01 Mar 2023
 | 01 Mar 2023
Status: this preprint is open for discussion.

Combining local model calibration with the emergent constraint approach to reduce uncertainty in the tropical land carbon cycle feedback

Nina Raoult, Tim Jupp, Ben Booth, and Peter Cox

Abstract. The role of the land carbon cycle in climate change remains highly uncertain. A key source of projection spread is related to the assumed response of photosynthesis to warming, especially in the tropics. The optimum temperature for photosynthesis determines whether warming positively or negatively impacts photosynthesis, thereby amplifying or suppressing CO2 fertilisation of photosynthesis under CO2-induced global warming. Land carbon cycle models have been extensively calibrated against local eddy flux measurements, but this has not previously been clearly translated into a reduced uncertainty in how the tropical land carbon sink will respond to warming. Using a previous parameter perturbation ensemble carried out with version 3 of the Hadley Centre coupled climate-carbon cycle model (HadCM3C), we identify an emergent relationship between the optimal temperature for photosynthesis, which is especially relevant in tropical forests, and the projected amount of atmospheric CO2 at the end of the century. We combine this with a constraint on the optimum temperature for photosynthesis, derived from eddy-covariance measurements using the adjoint of the JULES land-surface model. Taken together, the emergent relationship from the coupled model and the constraint on the optimum temperature for photosynthesis define an emergent constraint on future atmospheric CO2 in the HadCM3C coupled climate-carbon cycle under a common emissions scenario (A1B). The emergent constraint sharpens the probability density of simulated CO2 change (2100–1900) and moves its peak to a lower value: 497 ± 91 compared to 607 ± 128 ppmv when using the equal-weight prior. Although this result is likely to be model and scenario dependent, it demonstrates the potential of combining the large-scale emergent constraint approach with parameter estimation using detailed local measurements.

Nina Raoult et al.

Status: open (until 13 Apr 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Nina Raoult et al.

Nina Raoult et al.


Total article views: 297 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
255 36 6 297 4 4
  • HTML: 255
  • PDF: 36
  • XML: 6
  • Total: 297
  • BibTeX: 4
  • EndNote: 4
Views and downloads (calculated since 01 Mar 2023)
Cumulative views and downloads (calculated since 01 Mar 2023)

Viewed (geographical distribution)

Total article views: 283 (including HTML, PDF, and XML) Thereof 283 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
Latest update: 29 Mar 2023
Short summary
Climate models are used to predict the impact of climate change. However, poorly constrained parameters used in the physics of the models mean that we simulate a large spread of possible future outcomes. We can use real-world observations to reduce the uncertainty of parameter values, but we do not have observations to reduce the spread of possible future outcomes directly. We present a method for translating the reduction in parameter uncertainty into a reduction in possible model projections.