The Crowley and Untermann 2013 Global and Hemispheric Volcanic Climate Forcing data is located at the NCDC link. I am not using their forcing estimates "exactly". I am using their data by hemisphere and doing my own adjustments with the goal of estimating the Hemispheric imbalance impact on internal oscillations. The forcing estimates I will be looking for are not "global", but equatorial.
Briefly, I have used a spreadsheet to interpolate the hemispheric "forcing" into monthly pseudo-data with 50% decay over a period of 27 months. The 27 months, 2.25 years +/- .25 is a common internal settling frequency near the equator caused by unbalanced seasonal solar forcing. The 50% decay rate is an initial estimate to be used as a reference. The data is then scaled down by a factor of ten again as an initial estimate to be used as a reference.
The hemispheric volcanic estimates is then combine with the G. Kopp TSI reconstruction from 1610 to start of the SORCE satellite TSI composite data. The TSI data converted to anomaly using the current 1361.1 average TOA TSI. The Kopp-Comp is not scaled priopr to be combined with the interpolated NH and SH volcanic forcing estimates.
Then since filtering is always a matter of personal taste I am using a three tier 27 month cascade filter. This requires removing 27 months from the start and end of any filter time series. This should be similar to the filtering recommended by Gregg Goodman.
Since I am specifically looking for hemispheric imbalances I am using various latitude bands instead of "global" surface temperature data. ERSSTv3b has a variety of pre-selected bands like the Tropics with Extratropics.
This is the results of the 3 tier smoothing on those pre-selected bands. Unfortunately, some of the bands I would like to use are not "standard" for all data sets. The extended tropics, 30S to 30N is used by the Hadley Climate Reseach Unit and can be easily created from the ERSSTv3b 30 degree bands available. The 30S-30N band simplifies calculations since it is approximately 50% of the global surface area and less prone to seasonal noise. Volcanic aerosols have a greater seasonal impact since they tend to scatter solar energy. That don't happen at night and it can impact the seasonal cycle removed in smoothing the "global" surface temperature anomaly data adding to real "noise" generated in data manipulation.
Another reference I use is 0.8dT(CO2) which is 0.8*ln(CO2/CO2ref)/ln(2) where CO2 ref is the average CO2 concentration of the baseline period. For 1980 to 2012 that is ~370 ppmv. The 1980 to 2012 baseline is selected to provide the longest satellite era baseline. Next year it will be 1980-2013. If there are seasonal cycle issues, eventually they will resolve themselves with more data. I am not using an overall baseline because of the differences in uncertainty at the ends of the data. I am also not using 1951 to what ever because that includes more volcanic, solar and internal oscillation noise.
The CO2 reference here is used to compare data uncertainty. It is fit to the less noisy of the two latitudinal band since this is a search for imbalance impact.
Hopefully, this is clear enough for the curious so I can move into the more fun stuff :)