Predicted Future Changes in the Mean Seasonal Carbon Cycle Due to Climate Change

Author:

Morichetti Mauro1ORCID,Vangi Elia12,Collalti Alessio13ORCID

Affiliation:

1. National Research Council of Italy, Forest Modelling Laboratory, Institute for Agriculture and Forestry Systems in the Mediterranean (CNR-ISAFOM), Via Madonna Alta 128, 06128 Perugia, Italy

2. geoLAB—Laboratory of Forest Geomatics, Department of Agriculture, Food, Environment and Forestry, Università degli Studi di Firenze, Via San Bonaventura 13, 50145 Firenze, Italy

3. National Biodiversity Future Center (NBFC), Piazza Marina 61, 90133 Palermo, Italy

Abstract

Through photosynthesis, forests absorb annually large amounts of atmospheric CO2. However, they also release CO2 back through respiration. These two, opposite in sign, large fluxes determine how much of the carbon is stored or released back into the atmosphere. The mean seasonal cycle (MSC) is an interesting metric that associates phenology and carbon (C) partitioning/allocation analysis within forest stands. Here, we applied the 3D-CMCC-FEM model and analyzed its capability to represent the main C-fluxes, by validating the model against observed data, questioning if the sink/source mean seasonality is influenced under two scenarios of climate change, in five contrasting European forest sites. We found the model has, under current climate conditions, robust predictive abilities in estimating NEE. Model results also predict a consistent reduction in the forest’s capabilities to act as a C-sink under climate change and stand-aging at all sites. Such a reduction is predicted despite the number of annual days as a C-sink in evergreen forests increasing over the years, indicating a consistent downward trend. Similarly, deciduous forests, despite maintaining a relatively stable number of C-sink days throughout the year and over the century, show a reduction in their overall annual C-sink capacity. Overall, both types of forests at all sites show a consistent reduction in their future mitigating potential.

Funder

OptForEU Horizon Europe research and innovation programme

Publisher

MDPI AG

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