Continental biosphere atmosphere cloud climate feedback loop in boreal forests: Analysis of ground-based observations
|Theme||1. Environmental protection|
|Session Name||1.4 Sustainable development of the Arctic - boreal regions|
|Datetime||Sep 05, 2018 03:30 PM - 03:45 PM (UTC +3)|
|Author(s)||Ekaterina Ezhova (University of Helsinki, Finland), Ilona Ylivinkka (University of Helsinki, Finland), Joel Kuusk (Tartu Observatory, Estonia), Veli-Matti Kerminen (University of Helsinki, Finland), Markku Kulmala (University of Helsinki, Finland)|
Climate feedback loops play an important role for our understanding on the global climate change. Feedback loops are relevant for future large-scale models, which have to include self-regulatory processes controlling carbon exchange.
The idea behind the COBACC feedback loop can be formulated as follows. Increased concentration of carbon dioxide leads to the increase in the photosynthetic activity of plants or gross primary production (GPP). Active plants can emit more biogenic volatile organic compounds (BVOCs). Oxidized BVOCs form low-volatility organic vapours, favouring conditions for the new particle formation and growth. Aerosols in the atmosphere (quantified through condensation sink, CS) scatter and absorb solar radiation, increasing the diffuse fraction of global solar radiation (Rd/Rg). Diffuse radiation boosts light use efficiency of plants (LUE) increasing leaf area surface available for photosynthesis and, consequently, increasing GPP. Thus, carbon dioxide concentration decreases, resulting in a negative feedback loop.
We studied the feedback loop in boreal forests employing ground-based measurements. We used data sets from several sites at middle and relatively high latitudes during the growing season. GPP was related to atmospheric BVOC concentration via BVOC emissions. Further, CS dependence on organic vapour concentration was quantified. We estimated the diffuse fraction of global radiation due to aerosols and showed that it did not exceed 0.25 for all sites. We found nonlinear GPP dependences on Rd/Rg with a maximum around 0.3-0.5 depending on the LUE of an ecosystem. Our analysis makes it possible to estimate the interactions inside the feedback loop.
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