But in three party-line votes, the Republican members of the committee unanimously opposed them. To be clear, these amendments had no teeth whatsoever. No regulatory anything. Still, the comittee’s Republicans felt obliged to vote them down.

Of late, allusions to Soviet Russia have become quite popular in certain quarters.  While I suppose that progressive taxation and organized labor might look like Stalinism if  you squint really hard, it’s also worth noting that Stalin was incredibly multifaceted when it came to repression.  Not to go all Glenn Beck on you here—but are there other kinds of commie sleeper agents, besides the liberal ones we already know about? I submit that these comparisons can cut both ways.

For instance, did you know that during Stalin’s tenure, the law of large numbers and the idea of random deviations were declared “false theories?” If you haven’t heard of them, they are two of the bedrock mathematical principles that underlie all of statistics.  Or that Stalin embraced a biological fraud who denied the genetic theory of inheritance, and imprisoned or killed geneticists who disagreed?  There are many more examples of the Party “changing” science where it conflicted with the official ideology.

One guess which side won that conflict in the long run.

First, we will need rainfall records to line up agains the SOI. These are available from the Australian Bureau of Meteorology here. Let’s look at the period from 1970 to the present:

Monthly Queensland rainfall (in millimeters), 1970-2010.

Very spiky. Looking closer, the spikes are all occurring mid-summer down under (that is, in December, January, and February). Rainfall in Queensland is extremely seasonal. A few of the spikes are higher than the rest, most notably in 1974, when there were also major floods in Queensland. On further reflection, it doesn’t make much sense to compare the SOI directly with rainfall, since the rain changes more from summer to winter than it does between years. We’ll want to remove the average seasonal cycle from the data to leave us with anomalies, or departures from that average.

The graph below shows each year of rainfall since 1900 plotted as a separate line over the twelve months of the year, giving us a sense of how the annual cycle of rainfall varies from year to year. From these we can calculate the average monthly rainfalls, shown by the black line.

Queensland rain climatology. Each line shows one year of rain totals over the 12 months.

This graph shows us several things. January is the rainiest month, and August the driest. Rainfall is also much more variable during the Austral summer (November-March) than it is during the rest of the year. We can also compare particular years to the mean. The last year of disastrous flooding, 1973-74, is shown in red (the line starts in the middle in July 1973, then runs off the right edge and wraps around to start 1974 on the left). November and December 1973 were among the rainiest on record. January 1974 set the all-time record, half again as rainy as the next highest. Rainfall in 2010 has been above average since July, and well above average since September. Last month was the rainiest December on record.

Now that we have the average rainfall for each month (a.k.a. the “climatology”), we can subtract it from the data to get anomalies, which then look like this (again, just plotted from 1970-present):

Monthly rainfall anomalies 1970-2010. Monthly totals minus the monthly averages.

Still spiky, but with no immediately obvious periodicity. Let’s go ahead and do a simple regression of Queensland rainfall on the SOI:

Rainfall anomaly vs. SOI, with best-fit regression line.

Aha! There is a positive correlation—higher values of the SOI are associated with above-average rainfall in Queensland. This result is highly significant (p << 0.001), meaning that it is very, very unlikely this result would occur by chance. Still, it isn't a particularly strong relationship. There is still a lot of scatter around the best-fit line, and our simple regression model only explains about 13% of the variability. There are evidently (and not surprisingly) other, more complicated dynamics going on.

Finally, let's dig a little deeper into this correlation. It would be reasonable to wonder if rain perhaps lags the SOI by some amount of time. Maybe it takes a month or two for the oceanic and atmospheric conditions expressed in the SOI to manifest themselves as increased rain in Queensland. To check this idea, we use a statistical tool called the cross-correlation function.

Cross-correlation of rainfall anomaly with SOI. When the lag is negative, SOI leads rainfall. When it is positive, rainfall leads SOI.

The graph above shows the strength of the correlation of rainfall with SOI as a function of the time lag between the two series. When the lag is negative, rainfall is regressed on previous values of SOI. When the lag is positive, SOI is in effect regressed on previous values of Queensland rainfall. When the bars stick out past the dotted blue lines, they are significant at the 5% level.

So what does this show? SOI and rainfall are positively correlated for about seven months in either direction. The correlations look a bit stronger on the left side—that is, the SOI is a better advance predictor of rain than vice-versa. But generally, SOI doesn’t look like a super-precise predictor of rainfall from month to month, even if it is a good indicator at a seasonal or yearly scale.

I’ll close out with a snippet from the Australian poet Dorothea McKellar. I’m told that little Aussies learn this in school the same way I learned Longfellow growing up in Boston…Listen, my children, and you shall hear, of the midnight ride of Paul Revere…er, ahem.

I love a sunburnt country,
A land of sweeping plains,
Of ragged mountain ranges,
Of droughts and flooding rains.

—————
NOTES

First, I should note that this really is quick-and-dirty climatology. I’m not an expert in this, nor am I familiar with this part of the world, and I didn’t go that far beneath the surface here, statistically speaking. I could dig deeper, but I have other things I should be doing, such as my thesis and laundry. Take it with a grain of salt.

All data analyzed here came from the Australian Bureau of Meteorology, as mentioned above. With the help of some Python magic, I compiled them into two data files, soi.csv and qlnd_rain.csv. Each has three columns for the year, month, and data value. All graphs and analysis in this post were done using R. The code, for those interested, is here.

The winter of my freshman year, I took “Introduction to Earth Systems”, the eponymous introductory course for my major. Each lecture was by a different professor, covering his or her area of expertise. Schneider gave one of the talks on global warming. I was familiar with the basics of climate change from middle school science class and reading the newspaper. But, because of where I was coming from personally, I was predisposed to view it as cut and dried, right and wrong politics. To see not just the policy, but the science of climate change, in moral terms—one more reason why I was right and They were wrong.

Schneider’s lecture allowed me no such luxury. Andy Revkin was right to call him “caustically honest”—within five seconds of taking the podium, he was informing us of the immense uncertainty as to the extent and consequences of manmade climate change. Not just the uncertainty, but the impossibility of certainty when forecasting the climate system 50 or 100 years in the future. “We can’t know what will happen,” he said, “because it hasn’t happened yet.” I was expecting certain science followed by a call to arms. I did not receive it, at least not in the form I was expecting.

For the next 50 minutes, he took us on a wide-ranging tour of the science and politics of global warming, from black-body radiation to why certain skeptics were willfully ignorant, paid hacks, or both. That caustic honesty was present throughout. It was electric. I was unsettled to be thrown into a topic on a level that was both more technical and less certain than any previous experience. Looking back, though, his perspective worked its way into my head. It’s something like the perspective I tried to convey in this post: that dealing with uncertainty about frighteningly important questions is unnerving, to say the least. But ultimately, gauging that uncertainty while trusting in the facts you do know is profoundly liberating. For his part in leading me to that realization, I am truly indebted to Professor Schneider.

Closing his lecture, as the students were already ruffling their papers and bagging their notebooks, he left us with his three commandments of communicating science, which I have yet to forget. “Know thy audience,” he said. “Know thy self. And know thy stuff.”

You will be missed, Dr. Schneider.