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How much CO2 can trees remove from the atmosphere?

Prof Martyn Chipperfield (SEE), Prof E. Gloor (SoG), Dr W. Buermann (SEE), Dr C. Wilson (SEE)

Project partner(s): Hartmut Boesch, University of Leicester; John Miller, NOAA ESRL, Boulder, Colorado, USA; Luciana Gatti, INPE, Sao Jose dos Campos

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Fig. 1 Change in Leaf area density from 1982 to 2015 based on spectrometer measurements in the visible domain from space.

The increase in atmospheric CO2, primarily due to fossil fuel burning, is the main driver of anthropogenic warming of the earth's surface. Based on continuous measurements of atmospheric CO2, observations of substances like chlorofluorocarbons (CFCs) penetrating slowly into the oceans, as well as ocean carbon and partial pressure measurements, we know that approximately half of fossil fuel emissions is currently taken up by the oceans and land vegetation. While we understand well the key processes responsible for the ocean uptake, our knowledge of the land carbon sink is less complete. Forest inventories suggest - at least in some important areas - that trees have grown at a faster rate over recent decades and thus may be responsible for this land sink. However, these data are spatially incomplete and uncertainties are large. It is also unclear how long the sink will persist particularly in the tropics given increasingly hot conditions there. 

Fig. 2 Changes in the seasonal amplitude of atmospheric CO2 at Barrow (71°N 156°W) and Mauna Loa (19° N, 155° W) (from Graven et al. 2013).

The same atmospheric CO2 data also reveal a large seasonal cycle concentrated in the northern hemisphere. This is a result of carbon uptake by vegetation during the spring/summer and respiration during the autumn/winter. The CO2 concentration in the northern hemisphere reflects the larger land fraction compared to the southern hemisphere. This seasonal cycle has increased by approximately 40% at northern high latitude sites like Barrow since the 1960s, and approximately 10% at subtropical sites like Mauna Loa (Graven et al., 2013). This indicates a large-scale vegetation response to increasing levels of CO2, to warming itself, and to warming-induced changes of climate. The exact nature of these changes remains unclear. Furthermore, atmospheric concentration data reveal substantial interannual variation which co-varies strongly with El Nino / La Nina climate oscillations and related temperature variations.

This Project

Given the ongoing warming of the earth’s surface the focus of carbon cycle observations has shifted from simple source / sink attribution towards (i) quantifying and understanding changes in functioning primarily of the land vegetation with focus on tropics and high northern latitudes, (ii) monitoring of fossil fuel emissions and (iii) whether evolution of ocean uptake continues as expected based on our knowledge of air-sea gas exchange, ocean chemistry and ocean transport pathways from the surface to the interior.

One traditional approach to answer these questions is to integrate backwards in time from atmospheric concentration data to surface processes using atmospheric transport models in an inverse mode. Such an approach is not robust as atmospheric mixing dilutes signals, which are difficult to ‘un-mix’ in a unique way. However, over recent years increasing information from remote sensing about the land vegetation and how it is changing is becoming available which will help to constrain this approach. Relevant remote sensing data includes vegetation chlorophyll content and vegetation fluorescence (e.g. Guan et al. 2015). Together with precipitation and temperature data they can give information about status and activity of land vegetation. Similarly, satellites provide atmospheric column observations of CO2 and related gases such as CO.

This studentship will build on an existing 4D-Var inverse model of atmospheric transport for CH4 based on the Leeds atmospheric transport and chemistry model TOMCAT (Chipperfield et al. 2006, Wilson et al. 2014) to develop an analogous scheme for CO2. The aim will be to use / develop simple models for land vegetation functioning building on remote sensing data like fluorescence to represent land vegetation, existing air-sea flux models based on sea surface partial pressure difference measurements, fossil fuel emissions estimates and remote sensing-based biomass burning estimates to develop a 4D-Var flux estimation system. The ultimate goal is then to analyse recent variations and trends primarily of land vegetation functioning both in the tropics and northern hemisphere. The novel data assimilation system will also allow the student to investigate to what extent greenhouse concentration data from space (CO2, CO) can corroborate insights gained based on in-situ concentration measurements primarily from the NOAA network. Such data exist from a range of satellites like MOPITT (CO) and OCO (CO2).

A particular focus of the project could be the Amazon region based on a long-term close collaboration of Leeds with Luciana Gatti who runs a high-precision greenhouse gas laboratory at INPE Sao Jose dos Campos. This laboratory has regularly measured lower troposphere greenhouse gas concentration profiles. It is likely that novel tracers like COS (carbonyl sulfide) and 13CO2 which carry information of land vegetation functioning and response to drier than usual conditions will also soon be available.


The student will be part of the atmospheric chemistry group which uses the TOMCAT 3-D model to analyse observations to understand and quantify processes and sources and sinks of atmospheric constituents. The student will be learn how to run and use such geophysical computer codes to analyze atmospheric data. He/she will also learn how the carbon cycle and related biogeochemical cycles work. Finally, depending on the direction of the project, he/she will also process and analyse remote sensing data and examine how they can contribute to better understand past and ongoing changes of the carbon cycle.

Potential for high impact publications

The problem to be investigated is important for predictions of future atmospheric CO2 levels. As such the project is clearly of wide scientific and societal interest. It is expected that the student will be able to publish papers during the PhD and ahead of thesis submission.

Student profile

Applicants should have a strong interest in global environmental problems, a strong background in a quantitative science (math, physics, engineering, environmental sciences) and a flair for, and good familiarity with, programming and scientific computing.


  • Hartmut Boesch, University of Leicester, remote sensing of atmospheric greenhouse gas concentrations.

  • John Miller, NOAA ESRL, Boulder Colorado, USA.

  • Luciana Gatti, INPE, Sao Jose dos Campos, Brazil.

References and further reading

Anderegg, W.R.L., et al. (2015) Tropical nighttime warming as a dominant driver of variability in the terrestrial carbon sink, Proc. Nat. Acad. Sc., 112, 15,591-15,596, doi:10.1073/pnas.1521479112.

Brienen, Phillips, Feldpausch, Gloor, Galbraith et al. (2015) Long-term decline of the Amazon carbon sink, Nature 519, 344–348 doi:10.1038/nature14283

Chipperfield, M.P. (2006), New version of the TOMCAT/SLIMCAT off-line chemical transport model: Intercomparison of stratospheric tracer experiments, Q. J. R. Meteorol. Soc., 132, 1179-1203, doi:10.1256/qj.05.51.

Graven, H.D. et al. (2013), Enhanced seasonal exchange of CO2 by northern ecosystems since 1960, Science, doi:10.1126/science.1239207.

Guan, K., et al. (2015), Photosynthetic seasonality of global tropical forests constrained by hydroclimate, Nature Geoscience, 8, 284-289, doi:10.1038/ngeo2382.

Wilson, C., M. P. Chipperfield, M. Gloor, and F. Chevallier (2014), Development of a variational flux inversion system (INVICAT v1.0) using the TOMCAT chemical transport model, Geosci. Model Dev., 7, 2485-2500, doi:10.5194/gmd-7-2485-2014.

NASA Orbiting Carbon Observatory (OCO-2, satellite).

NOAA Earth System Research Laboratory (ESRL surface network).

Related undergraduate subjects:

  • Chemistry
  • Computing
  • Engineering
  • Environmental science
  • Geography
  • Meteorology
  • Physics