The impact of boreal deciduous and evergreen forests on atmospheric CO2 seasonality

The seasonal cycle of atmospheric CO2 is largely controlled by the terrestrial biosphere. It is well known that the seasonal amplitude of net ecosystem productivity (NEP) is the largest in the far north, where forest productivity is compressed into a short growing season. Since 1960, the seasonal amplitude of atmospheric CO2 north of 45N has increased by 35-55%. The increase in the seasonal amplitude is a difficult benchmark for coupled climate-carbon models to replicate. In fact, the models vary widely in their mean seasonal cycle representation. The boreal region has a strong influence on CO2 seasonality at Barrow. Deciduous and evergreen plant functional types (PFTs) have different patterns of NEP. We identified four pairs of nearby deciduous and evergreen forest PFTs with eddy covariance measurements. Evergreen forests show an early peak in NEP in May-June, while deciduous forests have a larger peak in NEP later in June-July. The influence of each PFT on the seasonal cycle at Barrow was computed from atmospheric transport results. We normalized the amplitude influence by the growing season NEP of the tower-based PFT flux and found that deciduous forests have 1.4 to 1.8 times more influence (per unit of growing season NEP) at Barrow than evergreen PFT. This diagnosis depends on the timing of the sharp seasonal draw-down at Barrow, which occurs too late to be explained by evergreen forests. The cycle at Barrow therefore appears to be strongly influenced by deciduous PFT, despite the dominance of evergreen PFTs in boreal forests. This paradoxical conclusion is also reached when examining the seasonality of land surface fluxes calculated using atmospheric inverse methods. We examine how these different PFTs, and possible trends in relative abundance, affect the seasonality of atmosphere CO2 using FluxNet data and atmospheric transport modelling. Our results highlight the importance of parameterizing multiple PFTs or individual species within grid cells in models in order to understand the recent responses of the northern ecosystems to climate change.