Tag Archives: physics

Climate change and the jet stream

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Here in the northern mid-latitudes (much of Canada and the US, Europe, and the northern half of Asia) our weather is governed by the jet stream. This high-altitude wind current, flowing rapidly from west to east, separates cold Arctic air (to the north) from warmer temperate air (to the south). So on a given day, if you’re north of the jet stream, the weather will probably be cold; if you’re to the south, it will probably be warm; and if the jet stream is passing over you, you’re likely to get rain or snow.

The jet stream isn’t straight, though; it’s rather wavy in the north-south direction, with peaks and troughs. So it’s entirely possible for Calgary to experience a cold spell (sitting in a trough of the jet stream) while Winnipeg, almost directly to the east, has a heat wave (sitting in a peak). The farther north and south these peaks and troughs extend, the more extreme these temperature anomalies tend to be.

Sometimes a large peak or trough will hang around for weeks on end, held in place by certain air pressure patterns. This phenomenon is known as “blocking”, and is often associated with extreme weather. For example, the 2010 heat wave in Russia coincided with a large, stationary, long-lived peak in the polar jet stream. Wildfires, heat stroke, and crop failure ensued. Not a pretty picture.

As climate change adds more energy to the atmosphere, it would be naive to expect all the wind currents to stay exactly the same. Predicting the changes is a complicated business, but a recent study by Jennifer Francis and Stephen Vavrus made headway on the polar jet stream. Using North American and North Atlantic atmospheric reanalyses (models forced with observations rather than a spin-up) from 1979-2010, they found that Arctic amplification – the faster rate at which the Arctic warms, compared to the rest of the world – makes the jet stream slower and wavier. As a result, blocking events become more likely.

Arctic amplification occurs because of the ice-albedo effect: there is more snow and ice available in the Arctic to melt and decrease the albedo of the region. (Faster-than-average warming is not seen in much of Antarctica, because a great deal of thermal inertia is provided to the continent in the form of strong circumpolar wind and ocean currents.) This amplification is particularly strong in autumn and winter.

Now, remembering that atmospheric pressure is directly related to temperature, and pressure decreases with height, warming a region will increase the height at which pressure falls to 500 hPa. (That is, it will raise the 500 hPa “ceiling”.) Below that, the 1000 hPa ceiling doesn’t rise very much, because surface pressure doesn’t usually go much above 1000 hPa anyway. So in total, the vertical portion of the atmosphere that falls between 1000 and 500 hPa becomes thicker as a result of warming.

Since the Arctic is warming faster than the midlatitudes to the south, the temperature difference between these two regions is smaller. Therefore, the difference in 1000-500 hPa thickness is also smaller. Running through a lot of complicated physics equations, this has two main effects:

  1. Winds in the east-west direction (including the jet stream) travel more slowly.
  2. Peaks of the jet stream are pulled farther north, making the current wavier.

Also, both of these effects reinforce each other: slow jet streams tend to be wavier, and wavy jet streams tend to travel more slowly. The correlation between relative 1000-500 hPa thickness and these two effects is not statistically significant in spring, but it is in the other three seasons. Also, melting sea ice and declining snow cover on land are well correlated to relative 1000-500 hPa thickness, which makes sense because these changes are the drivers of Arctic amplification.

Consequently, there is now data to back up the hypothesis that climate change is causing more extreme fall and winter weather in the mid-latitudes, and in both directions: unusual cold as well as unusual heat. Saying that global warming can cause regional cold spells is not a nefarious move by climate scientists in an attempt to make every possible outcome support their theory, as some paranoid pundits have claimed. Rather, it is another step in our understanding of a complex, non-linear system with high regional variability.

Many recent events, such as record snowfalls in the US during the winters of 2009-10 and 2010-11, are consistent with this mechanism – it’s easy to see that they were caused by blocking in the jet stream when Arctic amplification was particularly high. They may or may not have happened anyway, if climate change wasn’t in the picture. However, if this hypothesis endures, we can expect more extreme weather from all sides – hotter, colder, wetter, drier – as climate change continues. Don’t throw away your snow shovels just yet.

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A New Kind of Science

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Cross-posted from NextGen Journal

Ask most people to picture a scientist at work, and they’ll probably imagine someone in a lab coat and safety goggles, surrounded by test tubes and Bunsen burners. If they’re fans of The Big Bang Theory, maybe they’ll picture complicated equations being scribbled on whiteboards. Others might think of the Large Hadron Collider, or people wading through a swamp taking water samples.

All of these images are pretty accurate – real scientists, in one field or another, do these things as part of their job. But a large and growing approach to science, which is present in nearly every field, replaces the lab bench or swamp with a computer. Mathematical modelling, which essentially means programming the complicated equations from the whiteboard into a computer and solving them many times, is the science of today.

Computer models are used for all sorts of research questions. Epidemiologists build models of an avian flu outbreak, to see how the virus might spread through the population. Paleontologists build biomechanical models of different dinosaurs, to figure out how fast they could run or how high they could stretch their necks. I’m a research student in climate science, where we build models of the entire planet, to study the possible effects of global warming.

All of these models simulate systems which aren’t available in the real world. Avian flu hasn’t taken hold yet, and no sane scientist would deliberately start an outbreak just so they could study it! Dinosaurs are extinct, and playing around with their fossilized bones to see how they might move would be heavy and expensive. Finally, there’s only one Earth, and it’s currently in use. So models don’t replace lab and field work – rather, they add to it. Mathematical models let us perform controlled experiments that would otherwise be impossible.

If you’re interested in scientific modelling, spend your college years learning a lot of math, particularly calculus, differential equations, and numerical methods. The actual application of the modelling, like paleontology or climatology, is less important for now – you can pick that up later, or read about it on your own time. It might seem counter-intuitive to neglect the very system you’re planning to spend your life studying, but it’s far easier this way. A few weeks ago I was writing some computer code for our lab’s climate model, and I needed to calculate a double integral of baroclinic velocity in the Atlantic Ocean. I didn’t know what baroclinic velocity was, but it only took a few minutes to dig up a paper that defined it. My work would have been a lot harder if, instead, I hadn’t known what a double integral was.

It’s also important to become comfortable with computer programming. You might think it’s just the domain of software developers at Google or Apple, but it’s also the main tool of scientists all over the world. Two or three courses in computer science, where you’ll learn a multi-purpose language like C or Java, are all you need. Any other languages you need in the future will take you days, rather than months, to master. If you own a Mac or run Linux on a PC, spend a few hours learning some basic UNIX commands – it’ll save you a lot of time down the road. (Also, if the science plan falls through, computer science is one of the only majors which will almost definitely get you a high-paying job straight out of college.)

Computer models might seem mysterious, or even untrustworthy, when the news anchor mentions them in passing. In fact, they’re no less scientific than the equations that Sheldon Cooper scrawls on his whiteboard. They’re just packaged together in a different form.

How do climate models work?

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Also published at Skeptical Science

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.

Why Trust Science?

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Part 1 of a series of 5 for NextGen Journal.

What’s wrong with these statements?

  • I believe in global warming.
  • I don’t believe in global warming.
  • We should hear all sides of the climate change debate and decide for ourselves.

Don’t see it? How about these?

  • I believe in photosynthesis.
  • I don’t believe in Newton’s Laws of Motion.
  • We should hear all sides of the quantum mechanics debate and decide for ourselves.

Climate change is a scientific phenomenon, rooted in physics and chemistry. All I did was substitute in other scientific phenomena, and the statements suddenly sounded wacky and irrational.

Perhaps we have become desensitized by people conflating opinion with fact when it comes to climate change. However, the positions of politicians or media outlets do not make the climate system any less of a physical process. Unlike, say, ideology, there is a physical truth out there.

If there is a physical truth, there are also wrong answers and false explanations. In scientific issues, not every “belief” is equally valid.

Of course, the physical truth is elusive, and facts are not always clear-cut. Data requires interpretation and a lot of math. Uncertainty is omnipresent and must be quantified. These processes require training, as nobody is born with all the skills required to be a good scientist. Again, the complex nature of the physical world means that some voices are more important than others.

Does that mean we should blindly accept whatever a scientist says, just because they have a Ph.D.? Of course not. People aren’t perfect, and scientists are no exception.

However, the institution of science has a pretty good system to weed out incorrect or unsupported theories. It involves peer review, and critical thinking, and falsifiability. We can’t completely prove anything right – not one hundred percent – so scientists try really hard to prove a given theory wrong. If they can’t, their confidence in its accuracy goes up. Peter Watts describes this process in more colourful terms: “You put your model out there in the coliseum, and a bunch of guys in white coats kick the s**t out of it. If it’s still alive when the dust clears, your brainchild receives conditional acceptance. It does not get rejected. This time.”

Peer review is an imperfect process, but it’s far better than nothing. Combined with the technical skill and experience of scientists, it makes the words of the scientific community far more trustworthy than the words of a politician or a journalist. That doesn’t mean that science is always right. But, if you had to put your money on it, who would you bet on?

The issue is further complicated by the fact that scientists are rarely unanimous. Often, the issue at question is truly a mystery, and the disagreement is widespread. What causes El Niño conditions in the Pacific Ocean? Science can’t give us a clear answer yet.

However, sometimes disagreement is restricted to the extreme minority. This is called a consensus. It doesn’t imply unanimity, and it doesn’t mean that the issue is closed, but general confidence in a theory is so high that science accepts it and moves on. Even today, a few researchers will tell you that HIV doesn’t cause AIDS, or that secondhand smoke isn’t harmful to your health. But that doesn’t stop medical scientists from studying the finer details of such diseases, or governments from funding programs to help people quit smoking. Science isn’t a majority-rules democracy, but if virtually all scientists have the same position on an issue, they probably have some pretty good reasons.

If science is never certain, and almost never unanimous, what are we supposed to do? How do we choose who to trust? Trusting nobody but yourself would be a poor choice. Chances are, others are more qualified than you, and you don’t hold the entirety of human knowledge in your head. For policy-relevant science, ignoring the issue completely until one side is proven right could also be disastrous. Inaction itself is a policy choice, which we see in some governments’ responses to climate change.

Let’s bring the whole issue down to a more personal level. Imagine you were ill, and twenty well-respected doctors independently examined you and said that surgery was required to save your life. One doctor, however, said that your illness was all in your mind, that you were healthy as a horse. Should you wait in bed until the doctors all agreed? Should you go home to avoid surgery that might be unnecessary? Or should you pay attention to the relative size and credibility of each group, as well as the risks involved, and choose the course of action that would most likely save your life?

Beautiful Things

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This is what the last few days have taught me: even if the code for climate models can seem dense and confusing, the output is absolutely amazing.

Late yesterday I discovered a page of plots and animations from the Canadian Centre for Climate Modelling and Analysis. The most recent coupled global model represented on that page is CGCM3, so I looked at those animations. I noticed something very interesting: the North Atlantic, independent of the emissions scenario, was projected to cool slightly, while the world around it warmed up. Here is an example, from the A1B scenario. Don’t worry if the animation is already at the end, it will loop:

It turns out that this slight cooling is due to the North Atlantic circulation slowing down, as is very likely to happen from large additions of freshwater that change the salinity and density of the ocean (IPCC AR4 WG1, FAQ 10.2). This freshwater could come from either increased precipitation due to climate change, or meltwater from the Arctic ending up in the North Atlantic. Of course, we hear about this all the time – the unlikely prospect of the Gulf Stream completely shutting down and Europe going into an ice age, as displayed in The Day After Tomorrow – but, until now, I hadn’t realized that even a slight slowing of the circulation could cool the North Atlantic, while Europe remained unaffected.

Then, in chapter 8 of the IPCC, I read something that surprised me: climate models generate their own El Ninos and La Ninas. Scientists don’t understand quite what triggers the circulation patterns leading to these phenomena, so how can they be in the models? It turns out that the modellers don’t have to parameterize the ENSO cycles at all: they have done such a good job of reproducing global circulation from first principles that ENSO arises by itself, even though we don’t know why. How cool is that? (Thanks to Jim Prall and Things Break for their help with this puzzle.)

Jim Prall also pointed me to an HD animation of output from the UK-Japan Climate Collaboration. I can’t seem to embed the QuickTime movie (WordPress strips out some of the necessary HTML tags) so you will have to click on the link to watch it. It’s pretty long – almost 17 minutes – as it represents an entire year of the world’s climate system, in one-hour time steps. It shows 1978-79, starting from observational data, but from there it simulates its own circulation.

I am struck by the beauty of this output – the swirling cyclonic precipitation, the steady prevailing westerlies and trade winds, the subtropical high pressure belt clear from the relative absence of cloud cover in these regions. You can see storms sprinkling across the Amazon Basin, monsoons pounding South Asia, and sea ice at both poles advancing and retreating with the seasons. Scientists didn’t explicitly tell their models to do any of this. It all appeared from first principles.

Take 17 minutes out of your day to watch it – it’s an amazing stress reliever, sort of like meditation. Or maybe that’s just me…

One more quick observation: most of you are probably familiar with the naming conventions of IPCC reports. The First Assessment Report was FAR, the second was SAR, and so on, until the acronyms started to repeat themselves, so the Fourth Assessment Report was AR4. They’ll have to follow this alternate convention until the Eighth Annual Report, which will be EAR. Maybe they’ll stick with AR8, but that would be substantially less entertaining.

Learning Experiences

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I apologize for my brief hiatus – it’s been almost two weeks since I’ve posted. I have been very busy recently, but for a very exciting reason: I got a job as a summer student of Dr. Steve Easterbrook! You can read more about Steve and his research on his faculty page and blog.

This job required me to move cities for the summer, so my mind has been consumed with thoughts such as “Where am I and how do I get home from this grocery store?” rather than “What am I going to write a post about this week?” However, I have had a few days on the job now, and as Steve encourages all of his students to blog about their research, I will use this outlet to periodically organize my thoughts.

I will be doing some sort of research project about climate modelling this summer – we’re not yet sure exactly what, so I am starting by taking a look at the code for some GCMs. The NCAR Community Earth System Model is one of the easiest to access, as it is largely an open source project. I’ve only read through a small piece of their atmosphere component, but I’ve already seen more physics calculations in one place than ever before.

I quickly learned that trying to understand every line of the code is a silly goal, as much as I may want to. Instead, I’m trying to get a broader picture of what the programs do. It’s really neat to have my knowledge about different subjects converge so completely. Multi-dimensional arrays, which I have previously only used to program games of Sudoku and tic-tac-toe, are now being used to represent the entire globe. Electric potential, a property I last studied in the circuitry unit of high school physics, somehow impacts atmospheric chemistry. The polar regions, which I was previously fascinated with mainly for their wildlife, also present interesting mathematical boundary cases for a climate model.

It’s also interesting to see how the collaborative nature of CESM, written by many different authors and designed for many different purposes, impacts its code. Some of the modules have nearly a thousand lines of code, and some have only a few dozen – it all depends on the programming style of the various authors. The commenting ranges from extensive to nonexistent. Every now and then one of the files will be written in an older version of Fortran, where EVERYTHING IS IN UPPER CASE.

I am bewildered by most of the variable names. They seem to be collections of abbreviations I’m not familiar with. Some examples are “mxsedfac”, “lndmaxjovrdmdni”, “fxdd”, and “vsc_knm_atm”.

When we get a Linux machine set up (I have heard too many horror stories to attempt a dual-boot with Windows) I am hoping to get a basic CESM simulation running, as well as EdGCM (this could theoretically run on my laptop, but I prefer to bring that home with me each evening, and the simulation will probably take over a day).

I am also doing some background reading on the topic of climate modelling, including this book, which led me to the story of PHONIAC. The first weather prediction done on a computer (the ENIAC machine) was recreated as a smartphone application, and ran approximately 3 million times faster. Unfortunately, I can’t find anyone with a smartphone that supports Java (argh, Apple!) so I haven’t been able to try it out.

I hope everyone is having a good summer so far. A more traditional article about tornadoes will be coming at the end of the week.

Forcings

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Last time, we talked about the energy budget – the process of radiation coming in from the sun, being absorbed by the Earth, and then emitted as infrared radiation, which we perceive as heat when it hits us. Remember that this outgoing emission of energy is what determines the temperature of the Earth.

So how can the temperature of the Earth be changed? Naturally, there is a lot of year-to-year variation. For example, when the oceans absorb radiation from the sun, they don’t always emit it right away. They will store energy for a long time, and sometimes release lots at once, during El Nino. This kind of internal variability makes the average global temperature very zig-zaggy.

We need to revise the question, then. The question is not about the average global surface temperature – it’s about the amount of energy on the planet. That’s generally how the climate is changed, by increasing or decreasing the amount of energy the Earth emits as infrared radiation, and consequently, the temperature.

There are two ways to do this. The simplest method is to change the amount of incoming energy. By increasing or decreasing the amount of solar radiation that hits the Earth – either directly, by changing the sun’s output, or indirectly, by increasing the albedo or reflectivity of the Earth – the amount of infrared radiation emitted by the surface will also increase or decrease, because incoming has to be equal to outgoing. The change in outgoing radiation will often take a bit of time to catch up to the change in incoming radiation. Until the two reach a new equilibrium, the Earth will warm or cool.

Another way to change the Earth’s temperature is by artificially changing the amount of incoming energy. The same amount of solar radiation reaches the Earth, but when it is absorbed and emitted, some of the emitted infrared energy gets bounced back so the Earth has to absorb and emit it again. By processing the same energy multiple times, the temperature is a lot warmer that it would be without any bouncing. We refer to this bouncing as the “greenhouse effect”, even though greenhouses work in a completely different way, and we will be discussing it a lot more later. By increasing or decreasing the greenhouse effect, the temperature of the Earth will change too.

A change in incoming energy is referred to as a radiative forcing, because it “forces” the Earth’s temperature in a certain way, by a certain amount. It is measured in watts per square meter (W/m2), and it doesn’t take very many watts per square meter to make a big difference in the Earth’s temperature. The resulting change in temperature is called a response.

My favourite analogy to explain forcing and response uses one of the most basic physics equations – F=ma. Mass (m) is constant, so force (F) is proportional to acceleration (a). Applying a forcing to the Earth is just like pushing on a box. If the force is big enough to overcome friction, you get an acceleration – a response.

It’s also very important to use net force, not just any force. If there are two people pushing on the box in different directions with different amounts of force, the acceleration you observe will be equal to the result of those forces combined. Similarly, there are often multiple forcings acting on the climate at once. The sun might be getting slightly dimmer, the albedo might be decreasing, the greenhouse effect might be on the rise. The response of the climate will not match up to any one of those, but the sum of them all together.

Here is a video I made last year, in collaboration with Climate Change Connection, about this very analogy:

In future posts, I will be discussing different forcings in more detail. Stay tuned!