Friday, 17 May 2013

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:

Here's also an interview with Prof. Michael Mann from Penn State University:

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 -

Keeling Curve -

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:

JBLS = (c - Gs)TX(c - Gs) + (s - z)TW(- 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 (

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. 

Thursday, 2 May 2013

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: