Magnetic Topology of Actively Evolving and Passively Convecting Structures in the Turbulent Solar Wind.

Multipoint in situ observations of the solar wind are used to identify the magnetic topology and current density of turbulent structures. We find that at least 35% of all structures are both actively evolving and carrying the strongest currents, actively dissipating, and heating the plasma. These structures are comprised of ∼1/5 3D plasmoids, ∼3/5 flux ropes, and ∼1/5 3D X points consistent with magnetic reconnection. Actively evolving and passively advecting structures are both close to log-normally distributed. This provides direct evidence for the significant role of strong turbulence, evolving via magnetic shearing and reconnection, in mediating dissipation and solar wind heating.

On estimating local long-term climate trends.

Climate sensitivity is commonly taken to refer to the equilibrium change in the annual mean global surface temperature following a doubling of the atmospheric carbon dioxide concentration. Evaluating this variable remains of significant scientific interest, but its global nature makes it largely irrelevant to many areas of climate science, such as impact assessments, and also to policy in terms of vulnerability assessments and adaptation planning. Here, we focus on local changes and on the way observational data can be analysed to inform us about how local climate has changed since the middle of the nineteenth century. Taking the perspective of climate as a constantly changing distribution, we evaluate the relative changes between different quantiles of such distributions and between different geographical locations for the same quantiles. We show how the observational data can provide guidance on trends in local climate at the specific thresholds relevant to particular impact or policy endeavours. This also quantifies the level of detail needed from climate models if they are to be used as tools to assess climate change impact. The mathematical basis is presented for two methods of extracting these local trends from the data. The two methods are compared first using surrogate data, to clarify the methods and their uncertainties, and then using observational surface temperature time series from four locations across Europe.

Kinetic equation of linear fractional stable motion and applications to modeling the scaling of intermittent bursts.

Lévy flights and fractional Brownian motion have become exemplars of the heavy-tailed jumps and long-ranged memory widely seen in physics. Natural time series frequently combine both effects, and linear fractional stable motion (lfsm) is a model process of this type, combining alpha-stable jumps with a memory kernel. In contrast complex physical spatiotemporal diffusion processes where both the above effects compete have for many years been modeled using the fully fractional kinetic equation for the continuous-time random walk (CTRW), with power laws in the probability density functions of both jump size and waiting time. We derive the analogous kinetic equation for lfsm and show that it has a diffusion coefficient with a power law in time rather than having a fractional time derivative like the CTRW. We discuss some preliminary results on the scaling of burst "sizes" and "durations" in lfsm time series, with applications to modeling existing observations in space physics and elsewhere.

Pseudononstationarity in the scaling exponents of finite-interval time series.

The accurate estimation of scaling exponents is central in the observational study of scale-invariant phenomena. Natural systems unavoidably provide observations over restricted intervals; consequently, a stationary stochastic process (time series) can yield anomalous time variation in the scaling exponents, suggestive of nonstationarity. The variance in the estimates of scaling exponents computed from an interval of N observations is known for finite variance processes to vary as approximately 1N as N-->infinity for certain statistical estimators; however, the convergence to this behavior will depend on the details of the process, and may be slow. We study the variation in the scaling of second-order moments of the time-series increments with N for a variety of synthetic and "real world" time series, and we find that in particular for heavy tailed processes, for realizable N , one is far from this approximately 1N limiting behavior. We propose a semiempirical estimate for the minimum N needed to make a meaningful estimate of the scaling exponents for model stochastic processes and compare these with some "real world" time series.

Exactly solvable sandpile with fractal avalanching.

A simple one-dimensional sandpile model is constructed which possesses exact analytical solvability while displaying both scale-free behavior and fractal properties. The sandpile grows by avalanching on all scales, yet its shape and energy content are described by a simple, continuous (but nowhere differentiable) analytical formula. The avalanche energy distribution and the avalanche time series are both power laws with index -1 ("1/f spectra").