Light and Noise in the Anthropocene

There is a companion article exploring the issue from the perspective of environmental monitoring over at ArkFab.

Human influence on the environment has increased dramatically over the last 10,000 years, to the point that some geologists have argued that human reworking of the earth defines a new geologic age, The Anthropocene. (Zalasiewicz et al, 2008) Much of the focus has been on relatively robust, tangible changes in biogeochemistry. Examples include:

• the climate change and ocean acidification resulting from the addition of fossil fuel emissions to the carbon cycle.

• megafaunal extinction, accelerated erosion (Zalasiewicz et al, 2008) and nitrogen fixation resulting from the spread of intensive subsistence patterns
• the loss of stratospheric ozone resulting from the release of novel chlorofluorocarbons

However, fleeting and less tangible effects are also important. Two examples are:

• the light pollution resulting from urbanization and transportation infrastructure
• changes in the acoustic environment resulting from direct addition of sonic energy and memes, as well as indirect sources.

A year-long composite view of the earth at night, showing human light generation. White lights are cities; blue lights are fishing boats; green lights are natural gas flares, and red lights are ‘ephemeral light sources’, interpreted as fires. Image from  NOAA National Geophysical Data Center – click for source + discussion.

Light pollution, the scourge of urban astronomers, is a well-accepted phenomenon with serious consequences. A 2004 review begins:

In the past century, the extent and intensity of artificial night lighting has increased such that it has substantial effects on the biology and ecology of species in the wild. We distinguish “astronomical light pollution”, which obscures the view of the night sky, from “ecological light pollution”, which alters natural light regimes in terrestrial and aquatic ecosystems. Some of the catastrophic consequences of light for certain taxonomic groups are well known, such as the deaths of migratory birds around tall lighted structures, and those of hatchling sea turtles disoriented by lights on their natal beaches. The more subtle influences of artificial night lighting on the behavior and community ecology of species are less well recognized, and constitute a new focus for research in ecology and a pressing conservation challenge. (Longcore & Rich 2004)

The amount of sonic energy released by human activity is recognized as an urban nuisance as well as an occupational safety concern. It also has recognized ecological effects: urban European robins have begun singing at night, when they have less acoustic competition. (Fuller et al 2007) Frogs have begun changing the pitch of their croaks in order to talk over traffic noise (Paris et al 2009)  In addition to sonic energy, human activity has released sonic memes into the environment. A meme is a self-replicating information pattern; jokes and computer viruses are two examples of memes. A person or computer acquires a meme and then spreads it, through retelling or infected emails. Sonic memes, such as ambulance sirens and cellphone ringtones, have been picked and repeated by songbirds. (Stover 2009) This is very interesting: human memes, the basis of Richard Dawkins’ ‘extended phenotype’ concept, have organically extended into other animals’ extended phenotype. (Recent reports of dolphins mimicking human speech are also very interesting in this context. The reverse flow also occurs, as animal communications are repackaged as ringtones or ambient music.)

Global Warming Killed My Friend’s Dog

I sit at the Carrboro Really Free Market, on the first caturday in July. I sit in the shade and the banners are blowing lazily in the breeze; still it’s nearly 100 degrees; the humidity jacks it up to 103, and the breeze is welcome but ineffectual. Air quality is ‘Orange’: ozone levels ‘may approach or exceed unhealthy standards.’ A parade is planned but only a handful want to move; I’m definitely not going back out. I keep a cold pack in my bag to refridgerate my computer, but I worry that the condensation from the humid air will offset the benefits of a cool processor. Whatever; I need chill tunes if I’m going to bike around in this weather.

A constant source of frustration for me is communicating the local importance of global problems. Climate change is real, and it’s serious – but at the same time it can be intangible and diffuse. I live in the North Carolina piedmont, hours away from the beach. I can explain to my neighbors that ocean acidification is a serious problem, that the demise of coral reefs would mean the loss of food and resources for the third world. But even if they believe me, even if they agree that it’s bad news, it can still be hard to see how global warming effects them personally, as a homeowner, a farmer, a pet owner or the parent of a young child, a worker with a daily commute. How does carbon dioxide pollution impact North Carolina and beyond?

“rock me momma like the wind and the rain//rock me momma like a hurricane“

Let’s start at the beach. An obvious problem here is rising sea levels. As the ocean heats up, it expands; as ice heats up, it melts and drains into the sea (or, it calves, falls into the sea, and then melts). This causes a slow but steady rise in sea level. Sea level is predicted to rise by a meter (maybe more) over the 21st century, and 4-6 m over the next few centuries. This is bad news bears – in many coastal counties, more than 10% of the population lives within a meter of high tide. The threat to homes and businesses is worsened by storm surges, which will also be higher as the seas rise [Strauss 2012]. North Carolina has a unique relationship with sea level rise. The coastal salt marshes have recorded 2,100 years of sea level history in their smelly mucky sediments; the ocean stayed relatively stable up until about 1880, when it began to creep upwards. The average rate of sea level rise for the NC coast over the 20th century was ‘greater than any other persistent, century-scale trend’ in the marsh’s memory. During this time period, the seas rose 3.5 times faster than they did even during the Medieval Warm Period, and regional sea level rose faster than model predictions over the 20th century (though the uncertainties involved overlap.) [Kemp et al. 2011]

Sea level rise at the North Carolina coast over the past two millenia. Things are pretty stable, even during climatic episodes like the MWP – until we get to the late 19th century. Then the hockey stick gets hockey stuck. GIA is glacial isostatic adustment, an additional factor which must be considered. It deals with the fact that the North American landmass is still rebounding from the weight of Ice Age glaciers. Image from Figure 2 of Kemp et al. 2011

But what’s really special is the state legislature’s reaction to the rising tide. This June, the NC Senate infamously outlawed the use of accelerating sea level scenarios in planning urban development. The usual astroturfing seems to be at play: the money trail for this legislation leads back to the Locke Foundation; spokespeople and nonprofits proliferate to establish a consent factory. These hijinks are as cynical as they are asinine: not only is global sea level rise accelerating [Church & White 2006], but North Carolina is at the southern end of a ‘hotspot’ where the sea is rising 3-4 times as fast as the global average, [Sallenger et al. 2012] putting its coastline at exceptional risk. The legislation is also a lovely inversion on a popular skuptik trope, that of an authoritarian scientific Orthodoxy dictating Truth and squelching dissidents. In this case, it’s the state government which has declared which climatic scenarios are kosher and which are thought crimes, favoring the least alarming. The proposed law would not merely declare what course sea level rise will take in the years to come, but also prohibit state planning agencies from considering alternatives. Not content to legislate straight marriage as the only valid relationship, the Old North State is considering straight lines as the only acceptable graph.

“You need to move indoors right now.”

– Meteorologist Dr. Forbes, on Philadelphian extreme weather.

It’s Friday night, 29 June, and forecasts of a sweltering weekend have already started to come true. I am sifting through hardware at work when the power goes out.

Graphing Out Loud: ups and downs

A while back, we started looking at a poorly thought-out article from the website C3Headlines. C3 is starting to make a name for itself as a goldmine of climate comedy- their claims have recently been addressed at Tamino and SkepticalScience.

We’re going to keep digging into C3‘s claim that carbon dioxide concentrations have been increasing linearly over the 20th century. They seemed to draw this claim by eyeballing the graph of CO2 concentrations and qualitatively describing them as linear, apparently using the inset in their first figure to compare linear, quadratic, and exponential trends. This is a faulty method: it’s an elementary fact of calculus that ANY smooth curve, when viewed appropriately, will appear linear. The point has already been made but it’s worthwhile to keep looking because there are some interesting graphical follies at play; examining them further might help us understand how and why graphs are misunderstood.

Figure 1: From C3Headlines’ article on “The Left/Liberal Bizarro Anti-Science Hyperbole”, which claims that CO2 concentrations are increasing linearly. Click to read it, if you dare…

C3‘s second graph in this article measures the change in atmospheric CO2 by calculating a month-to-month percentage change. It’s not entirely clear why they are using a percent change, rather than the standard practice of expressing rate of change as concentration change per year (like the source of their data uses). Whereas ppm/year is an absolute measure, each datum generated by the percentage-change method depends strongly upon the value of the previous month. As a measure of long-term rate of change, it is a bit questionable.

My primary concern, though, is with their use of monthly data in the first place. In my last article, we noted that, without explanation, C3 confined their focus to January CO2 concentrations. Were they consistent, they’d also look at January rates of change – of course, doing so might lead to unacceptable conclusions.

 Figure 2. Rates of CO2 accumulation have been calculated for the month of January, consistent with earlier investigation of January CO2 concentration. Over the period of observation, rates have increased at a significant (P~0.0005) acceleration of 0.11 ppm/year^2. Monthly rates throughout this article have been calculated by considering the change in CO2 between adjacent months, and assuming that a month is 1/12 of a year. Interpolated values of CO2 were used to avoid annoying data holes early in the record.

Instead, they look at the rate of change for every single month on record. Why do I find that problematic? Well, let’s look at the full record, with monthly resolution:

temperature aNOMalies

If you are new to climate science, you might be wondering what, exactly, this ‘temperature anomaly’ thing is that you keep hearing about. I know I was a bit confused at first! This post explains the concept, using a real-world example.

Absolute temperatures (yearly averaged) from two sites in the UK: one urban (St. James Park, green) and one rural (Rothamsted, red). Although the urban site is consistently warmer, the two sites show the same warming trend. But is there a way to compare them directly? Data from Jones et al. 2008, kindly provided by Dr. Jones.

Cities tend to be warmer than their surrounding countrysides, a fact known as the urban heat island effect (UHI). This occasionally is offered as an alternative explanation for greenhouse warming, but it fails on closer inspection. We can use data from Jones et al. (2008) [PDF] to see one reason UHI can’t explain observed warming. One time series is from St. James Park, in the city of London; the other is from nearby Rothamsted, a rural site some tens of miles away. As you can see, the urban location is consistently about 2 C warmer; however, the warming is nearly identical at both sites (a strongly significant 0.03 deg C/year). Jones et al. note:

“… the evolution of the time series is almost identical. As for trends since 1961 all sites give similar values …  in terms of anomalies from a common base period, all sites would give similar values.”

This gives us a hint about what a temperature anomaly is:

You can’t predict the weather

Regarding climate models, physician and science fiction writer Michael Crichton had this to say:

“Since climate may be a chaotic system—no one is sure—these predictions are inherently doubtful, to be polite.” (Aliens Cause Global Warming)

What does he mean when he says that climate may be chaotic, and what impact does this have on climate modelling?

Flash back to the early 1960s. Meteorologist Edward Lorenz was studying a bare-bones weather model, consisting of three differential equations. Give the model an initial state and the differential equations would describe how the state changes over time, in much the same way that you can predict where Johnny will be in three hours’ time, given that he starts in Chicago and is driving west at 60 miles per hour. The hope was that with a big enough computer, a powerful enough model, and an accurately measured state of the atmosphere, the weather could one day be predicted far in advance.

Lorenz, the story goes, found a run of the model which interested him, and sat down to replay the simulation. He entered the initial conditions and set the model in motion, only to watch in bewilderment as the replay rapidly diverged from the original simulation.

"From nearly the same starting point, Edward Lorenz saw his computer weather produce patterns that grew farther and farther apart until all resemblance disappeared" (Image and caption from Chaos: Making a New Science, by James Gleick, 1987, p.17)

Lorenz tore his code apart looking for the error, only to realise that the error had been in his assumptions. In a distinctly Crichtonesque twist, the computer worked with numbers to six decimal places (0.123456) but only printed out values to three decimal places (0.123) in order to save space. It was these shortened number which Lorenz entered as the initial conditions for his model. Surely those last digits were inconsequential; after all, they were but a few hundred parts per million, comparable to the atmospheric concentration of the trace gas carbon dioxide.

Oh, but the consequences! Its roots stretched back to earlier anomalies and the term ‘chaos’ would not be introduced for another decade, but it was Lorenz’s observation which heralded the beginnings of chaos theory.

Lorenz had discovered that even very small changes in the state of a chaotic system can quickly and radically change the way that the system develops over time. This property is known as extreme sensitivity to initial conditions, also called the ‘Butterfly Effect’ because it suggested neglecting an event as small as the flapping of a butterfly’s wings could be enough to derail a weather forecast. There is more to chaotic systems than the Butterfly Effect, but this characteristic is one of their best know properties. Lorenz’s work put and end to hopes of long-term weather forecasting. The state of the atmosphere could only be known so well, and even the smallest of imprecisions would lead the simulations to catastrophic failure.

‘Nobody believes a weather prediction twelve hours ahead. Now we’re asked to believe a prediction that goes out 100 years into the future? And make financial investments based on that prediction? Has everybody lost their minds?’ – Crichton

But does chaos theory signal doom for climate modelling? Stay tuned for part II….