Preprints
https://doi.org/10.5194/egusphere-2022-623
https://doi.org/10.5194/egusphere-2022-623
 
18 Jul 2022
18 Jul 2022
Status: this preprint is open for discussion.

Opening Pandora's box: How to constrain regional projections of the carbon cycle

Lina Teckentrup1,2, Martin Gerard De Kauwe3, Gab Abramowitz1,2, Andrew John Pitman1,2, Anna Maria Ukkola1,2, Sanaa Hobeichi1,2, Bastien François4, and Benjamin Smith5,6 Lina Teckentrup et al.
  • 1ARC Centre of Excellence for Climate Extremes, Sydney, NSW, Australia
  • 2Climate Change Research Centre, University of New South Wales, Sydney, NSW, Australia
  • 3School of Biological Sciences, University of Bristol, England
  • 4Laboratoire des Sciences du Climat et l’Environnement (LSCE-IPSL) CNRS/CEA/UVSQ, UMR8212, Université Paris-Saclay, Gif-sur-Yvette, France
  • 5Hawkesbury Institute for the Environment, Western Sydney University, Penrith, NSW, Australia
  • 6Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden

Abstract. Climate projections from global circulation models (GCMs) part of the Coupled Model Intercomparison Project 6 (CMIP6) are often employed to study the impact of future climate on ecosystems. However, especially at regional scales, climate projections display large biases in key forcing variables such as temperature and precipitation, which hamper predictive capacity. In this study we examine different methods to constrain regional projections of the carbon cycle in Australia. We employ a dynamic global vegetation model (LPJ-GUESS) and force it with raw output from CMIP6 to assess the uncertainty associated with the choice of climate forcing. We then test different methods to either bias correct or calculate ensemble averages over the original forcing data to constrain the uncertainty in the regional projection of the Australian carbon cycle. We find that all bias correction methods reduce the bias of continental averages of steady-state carbon variables. Carbon pools are insensitive to the type of bias correction method applied for both individual GCMs and the arithmetic ensemble average across all corrected models. None of the bias correction methods consistently improve the change in carbon over time, highlighting the need to account for temporal properties in correction or ensemble averaging methods. Some bias correction methods reduce the ensemble uncertainty more than others. The vegetation distribution can depend on the bias correction method used. We further find that both the weighted ensemble averaging and random forest approach reduce the bias in total ecosystem carbon to almost zero, clearly outperforming the arithmetic ensemble averaging method. The random forest approach also produces the results closest to the target dataset for the change in the total carbon pool, seasonal carbon fluxes, emphasizing that machine learning approaches are promising tools for future studies.

Lina Teckentrup et al.

Status: open (until 31 Aug 2022)

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  • AC1: 'Comment on egusphere-2022-623', Lina Teckentrup, 25 Jul 2022 reply

Lina Teckentrup et al.

Lina Teckentrup et al.

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Short summary
Studies analysing the impact of the future climate on ecosystems employ climate projections simulated by global circulation models. These climate projections display biases which translate into significant uncertainty in projections of the future carbon cycle. Here, we test different methods to constrain the uncertainty in simulations of the carbon cycle over Australia. We find that all methods reduce the bias in the steady-state carbon variables but temporal properties do not improve.