1. Identifying the Role of Solar-Wind Number Density in Ring Current Evolution Paul O’Brien and Robert McPherron UCLA/IGPP

2. Outline Background –CIR Topology Model –Plasma Entry Model New Analysis –1994-1997 –1964-1997 –Solar Min –Solar Max Conclusions Does enhanced solar- wind number density drive the ring current? The observed relationship between solar-wind number density and Dst is best explained by the solar-wind topology of corotating interaction regions

3. CIR Topology Model IMF V Slow 6 hours BzBz V Fast x z y Rosenberg [JGR April 1982] proposed that the typical structure of corotating interaction regions (CIRs) gave rise to a dense plasma front 6 hours (time-of-flight) prior to a strong IMF-B z (North or South) –A strong southward B z would then cause a magnetic storm in Dst –25 density enhancements from 1978- 1979 were used in a super-posed epoch analysis Pizzo [JGR March 1994] reproduced this behavior in MHD simulations –North-South velocity and IMF were seen behind the CIR density front –CIRs are dominant only during Solar Minimum BzBz V Fast x z y V Slow 6 hours

4. Plasma Entry Model Borovsky et al. [JGR August 1998] suggested that solar wind plasma enters the ring current through the plasma sheet –Enhanced solar wind density leads to stronger ring current after 4+ hours –This process should occur at all phases of the Solar Cycle 2 hr Lag 4 hr Lag Solar Wind

5. Density Precursor Electric Field Driver Initial Observations Smith et al. [GRL July 1, 1999] found a strong density precursor 5-6 hours prior to the minimum Dst in a storm –The study covered 55 moderate storms 1994-1997 (solar minimum conditions) –The density “driver” was independent of the electric field (VB s )

6. More Observations Using the OMNI database from 1964-1997, it was not possible to detect the Smith et al. signal –Other methods also were not able to detect a signal –This analysis covered 440 moderate storms (solar minimum and solar maximum conditions) –There was no signal for a density “driver”

7. Solar Max Observations Using only storms within 2 years of Solar Maximum it was not possible to detect a density precursor –CIRs are not common at Solar Maximum –The analysis covered 155 moderate storms (solar maximum conditions only) –There was no signal for a density “driver”

8. Solar Min Observations Using only storms within 2 years of Solar Minimum an ambiguous density signal was detected –This would be consistent with a density enhancement at several hours lag associated with CIR topology –The analysis covered 176 moderate storms (solar minimum conditions only) –There was a possible response for density after 5 hours

9. Solar Cycle Dependence The only data subsets that showed a density precursor were those that excluded Solar Maximum data Therefore, the density precursor is a Solar Minimum phenomenon, probably associated with CIR topology Smoothed Sunspot Number

10. Conclusions The observed relationship between solar-wind number density and Dst is best explained by the solar-wind topology of corotating interaction regions Solar wind number density is probably a precursor but not a driver of the ring current at a lag of 5+ hours –The correlation is only seen at Solar Minimum –The strength of the ring current does not appear to be causally related to solar wind density enhancements –Other methods (not presented) support this conclusion: Statistical Dst phase-space analysis Analytical Dst dynamics optimization Neural Network Dst dynamics simulation

11. Solar Cycle Phenomena Lindsay et al. [JGR January 1994] analyzed CIRs and CMEs at 0.72 AU –CIRs occur most frequently at Solar Minimum –CMEs occur most frequently at Solar Maximum SunSpots CMEs CIRs 35 0 20 5 30 25 15 10 79888780818283848586 Year Yearly Normalized Monthly Occurrence Rate 180 0 120 60 160 140 100 80 40 20 PVO Disturbances Over Solar Cycle Solar Minimum

12. Bi-Linear Correlation Method A bi-linear model is built by linearly regressing the minimum Dst during a storm versus the solar wind electric field VBs and number density n sw : min(Dst)  Y l,m = x 0 + x 1 *VB s (t-l  t) + x 2 *n sw (t-m  t) The bi-linear correlation coefficient is: r l,m = corr(min(Dst),Y l,m ) A correlation coefficient of 0.0 indicates no correlation, 1.0 indicates a perfect model