The Development of a Space Climatology: 3. Models of the Evolution of Distributions of Space Weather Variables With Timescale

• Published in: Space weather 2019-01, Vol.17 (1), p.180-209
• Main Authors: Lockwood, Mike, Bentley, Sarah N., Owens, Mathew J., Barnard, Luke A., Scott, Chris J., Watt, Clare E., Allanson, Oliver, Freeman, Mervyn P.
• Format: Article
• Language: English
• Subjects: Algorithms; Climate; Climate models; Climatology; Distribution functions; DST Index; Electric power distribution; Empirical models; Evolution; Extreme values; geomagnetic indices; Geomagnetism; large and extreme events; long‐term variations; Magnetosphere; Magnetospheres; model algorithms; Probability distribution; Probability distribution functions; Solar cycle; Solar maximum; Solar minimum; Space weather; Sunspot cycle; Sunspots; Time
• ISSN: 1542-7390; 1539-4964; 1542-7390
• DOI: 10.1029/2018SW002017

We study how the probability distribution functions of power input to the magnetosphere Pα and of the geomagnetic ap and Dst indices vary with averaging timescale, τ, between 3 hr and 1 year. From this we develop and present algorithms to empirically model the distributions for a given τ and a given annual mean value. We show that lognormal distributions work well for ap, but because of the spread of Dst for low activity conditions, the optimum formulation for Dst leads to distributions better described by something like the Weibull formulation. Annual means can be estimated using telescope observations of sunspots and modeling, and so this allows the distributions to be estimated at any given τ between 3 hr and 1 year for any of the past 400 years, which is another important step toward a useful space weather climatology. The algorithms apply to the core of the distributions and can be used to predict the occurrence rate of large events (in the top 5% of activity levels): they may contain some, albeit limited, information relevant to characterizing the much rarer superstorm events with extreme value statistics. The algorithm for the Dst index is the more complex one because, unlike ap, Dst can take on either sign and future improvements to it are suggested. Plain Language Summary This is the third in a series of three papers aimed at developing a climatology of space weather that applies to all solar conditions between grand solar minimum and grand solar maximum. We generate empirical models to enable us to predict the probability of a given level of space weather disturbance, as quantified by either the ap of the Dst geomagnetic indices, in a year with a given average level of disturbance. The models can be used with averaging/integration times anywhere between 3 hr and 1 year. Key Points Core distributions and extreme events of geomagnetic activity are studied as a function of averaging timescale τ The autocorrelation is shown to have a dominant role determining how these core distributions vary with averaging timescale τ Models for computing the distribution of geomagnetic activity for a given timescale τ and annual mean are presented