Effects of alternative ways to estimate the CO 2 storage change term on net annual CO 2 surface exchange estimates

Storage change ( S ) is an important component of the mass balance equation and quantifies the accumulation or depletion of matter in the studied control volume, under the eddy covariance (EC) sensor. The quantification of S is required to estimate surface fluxes. This study compared four methods for calculating S of CO 2 , based on EC and profile measurements at a Danish temperate forest ICOS site (DK-Sor). The 12-heights sequential sampling system quantified in- and above-canopy S . Its design and physical averaging properties were thoroughly described. Two vertical configurations of the profile system were analyzed: (i) top-tower, (ii) full profile (incorporating all levels), along with two alternative calculation methods based on top-tower EC data alone, including the method proposed in an often used software. Results showed that the deviations between the S methods had a seasonal course and that the top-tower profile was on average 21% lower than the full profile method. The choice of S method also impacted the surface flux estimations on an annual scale, with relative differences in net ecosystem exchanges of up to 8%, represented by 22 g-C m −2 yr −1 . The S methods impacted the friction velocity threshold determination, leading to a variation in the amount of data retained during low-turbulence filtering. The full profile retained the most data. Lastly, the tailor-made calculation from EC concentration measurements were shown to fit the top-tower profile measurements closer, compared to the EddyPro-calculated S . These results highlight the importance of accurate storage change measurements in high and dense forest sites. • Tall and dense forest sites require a profile storage change system. • The uncertainty of top-tower storage measurements shows a seasonal course. • Storage change calculation methods impact the net annual CO 2 ecosystem exchanges. • Accurate storage change estimation decreases the u ∗ threshold and retains more data.