Serendipity

In honour of today’s announcement that Syukuro Manabe, Klaus Hasselmann and Giorgio Parisi have been awarded the Nobel prize in physics for their contributions to understanding and modeling complex systems, I’m posting here some extracts from my forthcoming book, “Computing the Climate”, describing Manabe’s early work on modeling the climate system. We’ll start the story with the breakthrough by Norman Phillips at Princeton University’s Institute for Advanced Study (IAS), which I wrote about in my last post.

The Birth of General Circulation Modeling

Phillips had built what we now acknowledge as the first general circulation model (GCM), in 1955. It was ridiculously simple, representing the earth as a cylinder rather than a globe, with the state of the atmosphere expressed using a single variable—air pressure—at two different heights, at each of 272 points around the planet (a grid of 16 x 17 points). Despite its simplicity, Phillips’ model did something remarkable. When started with a uniform atmosphere—the same values at every grid point—the model gradually developed its own stable jet stream, under the influence of the equations that describe the effect of heat from the sun and rotation of the earth. The model was hailed as a remarkable success, and inspired a generation of atmospheric scientists to develop their own global circulation models.

The idea of starting the model with the atmosphere at rest—and seeing what patterns emerge—is a key feature that makes this style of modelling radically different how models are used in weather forecasting. Numerical weather forecasting had taken off rapidly, and by 1960, three countries—the United States, Sweden and Japan—had operational numerical weather forecasting services up and running. So there was plenty of expertise already in numerical methods and computational modelling among the meteorological community, especially in those three countries. 

But whereas a weather model only simulates a few days starting from data about current conditions, a general circulation model has to simulate long-term stable patterns, which means many of the simplifications to the equations of motion that worked in early weather forecasting models don’t work in GCMs. The weather models of the 1950s all ignore fast moving waves that are irrelevant in short-term weather forecasts. But these simplifications make the model unstable over longer runs. The atmosphere would steadily lose energy—and sometimes air and moisture too—so that realistic climatic patterns don’t emerge. The small group of scientists interested in general circulation modelling began to diverge from the larger numerical weather forecasting community, choosing to focus on versions of the equations and numerical algorithms with conservation of mass and energy built in, to give stable long-range simulations.

In 1955, the US Weather Bureau established a General Circulation Research Laboratory, specifically to build on Phillips’ success. It was headed by Joseph Smagorinsky, one of the original members of the ENIAC weather modelling team. Originally located just outside Washington DC, the lab has undergone several name changes and relocations, and is now the Geophysical Fluid Dynamics Lab (GFDL), housed at Princeton University, where it remains a major climate modelling lab today.

In 1959, Smagorinsky recruited the young Japanese meteorologist, Syukuro Manabe from Tokyo, and they began work on a primitive equation model. Like Phillips, they began with a model that represented only one hemisphere. Manabe concentrated on the mathematical structure of the models, while Smagorinsky hired a large team of programmers to develop the code. By 1963, they had developed a nine-layer atmosphere model which exchanged water—but not heat—between the atmosphere and surface. The planet’s surface, however, was flat and featureless—a continuous swamp from which water could evaporate, but which had no internal dynamics of its own. The model could simulate radiation passing through the atmosphere, interacting with water vapour, ozone and CO2. Like most of the early GCMs, this model captured realistic global patterns, but had many of the details wrong. 

Meanwhile, at the University of California, Los Angeles (UCLA), Yale Mintz, the associate director of the Department of Meteorology, recruited another young Japanese meteorologist, Akio Arakawa, to help him build their own general circulation model. From 1961, Mintz and Arakawa developed a series of models, with Mintz providing the theoretical direction, and Arakawa designing the model, with help from the department’s graduate students. By 1964, their model represented the entire globe with a 2-layer atmosphere and realistic geography.

Computational limitations dominated the choices these two teams had to make. For example, the GFDL team modelled only the northern hemisphere, with a featureless surface, so that they could put more layers into the atmosphere, while the UCLA team chose the opposite route: an entire global model with realistic layout of continents and oceans, but with only 2 layers of atmosphere.

Early Warming Signals

Meanwhile, in the early 1950s, oceanographers at the Scripps Institute for Oceanography in California, under the leadership of their new director, Roger Revelle, were investigating the spread of radioactive fallout in the oceans from nuclear weapons testing. Their work was funded by the US military, who needed to know how quickly the oceans would absorb these contaminants, to assess the risks to human health. But Revelle had many other research interests. He had read about the idea that carbon dioxide from fossil fuels could warm the planet, and realized radiocarbon dating could be used to measure how quickly the ocean absorbs CO2. Revelle understood the importance of a community effort, so he persuaded a number of colleagues to do similar analysis, and in a coordinated set of three papers [Craig, 1957; Revelle & Süess, 1957; and Arnold & Anderson, 1957], published in 1957, the group presented their results.

They all found a consistent pattern: the surface layer of the ocean continuously absorbs CO2 from the atmosphere, so on average, a molecule of CO2 molecule stays in the atmosphere only for about 7 years, before being dissolved into the ocean. But the surface waters also release CO2, especially when they warm up in the sun. So the atmosphere and surface waters exchange CO2 molecules continuously—any extra CO2 will end up shared between them

All three papers also confirmed that the surface waters don’t mix much with the deeper ocean. So it takes hundreds of years for any extra carbon to pass down into deeper waters. The implications were clear—the oceans weren’t absorbing CO2 anywhere near as fast as we were producing it.

These findings set alarm bells ringing amongst the geosciences community. If this was correct, the effects of climate change would be noticeable within a few decades. But without data, it would be hard to test their prediction. At Scripps, Revelle hired a young chemist, David Charles Keeling, to begin detailed measurements. In 1958, Keeling set up an observing station on Mauna Loa in Hawaii, and a second station in the Antarctic, both far enough from any major sources of emissions to give a reliable baseline measurements of CO2 in the atmosphere. Funding for the Antarctic station was cut a few years later, but Keeling managed to keep the recordings going at Mauna Loa, where they are still collected regularly today. Within two years, Keeling had enough data to confirm Bolin and Ericsson’s analysis: CO2 levels in the atmosphere were rising sharply.

Keeling’s data helped to spread awareness of the issue rapidly among the ocean and atmospheric science research communities, even as scientists in other fields remained unaware of the issue. Alarm at the implications of the speed at which CO2 levels were rising led some scientists to alert the country’s political leaders. When President Lyndon Johnson commissioned a report on the state of the environment, in 1964, the president’s science advisory committee invited a small subcommittee—including Revelle, Keeling, and Smagorinsky—to write an appendix to the report, focusing on the threat of climate change. And so, on February 8th, 1965, President Johnson became the first major world leader to mention the threat of climate change, in speech to congress: “This generation has altered the composition of the atmosphere on a global scale through…a steady increase in carbon dioxide from the burning of fossil fuels.”

Climate Modeling Takes Off

So awareness of the CO2 problem was spreading rapidly through the scientific community just as the general circulation modelling community was getting established. However, it wasn’t clear that global circulation models would be suited to this task. Computational power was limited, and it wasn’t yet possible to run the models long enough to simulate the decades or centuries over which climate change would occur. Besides, the first generation of GCMs had so many simplifications, it seemed unlikely they could simulate the effects of increasing CO2—that wasn’t what they were designed for.

To do this properly, the models would need to include all the relevant energy exchanges between the surface, atmosphere and space. That would mean a model that accurately captured the vertical temperature profile of the atmosphere, along with the process of radiation, convection, evaporation and precipitation, all of which move energy vertically. None of these processes are adequately captured in the primitive equations, so they would all need to be added as parameterization schemes in the models.

Smagorinsky and Manabe at GFDL were the only group anywhere near ready to try running CO2 experiments in their global circulation model. Their nine-layer model already captured some of the vertical structure of the atmosphere, and Suki Manabe had built in a detailed radiation code from the start, with the help of a visiting German meteorologist, Fritz Möller. Manabe had a model of the relevant heat exchanges in the full height of the atmosphere working by 1967, and together with his colleague, Richard Wetherald, published what is now recognized as the first accurate computational experiment of climate change [Manabe and Wetherald, 1967].

Running the general circulation model for this experiment was still too computationally expensive, so they ignored all horizontal heat exchanges, and instead built a one dimensional model of just a single column of atmosphere. The model could be run with 9 or 18 layers, and included the effects of upwards and downwards radiation through the column, exchanges of heat through convection, and the latent heat of evaporation and condensation of water. Manabe and Wetherald first tested the model with current atmospheric conditions, to check it could reproduce the correct vertical distribution of temperatures in the atmosphere, which it did very well. They then doubled the amount of carbon dioxide in the model and ran it again. They found temperatures rose throughout the lower atmosphere, with a rise of about 2°C at the surface, while the stratosphere showed a corresponding cooling. This pattern—warming in the lower atmosphere and cooling in the stratosphere—shows up in all the modern global climate models, but wasn’t confirmed by satellite readings until the 2000s.

By the mid 1970s, a broad community of scientists were replicating Manabe and Wetherald’s experiment in a variety of simplified models, although it would take nearly a decade before anyone could run the experiment in a full 3-dimensional GCM. But the community was beginning to use the term climate modelling to describe their work—a term given much greater impetus when it was used as the title of a comprehensive survey of the field by two NCAR scientists, Steven Schneider and Robert Dickinson in 1975. Remarkably, their paper [Schneider and Dickinson, 1974] charts a massive growth of research, citing the work of over 150 authors who published work on climate modelling the in period from 1967-1975, after Manabe and Wetherald’s original experiment.

It took some time, however, to get the general circulation models to the point where they could also run a global climate change experiment. Perhaps unsurprisingly, Manabe and Wetherald were also the first to do this, in 1975. Their GCM produced a higher result for the doubled CO2 experiment—an average surface warming of 3°C—and they attributed this to the snow-albedo feedback, which is included in the GCM, but not in their original single column model. Their experiment [Manabe and Wetherald 1975] also showed an important effect first noted by Arrhenius: a much greater warming at the poles than towards the equator—because polar temperatures are much more sensitive to changes in the rate at which heat escapes to space. And their model predicted another effect—global warming would speed up evaporation and precipitation, and hence produce more intense rainfalls. This prediction has already been demonstrated in the rapid uptick of extreme weather events in the 2010s.

In hindsight, Manabe’s simplified models produced remarkably accurate predictions of future climate change. Manabe used his early experiments to predict a temperature rise of about 0.8°C by the year 2000, assuming a 25% increase in CO2 over the course of the twentieth century. Manabe’s assumption about the rate that CO2 would increase was almost spot on, and so was his calculation for the resulting temperature rise. CO2 levels rose from about 300ppm in 1900 to 370ppm in 2000, a rise of 23%. The change in temperature over this period, calculated as the change in decadal means in the HadCRUT5 dataset was 0.82°C. [Hausfather et al 2020].

References

Arnold, J. R., & Anderson, E. C. (1957). The Distribution of Carbon-14 in Nature. Tellus, 9(1), 28–32.

Craig, H. (1957). The Natural Distribution of Radiocarbon and the Exchange Time of Carbon Dioxide Between Atmosphere and Sea. Tellus, 9(1), 1–17.

Hausfather, Z., Drake, H. F., Abbott, T., & Schmidt, G. A. (2020). Evaluating the Performance of Past Climate Model Projections. Geophysical Research Letters, 47(1), 2019GL085378.

Manabe, S., & Wetherald, R. T. (1967). Thermal Equilibrium of the Atmosphere with a Given Distribution of Relative Humidity. Journal of the Atmospheric Sciences.

Manabe, S., & Wetherald, R. T. (1975). The Effects of Doubling the CO2 Concentration on the Climate of a General Circulation Model. Journal of the Atmospheric Sciences, 32(1), 3–15.

Revelle, R., & Suess, H. E. (1957). Carbon Dioxide Exchange Between Atmosphere and Ocean and the Question of an Increase of Atmospheric CO 2 during the Past Decades. Tellus, 9(1), 18–27.

Schneider, S. H., & Dickinson, R. E. (1974). Climate modeling. Reviews of Geophysics, 12(3), 447.

One of the biggest challenges in understanding climate change is that the timescales involved are far longer than most people are used to thinking about. Garvey points out that this makes climate change different from any other ethical question, because both the causes and consequences are smeared out across time and space:

“There is a sense in which my actions and the actions of my present fellows join with the past actions of my parents, grandparents and great-grandparents, and the effects resulting from our actions will still be felt hundreds, even thousands of years in the future. It is also true that we are, in a way, stuck with the present we have because of our past. The little actions I undertake which keep me warm and dry and fed are what they are partly because of choices made by people long dead. Even if I didn’t want to burn fossil fuels, I’m embedded in a culture set up to do so.” (Garvey, 2008, p60)

Part of the problem is that the physical climate system is slow to respond to our additional greenhouse gas emissions, and similarly slow to respond to reductions in emissions. The first part of this is core to a basic understanding of climate change, as it’s built into the idea of equilibrium climate sensitivity (roughly speaking, the expected temperature rise for each doubling of CO2 concentrations in the atmosphere). The extra heat that’s trapped by the additional greenhouse gases builds up over time, and the planet warms slowly, but the oceans have such a large thermal mass, it takes decades for this warming process to complete.

Unfortunately, the second part, that the planet takes a long time to respond to reductions in emissions, is harder to explain, largely because of the common assumption that CO2 will behave like other pollutants, which wash out of the atmosphere fairly quickly once we stop emitting them. This assumption underlies much of the common wait-and-see response to climate change, as it gives rise to the myth that once we get serious about climate change (e.g. because we start to see major impacts), we can fix the problem fairly quickly. Unfortunately, this is not true at all, because CO2 is a long-lived greenhouse gas. About half of human CO2 emissions are absorbed by the oceans and soils, over a period of several decades. The remainder stays in the atmosphere  There are several natural processes that remove the remaining CO2 from the atmosphere, but they take thousands of years, which means that even with zero greenhouse gas emissions, we’re likely stuck with the consequences of life on a warmer planet for centuries.

So the physical climate system presents us with two forms of inertia, one that delays the warming due to greenhouse gas emissions, and, one that delays the reduction in that warming in response to reduced emissions:

1. The thermal inertia of the planet’s surface (largely due to the oceans), by which the planet can keep absorbing extra heat for years before it makes a substantial difference to surface temperatures. (scale: decades)
2. The carbon cycle inertia by which CO2 is only removed from the atmosphere very slowly, and has a continued warming effect for as long as it’s there. (scale: decades to millennia)

For more on how these forms of inertia affect future warming scenarios, see my post on committed warming.

But these are not the only forms of inertia that matter. There are also various kinds of inertia in the socio-economic system that slow down our response to climate change. For example, Davis et. al. attempt to quantify the emissions from all the existing energy infrastructure (power plants, factories, cars, buildings, etc that already exist and are in use), because even under the most optimistic scenario, it will take decades to replace all this infrastructure with clean energy alternatives. Here’s an example of their analysis, under the assumption that things we’ve already built will not be retired early. This assumption is reasonable because (1) its rare that we’re willing to bear the cost of premature retirement of infrastructure and (2) it’s going to be hard enough building enough new clean energy infrastructure fast enough to replace stuff that has worn out while meeting increasing demand.

Expected ongoing carbon dioxide emissions from existing infrastructure. Includes primary infrastructure only – i.e. infrastructure that directly releases CO2 (e.g. cars & trucks), but not infrastructure that encourages the continued production of devices that emit CO2. (e.g. the network of interstate highways in the US). From Davis et al, 2010.

So that gives us our third form of inertia:

3. Infrastructural inertia from existing energy infrastructure, as emissions of greenhouse gases will continue from everything we’ve built in the past, until it can be replaced. (scale: decades)

We’ve known about the threat of climate change for decades, and various governments and international negotiations have attempted to deal with it, and yet have made very little progress. Which suggests there are more forms of inertia that we ought to be able to name and quantify. To do this, we need to look at the broader socio-economic system that ought to allow us as a society to respond to the threat of climate change. Here’s a schematic of that system, as a systems dynamic model:

The socio-geophysical system. Arrows labelled ‘+’ are positive influence links (“A rise in X tends to cause a rise in Y, and a fall in X tends to cause a fall in Y”). Arrows labelled ‘-‘ represent negative links, where a rise in X tends to cause a fall in Y, and vice versa. The arrow labelled with a tap (faucet) is an accumulation link: Y will continue to rise even while X is falling, until X reaches net zero.

Broadly speaking, decarbonization will require both changes in technology and changes in human behaviour. But before we can do that, we have to recognize and agree that there is a problem, develop an agreed set of coordinated actions to tackle it, and then implement the policy shifts and behaviour changes to get us there.

At first, this diagram looks promising: once we realise how serious climate change is, we’ll take the corresponding actions, and that will bring down emissions, solving the problem. In other words, the more carbon emissions go up, the more they should drive a societal response, which in turn (eventually) will reduce emissions again. But the diagram includes a subtle but important twist: the link from carbon emissions to atmospheric concentrations is an accumulation link. Even as emissions fall, the amount of greenhouse gases in the atmosphere continue to rise. The latter rise only stops when carbon emissions reach zero. Think of a tap on the bathtub – if you reduce the inflow of water, the level of water in the tub still rises, until you turn the tap off completely.

Worse still, there are plenty more forms of inertia hidden in the diagram, because each of the causal links takes time to operate. I’ve given these additional sources of inertia names:

Sources of inertia in the socio-geophysical climate system

For example, there are forms of inertia that delay the impacts of increased temperatures, both on ecosystems and on human society. Most of the systems that are impacted by climate change can absorb smaller changes in the climate without much noticeable difference, but then reach a threshold whereby they can no longer be sustained. I’ve characterized two forms of inertia here:

4. Natural variability (or “signal to noise”) inertia, which arises because initially, temperature increases due to climate change are much smaller than internal variability with daily and seasonal weather patterns. Hence it takes a long time for the ‘signal’ of climate change to emerge from the noise of natural variability. (scale: decades)
5. Ecosystem resilience. We tend to think of resilience as a good thing – defined informally as the ability of a system to ‘bounce back’ after a shock. But resilience can also mask underlying changes that push a system closer and closer to a threshold beyond which it cannot recover. So this form of inertia acts by masking the effect of that change, sometimes until it’s too late to act. (scale: years to decades)

Then, once we identify the impacts of climate change (whether in advance or after the fact), it takes time for these to feed into the kind of public concern needed to build agreement on the need for action:

6. Societal resilience. Human society is very adaptable. When storms destroy our buildings, we just rebuild them a little stronger. When drought destroys our crops, we just invent new forms of irrigation. Just as with ecosystems, there is a limit to this kind of resilience, when subjected to a continual change. But our ability to shrug and get on with things causes a further delay in the development of public concern about climate change. (scale: decades?)
   
7. Denial. Perhaps even stronger than human resilience is our ability to fool ourselves into thinking that something bad is not happening, and to look for other explanations than the ones that best fit the evidence. Denial is a pretty powerful form of inertia. Denial stops addicts from acknowledging they need to seek help to overcome addiction, and it stops all of us from acknowledging we have a fossil fuel addiction, and need help to deal with it. (scale: decades to generations?)

Even then, public concern doesn’t immediately translate into effective action because of:

8. Individualism. A frequent response to discussions on climate change is to encourage people to make personal changes in their lives: change your lightbulbs, drive a little less, fly a little less. While these things are important in the process of personal discovery, by helping us understanding our individual impact on the world, they are a form of voluntary action only available to the privileged, and hence do not constitute a systemic solution to climate change. When the systems we live in drive us towards certain consumption patterns, it takes a lot of time and effort to choose a low-carbon lifestyle. So the only way this scales is through collective political action: getting governments to change the regulations and price structures that shape what gets built and what we consume, and making governments and corporations accountable for cutting their greenhouse gas contributions. (scale: decades?)

When we get serious about the need for coordinated action, there are further forms of inertia that come into play:

9. Missing governance structures. We simply don’t have the kind of governance at either the national or international level that can put in place meaningful policy instruments to tackle climate change. The Kyoto process failed because the short term individual interests of the national governments who have the power to act always tend to outweigh the long term collective threat of climate change. The Paris agreement is woefully inadequate for the same reason. Similarly, national governments are hampered by the need to respond to special interest groups (especially large corporations), which means legislative change is a slow, painful process. (scale: decades!)
10. Bureaucracy. Hampers implementation of new policy tools. It takes time to get legislation formulated and agreed, and it takes time to set up the necessary institutions to ensure they are implemented. (scale: years)
11. Social Resistance. People don’t like change, and some groups fight hard to resist changes that conflict with their own immediate interests. Every change in social norms is accompanied by pushback. And even when we welcome change and believe in it, we often slip back into old habits. (scale: years? generations?)

Finally, development and deployment of clean energy solutions experience a large number of delays:

12. R&D lag. It takes time to ramp up new research and development efforts, due to the lack of qualified personnel, the glacial speed that research institutions such as universities operate, and the tendency, especially in academia, for researchers to keep working on what they’ve always worked on in the past, rather than addressing societally important issues. Research on climate solutions is inherently trans-disciplinary, and existing research institutions tend to be very bad at supporting work that crosses traditional boundaries. (scale: decades?)
13. Investment lag: A wholescale switch from fossil fuels to clean energy and energy efficiency will require huge upfront investment. Agencies that have funding to enable this switch (governments, investment portfolio managers, venture capitalists) tend to be very risk averse, and so prefer things that they know offer a return on investment – e.g. more oil wells and pipelines rather than new cleantech alternatives (scale: years to decades)
14. Diffusion of innovation: new technologies tend to take a long time to reach large scale deployment, following the classic s-shaped curve, with a small number of early adopters, and, if things go well, a steadily rising adoption curve, followed by a tailing off as laggards resist new technologies. Think about electric cars: while the technology has been available for years, they still only constitute less than 1% of new car sales today. Here’s a study that predicts this will rise to 35% by 2040. Think about that for a moment – if we follow the expected diffusion of innovation pattern, two thirds of new cars in 2040 will still have internal combustion engines. (scale: decades)

All of these forms of inertia slow the process of dealing with climate change, allowing the warming to steadily increase while we figure out how to overcome them. So the key problem isn’t how to address climate change by switching from the current fossil fuel economy to a carbon-neutral one – we probably have all the technologies to do this today. The problem is how to do it fast enough. To stay below 2°C of warming, the world needs to cut greenhouse gas emissions by 50% by 2030, and achieve carbon neutrality in the second half of the century. We’ll have to find a way of overcoming many different types of inertia if we are to make it.