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Abstract

Source/sink distributions of heat, CO₂ and water vapour in a Siberian Scots pine forest were estimated from measured concentration and temperature profiles using three inverse analysis methods. These methods include: a Eulerian second- order closure model (EUL); a localized near-field Lagrangian dispersion model (LNF); and a hybrid model (HEL) which uses the Eulerian second-order turbulence model to calculate the flow statistics combined with the regression analysis used with the Lagrangian model. Model predictions were compared to heat flux profiles measured at five levels in the canopy, and to CO₂ and water-vapour fluxes measured close to the ground and above the forest. Predictions of sensible-heat flux profiles by the LNF and HEL schemes were systematically better than results from the EUL analysis. This improvement was attributed to the redundancy in the measured profile (scalar concentration and temperature) data for LNF and HEL and to the imposed smoothness condition used in the regression analyses, whereas the EUL approach calculates a source for each level without any redundancy. The LNF and HEL schemes were also better than EUL in predicting source distributions for CO₂ and water vapour, although errors were larger than for sensible heat. The main novelty in our study is the use of EUL to decompose the vertical variability in scalar (or heat) sources into variability produced by the inhomogeneity in flow statistics and variability inferred from the measured mean scalar concentration (or temperature) profile. Hence, it is possible with this analysis to assess how much 'new information' about the source variability is attributed to vertical variation in the measured mean scalar concentration (or temperature) profiles. The analysis shows that measured water vapour concentration profiles provide little information on the inferred source distribution, whereas the CO₂ profiles contain more information. Monte Carlo simulations show that computed sources from all three inverse methods have similar sensitivities to errors in measured temperatures. Errors are reduced when the reference temperature above the canopy is held fixed, implying that errors in this temperature propagate throughout the entire domain. When information content and error estimations are combined, a valuable tool to assess the quality of source prediction by inverse methods can be generated.