Global CO2 flux

Global CO₂ flux

This Web-Project by Noel Cressie, Josh Jacobson, and Michael Bertolacci, features WOMBAT (the WOllongong Methodology for Bayesian Assimilation of Trace-gases), a framework for estimating fluxes on a global scale. Carbon dioxide (CO₂) is one of several greenhouse gases, so-called because they trap heat in Earth’s lower atmosphere. Locations across Earth’s surface where CO₂ is added to or removed from the atmosphere are known as sources and sinks – the rate at which this happens is known as flux. The aim of flux inversion is to characterise the pattern and scale of sources and sinks in both space and time. The nature of CO₂ sources and sinks can be very different from one location and time point to the next. For example, temperate forests occupy large parts of the terrestrial biosphere and transition from sinks to sources during the year, while volcanoes are local sources with sporadic and unpredictable outgassing of CO₂. Human activity has also caused changes to the natural processes that cause these sources and sinks. As a tool to improve our understanding of the scale, variability, and patterns of the sources and sinks of the leading greenhouse gas CO₂, WOMBAT produces flux estimates accompanied by uncertainty bounds on the estimates. The framework is designed to help scientists and policy-makers take uncertainty in CO₂ flux estimates into account and thus better respond to climate change.

• Introduction
• WOMBAT: A fully Bayesian global flux-inversion framework
• Global CO2 flux estimation from WOMBAT
• Conclusions and future work
• Acknowledgements
• References
• Links
• Image and figure credits

CO₂ is the leading greenhouse gas emitted by human activity, largely as a byproduct of burning fossil fuels. These anthropogenic emissions over the last 200 years have more than doubled the amount of CO₂ in the atmosphere. Now, as a result, the human influence on the climate is clear: Figure 1 shows that current CO₂ concentrations of more than 400 parts per million (ppm) are greater than anything seen for at least the last 800,000 years.

According to the 2021 IPCC synthesis report, “limiting human-induced global warming to a specific level requires limiting cumulative CO₂ emissions, reaching at least net zero CO₂ emissions.” This objective is simple to describe, but the actual monitoring and reduction of CO₂ emissions is difficult for several reasons. First, humans aren’t the only factor, as plants and animals both emit and absorb CO₂ all the time, as does the ocean. Second, the atmosphere is a connected system and the CO₂ that is emitted in any one region moves around via weather systems and affects CO₂ concentrations across the globe. Mitigating atmospheric CO₂ requires an understanding of both of these factors, as well as understanding when and where human activity results in emission of this leading greenhouse gas.

Science problem: global flux inversion

The rate of exchange of CO₂ between Earth’s surface and the atmosphere is known as CO₂ flux. ​​How the atmosphere responds to CO₂ fluxes can be studied using simulations. For example, in Figure 2, we simulate the effect of additional CO₂ emissions over parts of North America. The left panel shows a month of added CO₂ emissions (flux) in North America, and the right panel is an animation of of the estimated changes in atmospheric-column CO₂ concentration (XCO₂) showing how the extra CO₂ spreads around the globe. By adding together hundreds of such simulated changes in different parts of the globe, we can build a picture of how atmospheric transport works. This animation also illustrates the point made in the previous paragraph, that CO₂ emitted in one place moves around the globe—in fact, it moves quite quickly and becomes part of the background CO₂ concentration in the hemisphere it is emitted in within a matter of months.

CO₂ flux is a key quantity that scientists and policy-makers use to mitigate climate change, but it is usually not possible to measure it directly. It is a rate of change of CO₂ over time at every location on Earth’s surface, but a large amount of the flux happens over huge, remote areas, such as Siberia and the Amazon. Instead, the effect of flux, namely CO₂ concentration, is measured in parts per million (ppm) of CO₂. However, because of atmospheric transport, changes in concentration are not usually seen at the same time or location that the flux happened. To estimate when and where flux has occurred, we need to understand the process that moves CO₂ around the globe, called atmospheric transport. Then, in principle, we can work backwards from observations of CO₂ concentrations to estimate CO₂ fluxes, using a computational-statistical procedure known as flux inversion. Figure 3 shows an example of CO₂ concentrations (in ppm) on the left, and the corresponding flux estimates (in kilograms per square metre per year) on the right.

WOMBAT: A fully Bayesian global flux-inversion framework

Like an intricate, connected machine, the uncertainty in one layer of the statistical hierarchy can affect the uncertainty and estimates in another layer. To deal with this, the layers are connected using  statistical models and a mathematical result known as Bayes’ Theorem, which shows how different sources of information can be combined in the presence of uncertainty. This statistical framework is called a Bayesian hierarchical model (BHM), and WOMBAT is a geophysical  example of such a model. BHMs appear in many other areas of science.

A technical description of a BHM goes as follows: Let [A] denote the probability distribution of variable A; [A, B] denote the joint probability distribution of the variables A and B; and [A | B] denote the conditional distribution of A given B. Using this notation, the four layers of WOMBAT are connected by the following probability distributions:

            (1) [Z | Y, θ],

            (2) [Y | X, θ],

            (3) [X | θ], and

            (4) [θ].

What distinguishes WOMBAT from other flux inversions is the presence of [θ] and the consequent Markov Chain Monte Carlo (MCMC) sampling needed to obtain [X, Y, θ | Z], which is the posterior distribution of all the hidden variables given the data Z. Estimates of CO₂ flux and their uncertainties are obtained from this posterior, specifically from [Y | Z]. For more information, see the Tutorial on Bayesian Statistics for Geophysicists.

NASA’s OCO-2 satellite data

The data used in this Web-Project come from the Orbiting Carbon Observatory-2 (OCO-2) satellite, NASA’s first remote sensing mission with primary science objective to understand the global geographic distribution of CO₂. Data are available dating back to late 2014, a few months after the satellite was launched, and they cover much of each hemisphere, depending on the time of year. Figure 5 shows an example of these data, representing the observations accumulated over the course of one week in January 2016 – notice that the satellite’s orbital path can be seen in the spatial pattern of the observations (OCO-2’s current location is available at the OrbTrack website). The analysis given in this Web-Project follows closely that of Zammit-Mangion et al. (2022), which focuses on OCO-2 data (Version 7) for the two-year period from January 2015 through December 2016.

Monthly estimated fluxes for 2015-2016

The animation shown in Figure 6 gives estimated monthly CO₂ fluxes for 2015-01 to 2016-12 across the globe, estimated from WOMBAT and based on two years of OCO-2 satellite data (XCO₂ concentrations). Also shown are the uncertainties obtained from WOMBAT. Fossil-fuel fluxes are considered to be well known (at least compared to natural fluxes), so we subtract them from our statistical model and only estimate the natural fluxes. The left panel of the animation shows the map of monthly non-fossil-fuel estimated fluxes (posterior mean in each grid cell) over the globe for each month in (kg/metre squared/second x 1e-7). The right panel shows the map of the corresponding monthly posterior standard deviation in each grid cell (same units), which is a quantification of the uncertainty of the estimated fluxes shown on the left panel.

Notice that the tropics are relatively constant in their flux, while the temperate zones are very seasonal. This seasonal cycle of CO₂ is mostly driven by the vast forest areas in the northern hemisphere. Earth is breathing! In autumn and winter, trees drop their leaves, which decompose and release CO₂ into the atmosphere; in the spring and summer, leaves grow back and begin drawing down CO₂ through the process of photosynthesis; and the cycle repeats. Flux activity over the oceans is less intense at any given time and location, but because oceans cover more than two-thirds of Earth’s surface area, the ocean fluxes are substantial.

To control and reduce the build-up of atmospheric CO₂ over the next century and uphold the urgent commitments of COP21 and later agreements, it is essential to understand where CO₂ is being exchanged with the atmosphere, how these regions vary through time, and whether there are ways to mitigate the sources and enhance the sinks. As a framework for determining where CO₂ is emitted and absorbed across Earth’s surface, WOMBAT can aid scientists in the evaluation of CO₂ sinks, to understand their long-term viability as well as the possibility of replicating their characteristics in the future over new regions of Earth’s surface. The CO₂ flux estimates provided by WOMBAT, together with their statistical uncertainty, give scientists and policy-makers the clarity needed to better mitigate the effect of this leading greenhouse gas, carbon dioxide. 

There are updates and extensions we hope to address in future versions of WOMBAT. For example:

• Account for remaining uncertainties, particularly in the atmospheric transport model of how CO₂ moves around the globe.
• Carry out flux-inversion for other atmospheric greenhouse gases, such as methane, which are also important to monitor and mitigate.
• Investigate whether the results presented here could be used to improve smaller-scale, regional or country-scale flux inversions at very high resolutions, such as those of the CarbonWatch-NZ project that estimates fluxes over New Zealand.

This Web-Project was supported by Australian Research Council Discovery Project DP190100180 and NASA ROSES grants 17-OCO2-17-0012 and 20-OCOST20-0004.