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Today my very first scientific publication is appearing in Geophysical Research Letters. During my summer at UVic, I helped out with a model intercomparison project regarding the effect of climate change on Atlantic circulation, and was listed as a coauthor on the resulting paper. I suppose I am a proper scientist now, rather than just a scientist larva.

The Atlantic meridional overturning circulation (AMOC for short) is an integral part of the global ocean conveyor belt. In the North Atlantic, a massive amount of water near the surface, cooling down on its way to the poles, becomes dense enough to sink. From there it goes on a thousand-year journey around the world – inching its way along the bottom of the ocean, looping around Antarctica – before finally warming up enough to rise back to the surface. A whole multitude of currents depend on the AMOC, most famously the Gulf Stream, which keeps Europe pleasantly warm.

Some have hypothesized that climate change might shut down the AMOC: the extra heat and freshwater (from melting ice) coming into the North Atlantic could conceivably lower the density of surface water enough to stop it sinking. This happened as the world was coming out of the last ice age, in an event known as the Younger Dryas: a huge ice sheet over North America suddenly gave way, drained into the North Atlantic, and shut down the AMOC. Europe, cut off from the Gulf Stream and at the mercy of the ice-albedo feedback, experienced another thousand years of glacial conditions.

A shutdown today would not lead to another ice age, but it could cause some serious regional cooling over Europe, among other impacts that we don’t fully understand. Today, though, there’s a lot less ice to start with. Could the AMOC still shut down? If not, how much will it weaken due to climate change? So far, scientists have answered these two questions with “probably not” and “something like 25%” respectively. In this study, we analysed 30 climate models (25 complex CMIP5 models, and 5 smaller, less complex EMICs) and came up with basically the same answer. It’s important to note that none of the models include dynamic ice sheets (computational glacial dynamics is a headache and a half), which might affect our results.

Models ran the four standard RCP experiments from 2006-2100. Not every model completed every RCP, and some extended their simulations to 2300 or 3000. In total, there were over 30 000 model years of data. We measured the “strength” of the AMOC using the standard unit Sv (Sverdrups), where each Sv is 1 million cubic metres of water per second.

Only two models simulated an AMOC collapse, and only at the tail end of the most extreme scenario (RCP8.5, which quite frankly gives me a stomachache). Bern3D, an EMIC from Switzerland, showed a MOC strength of essentially zero by the year 3000; CNRM-CM5, a GCM from France, stabilized near zero by 2300. In general, the models showed only a moderate weakening of the AMOC by 2100, with best estimates ranging from a 22% drop for RCP2.6 to a 40% drop for RCP8.5 (with respect to preindustrial conditions).

Are these somewhat-reassuring results trustworthy? Or is the Atlantic circulation in today’s climate models intrinsically too stable? Our model intercomparison also addressed that question, using a neat little scalar metric known as Fov: the net amount of freshwater travelling from the AMOC to the South Atlantic.

The current thinking in physical oceanography is that the AMOC is more or less binary – it’s either “on” or “off”. When AMOC strength is below a certain level (let’s call it A), its only stable state is “off”, and the strength will converge to zero as the currents shut down. When AMOC strength is above some other level (let’s call it B), its only stable state is “on”, and if you were to artificially shut it off, it would bounce right back up to its original level. However, when AMOC strength is between A and B, both conditions can be stable, so whether it’s on or off depends on where it started. This phenomenon is known as hysteresis, and is found in many systems in nature.

This figure was not part of the paper. I made it just now in MS Paint.

Here’s the key part: when AMOC strength is less than A or greater than B, Fov is positive and the system is monostable. When AMOC strength is between A and B, Fov is negative and the system is bistable. The physical justification for Fov is its association with the salt advection feedback, the sign of which is opposite Fov: positive Fov means the salt advection feedback is negative (i.e. stabilizing the current state, so monostable); a negative Fov means the salt advection feedback is positive (i.e. reinforcing changes in either direction, so bistable).

Most observational estimates (largely ocean reanalyses) have Fov as slightly negative. If models’ AMOCs really were too stable, their Fov‘s should be positive. In our intercomparison, we found both positives and negatives – the models were kind of all over the place with respect to Fov. So maybe some models are overly stable, but certainly not all of them, or even the majority.

As part of this project, I got to write a new section of code for the UVic model, which calculated Fov each timestep and included the annual mean in the model output. Software development on a large, established project with many contributors can be tricky, and the process involved a great deal of head-scratching, but it was a lot of fun. Programming is so satisfying.

Beyond that, my main contribution to the project was creating the figures and calculating the multi-model statistics, which got a bit unwieldy as the model count approached 30, but we made it work. I am now extremely well-versed in IDL graphics keywords, which I’m sure will come in handy again. Unfortunately I don’t think I can reproduce any figures here, as the paper’s not open-access.

I was pretty paranoid while coding and doing calculations, though – I kept worrying that I would make a mistake, never catch it, and have it dredged out by contrarians a decade later (“Kate-gate”, they would call it). As a climate scientist, I suppose that comes with the job these days. But I can live with it, because this stuff is just so darned interesting.

During my summer at UVic, two PhD students at the lab (Andrew MacDougall and Chris Avis) as well as my supervisor (Andrew Weaver) wrote a paper modelling the permafrost carbon feedback, which was recently published in Nature Geoscience. I read a draft version of this paper several months ago, and am very excited to finally share it here.

Studying the permafrost carbon feedback is at once exciting (because it has been left out of climate models for so long) and terrifying (because it has the potential to be a real game-changer). There is about twice as much carbon frozen into permafrost than there is floating around in the entire atmosphere. As high CO2 levels cause the world to warm, some of the permafrost will thaw and release this carbon as more CO2 – causing more warming, and so on. Previous climate model simulations involving permafrost have measured the CO2 released during thaw, but haven’t actually applied it to the atmosphere and allowed it to change the climate. This UVic study is the first to close that feedback loop (in climate model speak we call this “fully coupled”).

The permafrost part of the land component was already in place – it was developed for Chris’s PhD thesis, and implemented in a previous paper. It involves converting the existing single-layer soil model to a multi-layer model where some layers can be frozen year-round. Also, instead of the four RCP scenarios, the authors used DEPs (Diagnosed Emission Pathways): exactly the same as RCPs, except that CO2 emissions, rather than concentrations, are given to the model as input. This was necessary so that extra emissions from permafrost thaw would be taken into account by concentration values calculated at the time.

As a result, permafrost added an extra 44, 104, 185, and 279 ppm of CO2 to the atmosphere for DEP 2.6, 4.5, 6.0, and 8.5 respectively. However, the extra warming by 2100 was about the same for each DEP, with central estimates around 0.25 °C. Interestingly, the logarithmic effect of CO2 on climate (adding 10 ppm to the atmosphere causes more warming when the background concentration is 300 ppm than when it is 400 ppm) managed to cancel out the increasing amounts of permafrost thaw. By 2300, the central estimates of extra warming were more variable, and ranged from 0.13 to 1.69 °C when full uncertainty ranges were taken into account. Altering climate sensitivity (by means of an artificial feedback), in particular, had a large effect.

As a result of the thawing permafrost, the land switched from a carbon sink (net CO2 absorber) to a carbon source (net CO2 emitter) decades earlier than it would have otherwise – before 2100 for every DEP. The ocean kept absorbing carbon, but in some scenarios the carbon source of the land outweighed the carbon sink of the ocean. That is, even without human emissions, the land was emitting more CO2 than the ocean could soak up. Concentrations kept climbing indefinitely, even if human emissions suddenly dropped to zero. This is the part of the paper that made me want to hide under my desk.

This scenario wasn’t too hard to reach, either – if climate sensitivity was greater than 3°C warming per doubling of CO2 (about a 50% chance, as 3°C is the median estimate by scientists today), and people followed DEP 8.5 to at least 2013 before stopping all emissions (a very intense scenario, but I wouldn’t underestimate our ability to dig up fossil fuels and burn them really fast), permafrost thaw ensured that CO2 concentrations kept rising on their own in a self-sustaining loop. The scenarios didn’t run past 2300, but I’m sure that if you left it long enough the ocean would eventually win and CO2 would start to fall. The ocean always wins in the end, but things can be pretty nasty until then.

As if that weren’t enough, the paper goes on to list a whole bunch of reasons why their values are likely underestimates. For example, they assumed that all emissions from permafrost were  CO2, rather than the much stronger CH4 which is easily produced in oxygen-depleted soil; the UVic model is also known to underestimate Arctic amplification of climate change (how much faster the Arctic warms than the rest of the planet). Most of the uncertainties – and there are many – are in the direction we don’t want, suggesting that the problem will be worse than what we see in the model.

This paper went in my mental “oh shit” folder, because it made me realize that we are starting to lose control over the climate system. No matter what path we follow – even if we manage slightly negative emissions, i.e. artificially removing CO2 from the atmosphere – this model suggests we’ve got an extra 0.25°C in the pipeline due to permafrost. It doesn’t sound like much, but add that to the 0.8°C we’ve already seen, and take technological inertia into account (it’s simply not feasible to stop all emissions overnight), and we’re coming perilously close to the big nonlinearity (i.e. tipping point) that many argue is between 1.5 and 2°C. Take political inertia into account (most governments are nowhere near even creating a plan to reduce emissions), and we’ve long passed it.

Just because we’re probably going to miss the the first tipping point, though, doesn’t mean we should throw up our hands and give up. 2°C is bad, but 5°C is awful, and 10°C is unthinkable. The situation can always get worse if we let it, and how irresponsible would it be if we did?

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.

Soliloquy at Twilight

And now for something completely different: this is one of my favourite poems by the brilliant Pablo Neruda, translated from the original Spanish by Alastair Reid.

It reminds me of all the bickering and politics that get in the way of climate science, and all the trials and tribulations of climate change communication.

Given that now perhaps
we are seriously alone,
I mean to ask some questions -
we’ll speak man to man.

With you, with that passerby,
with those born yesterday,
with all those who died,
and with those to be born tomorrow,
I want to speak without being overheard,
without them always whispering,
without things getting changed
in ears along the way.

Well then, where from, where to?
What made you decide to be born?
Do you know that the world is small,
scarcely the size of an apple,
like a little hard stone,
and that brothers kill each other
for a fistful of dust?

For the dead there’s land enough!

You know by now, or you will,
that time is scarcely one day
and a day is a single drop?

How will you be, how have you been?
Sociable, talkative, silent?
Are you going to outdistance
those who were born with you?
Or will you be sticking a pistol
grimly into their kidneys?

What will you do with so many days
left over, and even more,
with so many missing days?

Do you know there’s nobody in the streets
and nobody in the houses?

There are only eyes in the windows.

If you don’t have somewhere to sleep,
knock on a door and it will open,
open up to a certain point
and you’ll see it’s cold inside,
and that that house is empty
and wants nothing to do with you;
your stories are worth nothing,
and if you insist on being gentle,
the dog and cat will bite you.

Until later, till you forget me -
I’m going, since I don’t have time
to ask the wind more questions.

I can scarcely walk properly,
I’m in such a hurry.
Somewhere they’re waiting
to accuse me of something
and I have to defend myself;
nobody knows what it’s about
except that it’s urgent,
and if I don’t go, it will close,
and how can I hold my own
if I knock and nobody opens the door?

Until later, we’ll speak before then.
Or speak after, I don’t remember,
or perhaps we haven’t even met
or cannot communicate.
I have these crazy habits -
I speak, there is no one and I don’t listen,
I ask myself questions and never answer.

- Pablo Neruda

Since I Last Wrote…

Since I last wrote, I finished my summer research at Andrew Weaver’s lab (more on that in the weeks and months to come, as our papers work through peer review). I moved back home to the Prairies, which seem unnaturally hot, flat and dry compared to BC. Perhaps what I miss most is the ocean – the knowledge that the nearest coastline is more than a thousand kilometres away gives me an uncomfortable feeling akin to claustrophobia.

During that time, the last story I covered has developed significantly. Before September even began, Arctic sea ice extent reached record low levels. It’s currently well below the previous record, held in 2007, and will continue to decline for two or three more weeks before it levels off:

Finally, El Niño conditions are beginning to emerge in the Pacific Ocean. In central Canada we are celebrating, because El Niño tends to produce warmer-than-average winters (although last winter was mysteriously warm despite the cooling influence of La Niña – not a day below -30 C!) The impacts of El Niño are different all over the world, but overall it tends to boost global surface temperatures. Combine this effect with the current ascent from a solar minimum and the stronger-than-ever greenhouse gas forcing, and it looks likely that 2013 will break global temperature records. That’s still a long way away, though, and who knows what will happen before then?

Arctic sea ice is in the midst of a record-breaking melt season. This is yet another symptom of human-caused climate change progressing much faster than scientists anticipated.

Every year, the frozen surface of the Arctic Ocean waxes and wanes, covering the largest area in February or March and the smallest in September. Over the past few decades, these September minima have been getting smaller and smaller. The lowest sea ice extent on record occurred in 2007, followed closely by 2011, 2008, 2010, and 2009. That is, the five lowest years on record all happened in the past five years. While year-to-year weather conditions, like summer storms, impact the variability of Arctic sea ice cover, the undeniable downward trend can only be explained by human-caused climate change.

The 2012 melt season started off hopefully, with April sea ice extent near the 1979-2000 average. Then things took a turn for the worse, and sea ice was at record or near-record low conditions for most of the summer. In early August, a storm spread out the remaining ice, exacerbating the melt. Currently, sea ice is significantly below the previous record for this time of year. See the light blue line in the figure below:

The 2012 minimum is already the fifth-lowest on record for any day of the year – and the worst part is, it will keep melting for about another month. At this rate, it’s looking pretty likely that we’ll break the 2007 record and hit an all-time low in September. Sea ice volume, rather than extent, is in the same situation.

Computer models of the climate system have a difficult time reproducing this sudden melt. As recently as 2007, the absolute worst-case projections showed summer Arctic sea ice disappearing around 2100. Based on observations, scientists are now confident that will happen well before 2050, and possibly within a decade. Climate models, which many pundits like to dismiss as “alarmist,” actually underestimated the severity of the problem. Uncertainty cuts both ways.

The impacts of an ice-free Arctic Ocean will be wide-ranging and severe. Luckily, melting sea ice does not contribute to sea level rise (only landlocked ice does, such as the Greenland and Antarctic ice sheets), but many other problems remain. The Inuit peoples of the north, who depend on sea ice for hunting, will lose an essential source of food and culture. Geopolitical tensions regarding ownership of the newly-accessible Arctic waters are likely. Changes to the Arctic food web, from blooming phytoplankton to dwindling polar bears, will irreversibly alter the ecosystem. While scientists don’t know exactly what this new Arctic will look like, it is certain to involve a great deal of disruption and suffering.

Daily updates on Arctic sea ice conditions are available from the NSIDC website.

More on Phytoplankton

On the heels of my last post about iron fertilization of the ocean, I found another interesting paper on the topic. This one, written by Long Cao and Ken Caldeira in 2010, was much less hopeful.

Instead of a small-scale field test, Cao and Caldeira decided to model iron fertilization using the ocean GCM from Lawrence Livermore National Laboratory. To account for uncertainties, they chose to calculate an upper bound on iron fertilization rather than a most likely scenario. That is, they maxed out phytoplankton growth until something else became the limiting factor – in this case, phosphates. On every single cell of the sea surface, the model phytoplankton were programmed to grow until phosphate concentrations were zero.

A 2008-2100 simulation implementing this method was forced with CO2 emissions data from the A2 scenario. An otherwise identical A2 simulation did not include the ocean fertilization, to act as a control. Geoengineering modelling is strange that way, because there are multiple definitions of “control run”: a non-geoengineered climate that is allowed to warm unabated, as well as preindustrial conditions (the usual definition in climate modelling).

Without any geoengineering, atmospheric CO2 reached 965 ppm by 2100. With the maximum amount of iron fertilization possible, these levels only fell to 833 ppm. The mitigation of ocean acidification was also quite modest: the sea surface pH in 2100 was 7.74 without geoengineering, and 7.80 with. Given the potential side effects of iron fertilization, is such a small improvement worth the trouble?

Unfortunately, the ocean acidification doesn’t end there. Although the problem was lessened somewhat at the surface, deeper layers in the ocean actually became more acidic. There was less CO2 being gradually mixed in from the atmosphere, but another source of dissolved carbon appeared: as the phytoplankton died and sank, they decomposed a little bit and released enough CO2 to cause a net decrease in pH compared to the control run.

In the diagram below, compare the first row (A2 control run) to the second (A2 with iron fertilization). The more red the contours are, the more acidic that layer of the ocean is with respect to preindustrial conditions. The third row contains data from another simulation in which emissions were allowed to increase just enough to offest sequestration by phytoplankton, leading to the same CO2 concentrations as the control run. The general pattern – iron fertilization reduces some acidity at the surface, but increases it at depth – is clear.

depth vs. latitude at 2100 (left); depth vs. time (right)

The more I read about geoengineering, the more I realize how poor the associated cost-benefit ratios might be. The oft-repeated assertion is true: the easiest way to prevent further climate change is, by a long shot, to simply reduce our emissions.

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