Simulating Ips typographus L. outbreak dynamics and their influence on carbon balance estimates with ORCHIDEE r8627

MARIE Guillaume, Jina Jeong, Hervé Jactel, Gunnar Petter, Maxime Cailleret, Matthew J. McGrath, Vladislav Bastrikov, Josefine Ghattas, Bertrand Guenet, Anne Sofie Lansø, Kim Naudts, Aude Valade, Chao Yue, S. Luyssaert

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

Abstract. New (a)biotic conditions resulting from climate change are expected to change disturbance dynamics, such as windthrow, forest fires, droughts, and insect outbreaks, and their interactions. These unprecedented natural disturbance dynamics might alter the capability of forest ecosystems to buffer atmospheric CO2 increases, potentially leading forests to transform from sinks into sources of CO2. This study aims to enhance the ORCHIDEE land surface model to study the impacts of climate change on the dynamics of the bark beetle, Ips typographus, and subsequent effects on forest functioning. The Ips typographus outbreak model is inspired by previous work from Temperli et al. (2013) for the LandClim landscape model. The new implementation of this model in ORCHIDEE r8627 accounts for key differences between ORCHIDEE and LandClim: (1) the coarser spatial resolution of ORCHIDEE; (2) the higher temporal resolution of ORCHIDEE; and (3) the pre-existing process representation of windthrow, drought, and forest structure in ORCHIDEE. Simulation experiments demonstrated the capability of ORCHIDEE to simulate a variety of post-disturbance forest dynamics observed in empirical studies. Through an array of simulation experiments across various climatic conditions and windthrow intensities, the model was tested for its sensitivity to climate, initial disturbance, and selected parameter values. The results of these tests indicated that with a single set of parameters, ORCHIDEE outputs spanned the range of observed dynamics. Additional tests highlighted the substantial impact of incorporating Ips typographus outbreaks on carbon dynamics. Notably, the study revealed that modeling abrupt mortality events as opposed to a continuous mortality framework provides new insights into the short-term carbon sequestration potential of forests under disturbance regimes by showing that the continuous mortality framework tends to overestimate the carbon sink capacity of forests in the 20- to 50-year range in ecosystems under high disturbance pressure compared to scenarios with abrupt mortality events. This model enhancement underscores the critical need to include disturbance dynamics in land surface models to refine predictions of forest carbon dynamics in a changing climate.
Original languageEnglish
Pages (from-to)8023-8047
JournalGeoscientific Model Development
Volume17
Issue number21
DOIs
Publication statusPublished - 11 Nov 2024

Funding

Guillaume Marie was funded by MSCF (CLIMPRO; grant no. 895455) and ADEME (DIPROG; grant no. 2003C0002). Sebastiaan Luyssaert and Kim Naudts were funded by the EU Horizon 2020 HoliSoils program (grant no. SEP-210673589) and the Horizon Europe INFORMA program (grant no. 101060309). Jina Jeong and Bertrand Guenet were funded by the EU Horizon 2020 HoliSoils program (grant no. SEP-210673589). Gunnar Petter was funded by the Swiss National Science Foundation (grant no. SNF 163250). Anne Sofie Lans\u00F8 was funded by the EU Horizon 2020 Crescendo program (grant no. 641816). Chao Yue was funded by the National Science Foundation of China (grant nos. U20A2090 and 41971132). Matthew J. McGrath was supported by the European Commission Horizon 2020 framework program (VERIFY; grant no. 776810) and the European Union\u2019s Horizon 2020 research and innovation program (CoCO2; grant no. 958927). Aude Valade was funded by the Agropolis Fondation (grant no. 2101-048). This work was performed using high-performance computing (HPC) resources from GENCI-TGCC (grant no. 2022-06328). The Open AI textual AI GPT4 ( https://chat.openai.com/ , last access: March 2024) was used for language editing at an early stage of manuscript preparation.

FundersFunder number
ADEME2003C0002
Agropolis Fondation2101-048
Agropolis Fondation
Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungSNF 163250
Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
Horizon Europe INFORMA program101060309
Horizon 2020 Framework Programme958927
Horizon 2020 Framework Programme
Horizon 2020641816
Horizon 2020
Grand Équipement National De Calcul Intensif2022-06328
Grand Équipement National De Calcul Intensif
European Commission Horizon 2020 framework program776810
EU Horizon 2020 HoliSoils programSEP-210673589
National Natural Science Foundation of China41971132, U20A2090
National Natural Science Foundation of China
Mount Sinai Community Foundation895455
Mount Sinai Community Foundation

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