Reducing atmospheric soot a leveraged global warming play

There's been a lot of discussion of late regarding the contribution of soot and other particulate to global warming. Washington's blog did a nice job of summarizing why soot reduction is important to climate efforts. NASA has been studying the effects of soot for some time. And when Scientific American, BusinessWeek and U.S. News wrote about it, it got me thinking...

In specific, I wondered how much warming comes from areas which are "susceptible" to melting, as a proxy to measure soot's potential influence (light absorption, inducing precipitation, and resulting exposure of darker surfaces under snow & ice)? Fortunately, it's reasonably easy to do a proxy study after a trip to NASA's Goddard Institute for Space Studies to download some GISTEMP data and after doing some programming handywork.

The findings are telling! Warming is accelerated much more (2x or 3x) in areas which fluctuate on either side of freezing, and no so much in areas which remain frozen or never freeze. This may warrant more serious consideration of soot's effects.

What I calculated was the extent (in terms of time and degrees Celsius) spent on both sides of the freezing point for each station. If for example, a given station in the GISTEMP list experienced temperatures always below (or always above) freezing, then it is taken to have no vulnerability to soot-induced thawing. For each of a station's monthly datapoints, I summed up separately the distances above and below freezing and took the minimum of the two. The result is an approximation of the potential for warm days to thaw the freezing from the colder days. Below is a graph showing the results, where I binned and averaged stations together that fell into various "thawing vulnerability" buckets (X-axis), along with corresponding averages within each bucket of 100-year warming trends for the related stations (Y-axis):


The bin labeled "None" holds stations which had all temperature readings below or all above freezing, but not a mix. As the graph progresses to the right, the X-axis shows increasing amounts of vulnerability to thawing, as an increasing amount of freezing temperatures were offset by thawing temperatures (and shown parenthetically is the number of stations which were binned into the designated range). To clarify/exemplify the methodology, if 4 months of data read [-5, -5, +5, +10], then I would sum 10 degrees of freezing and 15 degrees of thawing, resulting in only 10 degrees worth of "thaw vulnerability". Over 4 months that would be (+10 / 4) = 2.5° C/mo. That would dictate the bin in which I would then include the station's normal 100-year warming trend (as calculated by a least squares regression across the most recent 100 years of the station's temperature data). And within each bin I used a simple average of all included stations' 100-year trends, the Y-axis above.

As can be seen from the graph, stations which have more "thaw vulnerability", record substantially more average global warming trends. I added the overall 100-year warming trendline on the graph, the average of participating stations as if they were placed all in one bin. Apparently, "thaw vulnerable" stations are leveraged to global warming at 2x to 3x overall rates. And so, a reasonable inference may be that reducing soot is a highly leveraged way to reduce global warming as soot would have the most impact on areas which are vulnerable to its thawing effects (and those areas happen to be leveraged to warming trends).

As "soot capture" is relatively less expensive than "carbon capture", and given its potential leverage, it also makes for a strong investment case. And by the way, reducing soot comes with an enormous "knock-on effect" of reducing a massive amount of health-care related issues surrounding respiratory problems.

Methodology note: I screened for stations which had a 100-year data series which ended within the current decade (assuming the most recent 100 years would likely yield more accurate readings than otherwise). Fwiw, the chart looks fairly similar if I use recent 50 or 75 year series instead. I also screened out stations in areas which were marked as having 2mm or greater populations (to remove "heat island" effects), though this didn't matter very much.

Disclosure: no positions