Opening Pandora's box: reducing global circulation model uncertainty in Australian simulations of the carbon cycle
| العنوان: | Opening Pandora's box: reducing global circulation model uncertainty in Australian simulations of the carbon cycle |
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| المؤلفون: | Lina Teckentrup, Martin G. De Kauwe, Gab Abramowitz, Andrew J. Pitman, Anna M. Ukkola, Sanaa Hobeichi, Bastien François, Benjamin Smith |
| المساهمون: | Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Australian Research Council, ARC: DE200100086, DP190101823, Climate Extremes, CLEX: CE170100023, The research was funded by the ARC Centre of Excellence for Climate Extremes (CE170100023) and by the New South Wales Department of Planning, Industry and Environment. Martin G. De Kauwe and Andrew J. Pitman received support from the ARC Discovery Grant (DP190101823). Martin G. De Kauwe received support from the NSW Research Attraction and Acceleration Program (RAAP). Anna M. Ukkola received support from the Australian Research Council (DE200100086). |
| المصدر: | Earth System Dynamics Earth System Dynamics, 2023, 14 (3), pp.549-576. ⟨10.5194/esd-14-549-2023⟩ |
| سنة النشر: | 2023 |
| مصطلحات موضوعية: | [SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, Climate, Australia, General Earth and Planetary Sciences, GCM, Coupled Model Intercomparison Project 6 (CMIP6) |
| الوصف: | 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. These biases have been identified as a major source of uncertainty in carbon cycle projections, hampering predictive capacity. In this study, we open the proverbial Pandora's box and peer under the lid of strategies to tackle climate model ensemble uncertainty. 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 reduce the climate-driven 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. Bias correction can improve model carbon outputs, but 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 simulated carbon over time compared to the target dataset, highlighting the need to account for temporal properties in correction or ensemble-averaging methods. Multivariate bias correction methods tend to reduce the uncertainty more than univariate approaches, although the overall magnitude is similar. Even after correcting the bias in the meteorological forcing dataset, the simulated vegetation distribution presents different patterns when different GCMs are used to drive LPJ-GUESS. Additionally, we found 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. This highlights that, where possible, an arithmetic ensemble average should be avoided. However, potential target datasets that would facilitate the application of machine learning approaches, i.e., that cover both the spatial and temporal domain required to derive a robust informed ensemble average, are sparse for ecosystem variables. |
| وصف الملف: | application/pdf |
| اللغة: | English |
| تدمد: | 2190-4987 2190-4979 |
| DOI: | 10.5194/esd-14-549-2023⟩ |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::59e030e6099f2d9a47c69b38de2b3645 https://esd.copernicus.org/articles/14/549/2023/ |
| Rights: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....59e030e6099f2d9a47c69b38de2b3645 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 21904987 21904979 |
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| DOI: | 10.5194/esd-14-549-2023⟩ |