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.
Posted in Research Blogging | Tagged climate change, climate models, education, global warming, math, numerical methods, programming, science, stochastic | 9 Comments »
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.
Posted in Mitigation and Policy, News and Reports, Research Blogging | Tagged andrew weaver, british columbia, canada, carbon dioxide, climate change, climate models, emic, global warming, green party, john cook, mauna loa, skeptical science, the consensus project, UVic | 6 Comments »
It seems that every post I write begins with an apology for not writing more. I’ve spent the past few months writing another set of exams (only one more year to go), building and documenting two simple climate models for term projects (much more on that later), and moving to Australia!
This (Northern Hemisphere) summer I have a job at the Climate Change Research Centre at the University of New South Wales in Sydney, which has a close partnership with the UVic Climate Lab (where I worked last summer). I am working with Dr. Katrin Meissner, who primarily studies ocean, carbon cycle, and paleoclimate modelling. We have lots of plans for exciting projects to work on over the next four months.
Australia is an interesting place. Given that it’s nearly 20 hours away by plane, it has a remarkably similar culture to Canada. The weather is much warmer, though (yesterday it dropped down to 15 C and everyone was complaining about the cold) and the food is fantastic. The birds are more colourful (Rainbow Lorikeets are so common that some consider them pests) and the bats are as big as ravens. Best of all, there is an ocean. I think I am going to like it here.
Posted in Research Blogging | Tagged australia, canada, ccrc, climate change, global warming, science, sydney, unsw, UVic | 4 Comments »
The back gardens of Mayflower, Arkansas aren’t looking too good:
Yes, that’s oil. Canadian oil, no less. You’re welcome.
I’ve heard surprisingly little about this event, which occurred when an Exxon Mobil pipeline ruptured on Friday. It appears that the press have limited access while the cleanup crews are at work. National Geographic had a good piece, though.
Call me cynical, but I think the Canadian media are purposely keeping quiet on this one. It’s a very inconvenient time for a pipeline to burst, given that all levels of government and industry are pushing for Keystone, Northern Gateway, Energy East, etc., etc.
News of this event is largely relying on Mayflower citizens leveraging social media. There’s no way to verify their photos and videos, but they’re striking nonetheless. Here’s a video of the situation on a residential street – note the lack of cleanup crews.
The oil is going straight into the storm drain, the man in the video says, which makes me shudder. I don’t know anything about Mayflower’s stormwater system, but where I live those storm drains are about three steps removed from the Red River. Once oil got in there, I can’t imagine it ever getting out.
I find it puzzling that the negative impacts of pipelines are so often catalogued as “environmentalists’ problems” in the Canadian media – here’s a typical example. In reality, they’re everyone’s problems. Environmentalists (as much as I detest that label) are just the people who realize it. We are not a special interest group; we represent everyone. When it comes to disasters, from short-term spills like the one in Mayflower to millennial-scale impacts like climate change, Canadian oil will affect everyone indiscriminately.
Side note: Sorry I have been so absurdly quiet recently. I am busy building two climate models – just small ones for term projects, but so enjoyable that everything else is getting neglected. I’ll be posting much more on that in about a month.
Posted in Media and the Public, News and Reports | Tagged canada, climate change, energy east, environment, exxon, global warming, keystone, mayflower, media, northern gateway, oil spill, pipelines | 10 Comments »
Last week I was lucky enough to attend the Second Workshop on Coupling Technologies for Earth System Models, held at the National Center for Atmospheric Research (NCAR) in Boulder, Colorado, USA. I was excited just to visit NCAR, which is one of the top climate research facilities in the world. Not only is it packed full of interesting scientists and great museum displays, but it’s nestled in the Rocky Mountains and so the view from the conference room looks like this:
Many of the visitors would spend large portions of the coffee breaks just staring out the window…
The conference was focused on couplers – the part of a climate model that ties all the other components (atmosphere, ocean, land, etc.) together. However, the presentations covered (as Rob Jacob put it) “everything that physical scientists don’t care about unless it stops working”. Since I consider myself a physical scientist, this included a lot of concepts I hadn’t thought about before:
- Parallel processing: Since climate models are so big, it makes sense to multitask by splitting the work over many computer processors. You have to allocate the right number of processors to each component, though: if the atmosphere has too many processors, it will finish its timestep too quickly and sit there waiting until the ocean is done, and vice versa. This is called load balancing, and it gets very tricky as soon as the number of components exceeds two.
- Scalability: The more processors you use, the faster the model runs, but the speed has diminishing returns. If you double the number of processors, you won’t quite double the speed, particularly if the number of processors exceeds 104 (a setup which is becoming increasingly affordable for large research groups). Historically, the coupler has not been a code bottleneck (limiting factor for model speed), but as the number of processors gets very large, that scenario is changing. We have to figure out the most efficient way to couple many small components together, so that climate model speed can continue to keep up with advances in computer hardware.
- Standardization: Modelling groups across the world are communicating with each other more and more, and using each other’s code. Currently this requires a lot of modifications, because every climate model has a different structure. Everyone seems to agree that it would be great to have a standard interface that allowed you to plug any combination of components together, but of course everyone has a different idea of what that standard should be.
- Fortran is still the best language for climate models, believe it or not, because it is the fastest language for the kinds of operations required. If a modern, accessible language like Python could compete based on speed, you can bet that new climate models like MPAS would use it.
I was at the conference with Steve Easterbrook and his new M.Sc. student Daniel Levy, presenting our bubble diagrams of model architecture. (If you haven’t already, read my AGU poster schpiel first, or none of this will make sense!) As interesting and useful as these diagrams are, there were some flaws in our original analysis:
- We didn’t use preprocessed code, meaning that each “model” is actually the code base for many different model configurations. So our estimate of model complexity based on line count is biased towards models which are very configurable, but might not actually be very complex. We can fix this by choosing specific configurations of each model (for consistency, the configuration used in CMIP5 or the equivalent EMIC AR5 intercomparison project) and obtaining preprocessed code from the corresponding institutions.
- We sorted the code into components (eg atmosphere) and sub-components (eg atmospheric aerosols) based on folder structure, which might not reflect the hierarchy of routines formed at runtime. Some modelling groups keep their files very organized, but often code from different parts of the model was mixed together, and separating it out was very much a judgement call. To fix this, we can sort based on the dependency structure (a massive tree graph showing which routines call which): all the descendants of the atmosphere driver are part of the atmosphere component, and so on.
- We made our diagrams in Microsoft PowerPoint, which is quite limited, and didn’t allow us to size the bubbles so their area was perfectly proportional to line count. Instead, we just had to eyeball it. We can fix this by using Adobe Illustrator, which is much more advanced and has this capability.
So far, we’ve repeated the analysis for the UK Met Office Model, version HadGEM2-ES. I created the dependency structure by going manually through every file and making good use of grep, which took hours and hours (although it was a nice, menial way to avoid studying for my courses!). Daniel is going to write a Fortran parser to make the job easier next time around. In the meantime, our HadGEM2-ES diagram is absolutely gorgeous and wonderfully accurate:
I will post future diagrams as they become available. We think the main use of these diagrams will be as communication tools between scientists, so they are free to use with attribution.
Just a few more weeks of classes, then I can enjoy some full-time research. Now that I’ve had a taste of being a proper scientist, it’s hard to go back!
Posted in Research Blogging | Tagged climate change, climate models, colorado, computers, fortran, global warming, ncar, parallel processing, science | 4 Comments »
Me: Yes, hello?
Caller: Hi, I’m from the local paper.
Me: Okay. (We get these calls several times a week. It’s getting kind of tiresome.)
Caller: Do you currently receive the paper?
Caller: Would you like to become a subscriber?
Me: No, thank you.
Caller: Well, have you ever read it?
Me: Yes, and that’s why I don’t want to buy it.
Caller: Say again?
Me: I’m not happy with your science coverage.
Me: Yes. I’m a scientist and I’m not happy with the quality of science journalism in the paper.
Caller: Well, I didn’t write it.
Me: I know. I’m just telling you why I don’t want to buy it.
Caller: Is it anything in particular? I mean, you say “science”…
Me: Climate science, in particular.
Caller: Climate science.
Caller: Was it a specific article?
Me: No, it was a pattern of articles over several years, relating to climate change. I’m a climate scientist and your coverage of this issue was so far off base that I could no longer support the paper.
Me: Thanks for your interest. Please don’t call again.
End of call
Posted in Musings | Tagged climate change, global warming, journalism, science | 11 Comments »
Sometimes I look back to 2010 and wonder how we all got through it. I remember the stomachache I’d get every time I opened a newspaper, wondering what awful lies had been printed about climate science that day. I remember the disdain with which people treated the science I love and the scientists I look up to. 2010 was the year when climate change conspiracy theories went mainstream, and the year when the whole issue was a lump of dread I carried around in my pocket.
Things are easier now. The climate system is in really bad shape (and it can only get worse from here), but somehow I find this fact easier to deal with than the judgement of well-meaning, but highly misinformed and misled, people. Maybe this is because I find human conflict scary but graphs and numbers comforting. Or maybe I am just too good at thinking of model simulations as hypothetical.
In my own way I am in denial, because when I think about my future I always picture a world without climate change.
Regardless, the attacks on our integrity have largely abated, and everything feels so peaceful in comparison. Part of this, of course, has to do with policy: the misinformation campaign “ClimateGate” was clearly timed to derail the Copenhagen talks, the likes of which won’t happen again for another few years. I think there is another factor, though, which will protect us the next time around: the scientists are now officially pissed off.
As scientists, we are shy creatures by nature (remember what I said about human conflict and graphs?) but even we can be provoked. Recall that we have discovered important information that could be vital to the future of our civilization, and yet there are many who seek to discredit us and make sure this information is not taken seriously. When those people go so far as to slander and harass individual researchers, nearly to the point of suicide, they have stepped on all of our toes. We are mad scientists, but not in the cartoon sense.
Now, when deniers attempt to construct a scandal, it doesn’t get off the ground. Scientists are there immediately to set the record straight, and the media realizes it is a non-story. When shady politicians try to press charges against researchers, those researchers hire accomplished lawyers because there is a fund for that now. Geoscience conferences feature so many communications workshops that you could attend nothing else if you chose to. Scientific societies are publishing handbooks on how to respond if your research is publicly attacked, you face charges of fraud, or you fear for your safety.
I like this new attitude of climate scientists. Forget the old paradigm that scientists should steer clear of political speech and stick to pure research. We didn’t give up our rights as citizens when we decided to be scientists. Maybe 2010 was a necessary evil, because it made us realize that we have a responsibility to fight for truth.
Posted in Musings | Tagged climate change, climategate, denial, global warming, science | 23 Comments »