A New Approach for Reducing Uncertainty in Biospheric CO<sub>2</sub> Flux

In the 2010 Global Carbon Project Report it was estimated that the terrestrial biosphere sequesters 2.4 Pg of anthropogenic carbon emissions per year although the uncertainty on that value is undoubtedly high as no error bars were given. As man-made emissions of CO<sub>2</sub> continue to increase unabated in the atmosphere, quantifying the fate of these anthropogenic sources requires both knowing the magnitude and uncertainty of background, natural CO<sub>2</sub> fluxes. Thus, reducing uncertainty in biospheric CO<sub>2</sub> flux measurements will not only give us direct, accurate observations for future carbon accounting and climate treaties but also indirectly help us quantify anthropogenic emissions for the same purposes. In conjuncture with ecosystem models or remote-sensing techniques, these measurements also provide accurate constraints on global and continental terrestrial CO<sub>2</sub> budgets. With the motivation stated above, we designed and executed a three-year Laboratory Directed Research and Development (LDRD) project with the aim of reducing the uncertainty in eddy-covariance biospheric CO<sub>2</sub> flux. Reducing uncertainty was approached in two ways. The first utilized nontraditional eddy covariance instrumentation to identify and characterize atmospheric flows above and within the plant canopy. Here, atmospheric laser detection and ranging (lidar) instrumentation were used to capture unique flow features at night which may explain erroneous or anomalous carbon fluxes. The second approach utilized a multi-layer, 3rd order closure canopy-atmosphere model to simulate fluxes at each field site. Three sites were chosen for field instrument deployment and modeling. These included the Wind River AmeriFlux tower in Washington State and the Tonzi AmeriFlux and Diablo AmeriFlux towers in northern California. These sites represent some of the extremes in the biological and meteorological conditions over which eddy covariance techniques are used. Wind River is a multi-layered, 60-m tall seasonal rainforest, Diablo is a 1-m tall grassland with a very short growing season, and Tonzi is a 2- layered savannah canopy with complex ecohydrology. All three are surrounded by complex terrain in varying degrees. The towers provided very different test sites for validating the UC Davis Advanced Canopy Atmosphere Soil Algorithm (ACASA) model. Such validation gives promise that the model can be used to independently verify and gap-fill biospheric CO<sub>2</sub> measurements from the network of ~ 550 global flux towers for future greenhouse gas emissions monitoring and verification studies.