Preprints
https://doi.org/10.5194/egusphere-2022-623
https://doi.org/10.5194/egusphere-2022-623
18 Jul 2022
 | 18 Jul 2022

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

Lina Teckentrup, Martin Gerard De Kauwe, Gab Abramowitz, Andrew John Pitman, Anna Maria Ukkola, Sanaa Hobeichi, Bastien François, and Benjamin Smith

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.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
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Journal article(s) based on this preprint

05 May 2023
Opening Pandora's box: reducing global circulation model uncertainty in Australian simulations of the carbon cycle
Lina Teckentrup, Martin G. De Kauwe, Gab Abramowitz, Andrew J. Pitman, Anna M. Ukkola, Sanaa Hobeichi, Bastien François, and Benjamin Smith
Earth Syst. Dynam., 14, 549–576, https://doi.org/10.5194/esd-14-549-2023,https://doi.org/10.5194/esd-14-549-2023, 2023
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.

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Studies analysing the impact of the future climate on ecosystems employ climate projections...
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