Hank’s new spin-off channel SciShow, which publishes videos about popular science topics, has only being going for a month but already has 90 000 subscribers and 1 million views. So I was very excited when Hank created this entertaining, polished, and wonderfully accurate video about climate change. He discusses sea level rise, anoxic events, and even the psychology of denial:

In 2007, the Intergovernmental Panel on Climate Change published its fourth assessment report, internationally regarded as the most credible summary of climate science to date. It concluded that “most of the observed increase in global average temperatures since the mid-20th century is very likely due to the observed increase in anthropogenic greenhouse gas concentrations”.

A clear question remains: How much is “most”? 51%? 75%? 99%? At the time that the IPCC report was written, the answer was unclear. However, a new frontier of climate research has emerged since, and scientists are working hard to quantify the answer to this question.

I recently attended the 2011 American Geophysical Union Fall Meeting, a conference of over 20 000 scientists, many of whom study the climate system. This new area of research was a hot topic of discussion at AGU, and a phrase that came up many times was “more than 100%”.

That’s right, humans are probably causing more than 100% of observed global warming. That means that our influences are being offset by natural cooling factors. If we had never started burning fossil fuels, the world would be cooling slightly.

In the long term, oscillations of the Earth’s orbit show that, without human activity, we would be very slowly descending into a new ice age. There are other short-term cooling influences, though. Large volcanic eruptions, such as Mount Pinatubo in 1991, have thrown dust into the upper atmosphere where it blocks a small amount of sunlight. The sun, particularly in the last few years, has been less intense than usual, due to the 11-year sunspot cycle. We have also experienced several strong La Niña events in the Pacific Ocean, which move heat out of the atmosphere and into the ocean.

However, all of these cooling influences pale in comparison to the strength of the human-caused warming influences. The climate change communication project Skeptical Science recently summarized six scientific studies in this graphic:

Most of the studies estimated that humans caused over 100% of the warming since 1950, and all six put the number over 98%. Additionally, most of the studies find natural influences to be in the direction of cooling, and all six show that number to be close to zero.

If you are interested in the methodologies and uncertainty ranges of these six studies, Skeptical Science goes into more detail, and also provides links to the original journal articles.

To summarize, the perception that humans are accelerating a natural process of warming is false. We have created this problem entirely on our own. Luckily, that means we have the power to stop the problem in its tracks. We are in control, and we choose what happens in the future.

This is a climate model:

T = [(1-α)S/(4εσ)]^1/4

(T is temperature, α is the albedo, S is the incoming solar radiation, ε is the emissivity, and σ is the Stefan-Boltzmann constant)

An extremely simplified climate model, that is. It’s one line long, and is at the heart of every computer model of global warming. Using basic thermodynamics, it calculates the temperature of the Earth based on incoming sunlight and the reflectivity of the surface. The model is zero-dimensional, treating the Earth as a point mass at a fixed time. It doesn’t consider the greenhouse effect, ocean currents, nutrient cycles, volcanoes, or pollution.

If you fix these deficiencies, the model becomes more and more complex. You have to derive many variables from physical laws, and use empirical data to approximate certain values. You have to repeat the calculations over and over for different parts of the Earth. Eventually the model is too complex to solve using pencil, paper and a pocket calculator. It’s necessary to program the equations into a computer, and that’s what climate scientists have been doing ever since computers were invented.

A pixellated Earth

Today’s most sophisticated climate models are called GCMs, which stands for General Circulation Model or Global Climate Model, depending on who you talk to. On average, they are about 500 000 lines of computer code long, and mainly written in Fortran, a scientific programming language. Despite the huge jump in complexity, GCMs have much in common with the one-line climate model above: they’re just a lot of basic physics equations put together.

Computers are great for doing a lot of calculations very quickly, but they have a disadvantage: computers are discrete, while the real world is continuous. To understand the term “discrete”, think about a digital photo. It’s composed of a finite number of pixels, which you can see if you zoom in far enough. The existence of these indivisible pixels, with clear boundaries between them, makes digital photos discrete. But the real world doesn’t work this way. If you look at the subject of your photo with your own eyes, it’s not pixellated, no matter how close you get – even if you look at it through a microscope. The real world is continuous (unless you’re working at the quantum level!)

Similarly, the surface of the world isn’t actually split up into three-dimensional cells (you can think of them as cubes, even though they’re usually wedge-shaped) where every climate variable – temperature, pressure, precipitation, clouds – is exactly the same everywhere in that cell. Unfortunately, that’s how scientists have to represent the world in climate models, because that’s the only way computers work. The same strategy is used for the fourth dimension, time, with discrete “timesteps” in the model, indicating how often calculations are repeated.

It would be fine if the cells could be really tiny – like a high-resolution digital photo that looks continuous even though it’s discrete – but doing calculations on cells that small would take so much computer power that the model would run slower than real time. As it is, the cubes are on the order of 100 km wide in most GCMs, and timesteps are on the order of hours to minutes, depending on the calculation. That might seem huge, but it’s about as good as you can get on today’s supercomputers. Remember that doubling the resolution of the model won’t just double the running time – instead, the running time will increase by a factor of sixteen (one doubling for each dimension).

Despite the seemingly enormous computer power available to us today, GCMs have always been limited by it. In fact, early computers were developed, in large part, to facilitate atmospheric models for weather and climate prediction.

Cracking the code

A climate model is actually a collection of models – typically an atmosphere model, an ocean model, a land model, and a sea ice model. Some GCMs split up the sub-models (let’s call them components) a bit differently, but that’s the most common arrangement.

Each component represents a staggering amount of complex, specialized processes. Here are just a few examples from the Community Earth System Model, developed at the National Center for Atmospheric Research in Boulder, Colorado:

• Atmosphere: sea salt suspended in the air, three-dimensional wind velocity, the wavelengths of incoming sunlight
• Ocean: phytoplankton, the iron cycle, the movement of tides
• Land: soil hydrology, forest fires, air conditioning in cities
• Sea Ice: pollution trapped within the ice, melt ponds, the age of different parts of the ice

Each component is developed independently, and as a result, they are highly encapsulated (bundled separately in the source code). However, the real world is not encapsulated – the land and ocean and air are very interconnected. Some central code is necessary to tie everything together. This piece of code is called the coupler, and it has two main purposes:

1. Pass data between the components. This can get complicated if the components don’t all use the same grid (system of splitting the Earth up into cells).
2. Control the main loop, or “time stepping loop”, which tells the components to perform their calculations in a certain order, once per time step.

For example, take a look at the IPSL (Institut Pierre Simon Laplace) climate model architecture. In the diagram below, each bubble represents an encapsulated piece of code, and the number of lines in this code is roughly proportional to the bubble’s area. Arrows represent data transfer, and the colour of each arrow shows where the data originated:

We can see that IPSL’s major components are atmosphere, land, and ocean (which also contains sea ice). The atmosphere is the most complex model, and land is the least. While both the atmosphere and the ocean use the coupler for data transfer, the land model does not – it’s simpler just to connect it directly to the atmosphere, since it uses the same grid, and doesn’t have to share much data with any other component. Land-ocean interactions are limited to surface runoff and coastal erosion, which are passed through the atmosphere in this model.

You can see diagrams like this for seven different GCMs, as well as a comparison of their different approaches to software architecture, in this summary of my research.

Show time

When it’s time to run the model, you might expect that scientists initialize the components with data collected from the real world. Actually, it’s more convenient to “spin up” the model: start with a dark, stationary Earth, turn the Sun on, start the Earth spinning, and wait until the atmosphere and ocean settle down into equilibrium. The resulting data fits perfectly into the cells, and matches up really nicely with observations. It fits within the bounds of the real climate, and could easily pass for real weather.

Scientists feed input files into the model, which contain the values of certain parameters, particularly agents that can cause climate change. These include the concentration of greenhouse gases, the intensity of sunlight, the amount of deforestation, and volcanoes that should erupt during the simulation. It’s also possible to give the model a different map to change the arrangement of continents. Through these input files, it’s possible to recreate the climate from just about any period of the Earth’s lifespan: the Jurassic Period, the last Ice Age, the present day…and even what the future might look like, depending on what we do (or don’t do) about global warming.

The highest resolution GCMs, on the fastest supercomputers, can simulate about 1 year for every day of real time. If you’re willing to sacrifice some complexity and go down to a lower resolution, you can speed things up considerably, and simulate millennia of climate change in a reasonable amount of time. For this reason, it’s useful to have a hierarchy of climate models with varying degrees of complexity.

As the model runs, every cell outputs the values of different variables (such as atmospheric pressure, ocean salinity, or forest cover) into a file, once per time step. The model can average these variables based on space and time, and calculate changes in the data. When the model is finished running, visualization software converts the rows and columns of numbers into more digestible maps and graphs. For example, this model output shows temperature change over the next century, depending on how many greenhouse gases we emit:

Predicting the past

So how do we know the models are working? Should we trust the predictions they make for the future? It’s not reasonable to wait for a hundred years to see if the predictions come true, so scientists have come up with a different test: tell the models to predict the past. For example, give the model the observed conditions of the year 1900, run it forward to 2000, and see if the climate it recreates matches up with observations from the real world.

This 20th-century run is one of many standard tests to verify that a GCM can accurately mimic the real world. It’s also common to recreate the last ice age, and compare the output to data from ice cores. While GCMs can travel even further back in time – for example, to recreate the climate that dinosaurs experienced – proxy data is so sparse and uncertain that you can’t really test these simulations. In fact, much of the scientific knowledge about pre-Ice Age climates actually comes from models!

Climate models aren’t perfect, but they are doing remarkably well. They pass the tests of predicting the past, and go even further. For example, scientists don’t know what causes El Niño, a phenomenon in the Pacific Ocean that affects weather worldwide. There are some hypotheses on what oceanic conditions can lead to an El Niño event, but nobody knows what the actual trigger is. Consequently, there’s no way to program El Niños into a GCM. But they show up anyway – the models spontaneously generate their own El Niños, somehow using the basic principles of fluid dynamics to simulate a phenomenon that remains fundamentally mysterious to us.

In some areas, the models are having trouble. Certain wind currents are notoriously difficult to simulate, and calculating regional climates requires an unaffordably high resolution. Phenomena that scientists can’t yet quantify, like the processes by which glaciers melt, or the self-reinforcing cycles of thawing permafrost, are also poorly represented. However, not knowing everything about the climate doesn’t mean scientists know nothing. Incomplete knowledge does not imply nonexistent knowledge – you don’t need to understand calculus to be able to say with confidence that 9 x 3 = 27.

Also, history has shown us that when climate models make mistakes, they tend to be too stable, and underestimate the potential for abrupt changes. Take the Arctic sea ice: just a few years ago, GCMs were predicting it would completely melt around 2100. Now, the estimate has been revised to 2030, as the ice melts faster than anyone anticipated:

Answering the big questions

At the end of the day, GCMs are the best prediction tools we have. If they all agree on an outcome, it would be silly to bet against them. However, the big questions, like “Is human activity warming the planet?”, don’t even require a model. The only things you need to answer those questions are a few fundamental physics and chemistry equations that we’ve known for over a century.

You could take climate models right out of the picture, and the answer wouldn’t change. Scientists would still be telling us that the Earth is warming, humans are causing it, and the consequences will likely be severe – unless we take action to stop it.

The completed post should be up in a day or two. Thanks for your patience.

I am working on a larger piece about climate models: an introduction to how they work and why they are useful. That will take about a week to finish, so in the mean time, here is an open thread to keep things moving.

Some possible discussion topics from posts I’ve enjoyed:

• Deep Climate’s analysis of Canada’s emissions, and why we probably won’t meet even the most meagre targets
• Bart Verheggen’s thoughts on climate scientists practicing what they preach
• David Archer on the methane situation

Enjoy!

Do not burn yourself out. Be as I am – a reluctant enthusiast… a part time crusader, a half-hearted fanatic. Save the other half of yourselves and your lives for pleasure and adventure. It is not enough to fight for the land; it is even more important to enjoy it. While you can. While it is still there. So get out there and mess around with your friends, ramble out yonder and explore the forests, encounter the grizz, climb the mountains. Run the rivers, breathe deep of that yet sweet and lucid air, sit quietly for a while and contemplate the precious stillness, that lovely, mysterious and awesome space. Enjoy yourselves, keep your brain in your head and your head firmly attached to your body, the body active and alive, and I promise you this much: I promise you this one sweet victory over our enemies, over those deskbound people with their hearts in a safe deposit box and their eyes hypnotized by desk calculators. I promise you this: you will outlive the bastards.

So writes Edward Abbey, in a passage that Ken sent to me nearly two years ago. The quote is now stuck to my fridge, and I abide by it as best I can.

It’s pretty easy to find areas of untouched forest within my city. Living in a floodplain, it’s only practical to leave natural vegetation growing around the rivers – it acts as a natural sponge when the water rises. In the warmer months, hiking in the woods is convenient, particularly because I can bike to the edge of the river. But in the winter, it’s not so easy. The past few months have consistently been about 10 C above normal, though, and today I found a shortcut that made the trip to the woods walkable.

The aspen parkland in winter is strange. Most wildlife travel south or begin hibernating by early October, and no evergreen species grow here naturally. As you walk through the naked branches, it’s easy to think of the woods as desolate. But if you slow down, pay attention, and look around more carefully, you see signs of life in the distance:

Black-capped Chickadee

White-tailed Deer

If you stand still and do your best to look non-threatening, some of the more curious animals might come for a closer inspection:

If you imitate a bird's call well enough, it will come right up to you

A mother deer and her fawn, probably about eight months old

The species that live here year-round are some of the most resilient on the continent. They have survived 40 above and 40 below, near-annual droughts and floods, and 150 years of colonization. The Prairies is a climate of extremes, and life has evolved to thrive in those extremes.

So maybe this isn’t the land I am fighting for – it will probably be able to handle whatever climate change throws at it – but it is the land I love regardless.

Happy Christmas to everyone, and please go out and enjoy the land you’re fighting for, as a gift to yourself.

Share your thoughts, and other articles you like, in the comments.

Following the COP17 talks in Durban, South Africa – the latest attempt to create a global deal to cut carbon emissions and solve global warming – world leaders claimed they had “made history”, calling the conference “a great success” that had “all the elements we were looking for”.

So what agreement did they all come to, that has them so proud? They agreed to figure out a deal by 2015. As James Hrynyshyn writes, it is “a roadmap to a unknown strategy that may or may not produce a plan that might combat climate change”.

Did I miss a meeting? Weren’t we supposed to figure out a deal by 2010, so it could come into force when the Kyoto Protocol expires in 2012? This unidentified future deal, if it even comes to pass, will not come into force until 2020 – that’s 8 years of unchecked global carbon emissions.

At COP15 in Copenhagen, countries agreed to limit global warming to 2 degrees Celsius. The German Advisory Council on Global Change crunched the numbers and discovered that the sooner we start reducing emissions, the easier it will be to attain this goal. This graph shows that if emissions peak in 2011 we have a “bunny slope” to ride, whereas if emissions peak in 2020 we have a “triple black diamond” that’s almost impossible, economically. (Thanks to Richard Sommerville for this analogy).

If we stay on the path that leaders agreed on in Durban, emissions will peak long after 2020 – in the best case scenario, they will only start slowing in 2020. If the triple black diamond looks steep, imagine a graph where emissions peak in 2030 or 2040 – it’s basically impossible to achieve our goal, no matter how high we tax carbon or how many wind turbines we build.

World leaders have committed our generation to a future where global warming spins out of our control. What is there to celebrate about that?

However, we shouldn’t throw our hands in the air and give up. 2 degrees is bad, but 4 degrees is worse, and 6 degrees is awful. There is never a point at which action is pointless, because the problem can always get worse if we ignore it.

There was the token crazy guy who stood up and said “The sun is getting hotter every day and one day we’re all going to FRY! So what does that say about your global warming theory? Besides, if it was CO2 we could all just stop breathing!” Luckily, everybody laughed at his comments…

There were also some more reasonable-sounding people, repeating common myths like “It’s a natural cycle” and “Volcanoes emit more CO2 than humans“. The speakers did a good job of explaining why these claims were false, but I still wanted to pull out the Skeptical Science app and wave it in the air…

Overall, though, the audience seemed to be composed of concerned citizens who understood the causes and severity of climate change, and were eager to learn about impacts, particularly on extreme weather. It was nice to see an audience moving past this silly public debate into a more productive one about risk management.

The best moment, though, was on the bus home. There was a first-year student in the seat behind me – I assume he came to see the lecture as well, but maybe he just talks about climate change on the bus all the time. He was telling his friend about sea level rise, and he was saying all the right things – we can expect one or two metres by the end of the century, which doesn’t sound like a lot, but it’s enough to endanger many densely populated coastal cities, as well as kill vegetation due to seawater seeping in.

He even had the statistics right! I was so proud! I was thinking about turning around to join in the conversation, but by then I had been listening in for so long that it would have been embarrassing.

It’s nice to see evidence of a shift in public understanding, even if it’s only anecdotal. Maybe we’re doing something right after all.

• COSMOS (COmmunity earth System MOdelS) 1.2.1
• Model E: Oct. 11, 2011 snapshot
• HadGEM3 (Hadley Centre Global Environmental Model, version 3): August 2009 snapshot
• CESM (Community Earth System Model) 1.0.3
• GFDL (Geophysical Fluid Dynamics Laboratory), Climate Model 2.1 coupled to MOM (Modular Ocean Model) 4.1
• IPSL (Institut Pierre Simon Laplace), Climate Model 5A
• UVic ESCM (Earth System Climate Model) 2.9

And, since the most important part of poster sessions is the schpiel you give and the conversations you have, here is my schpiel:

Steve and I realized that while comparisons of the output of global climate models are very common (for example, CMIP5: Coupled Model Intercomparison Project Phase 5), nobody has really sat down and compared their software structure. We tried to fill this gap in research with a qualitative comparison study of seven models. Six of them are GCMs (General Circulation Models – the most complex climate simulations) in the CMIP5 ensemble; one, the UVic model, is not in CMIP because it’s really more of an EMIC (Earth System Model of Intermediate Complexity – simpler than a GCM). However, it’s one of the most complex EMICs, and contains a full GCM ocean, so we thought it would present an interesting boundary case. (Also, the code was easier to get access to than the corresponding GCM from Environment Canada. When we write this up into a paper we will probably use that model instead.)

I created a diagram of each model’s architecture. The area of each bubble is roughly proportional to the lines of code in that component, which we think is a pretty good proxy for complexity – a more complex model will have more subroutines and functions than a simple one. The bubbles are to scale within each model, but not between models, as the total lines of code in a model varies by about a factor of 10. A bit difficult to fit on a poster and still make everything readable! Fluxes from each component are represented by coloured arrows (the same colour as the bubble), and often pass through the coupler before reaching another component.

We examined the amount of encapsulation of components, which varies widely between models. CESM, on one end of the spectrum, isolates every component completely, particularly in the directory structure. Model E, on the other hand, places nearly all of its files in the same directory, and has a much higher level of integration between components. This is more difficult for a user to read, but it has benefits for data transfer.

While component encapsulation is attractive from a software engineering perspective, it poses problems because the real world is not so encapsulated. Perhaps the best example of this is sea ice. It floats on the ocean, its extent changing continuously. It breaks up into separate chunks and can form slush with the seawater. How do you split up ocean code and ice code? CESM keeps the two components completely separate, with a transient boundary between them. IPSL represents ice as an encapsulated sub-component of their ocean model, NEMO (Nucleus for European Modeling of the Ocean). COSMOS integrates both ocean and ice code together in MPI-OM (Max Planck Institute Ocean Model).

GFDL took a completely different, and rather innovative, approach. Sea ice in the GFDL model is an interface, a layer over the ocean with boolean flags in each cell indicating whether or not ice is present. All fluxes to and from the ocean must pass through the “sea ice”, even if they’re at the equator and the interface is empty.

Encapsulation requires code to tie components together, since the climate system is so interconnected. Every model has a coupler, which fulfills two main functions: controlling the main time-stepping loop, and passing data between components. Some models, such as CESM, use the coupler for every interaction. However, if two components have the same grid, no interpolation is necessary, so it’s often simpler just to pass them directly. Sometimes this means a component can be completely disconnected from the coupler, such as the land model in IPSL; other times it still uses the coupler for other interactions, such as the HadGEM3 arrangement with direct ocean-ice fluxes but coupler-controlled ocean-atmosphere and ice-atmosphere fluxes.

While it’s easy to see that some models are more complex than others, it’s also interesting to look at the distribution of complexity within a model. Often the bulk of the code is concentrated in one component, due to historical software development as well as the institution’s conscious goals. Most of the models are atmosphere-centric, since they were created in the 1970s when numerical weather prediction was the focus of the Earth system modelling community. Weather models require a very complex atmosphere but not a lot else, so atmospheric routines dominated the code. Over time, other components were added, but the atmosphere remained at the heart of the models. The most extreme example is HadGEM3, which actually uses the same atmosphere model for both weather prediction and climate simulations!

The UVic model is quite different. The University of Victoria is on the west coast of Canada, and does a lot of ocean studies, so the model began as a branch of the MOM ocean model from GFDL. The developers could have coupled it to a complex atmosphere model in an effort to mimic full GCMs, but they consciously chose not to. Atmospheric routines need very short time steps, so they eat up most of the run time, and make very long simulations not feasible. In an effort to keep their model fast, UVic created EMBM (Energy Moisture Balance Model), an extremely simple atmospheric model (for example, it doesn’t include dynamic precipitation – it simply rains as soon as a certain humidity is reached). Since the ocean is the primary moderator of climate over the long run, the UVic ESCM still outputs global long-term averages that match up nicely with GCM results.

Finally, CESM and Model E could not be described as “land-centric”, but land is definitely catching up – it’s even surpassed the ocean model in both cases! These two GCMs are cutting-edge in terms of carbon cycle feedbacks, which are primarily terrestrial, and likely very important in determining how much warming we can expect in the centuries to come. They are currently poorly understood and difficult to model, so they are a new frontier for Earth system modelling. Scientists are moving away from a binary atmosphere-ocean paradigm and towards a more comprehensive climate system representation.

I presented this work to some computer scientists in the summer, and many of them asked, “Why do you need so many models? Wouldn’t it be better to just have one really good one that everyone collaborated on?” It might be simpler from a software engineering perspective, but for the purposes of science, a variety of diverse models is actually better. It means you can pick and choose which model suits your experiment. Additionally, it increases our confidence in climate model output, because if dozens of independent models are saying the same thing, they’re more likely to be accurate than if just one model made a given prediction. Diversity in model architecture arguably produces the software engineering equivalent of perturbed physics, although it’s not systematic or deliberate.

A common question people asked me at AGU was, “Which model do you think is the best?” This question is impossible to answer, because it depends on how you define “best”, which depends on what experiment you are running. Are you looking at short-term, regional impacts at a high resolution? HadGEM3 would be a good bet. Do you want to know what the world will be like in the year 5000? Go for UVic, otherwise you will run out of supercomputer time! Are you studying feedbacks, perhaps the Paleocene-Eocene Thermal Maximum? A good choice would be CESM. So you see, every model is the best at something, and no model can be the best at everything.

You might think the ideal climate model would mimic the real world perfectly. It would still have discrete grid cells and time steps, but it would be like a digital photo, where the pixels are so small that it looks continuous even when you zoom in. It would contain every single Earth system process known to science, and would represent their connections and interactions perfectly.

Such a model would also be a nightmare to use and develop. It would run slower than real time, making predictions of the future useless. The code would not be encapsulated, so organizing teams of programmers to work on certain aspects of the model would be nearly impossible. It would use more memory than computer hardware offers us – despite the speed of computers these days, they’re still too slow for many scientific models!

We need to balance complexity with feasibility. A hierarchy of complexity is important, as is a variety of models to choose from. Perfectly reproducing the system we’re trying to model actually isn’t the ultimate goal.

Please leave your questions below, and hopefully we can start a conversation – sort of a virtual poster session!