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Persistence in Solar Physics

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If you have a question about this talk, please contact Roger Dufresne.

Persistence, or long memory, is of longstanding interest in solar physics, having first been identified in time series of sunspot numbers in the seminal paper by Mandelbrot and Wallis (1969): “Some long‐run properties of geophysical records”. They used a method called Rescaled Range Analysis (R/S) to determine a Hurst exponent, H=0.93, which is indicative of strong persistence. It has since been suggested that for sunspot numbers, and indeed most times series of solar quantities, R/S is not an appropriate method for estimating persistence due to the non-stationary nature of the time series. Detrended fluctuation analysis (DFA) has been proposed as a more suitable method for estimating persistence, and has since been widely used in the analysis of solar and geo-physical time series. However, DFA is known to introduce uncontrolled bias and is in fact inappropriate for non-stationary processes (Bryce & Sprague, 2012).

Here, we assume an alternative class of long-memory models, more commonly found in statistics and econometrics: fractionally integrated processes. We revisit solar physics time series such as sunspot number and total solar irradiance with more robust estimators, and identify higher persistence than previous studies, as well as persistence over timescales significantly shorter than previously identified.

We also consider persistence in time series of quantities derived from solar physics simulations, demonstrating that these simulations capture the memory structure that is present in the observational input data. Further, we provide an algorithm for the quantitative assessment of simulation burn-in: the time after which a quantity has evolved away from its arbitrary initial condition to a physically more realistic state.

This talk is part of the DAMTP Astrophysics Seminars series.

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