Abstract
Abstract. Variation of the cross polar cap potential (CPCP) with the interplanetary electric field (IEF), the merging electric field EKL, the Polar Cap North (PCN) magnetic index, and the solar wind-magnetosphere coupling function EC of Newell et al. (2007) is investigated by considering convection data collected by the Super Dual Auroral Radar Network (SuperDARN) in the Northern Hemisphere. Winter and summer observations are considered separately. All variations considered show close to linear trend at small values of the parameters and tendency for the saturation at large values. The threshold values starting from which the non-linearity was evident were estimated to be IEF*~EKL*~3 mV/m, PCN*~3–4, and EC*~1.5×104. The data indicate that saturation starts at larger values of the above parameters and reaches larger (up to 10 kV) saturation levels during summer. Conclusions are supported by a limited data set of simultaneous SuperDARN observations in the Northern (summer) and Southern (winter) Hemispheres. It is argued that the SuperDARN CPCP saturation levels and the thresholds for the non-linearity to be seen are affected by the method of the CPCP estimates.
Highlights
Observations show that the ionospheric cross polar cap potential (CPCP) tends to saturate at extreme solar wind conditions (e.g., Siscoe et al, 2004; Shepherd, 2007)
Maps that we considered had at least 200 points and, for the vast majority of cases (91% winter and 70% summer time), had more than 300 points (this threshold value was adopted by Khachikjan et al (2008) whose events have mostly been included into the present data set)
Based on ∼1700 CPCP estimates for summer and ∼1400 CPCP estimates for winter, all in the Northern Hemisphere, we showed that
Summary
Observations show that the ionospheric cross polar cap potential (CPCP) tends to saturate at extreme solar wind conditions (e.g., Siscoe et al, 2004; Shepherd, 2007). The effect has been demonstrated by considering E×B ion drift data from satellites crossing the polar cap (e.g., Hairston et al., 2003), by modeling the CPCP with AMIE technique based on magnetometer data (e.g., Russell et al, 2001; Liemohn and Ridley, 2002) and by analyzing the SuperDARN radar convection maps (e.g., Shepherd et al, 2003; Khachikjan et al, 2008). Ridley and Kihn (2004) demonstrated little nonlinearity in the CPCP-PCN relationship, they considered only moderately disturbed conditions (PCN
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