn, rigorous physical or mathematical underpinnings of this analogy remain largely unexplored. Here, we apply a statistical and graph theoretic approach to the study of two datasets - the time series of 90 stocks from the New York stock exchange over a 3-year period, and the fMRI-derived time series acquired from 90 brain regions over the course of a 10-min-long functional MRI scan of resting brain function in healthy volunteers. Despite the many obvious substantive differences between these two datasets, graphical analysis demonstrated striking commonalities in terms of global network topological properties.

Compressibility in solar wind plasma turbulence.

Incompressible magnetohydrodynamics is often assumed to describe solar wind turbulence. We use extended self-similarity to reveal scaling in the structure functions of density fluctuations in the solar wind. The obtained scaling is then compared with that found in the inertial range of quantities identified as passive scalars in other turbulent systems.

Intermittency, scaling, and the Fokker-Planck approach to fluctuations of the solar wind bulk plasma parameters as seen by the WIND spacecraft.

The solar wind provides a natural laboratory for observations of magnetohydrodynamic (MHD) turbulence over extended temporal scales. Here, we apply a model independent method of differencing and rescaling to identify self-similarity in the probability density functions (PDF) of fluctuations in solar wind bulk plasma parameters as seen by the WIND spacecraft. Whereas the fluctuations of speed v and interplanetary magnetic field (IMF) magnitude B are multifractal, we find that the fluctuations in the ion density rho, energy densities B2 and rhov(2) as well as MHD-approximated Poynting flux vB(2) are monoscaling on the time scales up to 26 hr.