« Idol with feet of clay »:Reliable predictions of forest ecosystem functioning require flawless climate forcings
Résumé
Climate change affects various aspects of ecosystem functioning, especially
photosynthesis, respiration and carbon storage. We need accurate modelling
approaches (impact models) to simulate forest functioning and vitality in a warmer
world so that forest models can estimate multiple changes in ecosystem service
provisions (e.g., productivity and carbon storage) and test management strategies to
promote forest resilience.
Here, we aimed to quantify the bias in these models, addressing three questions: (1)
Do the predictions of impact models vary when forcing them with different climate
models, and how do the predictions differ under climate model vs. observational
climate forcings? (2) Does the climate impact simulation variability caused by climate
forcings fade out at large spatial scales? (3) How does using simulated climate data
affect process-based model predictions in stressful drought events? To answer these
questions, we present historical results for 1960-2010 from the CASTANEA
ecophysiological forest model and use the data from three climate models. Our
analysis focuses on monospecific stands of European beech (Fagus sylvatica),
temperate deciduous oaks (Quercus robur and Q. petraea), Scots pine (Pinus
sylvestris) and spruce (Picea abies) in French forests.
We show that prediction of photosynthesis, respiration and wood growth highly
depends on the climate model used and species and region considered. Predictions
were improved after a monthly mean bias or monthly quantile mapping correction for
the three models considered. The processes simulated by the impact model exhibited
large variability under different climate forcings at the plot scale (i.e., a few hectares).
This variability faded out at larger scales (i.e., an ecological region, 100 km²), owing to
an aggregation effect. Moreover, process predictions obtained under different climate
forcings were more variable during the driest years. These results highlight the
necessity of quantifying the bias correction effect on process predictions before
predicting flux dynamics with a process-based model.
Domaines
Sciences de l'environnement
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