I doubt you're wrong and I think anyone in the industry would anecdotally confirm both that there is variations between programmers and devs, but I think the issue is much more interesting than that. The article you linked brings of an interesting point: you are not likely to find a good metric of productivity that fits all definitions of a developer. The 6 archetypes (ok, 5, because one is a joke) have different criteria - the workhorse may produce more code, but the innovator doesn't because he's thinking of new, crazy ways to do things. There are different pathways to being a good coder and not everyone agrees what they are.
This probably applies to the variance in your day to day work, too. You can measure this by, say, KLOCs, but that probably is only a facet of your productivity. Improving this will improve your productivity, but the kicker is that if your metric/model of productivity doesn't include factors out of your control (meetings, for example) but that are highly correlated with your the factors that are (KLOCs), you may
The original paper measures problem solving on simple, quantifiable puzzles. It's hard to do that in the real world, so you could use the warm and fuzzy approach of giving yourself a subjective judgment (or your manager) of how productive you were that day - this is likely to be a better measure given the difficulties quantifying this.
If you want to measure it yourself, the answer is probably specific to you and your workplace. Keep a log for a few weeks then have some fun dicing up your data. A few ideas: to answer your basic question, if you partition the data into two sets randomly and perform a t-test, you can get an idea whether there is day to day variability. You could bucket your days by day of week and do an ANOVA or pairwise t-tests to see if there are differences on days of the week.