Reliable predictions of forest ecosystem functioning require flawless climate forcings

Jourdan, Marion; François, Christophe; Delpierre, Nicolas; St-Paul, Nicolas Martin; Dufrêne, Éric

doi:10.1016/j.agrformet.2021.108703

Cited by 6 publications

(5 citation statements)

References 92 publications

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“…First, we calibrated some of the model most sensitive parameters (Supplementary notes SN1) in order to simulate GPP and ETR accurately over periods with no water stress over the period of 2006-2019. Then, we performed prospective simulations under a changing climate, considering three climate models under two RCP scenarios (RCP4.5 and 8.5, see Jourdan et al, 2021 for details on the climate models). With those simulations, we quantified the sensitivity of simulated GPP to the SWHC parameter.…”

Section: Methodsmentioning

confidence: 99%

Contribution of deep soil layers to the transpiration of a temperate deciduous forest: quantification and implications for the modelling of productivity

Maysonnave

Delpierre

François

et al. 2022

Preprint

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Climate change is imposing drier atmospheric and edaphic conditions on temperate forests. Here, we investigated how subsoil (down to 300 cm) water extraction contributed to the provision of water in the Fontainebleau-Barbeau temperate oak forest over two years, including the 2018 record drought. Deep water provision was key to sustain canopy transpiration during drought, with layers below 150 cm contributing up to 60% of the transpired water in August 2018, despite their very low density of fine roots. We further showed that soil databases used to parameterize ecosystem models clearly underestimated the amount of water extractable from the soil by trees. The consensus database established for France gave an estimate of 207 mm for the soil water holding capacity (SWHC) at our study site, when our estimate based on the analysis of soil water content measurements was 1.9 times higher, reaching 390+/-17 mm. Running the CASTANEA forest model with the database-derived SWHC yielded a 350 gC m-2 y-1 average underestimation of annual gross primary productivity under Global Change Biology For Review Only current climate, reaching up to 700 gC m-2 y-1 under climate change scenario RCP8.5. Correctly estimating SWHC is a challenge for accurate simulations of the carbon cycle in a changing climate and we showed that subsoil SWC measurements are needed for this.

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Section: Methodsmentioning

confidence: 99%

Contribution of deep soil layers to the transpiration of a temperate deciduous forest: quantification and implications for the modelling of productivity

Maysonnave

Delpierre

François

et al. 2022

Preprint

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“…Modelled weather data can be biased, which affects model outputs based on these data. For example, correcting climate projections for bias increased the accuracies of projected forest ecosystem function and of the simulated timing of leaf phenology Jourdan et al, 2021). Here, we refrained from bias-correcting the meteorological data for the past and future, which likely negatively affected the accuracy of the simulated timing of autumn leaf phenology for the past and future.…”

Section: Driver Datamentioning

confidence: 99%

Climate change-induced shifts in leaf phenology of trees: past and future trends

Meier

2024

Preprint

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Forests and atmosphere interact, for example when trees assimilate and respire CO2 during photosynthesis. Depending on their productivity and net assimilation, forests may function as a carbon sink or source. Further, the amounts of synthesized sugars assigned to growth, reproduction, and defense affect the fitness of the trees and eventually the distribution of species. Climate strongly impacts such forest-atmosphere interactions, which affect forests and in turn feed back to the climate. For deciduous trees, the beginning and end of the photosynthetically active period (i.e., the ‘growing season’) relate to spring and autumn leaf phenology (i.e., leaf unfolding and leaf senescence), respectively. The climatic conditions during the growing season (i.e., the ‘bioclimate’) are directly and indirectly influenced by climate change, as climate change alters the timing and length of the growing season through shifts in leaf unfolding and leaf senescence. While past changes in leaf unfolding and leaf senescence can be analyzed by in-situ observations, the underlying drivers and particularly possible future changes are often studied with process-oriented models. However, these models may suffer from a bias towards the mean (BTM), which causes the simulated values to be closer to the average than the observations and could result in overly flat simulated trends. Here I discuss (1) changes in the growing season and bioclimate in Switzerland during recent decades, (2) impacts of the calibration approach on the performance of 21 leaf senescence models tested with observations from Central Europe, and (3) effects of the BTM in these models on their performance and projections. Growing seasons have predominately lengthened at elevation-specific rates, which was primarily caused by changes in leaf senescence and increased the number of days with a negative atmospheric water balance at low elevations. Calibrations with the Generalized Simulated Annealing algorithm and with systematically balanced or stratified samples yielded the best performing leaf senescence models, while their performance was most influenced by their structure. The BTM caused the performance of current leaf senescence models to be only slightly better than the performance of a null model that constantly simulates the average of the calibration sample. Standard model comparisons favored models with stronger BTM, while models with weaker BTM projected smaller backward shifts in future leaf senescence. The latter is counter-intuitive, since smaller shifts result from flatter trends and are therefore associated with stronger rather than weaker BTM. I conclude that (1) the effects of phenological changes on the bioclimate should be considered when studying past and future forest productivity and species composition, (2) inference from process-oriented models to the underlying processes and drivers of leaf senescence is valid, and (3) current leaf senescence projections are highly uncertain and thus unreliable. While it is likely that current projections of future biosphere behavior under global change are distorted by erroneous state-of-the-art leaf senescence models, there is ample need and potential for the development of more accurate process-oriented models.

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“…Modeled weather data can be biased, which affects model outputs based on these data. For example, correcting climate projections for bias increased the accuracies of the projected forest ecosystem function and of the simulated timing of leaf phenology (Drepper et al, 2022;Jourdan et al, 2021). Here, we refrained from bias-correcting the meteorological data for the past and future, which likely negatively affected the accuracy of the simulated timing of autumn leaf phenology for the past and future.…”

Section: Driver Datamentioning

confidence: 99%

Process-oriented models of autumn leaf phenology: ways to sound calibration and implications of uncertain projections

Meier,

Bigler

2023

Geosci. Model Dev.

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Abstract. Autumn leaf phenology marks the end of the growing season, during which trees assimilate atmospheric CO2. The length of the growing season is affected by climate change because autumn phenology responds to climatic conditions. Thus, the timing of autumn phenology is often modeled to assess possible climate change effects on future CO2-mitigating capacities and species compositions of forests. Projected trends have been mainly discussed with regards to model performance and climate change scenarios. However, there has been no systematic and thorough evaluation of how performance and projections are affected by the calibration approach. Here, we analyzed >2.3 million performances and 39 million projections across 21 process-oriented models of autumn leaf phenology, 5 optimization algorithms, ≥7 sampling procedures, and 26 climate model chains from two representative concentration pathways. Calibration and validation were based on >45 000 observations for beech, oak, and larch from 500 central European sites each. Phenology models had the largest influence on model performance. The best-performing models were (1) driven by daily temperature, day length, and partly by seasonal temperature or spring leaf phenology; (2) calibrated with the generalized simulated annealing algorithm; and (3) based on systematically balanced or stratified samples. Autumn phenology was projected to shift between −13 and +20 d by 2080–2099 compared to 1980–1999. Climate scenarios and sites explained more than 80 % of the variance in these shifts and thus had an influence 8 to 22 times greater than the phenology models. Warmer climate scenarios and better-performing models predominantly projected larger backward shifts than cooler scenarios and poorer models. Our results justify inferences from comparisons of process-oriented phenology models to phenology-driving processes, and we advocate for species-specific models for such analyses and subsequent projections. For sound calibration, we recommend a combination of cross-validations and independent tests, using randomly selected sites from stratified bins based on mean annual temperature and average autumn phenology, respectively. Poor performance and little influence of phenology models on autumn phenology projections suggest that current models are overlooking relevant drivers. While the uncertain projections indicate an extension of the growing season, further studies are needed to develop models that adequately consider the relevant processes for autumn phenology.

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Reliable predictions of forest ecosystem functioning require flawless climate forcings

Cited by 6 publications

References 92 publications

Contribution of deep soil layers to the transpiration of a temperate deciduous forest: quantification and implications for the modelling of productivity

Contribution of deep soil layers to the transpiration of a temperate deciduous forest: quantification and implications for the modelling of productivity

Climate change-induced shifts in leaf phenology of trees: past and future trends

Process-oriented models of autumn leaf phenology: ways to sound calibration and implications of uncertain projections

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