Natascha Kljun

To understand the dynamics of ecosystem carbon cycling more than 10 years of eddy covariance data, measured over an evergreen, temperate, wet sclerophyll forest, were analysed and related to climate drivers on time scales ranging from hours to years. On hourly timescales we find that incoming shortwave radiation is the major meteorological driver of net ecosystem carbon exchange (NEE). Light use efficiency is higher under diffuse light conditions and carbon uptake is further modulated by the effects of variable and suboptimal temperatures (optimal temperature Topt = 18 °C) as well as by water demand (critical vapour pressure deficit VPDcrit = 12 hPa). Incoming shortwave radiation is also the major driver on daily time scales. Effects of increased light use efficiency under diffuse conditions, however, are overcompensated by the increased carbon uptake with larger amounts of total incoming shortwave radiation under clear sky conditions. On synoptic time scales a low ratio of actual to potential incoming shortwave radiation is also related to a reduced carbon uptake, or carbon release, and associated with precipitation events. Overcast conditions during an extended wet period (2010–2011) led to lower than average carbon uptake as did extended dry periods during 2003 and 2006. The drought in 2003 triggered an insect attack which turned the ecosystem into a net source of carbon for almost one year. The annual average normalised difference vegetation index (NDVI) is highly correlated with NEE at this site and multiple linear regression shows that NDVI, incoming solar radiation and air temperature explain most of the variance in NEE (r2 = 0.87, p < 0.001). Replacing air temperature with average spring air temperatures further increases the correlation (r2 = 0.91, p < 0.001). Results demonstrate that carbon uptake in this ecosystem is highly dynamic, that wavelet analysis is a suitable tool to analyse the coherence between the carbon exchange and drivers seamlessly, and that long time series are needed to capture the variability.