The 9th International Carbon Dioxide Conference - Beijing

Firstly, apologies for my long absence.

Not so long ago I was fortunate enough to attend the 9th International Carbon Dioxide Conference, hosted in Beijing by the Chinese Academy of Sciences. It was truly my first big international conference, and an opportunity to present the first set of results I had obtained. Well of course that’s what I said I was going to do ... It seemed that almost as soon as I submitted my abstract and it was accepted for a poster presentation (I was at first a bit disappointed, until I saw the author list of those presenting orals and realised that perhaps just listening would be a good thing this time round) that things started to delay my ambitions of having all my results wrapped up by the time of the conference. But through many late nights and many more antacids later, I was able to assemble something with which I was rather pleased. This in part played a role in my disappearance from the carbon science blogging scene.

When I finished off my poster, I thought I was being terribly clever when I put the link to the @CapeCarbon twitter account at the end of the poster. I then discovered that Twitter is not accessible in China – which was a bit of a buzz kill. So much for that grand idea.

The conference was an eye opening experience and a carbon science nerd’s dream! I can honestly say that almost my entire PhD reference list was walking about the conference. And if someone wasn't there themselves, then their students were there presenting collaborative work. I very often had to restrain myself from asking for autographs and taking pictures with famous (in my mind) scientists whose work I had poured over for hours and hours, resulting in severely dog-eared papers with plenty of my own scribbles as I’d tried to mould it into something I could understand and grasp.

Two such scientists based at the LSCE in Paris (Laboratoire des Sciences du Climat et de l’Environnement -  http://www.lsce.ipsl.fr/en/) are Philippe Ciais and Philippe Peylin. Their work features heavily in my literature review, and at one stage I had one of them standing on my left and the other on my right while they were having a discussion with my supervisor. I found myself searching the crowd for my South African colleague so that I could telepathically communicate to him to please take a picture!

A picture of Ralph Keeling who features in an earlier post

That was the other great opportunity presented to me by the conference – I got to meet up with my Australian supervisor, Dr. Peter Rayner from the University of Melbourne, and have an entire week to discuss, in person, the technical issues I’d been wrestling with over the past weeks leading up to the conference, as well as to meet in person a fellow collaborator with whom I’d had many an email conversation. For those people starting a PhD, I can only hope for you that you have a supervisor like Dr. Rayner. He has a unique grasp of the science behind atmospheric transport as well as the statistics of inverse modelling, which he is able to elucidate to another person in such a way that you can actually see the light bulb blinking on as a once complicated and hostile battlefield of detail and facts is turned into a tangible, achievable process. Preceding the conference, I had gone through a bit of the “PhD blues”, and had avoided discussing my concerns with any of my supervisors. This was a terrible mistake, and once I’d had my first conversation with Dr. Rayner in the weeks before the conference (a marathon two hour Skype meeting), I felt the glumness lift off of me, and I was given direction once again and able to forge on ahead. A PhD is not for the weak, let me tell you. If you thought you were emotionally unhinged before, just try one of these on for size.

Anyway! What did I take away from the conference? Well many of the presentations were on trends in carbon dioxide levels – and yes, carbon dioxide concentrations are going up. We are definitely not doing enough yet to prevent a more than two degree average temperature increment into the future. It may level off now and then, but now that we are starting to have the first really long term carbon dioxide time series datasets, it’s clear that levels are rising. And from these kinks in the carbon dioxide trend we also know that we are not doing such a great job yet in predicting the complicated interaction between the climate and the carbon cycle, particularly the component related to the land surface. That means that we need more measurements and we need better models. I also learnt that carbon scientists lean a little bit towards the cynical side – you need to in order to survive the carbon science/climate science game.

A rather thought provoking cartoon posted by one of the presenters on the last day of the conference

When I wasn't gawking at famous scientists or straining to take in every bit of information I could sponge from the presentations, the conference organisers were doing a great job of keeping us busy on tours around Beijing. I even managed, along with my South African colleague, to venture the streets of Beijing and discover a bit of the history and culture of the city. 

At The Summer Palace - Residence of The Dragon Lady

The Great Wall of China

The Forgotten City

More to follow soon!

Another article on the breaching of the 400ppm carbon dioxide level in the New York Times: http://www.nytimes.com/2013/05/11/science/earth/carbon-dioxide-level-passes-long-feared-milestone.html?pagewanted=all&_r=0

Global Carbon Dioxide Concentrations Surpass the 400ppm Mark

Here's an interview with Ralph Keeling, son of the climate scientist Charles David Keeling, who developed the well known and much publicized Keeling curve, on the approach of the 400ppm Global Carbon Dioxide Concentration mark: http://www.guardian.co.uk/environment/2013/may/14/record-400ppm-co2-carbon-emissions

Here's also an interview with Prof. Michael Mann from Penn State University:
http://www.youtube.com/watch?v=bvC-VI2EdBY

And if you haven't seen enough yet, here's an article which explains the significance of 400ppm, as well as a really honest talk by David Roberts on the reality we may be facing. This level of CO₂ concentrations has not been seen for three million years - http://www.treehugger.com/climate-change/what-does-world-400-ppm-co2-look.html

Keeling Curve - http://www.treehugger.com/climate-change/what-does-world-400-ppm-co2-look.html

Recipe for Obtaining Carbon Dioxide Emission Estimates - Second Ingredient: Transport Model

WARNING: The Presence of Equations is Detected in this Post!

It’s been a while since my last post regarding carbon dioxide emission estimates, and since I'm currently busy working on the Transport Model for my analysis, I thought it would be a good time to introduce the second ingredient needed for carbon flux estimation through inverse modelling.

To adequately explain why the transport model is needed, I'm going to have to introduce an equation. This is the Bayesian cost function which needs to be optimized in order to solve for the carbon dioxide fluxes:

J_BLS = (c - Gs)^TX(c - Gs) + (s - z)^TW(s - z)

where c are the observed concentrations at a particular station, s are the carbon dioxide fluxes (which is what we want to solve for), G is the matrix which describes how to fluxes influence the concentrations (and so is referred to as the influence function), X is the inverse covariance matrix of the concentrations, z are the best estimates of the carbon dioxide fluxes, and W is the inverse covariance matrix of the estimated fluxes (see Enting, 2002 Inverse Problems in Atmospheric Constituent Transport). The influence function G is the part of the equation which requires the transport model. This is the component which links the rates of CO₂ emissions to the observed CO₂ concentrations, so that c ≈ Gs.

The idea behind the optimization is to estimate the best values for s (the fluxes) so that c – Gs is as close to zero as possible. But because there are many sets of values that can be assigned to s so that the optimization equation is minimized, the Bayesian approach to optimization adds a second component, which is the minimization of the difference between the modelled values for s  and the best estimates for s (z which is referred to the as the prior estimates). The Bayesian way of thinking is that we aren't starting from a clean slate – we already know something about the carbon dioxide fluxes on the ground. We know which areas are going have lots of anthropogenic emissions, we know which areas are vegetated and are going to have photosynthesis and ecosystem respiration processes occurring, and we know which areas are barren where very little emissions are going to take place. So let’s add that information to the optimization routine so that we can further restrict the set of best possible solutions for the fluxes, s. And what we end up with is then a probability distribution for the solution of s. So not one answer, but a set of answers with associated probabilities.

The approach I'm using to obtain the influence function is based on a atmospheric modelling tool often used in pollution tracking called a Lagrangian Particle Distribution Model run in backward mode. So what does that mean? When a Lagrangian Particle Distribution Model (LPDM) is run in backward mode, particles are released from the measurement sites and travel to the surface and boundaries (Seibert and Frank, 2002, Source-receptor matrix calculation with a Lagrangian particle distribution model in backward mode) That’s why it is referred to as backward mode – we start at the measurement point and then go backward in time to see which surfaces would have influenced that particle. In order to correctly move the particles around, LPDM needs meteorological inputs at a resolution which matches up with the size of the area you’re interested in. Because in my study I'm going to be concentrating of Cape Town and the surrounding areas, I need to have meteorological inputs that are at a pretty high resolution. I'm going to be using values from the atmospheric model CCAM (CSIRO’s Conformal-Cubic Atmospheric Model - CCAM) which provides three dimensional estimates for the wind components (u, v, and w) and temperature and turbulent kinetic energy. And I'm going to be using an LPDM developed by Uliasz (Uliasz, M., 1994. Lagrangian particle modeling in mesoscale applications, Environmental Modeling II).

An example of the wind output from CCAM at a 1km resolution around Tasmania South Esk Region (http://www.csiro.au/sensorweb/ccam/index.html)

The LPDM model has been written in FORTRAN code, and the CCAM model outputs it meteorological variables as NETCDF files. I'll soon write a post on the joys of compiling code in LINUX and converting my met NETCDF files into a binary format that LPDM can use. 

How Rising CO2 Levels are Affecting the Ocean

I recently had the opportunity to chat to a local radio host on the impacts of rising CO₂ levels. I was surprised to find out that, in fact, very few people are aware of how important the world's oceans are in absorbing carbon dioxide, and what the affect of rising CO₂ levels in the atmosphere will have on ocean acidity, and the consequences of this. In the long term, oceans may absorb up to 85% of the anthropogenic CO₂ emissions. But with all this absorbing of CO₂, the oceans are becoming more acidic. It might seem like a small change in pH, but the drop of 0.1 in pH from pre-industrial to present, over only a few decades, would be similar to a natural shift in ocean pH of 5000 to 10000 years. So for ocean life which has adapted over thousands of years to a particular pH, it's a lot to deal with.

Ocean acidification not only affects marine life, but the acidification of the ocean means that the ocean can absorb less CO₂. Which has a negative feedback on climate change - the rates of CO₂ in the atmosphere may increase faster than predicted because the ability of the ocean to act as a sink for CO₂ is diminishing. In addition to the ocean becoming more acidic, the average ocean temperature is also rising, and this further diminishes the oceans ability to absorb CO₂

The affect of rising CO₂ levels on the ocean is rather complicated and multifaceted. There's a whole load of ocean chemistry going on there, not just one simple straight forward equation.

To read more see this Scientific American article or see this short video:

And here's a second video from New Zealand's NIWA which I think tells the story a little better:

New Scientist article on recent warming

Found this insightful article on the New Scientist website on the recent trend in warming. Global Warming Article in New Scientist

Follow the link to the Science article to get the full story!

Marcott, S.A., Shakun, J.D., Clark, P.U., Mix, A.C., 2013. A Reconstruction of Regional and Global Temperature for the Past 11300 years, Science, vol.339, DOI: 10.1126/science.1228026 http://www.sciencemag.org/content/339/6124/1198.full

Also an update! Hangklip monitoring station is back online!! Follow on twitter at @CapeCarbon.

My Cowboy Method of Estimating Carbon Dioxide Emissions

If you’ve been following the twitter feed (@CapeCarbon) – thanks to Laurie Butgereit of Meraka, CSIR, for getting it up and running – then you might have noticed, that not only is the carbon dioxide concentration at the two sites being tweeted, but under certain conditions, the carbon dioxide emissions from Cape Town and surrounds as well.

So how am I doing this exactly? As my last post revealed, normally a super computer, or at least a pretty amazing desktop, is needed to carry out an atmospheric inversion for obtaining estimates of carbon emissions. Well that certainly is the ultimate goal, and it’s the only estimate that’s really publishable. But in the mean time, to give people a taste of what’s possible with atmospheric measurements of carbon dioxide, I came up with a method to get a “ballpark” figure of what the carbon dioxide flux is. This is not a statistical approach. This is not a physics approach. But rather an “applied maths” approach. The same type of approach you need to use when your lecturer asks you to estimate how many ping pong balls can fit inside the lecture theatre.

Now the name comes from one of my favourite movies, Armegeddon (which probably popped into my mind because of all the meteors that have been crashing down to Earth lately - http://www.geekosystem.com/nasa-explains-russian-meteor/), specifically from the Russian cosmonaut, Lev Andropov, where he calls the America astronauts a “bunch of cowboys” after they've just caused his space station to blow up. There’s a scene where he bashes a spanner against the space ship to get it to work, and it springs to life. Well this is my way of smashing a spanner against the measurements to get them to give emission estimates.

The first thing that needs to happen is that the wind needs to be blowing in the right direction. It needs go from one of the measurement stations, over Cape Town, and directly to the next measurement station. So this is either when the wind is blowing from the South East or from the North West. We also need to know how fast the wind is blowing. Fortunately we can get all of this from the South African Weather Service (SAWS Cape Town Weather).

Map showing the required wind directions in order to make flux estimates based on the difference in CO₂ concentrations between the Robben Island and Hangklip sites. Map generated using Google Earth.

Then we need to get the difference in carbon dioxide concentration between the two sites. If the wind is coming from the North West, then we want Hangklip – Robben Island, and if it’s from the South East, then we want Robben Island – Hangklip. This difference then needs to be converted into mg of carbon dioxide per cubed m. This can be done using the ideal gas law.

mg CO₂ per m³ = CO₂ ppm × 44.01/(8.3145 × Temperature/Pressure)

Now this is where the serious wangling comes in.

The first major assumption that we need to make is that the wind is travelling in a straight line from station A to station B. Then we need to assume that the wind speed is constant. This won’t be 100% correct, but we’re not going to try and model the wind fields for this exercise (that’s what the super computer is used for).

Then my thinking is: imagine this concentration of CO₂ as a cylinder with a 1m² base and 1m in height. The cylinder starts off at station A and then travels towards station B at the speed of the wind in a straight line, a total distance of 77.4km. Along the way, the cylinder is going to collect or lose CO₂ due to processes taking place on the surface. There is also not just one cylinder at a particular point, but cylinders stacked up on top of each other until they reach the planetary boundary layer. To start off with, just to keep things simple and manageable, we’re going to assume that all the cylinders are moving at the same speed. This is not true, because the higher up in the atmosphere you go, the faster the wind is going. What I plan to use in the future is the wind profile power law (http://en.wikipedia.org/wiki/Wind_profile_power_law).

The planetary boundary layer extends from the surface to height of up to 3km, depending on the temperature and other factors (http://www.sciencedaily.com/articles/p/planetary_boundary_layer.htm). It’s where the air is most influenced by what’s happening on the surface, and it’s pretty hard to model. This height is required in order to make the estimation of the CO₂ fluxes possible, and so, for starters we’re just going to assume that the height of the planetary boundary layer is 1000m, which I grabbed from a study on North America, but for an area of similar latitude (http://www.meteor.iastate.edu/~jdduda/portfolio/605_paper.pdf). As the tweets get a bit more advanced, I will try to change the PBL depending on the time of the day and the current season.

An illustration of the approach used to estimate the CO₂ fluxes and the assumptions made 

We can now come up with an empirical relationship

The difference between the two CO₂ concentrations = ΔCO₂
= The amount of CO₂ emitted or absorbed per m² per second × the total distance travelled × the total amount of time spent per m² / the total number of cylinders in the stack.

We know the difference in concentration between the two sites, we know the total distance travelled which is 77400m, the amount of time spent at each m² is the inverse of the wind speed, and the total number of cylinders is 1000. So all that’s left is the emission (or absorption) of CO₂:

CO₂ flux = ΔCO₂ × wind speed × 1000 / 77400

So far the values that I've tested this on are in the right range, so I'm happy that the estimates are at least of the right order of magnitude.

During the mornings we have seen that there are negative fluxes observed. This is because photosynthesis has kicked in. During the morning, the stomata open up, and allow CO₂ to passes into the plant cells. At the same time water vapour can also leave the plant cells. If it starts to get too hot, the plant needs to be careful that it doesn't dessicate, and so it has to control how open it's stomata are going to be, which then limits the amount of CO₂ that can be absorbed through photosynthesis. If there's one thing I hope people take away, it's the important role that our natural systems play in regulating the atmosphere in which we exist.

Illustration of photosynthesis from www.butler.edu

Couldn't have done it without a little help from my friends...

Couldn't have done it without a little help from my friends...

Getting the Picarros up and running has been a journey in itself. As I've mentioned before, scientific equipment doesn't just up and install itself. And boy, you better get it right, or else you might as well just chuck the data or else spend the rest of your life trying to justify corrections and tweaks to the data.

My journey started in Australia, where I was lucky enough to spend a few weeks with my now co-supervisor, Dr. Peter Rayner. Peter taught me all the in's and out's of atmospheric inversion and how to go about getting one to run. This was my first experience with working on a super computer, and I think I learnt to code in about three different languages during those six weeks - IDL, Python and Fortran. As a bonus, Peter also took me on a tour of Lygon Street, Melbourne. This is a delightful place, which has restaurants of all kinds stretching from end to end. Every day we would visit a different restaurant so I got to enjoy a culinary tour of Melbourne, Australia, as well. I had possibly one of the best burgers ever at a place called Grill'd (http://www.grilld.com.au/). Unfortunately I'm not a ginger, or otherwise I would have been eligible for the special.

A view down Lygon Street, Melbourne - http://www.melbourneplaces.com

As an aside - there is a wonderful little cinema in Lygon Street, Cinema Nova (www.cinemanova.com.au/), which I discovered while staying there. It's an arthouse cinema, but they do show some of the movies on the regular circuit. My normal experience at a cinema, which I do love going to in general, is that you get your ticket and then you can maybe get a popcorn and some soda, and not much else. But at Cinema Nova, not only can you get your box of popcorn (so you can have the full cinema experience), but you can order a glass of wine as well!! In a GLASS! This was an entirely new experience for me. There's nothing like a glass of wine to help you get thoroughly enthralled in a soppy movie.

The perfect movie combo

Just around the corner from the University of Melboure are the Aspendale CSIRO (Commonwealth Scientific and Industrial Organisation) offices. Peter has a long standing relationship with CSIRO and works often in collaboration with the CSIRO Marine and Atmospheric Research group. Lucky for me! This group manages several atmospheric monitoring sites, including Cape Grim on Tasmania. Who better to ask how to go about setting up my own system? I was hoping for a few pointers and maybe a setup diagram or two. Well I got that and much more. On the day I visited, Zoë Loh (http://www.youtube.com/watch?v=n6313xRIdck) and Ann Stavert (http://www.youtube.com/watch?v=oYmJJerYi-w) graciously and enthusiastically showed me around their labs, and then took me through their entire setup of the Picarro systems, and the do’s and don’ts of plumbing. And on top of that, Zoë arranged for me to go into the field with David Etheridge to see two Picarro monitoring sites in action at a geosequestration site, as well as to get some hands on training in collecting flask samples for isotope analysis.

Atmospheric monitoring station near a geosequestration site in Otway, Australia

Assistance from CSIRO didn't stop there. I also got to visit the Marine and Atmospheric Research group based in Canberra. This was to get some hands on experience with code related to the CABLE (CSIRO Atmosphere Biosphere Land Exchange) model, which our group in South Africa had planned to start using and adapting for our purpose. This model represents the interactions between soil, vegetation and the atmosphere, and includes linkages between hydrology, plant physiology and their micro climate (http://cawcr.gov.au/projects/access/cable/cable_technical_description.pdf). Vanessa Haverd very kindly took me through all the code and explained how to get it to run.

When I arrived back home, it was time to start ordering the bits and pieces. If there was one thing that I had taken away from the trip, it was that it was going to be a lot harder to set everything up than I had anticipated. There are a lot of little parts that are needed to make the whole measurement system run properly. We had decided to go with the same Picarro CRDS measurement systems that our colleagues at CSIRO were using, and Picarro, although thousands of kilometres away were very helpful in getting us all the Picarro bits and pieces that we needed, and never hesitated to call if they needed to explain anything.  

Once the Picarro’s arrived, it was time for me to sort out the plumbing, It was something that I had been dreading since the Australia trip because I knew that it was more complicated than I had originally thought, and that it could make or break the whole measurement system.

Fortunately, just after getting back from Australia, I had the opportunity to attend the conference for the South African Society of Atmospheric Sciences, where I met in person Ernst Brunke, of the Cape Point GAW station, run by the South African Weather Service. Instead of treating me like someone who was trespassing onto his turf, Ernst was happy to explain in any required detail what I needed to get for the plumbing system to work, and also to share and collaborate with his lab at Cape Point.

The Robben Island and Hangklip Picarro's visiting Cape Point GAW station for a concurrent calibration

Lucky for me, the suppliers of Swagelok connectors (http://www.swagelok.com/johannesburg), the same connectors used on the Picarro CRDS instrument, were literally just around the corner from where I lived. Here I spent several hours while the sales personal helped to identify all the correct components needed for my plumbing diagram to turn into a reality. They even helped me to assemble the bits and pieces. I now know what a ferrule is. 

When it came to the actual installation, I needed permission from the Port Authority, Transnet, in Cape Town. Mr. Robin Poggenpoel, regional manager, was glad to get on board with the research project, and provided permission for me to access two of the lighthouses to install the CO₂ measurement instruments. With help from my family, and from the lighthouse keepers at Robben Island and Hangklip - Peter and George - I was able to finally get the instruments installed. After about a year of planning, everything finally came together.

Peter, the Robben Island lighthouse keeper, accompanying me to Robben Island for installation

So as you can see, this research project would not be possible without the help of a lot people, who really didn't have to, but did anyway. Thank you!