Postcards from the Anthropocene: Pollution's impact on tornadoes

Daniel Rosenfeld and Thomas L. Bell have a new paper out in the Journal of Geophysical Research arguing that the weekly cycle of aerosol pollutants resulting from human activity is likely to blame for the similarly-timed weekly cycle in tornado activity (h/t Luke):

Why do tornados and hailstorms rest on weekends?

This study shows for the first time statistical evidence that when anthropogenic aerosols over the eastern United States during summertime are at their weekly mid-week peak, tornado and hailstorm activity there is also near its weekly maximum. The weekly cycle in summertime storm activity for 1995–2009 was found to be statistically significant and unlikely to be due to natural variability. It correlates well with previously observed weekly cycles of other measures of storm activity. The pattern of variability supports the hypothesis that air pollution aerosols invigorate deep convective clouds in a moist, unstable atmosphere, to the extent of inducing production of large hailstones and tornados. This is caused by the effect of aerosols on cloud drop nucleation, making cloud drops smaller and hydrometeors larger. According to simulations, the larger ice hydrometeors contribute to more hail. The reduced evaporation from the larger hydrometeors produces weaker cold pools. Simulations have shown that too cold and fast-expanding pools inhibit the formation of tornados. The statistical observations suggest that this might be the mechanism by which the weekly modulation in pollution aerosols is causing the weekly cycle in severe convective storms during summer over the eastern United States. Although we focus here on the role of aerosols, they are not a primary atmospheric driver of tornados and hailstorms but rather modulate them in certain conditions. 

For a general audience article check out the write up at National Geographic, here. Of relevance is a variety of prior work on weekly weather cycles such as this paper in Nature and this paper also by Dr. Bell.

Note the graph showing weekly cycles of tornadoes, hail storms, and PMs 10 and 2.5. I think someone just violated a whole bunch of exclusion restrictions...

Are we producing negative wealth?

Environmental Accounting for Pollution in the United States Economy
Nicholas Z. Muller, Robert Mendelsohn and William Nordhaus

Abstract: This study presents a framework to include environmental externalities into a system of national accounts. The paper estimates the air pollution damages for each industry in the United States. An integrated-assessment model quantifies the marginal damages of air pollution emissions for the US which are multiplied times the quantity of emissions by industry to compute gross damages. Solid waste combustion, sewage treatment, stone quarrying, marinas, and oil and coal-fired power plants have air pollution damages larger than their value added. The largest industrial contributor to external costs is coal-fired electric generation, whose damages range from 0.8 to 5.6 times value added.

Recent advances in Lagrangian atmospheric transport models

I liked this brief review article in Eos of some recent advances in the modeling of chemical transport models. Excerpt below, since the article is behind a pay-wall.

click to enlarge

Lagrangian models (LMs) track the movement of fluid parcels in their moving frame of reference. As such, scientists using LMs are forced, in a way, to imagine themselves moving with the parcel and experiencing the effects of advection, turbulence, and changes in the parcel’s environment.

LMs have advanced in sophistication over recent decades, allowing them to be used increasingly for both scientific and societal purposes. For example, it is common practice now for researchers around the world to apply LMs to examine a wide spectrum of geophysical phenomena. Atmospheric chemists can track intercontinental transport of pollution plumes [Stohl et al., 2002] or airborne radioactivity [Wotawa et al.,2006]. By running LMs backward in time [Flesch et al., 1995; Lin et al., 2003], instrumentalists can establish the source regions of observed atmospheric species with high computational efficiency [Ryall et al., 2001]. Therefore, LMs are being used increasingly to quantify sources and sinks of greenhouse gases by combining simulations with observations in an inverse modeling framework [Trusilova et al., 2010]. Such “top-down”emissions estimation is receiving growing acceptance as an independent tool to test the veracity of emissions inventories and to verify adherence to treaties. 

A recent indication of the tremendous societal importance of LMs was their role in predicting the spread of volcanic ash from the eruption of Eyjafjallajökull volcano inIceland. Figure 1 demonstrates the power ofLMs to accurately track the multiday dispersion of a plume as it eventually transforms into a complicated filamentary structure. The example further demonstrates the great potential of applying LMs in combination with data assimilation and inverse modeling to improve source estimates and the simulation of hazardous plumes.

As Lagrangian modeling increases in complexity and popularity, it is imperative to reexamine the physical foundations and implementation aspects of LMs used today.From this, scientists can build a road map of further steps needed to move Lagrangian modeling forward and to ensure its successful application in the future.

As opposed to Eulerian models (which use grid cells that are fixed in place), LMs are known to create minimal numerical diffusion and thus are capable of preserving gradients in tracer concentration. Additionally,Lagrangian integration is numerically stable, meaning that models can take bigger time steps. Furthermore, the Lagrangian framework is a natural way to model turbulence,as it is a closer physical analog to the pathways traced by eddies.

These advantages served as the inspiration from which Lagrangian particle dispersion models (LPDMs) have evolved, in which air parcels are modeled as infinitesimally small particles that are transported with random velocities representing turbulence. LPDMs often track many thousands to millions of particles in three dimensions and are more sophisticated than simple trajectory or puff models. With the availability of computational resources, full three-dimensional LPDM simulations that were expensive to run just a decade ago are now routinely carried out.