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The Arctic Has Barfed

I was scanning my blog stats the other day – partly to see if people were reading my new post on the Blue Mountains bushfires, partly because I just like graphs – when I noticed that an article I wrote nearly two years ago was suddenly getting more views than ever before:

The article in question highlights the scientific inaccuracies of the 2004 film The Day After Tomorrow, in which global warming leads to a new ice age. Now that I’ve taken more courses in thermodynamics I could definitely expand on the original post if I had the time and inclination to watch the film again…

I did a bit more digging in my stats and discovered that most viewers are reaching this article through Google searches such as “is the day after tomorrow true”, “is the day after tomorrow likely to happen”, and “movie review of a day after tomorrow if it is possible or impossible.” The answers are no, no, and impossible, respectively.

But why the sudden surge in interest? I think it is probably related to the record cold temperatures across much of the United States, an event which media outlets have dubbed the “polar vortex”. I prefer “Arctic barf”.

Part of the extremely cold air mass which covers the Arctic has essentially detached and spilled southward over North America. In other words, the Arctic has barfed on the USA. Less sexy terminology than “polar vortex”, perhaps, but I would argue it is more enlightening.

Greg Laden also has a good explanation:

The Polar Vortex, a huge system of swirling air that normally contains the polar cold air has shifted so it is not sitting right on the pole as it usually does. We are not seeing an expansion of cold, an ice age, or an anti-global warming phenomenon. We are seeing the usual cold polar air taking an excursion.

Note that other regions such as Alaska and much of Europe are currently experiencing unusually warm winter weather. On balance, the planet isn’t any colder than normal. The cold patches are just moving around in an unusual way.

Having grown up in the Canadian Prairies, where we experience daily lows below -30°C for at least a few days each year (and for nearly a month straight so far this winter), I can’t say I have a lot of sympathy. Or maybe I’m just bitter because I never got a day off school due to the cold? But seriously, nothing has to shut down if you plug in the cars at night and bundle up like an astronaut. We’ve been doing it for years.

The Blue Mountains, Then and Now

During our time in Australia, my partner and I decided on a whim to spend a weekend in the Blue Mountains. This national park, a two-hour train ride west of Sydney, forms part of the Great Dividing Range: a chain of mountains which stretches from north to south across the entire country, separating the vast outback to the west from the narrow strip of coastal rainforest to the east.

For a region so close to Sydney, the Blue Mountains feel surprisingly remote. You can stand at any number of clifftops, gaze out over a seemingly endless stretch of land, and see no sign of civilization whatsoever. Or you can walk down into the valleys between the mountains and explore the rainforest, a vast expanse of ancient gumtrees that’s managed to hide koalas previously thought to have vanished, and possibly even an escaped panther.

Four months later, when we were safely back in Canada, the Blue Mountains bushfires began. It was October, barely even spring in the Southern Hemisphere. To have fires starting so early in the season was virtually unheard of.

The triggers for the fires were decidedly human-caused: arson, a botched army exercise, and sparking power lines. However, unusually hot, dry, and windy conditions allowed the fires to spread far more quickly than they would have in a more normal October.

To get from the clifftops of Echo Point to the walking trails in the valley below, we took the Giant Stairway, which is exactly what it sounds like. Imagine the steepest and narrowest stairway you can manage, cut into the stone cliff and reinforced with metal, and a handrail which you cling to for dear life. Make it 902 stairs long (by my count, so let’s say plus or minus 5) and wind it back and forth around the cliff. After a few minutes walking down the stairway your knees start to buckle, and you require more and longer breaks, but you still can’t see the bottom.

The exhaustion is worth it simply due to the view.

Sometimes we would see swarms of sulfur-crested cockatoos flying over the treetops hundreds of metres below. They looked like tiny white specks at such a distance, but we could still hear them squawking to one another.

The bushfires of 2013 didn’t affect any of the areas we visited in the Blue Mountains – in fact, none of the main tourism regions were damaged. The main losses occurred in residential areas in and around the Blue Mountains. As of October 19th, 208 houses and 40 non-residential buildings had been destroyed.

Despite the huge amount of property loss, there were only two fatalities from the bushfires. This relatively successful outcome was due to mass evacuations organized by the government of New South Wales. At one point a state of emergency was declared, which authorized police to force residents to leave their houses.

As the fires continued to burn out of control, westerly winds blew the smoke and ash right over Sydney. During sunsets the sky over Sydney Harbour turned a bright orange, giving the illusion of a city built on the surface of Mars.

I had heard about lyre birds, widely considered to be among the best mimics of the animal kingdom, many times before. In an elaborate courtship display, the male lyre bird perfectly imitates the songs of nearly every other bird in the forest, one after another like some kind of avian pop-music mashup. Lyre birds blow mockingbirds right out of the water.

Footage from the BBC of a lyre bird imitating camera shutters and chainsaws seemed too good to be true, but its authenticity was bolstered by a similar story from my friend at the climate lab in Sydney. Her neighbours had been doing renovations, and when they were finished the construction equipment went away but the sounds kept going. That’s when they discovered the lyre bird living in the garden.

We saw three or four lyre birds while hiking in the valley that weekend, but for the most part they just wandered around the forest floor, combing through the leaf litter with an outstretched foot and keeping their beaks firmly shut. It was winter in Australia, after all – not courtship season for most birds. On the last day of hiking, we sat by the side of the trail for a rest and a drink of water, while my partner quizzed me on the local bird calls.

“What kind of bird is making that song?”

“An eastern whip-bird, I think.

“Hang on, it just changed into a kookaburra.

“And now it’s a currawong?”

A few minutes later, a male lyre bird strolled out onto the path ahead of us, showing off his fantastic tail feathers and looking extremely pleased with himself.

It is well known among scientists that human-caused climate change increases the risk of severe bushfires. Spells of hot weather will obviously become more common as the planet warms, but so will prolonged droughts, especially in subtropical regions like Australia. Add an initial trigger, like a lightning strike or an abandoned campfire, and you have the perfect recipe for a bushfire.

The current Australian government, which has a history of questionable statements on climate change, really doesn’t want to believe this. Prime Minister Tony Abbott asserted that “these fires are certainly not a function of climate change, they’re a function of life in Australia”, while Environment Minister Greg Hunt cited Wikipedia during a similar statement. I was actually heartened by these events: the ensuing public outcry convinced me that Australians, by and large, do not buy into their government’s indifference on this issue.

It came as a surprise to nobody in the climate science community, and probably nobody in Australia, that 2013 was Australia’s warmest year on record. The previous record, set in 2005, was exceeded by a fairly significant 0.17°C. Even more remarkable was the fact that 2013 was an ENSO-neutral year. For Australia to shatter this temperature record without the help of El Niño indicates that something else (*cough cough climate change*) is at work.

Would the Blue Mountains bushfires have been so devastating without the help of human-caused climate change? In a cooler and wetter October, closer to the historical average, would the initial fire triggers have developed into anything significant? We’ll never know for sure. What we can say, though, is that bushfires like these will only become more common as climate change continues. This is what the future will look like.

At public hearings on the environmental impacts of proposed oil pipelines, Canadians are no longer allowed to discuss climate change: any testimonials concerning how the oil was produced (“upstream effects”) and what will happen when it is burned (“downstream effects”) are considered inadmissible. This new policy was part of a 2012 omnibus bill by the federal government.

So if we refuse to consider the risks, they don’t exist? Or does this government just not care? I’m not sure I want to know the answer.

See the very thoughtful article by Andy Skuce, a geologist who formerly worked in the Alberta oil sands.

A Simple Stochastic Climate Model: Climate Sensitivity

Last time I derived the following ODE for temperature T at time t:



where S and τ are constants, and F(t) is the net radiative forcing at time t. Eventually I will discuss each of these terms in detail; this post will focus on S.

At equilibrium, when dT/dt = 0, the ODE necessitates T(t) = S F(t). A physical interpretation for S becomes apparent: it measures the equilibrium change in temperature per unit forcing, also known as climate sensitivity.

A great deal of research has been conducted with the aim of quantifying climate sensitivity, through paleoclimate analyses, modelling experiments, and instrumental data. Overall, these assessments show that climate sensitivity is on the order of 3 K per doubling of CO2 (divide by 5.35 ln 2 W/m2 to convert to warming per unit forcing).

The IPCC AR4 report (note that AR5 was not yet published at the time of my calculations) compared many different probability distribution functions (PDFs) of climate sensitivity, shown below. They follow the same general shape of a shifted distribution with a long tail to the right, and average 5-95% confidence intervals of around 1.5 to 7 K per doubling of CO2.

Box 10.2, Figure 1 of the IPCC AR4 WG1: Probability distribution functions of climate sensitivity (a), 5-95% confidence intervals (b).

These PDFs generally consist of discrete data points that are not publicly available. Consequently, sampling from any existing PDF would be difficult. Instead, I chose to create my own PDF of climate sensitivity, modelled as a log-normal distribution (e raised to the power of a normal distribution) with the same shape and bounds as the existing datasets.

The challenge was to find values for μ and σ, the mean and standard deviation of the corresponding normal distribution, such that for any z sampled from the log-normal distribution,





Since erf, the error function, cannot be evaluated analytically, this two-parameter problem must be solved numerically. I built a simple particle swarm optimizer to find the solution, which consistently yielded results of μ = 1.1757, σ = 0.4683.

The upper tail of a log-normal distribution is unbounded, so I truncated the distribution at 10 K, consistent with existing PDFs (see figure above). At the beginning of each simulation, climate sensitivity in my model is sampled from this distribution and held fixed for the entire run. A histogram of 106 sampled points, shown below, has the desired characteristics.

Histogram of 106 points sampled from the log-normal distribution used for climate sensitivity in the model.

Note that in order to be used in the ODE, the sampled points must then be converted to units of Km2/W (warming per unit forcing) by dividing by 5.35 ln 2 W/m2, the forcing from doubled CO2.

Now that the academic summer is over, I have left Australia and returned home to Canada. It is great to be with my friends and family again, but I really miss the ocean and the giant monster bats. Not to mention the lab: after four months as a proper scientist, it’s very hard to be an undergrad again.

While I continue to settle in, move to a new apartment, and recover from jet lag (which is way worse in this direction!), here are a few pieces of reading to tide you over:

Scott Johnson from Ars Technica wrote a fabulous piece about climate modelling, and the process by which scientists build and test new components. The article is accurate and compelling, and features interviews with two of my former supervisors (Steve Easterbrook and Andrew Weaver) and lots of other great communicators (Gavin Schmidt and Richard Alley, to name a few).

I have just started reading A Short History of Nearly Everything by Bill Bryson. So far, it is one of the best pieces of science writing I have ever read. As well as being funny and easy to understand, it makes me excited about areas of science I haven’t studied since high school.

Finally, my third and final paper from last summer in Victoria was published in the August edition of Journal of Climate. The full text (subscription required) is available here. It is a companion paper to our recent Climate of the Past study, and compares the projections of EMICs (Earth System Models of Intermediate Complexity) when forced with different RCP scenarios. In a nutshell, we found that even after anthropogenic emissions fall to zero, it takes a very long time for CO2 concentrations to recover, even longer for global temperatures to start falling, and longer still for sea level rise (caused by thermal expansion alone, i.e. neglecting the melting of ice sheets) to stabilize, let alone reverse.

The Mueller Glacier

Recently I was lucky enough to pay a visit to the South Island of New Zealand. I am actually a Kiwi by birth (that’s why it’s so easy for me to work in Australia) but grew up in Canada. This was my first visit back since I left as a baby – in fact, we were in my hometown exactly 20 years to the day after I left. We didn’t plan this, but it was a neat coincidence to discover.

Among the many places we visited was Aoraki / Mt. Cook National Park in the Southern Alps. It was my first experience of an alpine environment and I absolutely loved it. It was also my first glacier sighting – a momentous day in the life of any climate scientist.

There are 72 named glaciers in the park, of which we saw two: the Hooker Glacier and the Mueller Glacier. The latter is pictured below as seen from the valley floor – the thick, blue-tinged ice near the bottom of the visible portion of the mountain. As it flows downward it becomes coated in dirt and is much less pretty.

Along with most of the world’s glaciers, the Mueller is retreating (see these satellite images by NASA). At the base of the mountain on which it flows, there is a large terminal lake, coloured bright blue and green from the presence of “glacial flour” (rock ground up by the ice). According to the signs at the park, this lake has only existed since 1974.

In the photo above, you can see a large black “sill” behind the lake, which is the glacial moraine showing the previous extent of the ice. Here’s a photo of the moraines on the other side, looking down the valley:

It’s hard to capture the scale of the melt, even in photos. But when you stand beside it, the now-empty glacial valley is unbelievably huge. The fact that it was full of ice just 50 years ago boggles my mind. Changes like that don’t happen for no reason.

A Simple Stochastic Climate Model: Deriving the Backbone

Last time I introduced the concept of a simple climate model which uses stochastic techniques to simulate uncertainty in our knowledge of the climate system. Here I will derive the backbone of this model, an ODE describing the response of global temperature to net radiative forcing. This derivation is based on unpublished work by Nathan Urban – many thanks!

In reality, the climate system should be modelled not as a single ODE, but as a coupled system of hundreds of PDEs in four dimensions. Such a task is about as arduous as numerical science can get, but dozens of research groups around the world have built GCMs (General Circulation Models, or Global Climate Models, depending on who you talk to) which come quite close to this ideal.

Each GCM has taken hundreds of person-years to develop, and I only had eight weeks. So for the purposes of this project, I treat the Earth as a spatially uniform body with a single temperature. This is clearly a huge simplification but I decided it was necessary.

Let’s start by defining T1(t) to be the absolute temperature of this spatially uniform Earth at time t, and let its heat capacity be C. Therefore,

$C \: T_1(t) = E$

where E is the change in energy required to warm the Earth from 0 K to temperature T1. Taking the time derivative of both sides,

$C \: \frac{dT_1}{dt} = \frac{dE}{dt}$

Now, divide through by A, the surface area of the Earth:

$c \: \frac{dT_1}{dt} = \frac{1}{A} \frac{dE}{dt}$

where c = C/A is the heat capacity per unit area. Note that the right side of the equation, a change in energy per unit time per unit area, has units of W/m2. We can express this as the difference of incoming and outgoing radiative fluxes, I(t) and O(t) respectively:

$c \: \frac{dT_1}{dt} = I(t)- O(t)$

By the Stefan-Boltzmann Law,

$c \: \frac{dT_1}{dt} = I(t) - \epsilon \sigma T_1(t)^4$

where ϵ is the emissivity of the Earth and σ is the Stefan-Boltzmann constant.

To consider the effect of a change in temperature, suppose that T1(t) = T0 + T(t), where T0 is an initial equilibrium temperature and T(t) is a temperature anomaly. Substituting into the equation,

$c \: \frac{d(T_0 + T(t))}{dt} = I(t) - \epsilon \sigma (T_0 + T(t))^4$

Noting that T0 is a constant, and also factoring the right side,

$c \: \frac{dT}{dt} = I(t) - \epsilon \sigma T_0^4 (1 + \tfrac{T(t)}{T_0})^4$

Since the absolute temperature of the Earth is around 280 K, and we are interested in perturbations of around 5 K, we can assume that T(t)/T0 ≪ 1. So we can linearize (1 + T(t)/T0)4 using a Taylor expansion about T(t) = 0:

$c \: \frac{dT}{dt} = I(t) - \epsilon \sigma T_0^4 (1 + 4 \tfrac{T(t)}{T_0} + O[(\tfrac{T(t)}{T_0})^2])$

$\approx I(t) - \epsilon \sigma T_0^4 (1 + 4 \tfrac{T(t)}{T_0})$

$= I(t) - \epsilon \sigma T_0^4 - 4 \epsilon \sigma T_0^3 T(t)$

Next, let O0 = ϵσT04 be the initial outgoing flux. So,

$c \: \frac{dT}{dt} = I(t) - O_0 - 4 \epsilon \sigma T_0^3 T(t)$

Let F(t) = I(t) – O0 be the radiative forcing at time t. Making this substitution as well as dividing by c, we have

$\frac{dT}{dt} = \frac{F(t) - 4 \epsilon \sigma T_0^3 T(t)}{c}$

Dividing each term by 4ϵσT03 and rearranging the numerator,

$\frac{dT}{dt} = - \frac{T(t) - \tfrac{1}{4 \epsilon \sigma T_0^3} F(t)}{\tfrac{c}{4 \epsilon \sigma T_0^3}}$

Finally, let S = 1/(4ϵσT03) and τ = cS. Our final equation is

$\frac{dT}{dt} = - \frac{T(t) - S F(t)}{\tau}$

While S depends on the initial temperature T0, all of the model runs for this project begin in the preindustrial period when global temperature is approximately constant. Therefore, we can treat S as a parameter independent of initial conditions. As I will show in the next post, the uncertainty in S based on climate system dynamics far overwhelms any error we might introduce by disregarding T0.

A Simple Stochastic Climate Model: Introduction

This winter I took a course in computational physics, which has probably been my favourite undergraduate course to date. Essentially it was an advanced numerical methods course, but from a very practical point of view. We got a lot of practice using numerical techniques to solve realistic problems, rather than just analysing error estimates and proving conditions of convergence. As a math student I found this refreshing, and incredibly useful for my research career.

We all had to complete a term project of our choice, and I decided to build a small climate model. I was particularly interested in the stochastic techniques taught in the course, and given that modern GCMs and EMICs are almost entirely deterministic, it was possible that I could contribute something original to the field.

The basic premise of my model is this: All anthropogenic forcings are deterministic, and chosen by the user. Everything else is determined stochastically: parameters such as climate sensitivity are sampled from probability distributions, whereas natural forcings are randomly generated but follow the same general pattern that exists in observations. The idea is to run this model with the same anthropogenic input hundreds of times and build up a probability distribution of future temperature trajectories. The spread in possible scenarios is entirely due to uncertainty in the natural processes involved.

This approach mimics the real world, because the only part of the climate system we have full control over is our own actions. Other influences on climate are out of our control, sometimes poorly understood, and often unpredictable. It is just begging to be modelled as a stochastic system. (Not that it is actually stochastic, of course; in fact, I understand that nothing is truly stochastic, even random number generators – unless you can find a counterexample using quantum mechanics? But that’s a discussion for another time.)

A word of caution: I built this model in about eight weeks. As such, it is highly simplified and leaves out a lot of processes. You should never ever use it for real climate projections. This project is purely an exercise in numerical methods, and an exploration of the possible role of stochastic techniques in climate modelling.

Over the coming weeks, I will write a series of posts that explains each component of my simple stochastic climate model in detail. I will show the results from some sample simulations, and discuss how one might apply these stochastic techniques to existing GCMs. I also plan to make the code available to anyone who’s interested – it’s written in Matlab, although I might translate it to a free language like Python, partly because I need an excuse to finally learn Python.

I am very excited to finally share this project with you all! Check back soon for the next installment.

Milestones

You may have already heard that carbon dioxide concentrations have surpassed 400 ppm. The most famous monitoring station, Mauna Loa Observatory in Hawaii, reached this value on May 9th. Due to the seasonal cycle, CO2 levels began to decline almost immediately thereafter, but next year they will easily blow past 400 ppm.

Of course, this milestone is largely arbitrary. There’s nothing inherently special about 400 ppm. But it’s a good reminder that while we were arguing about taxation, CO2 levels continued to quietly tick up and up.

In happier news, John Cook and others have just published the most exhaustive survey of the peer-reviewed climate literature to date. Read the paper here (open access), and a detailed but accessible summary here. Unsurprisingly, they found the same 97% consensus that has come up over and over again.

Cook et al read the abstracts of nearly 12 000 papers published between 1991 and 2011 – every single hit from the ISI Web of Science with the keywords “global climate change” or “global warming”. Several different people categorized each abstract, and the authors were contacted whenever possible to categorize their own papers. Using several different methods like this makes the results more reliable.

Around two-thirds of the studies, particularly the more recent ones, didn’t mention the cause of climate change. This is unsurprising, since human-caused warming has been common knowledge in the field for years. Similarly, seismology papers don’t usually mention that plate tectonics cause earthquakes, particularly in the abstracts where space is limited.

Among the papers which did express a position, 97.1% said climate change was human-caused. Again, unsurprising to anyone working in the field, but it’s news to many members of the public. The study has been widely covered in the mainstream media – everywhere from The Guardian to The Australian – and even President Obama’s Twitter feed.

Congratulations are also due to Andrew Weaver, my supervisor from last summer, who has just been elected to the British Columbia provincial legislature. He is not only the first-ever Green Party MLA in BC’s history, but also (as far as I know) the first-ever climate scientist to hold public office.

Governments the world over are sorely in need of officials who actually understand the problem of climate change. Nobody fits this description better than Andrew, and I think he is going to be great. The large margin by which he won also indicates that public support for climate action is perhaps higher than we thought.

Finally, my second publication came out this week in Climate of the Past. It describes an EMIC intercomparison project the UVic lab conducted for the next IPCC report, which I helped out with while I was there. The project was so large that we split the results into two papers (the second of which is in press in Journal of Climate). This paper covers the historical experiments – comparing model results from 850-2005 to observations and proxy reconstructions – as well as some idealized experiments designed to measure metrics such as climate sensitivity, transient climate response, and carbon cycle feedbacks.