Process-based modelling of biogenic monoterpene emissions: sensitivity to temperature and light
1Lund University, Department of Physical Geography and Ecosystems Analysis, Sölvegatan 12, 223 62 Lund, Sweden
2Utrecht University, Institute for Marine and Atmospheric Research, Utrecht, The Netherlands
3University of California at Berkeley, Department of Environmental Science, Policy and Management, Berkeley, CA, USA
Abstract. Monoterpenes, primarily emitted by terrestrial vegetation, can influence atmospheric ozone chemistry, and can form precursors for secondary organic aerosol. The short-term emissions of monoterpenes have been well studied and understood, but their long-term variability, which is particularly important for atmospheric chemistry, has not. This understanding is crucial for the understanding of future changes.
In this study, two algorithms of terrestrial biogenic monoterpene emissions, the first one based on the short-term volatilization of monoterpenes, as commonly used for temperature-dependent emissions, and the second one based on long-term production of monoterpenes (linked to photosynthesis) combined with emissions from storage, were compared and evaluated with measurements from a Ponderosa pine plantation (Blodgett Forest, California). The measurements were used to parameterize the long-term storage of monoterpenes, which takes place in specific storage organs and which determines the temporal distribution of the emissions over the year. The difference in assumptions between the first (emission-based) method and the second (production-based) method, which causes a difference in upscaling from instantaneous to daily emissions, requires roughly a doubling of emission capacities to bridge the gap to production capacities. The sensitivities to changes in temperature and light were tested for the new methods, the temperature sensitivity was slightly higher than that of the short-term temperature dependent algorithm.
Applied on a global scale, the first algorithm resulted in annual total emissions of 29.6 Tg C a−1, the second algorithm resulted in 31.8 Tg C a−1 when applying the correction factor 2 between emission capacities and production capacities. However, the exact magnitude of such a correction is spatially varying and hard to determine as a global average.