Modelling Geoengineering, Part II

Near the end of my summer at the UVic Climate Lab, all the scientists seemed to go on vacation at the same time and us summer students were left to our own devices. I was instructed to teach Jeremy, Andrew Weaver’s other summer student, how to use the UVic climate model – he had been working with weather station data for most of the summer, but was interested in Earth system modelling too.

Jeremy caught on quickly to the basics of configuration and I/O, and after only a day or two, we wanted to do something more exciting than the standard test simulations. Remembering an old post I wrote, I dug up this paper (open access) by Damon Matthews and Ken Caldeira, which modelled geoengineering by reducing incoming solar radiation uniformly across the globe. We decided to replicate their method on the newest version of the UVic ESCM, using the four RCP scenarios in place of the old A2 scenario. We only took CO2 forcing into account, though: other greenhouse gases would have been easy enough to add in, but sulphate aerosols are spatially heterogeneous and would complicate the algorithm substantially.

Since we were interested in the carbon cycle response to geoengineering, we wanted to prescribe CO2 emissions, rather than concentrations. However, the RCP scenarios prescribe concentrations, so we had to run the model with each concentration trajectory and find the equivalent emissions timeseries. Since the UVic model includes a reasonably complete carbon cycle, it can “diagnose” emissions by calculating the change in atmospheric carbon, subtracting contributions from land and ocean CO2 fluxes, and assigning the residual to anthropogenic sources.

After a few failed attempts to represent geoengineering without editing the model code (e.g., altering the volcanic forcing input file), we realized it was unavoidable. Model development is always a bit of a headache, but it makes you feel like a superhero when everything falls into place. The job was fairly small – just a few lines that culminated in equation 1 from the original paper – but it still took several hours to puzzle through the necessary variable names and header files! Essentially, every timestep the model calculates the forcing from CO2 and reduces incoming solar radiation to offset that, taking changing planetary albedo into account. When we were confident that the code was working correctly, we ran all four RCPs from 2006-2300 with geoengineering turned on. The results were interesting (see below for further discussion) but we had one burning question: what would happen if geoengineering were suddenly turned off?

By this time, having completed several thousand years of model simulations, we realized that we were getting a bit carried away. But nobody else had models in the queue – again, they were all on vacation – so our simulations were running three times faster than normal. Using restart files (written every 100 years) as our starting point, we turned off geoengineering instantaneously for RCPs 6.0 and 8.5, after 100 years as well as 200 years.

Results

Similarly to previous experiments, our representation of geoengineering still led to sizable regional climate changes. Although average global temperatures fell down to preindustrial levels, the poles remained warmer than preindustrial while the tropics were cooler:

Also, nearly everywhere on the globe became drier than in preindustrial times. Subtropical areas were particularly hard-hit. I suspect that some of the drying over the Amazon and the Congo is due to deforestation since preindustrial times, though:

Jeremy also made some plots of key one-dimensional variables for RCP8.5, showing the results of no geoengineering (i.e. the regular RCP – yellow), geoengineering for the entire simulation (red), and geoengineering turned off in 2106 (green) or 2206 (blue):

It only took about 20 years for average global temperature to fall back to preindustrial levels. Changes in solar radiation definitely work quickly. Unfortunately, changes in the other direction work quickly too: shutting off geoengineering overnight led to rates of warming up to 5 C / decade, as the climate system finally reacted to all the extra CO2. To put that in perspective, we’re currently warming around 0.2 C / decade, which far surpasses historical climate changes like the Ice Ages.

Sea level rise (due to thermal expansion only – the ice sheet component of the model isn’t yet fully implemented) is directly related to temperature, but changes extremely slowly. When geoengineering is turned off, the reversals in sea level trajectory look more like linear offsets from the regular RCP.

Sea ice area, in contrast, reacts quite quickly to changes in temperature. Note that this data gives annual averages, rather than annual minimums, so we can’t tell when the Arctic Ocean first becomes ice-free. Also, note that sea ice area is declining ever so slightly even with geoengineering – this is because the poles are still warming a little bit, while the tropics cool.

Things get really interesting when you look at the carbon cycle. Geoengineering actually reduced atmospheric CO2 concentrations compared to the regular RCP. This was expected, due to the dual nature of carbon cycle feedbacks. Geoengineering allows natural carbon sinks to enjoy all the benefits of high CO2 without the associated drawbacks of high temperatures, and these sinks become stronger as a result. From looking at the different sinks, we found that the sequestration was due almost entirely to the land, rather than the ocean:

In this graph, positive values mean that the land is a net carbon sink (absorbing CO2), while negative values mean it is a net carbon source (releasing CO2). Note the large negative spikes when geoengineering is turned off: the land, adjusting to the sudden warming, spits out much of the carbon that it had previously absorbed.

Within the land component, we found that the strengthening carbon sink was due almost entirely to soil carbon, rather than vegetation:

This graph shows total carbon content, rather than fluxes – think of it as the integral of the previous graph, but discounting vegetation carbon.

Finally, the lower atmospheric CO2 led to lower dissolved CO2 in the ocean, and alleviated ocean acidification very slightly. Again, this benefit quickly went away when geoengineering was turned off.

Conclusions

Is geoengineering worth it? I don’t know. I can certainly imagine scenarios in which it’s the lesser of two evils, and find it plausible (even probable) that we will reach such a scenario within my lifetime. But it’s not something to undertake lightly. As I’ve said before, desperate governments are likely to use geoengineering whether or not it’s safe, so we should do as much research as possible ahead of time to find the safest form of implementation.

The modelling of geoengineering is in its infancy, and I have a few ideas for improvement. In particular, I think it would be interesting to use a complex atmospheric chemistry component to allow for spatial variation in the forcing reduction through sulphate aerosols: increase the aerosol optical depth over one source country, for example, and let it disperse over time. I’d also like to try modelling different kinds of geoengineering – sulphate aerosols as well as mirrors in space and iron fertilization of the ocean.

Jeremy and I didn’t research anything that others haven’t, so this project isn’t original enough for publication, but it was a fun way to stretch our brains. It was also a good topic for a post, and hopefully others will learn something from our experiments.

Above all, leave over-eager summer students alone at your own risk. They just might get into something like this.

Cumulative Emissions and Climate Models

As my summer research continues, I’m learning a lot about previous experiments that used the UVic ESCM (Earth System Climate Model), as well as beginning to run my own. Over the past few years, the UVic model has played an integral role in a fascinating little niche of climate research: the importance of cumulative carbon emissions.

So far, global warming mitigation policies have focused on choosing an emissions pathway: making a graph of desired CO2 emissions vs. time, where emissions slowly reduce to safer levels. However, it turns out that the exact pathway we take doesn’t actually matter. All that matters is the area under the curve: the total amount of CO2 we emit, or “cumulative emissions” (Zickfeld et al, 2009). So if society decides to limit global warming to 2°C (a common target), there is a certain amount of total CO2 that the entire world is allowed to emit. We can use it all up in the first ten years and then emit nothing, or we can spread it out – either way, it will lead to the same amount of warming.

If you delve a little deeper into the science, it turns out that temperature change is directly proportional to cumulative emissions (Matthews et al, 2009). In other words, if you draw a graph of the total amount of warming vs. total CO2 emitted, it will be a straight line.

This is counter-intuitive, because the intermediate processes are definitely not straight lines. Firstly, the graph of warming vs. CO2 concentrations is logarithmic: as carbon dioxide builds up in the atmosphere, each extra molecule added has less and less effect on the climate.

However, as carbon dioxide builds up and the climate warms, carbon sinks (which suck up some of our emissions) become less effective. For example, warmer ocean water can’t hold as much CO2, and trees subjected to heat stress often die and stop photosynthesizing. Processes that absorb CO2 become less effective, so more of our emissions actually stay in the air. Consequently, the graph of CO2 concentrations vs. CO2 emissions is exponential.

These two relationships, warming vs. concentrations and concentrations vs. emissions, more or less cancel each other out, making total warming vs. total emissions linear. It doesn’t matter how much CO2 was in the air to begin with, or how fast the allowable emissions get used up. Once society decides how much warming is acceptable, all we need to do is nail down the proportionality constant (the slope of the straight line) in order to find out how much carbon we have to work with. Then, that number can be handed to economists, who will figure out the best way to spread out those emissions while causing minimal financial disruption.

Finding that slope is a little tricky, though. Best estimates, using models as well as observations, generally fall between 1.5°C and 2°C for every trillion tonnes of carbon emitted (Matthews et al, 2009; Allen et al, 2009; Zickfeld et al, 2009). Keep in mind that we’ve already emitted about 0.6 trillion tonnes of carbon (University of Oxford). Following a theme commonly seen in climate research, the uncertainty is larger on the high end of these slope estimates than on the low end. So if the real slope is actually lower than our best estimate, it’s probably only a little bit lower; if it’s actually higher than our best estimate, it could be much higher, and the problem could be much worse than we thought.

Also, this approach ignores other human-caused influences on global temperature, most prominently sulfate aerosols (which cause cooling) and greenhouse gases other than carbon dioxide (which cause warming). Right now, these two influences basically cancel, which is convenient for scientists because it means we can ignore both of them. Typically, we assume that they will continue to cancel far into the future, which might not be the case – there’s a good chance that developing countries like China and India will reduce their emissions of sulfate aerosols, allowing the non-CO2 greenhouse gases to dominate and cause warming. If this happened, we couldn’t even lump the extra greenhouse gases into the allowable CO2 emissions, because the warming they cause does depend on the exact pathway. For example, methane has such a short atmospheric lifetime that “cumulative methane emissions” is a useless measurement, and certainly isn’t directly proportional to temperature change.

This summer, one of my main projects at UVic is to compare what different models measure the slope of temperature change vs. cumulative CO2 emissions to be. As part of the international EMIC intercomparison project that the lab is coordinating, different modelling groups have sent us their measurements of allowable cumulative emissions for 1.5°C, 2°C, 3°C, and 4°C global warming. Right now (quite literally, as I write this) I’m running the same experiments on the UVic model. It’s very exciting to watch the results trickle in. Perhaps my excitement towards the most menial part of climate modelling, watching as the simulation chugs along, is a sign that I’m on the right career path.

Does Breathing Contribute to CO2 Buildup in the Atmosphere?

I was recently honoured to join Skeptical Science, a comprehensive database of rebuttals to common climate change misconceptions, as an author. Here I am republishing my first article regarding the common myth that breathing out contributes to the buildup of atmospheric carbon dioxide. It is the Intermediate version, and I have also written a very similar Basic version, which includes a diagram by John Cook. Enjoy!

The very first time you learned about carbon dioxide was probably in grade school: We breathe in oxygen and breathe out carbon dioxide. Any eight-year-old can rattle off this fact.

More specifically, the mitochondria within our cells perform cellular respiration: they burn carbohydrates (in the example shown below, glucose) in the oxygen that we breathe in to yield carbon dioxide and water, which we exhale as waste products, as well as energy, which is required to maintain our bodily processes and keep us alive.

carbohydrates + oxygen → carbon dixoide + water + energy

It should come as no surprise that, when confronted with the challenge of reducing our carbon emissions from the burning of fossil fuels, some people angrily proclaim, “Why should we bother? Even breathing out creates carbon emissions!”

This statement fails to take into account the other half of the carbon cycle. As you also learned in grade school, plants are the opposite to animals in this respect: Through photosynthesis, they take in carbon dioxide and release oxygen, in a chemical equation opposite to the one above. (They also perform some respiration, because they need to eat as well, but it is outweighed by the photosynthesis.) The carbon they collect from the CO2 in the air forms their tissues – roots, stems, leaves, and fruit.

These tissues form the base of the food chain, as they are eaten by animals, which are eaten by other animals, and so on. As humans, we are part of this food chain. All the carbon in our body comes either directly or indirectly from plants, which took it out of the air only recently.

Therefore, when we breathe out, all the carbon dioxide we exhale has already been accounted for. By performing cellular respiration, we are simply returning to the air the same carbon that was there to begin with. Remember, it’s a carbon cycle, not a straight line – and a good thing, too!