Occasional, rare measurements of stocks have limited use in constraining the estimates of other components of the C cycle. Long time series are particularly crucial for improving the analysis of pools with long time constants, such as SOM, woody biomass, and woody debris. Long-running forest stem surveys, and tree ring data, offer a rich resource that could be assimilated to provide an important constraint on C cycling of slow pools. For extending estimates of NEE across regions, DA can play a further important role, by assimilating remote-sensing data into the analysis of C cycles.We show, via sensitivity analysis, how assimilating an estimate of photosynthesiswhich might be provided indirectly by remotely sensed data -improves the analysis of NEE. I I I I I I I I I I f I 1 I f I I 1 estimates of ecosystem C stocks and fluxes, with reduced uncertainty compared with the original observations, or the model alone. The argument of this paper is that combining measurements and modelling through DA generates more precise estimates of C dynamics, and simultaneously highlights areas where model improvement is required. MethodsThe premise of DA is that neither models nor observations can perfectly describe a system, but an analysis that combines model and data will provide a better estimate of system dynamics than model or observations alone. DA is a process for the optimal combination of information about a system, which evolved from the engineering approaches to filtering and control theory applied in missile guidance and interception (Maybeck, 1979). DA has been applied in meteorology for forecasting (Lorenc, 1986), and ex- DA is the process of finding the model representation that is most consistent with observations (Lorenc, 1995). DA recognizes that there are never sufficient observations to represent the state of a system at any one time. For a detailed, complete picture, further information is required, such as knowledge of the behaviour and probable structure of the system. In DA, knowledge of system evolution in time is usually embodied in a model. In sequential assimilation, the approach we demonstrate here, the model organizes and propagates forward information from previous observations (Lorenc, 1995). When new information becomes available, the prediction, or forecast, of the model can be compared with these observations and corrected. A poor model will drift and will be frequently and heavily corrected; an effective model will require little ~initializa-tion by observations. However, it is not simply a question of fitting the model to the new data, as the assimilation process must also conserve the information provided by the model itself and by previous observations.The DA technique that we use here is the Kalman filter (KF) (Kalman, 19601, which has been widely used (Grewal, 1993), and, given various assumptions, has been shown to be an optimal, variance-minimizing analysis (Maybeck, 1979). The basic KF requires three assumptions: that a linear model can describe the system, and that ...

Tropospheric ozone (O3) effectively deposits to forested ecosystems but the fate of O3 within the forest canopy is unresolved. We partitioned total measured ecosystem daytime O3 deposition to a ponderosa pine (Pinus ponderosa) forest into its major loss pathways; stomatal uptake, non‐stomatal surface deposition, and gas‐phase chemistry. Total O3 flux was dominated by gas‐phase chemistry during the summer and by stomatal uptake during winter. O3 loss due to gas‐phase chemistry was exponentially dependent on temperature, with the same functionality as biogenic hydrocarbon emissions, implicating reactions with biogenic hydrocarbons as the likely gas‐phase chemical O3 loss process within the canopy. The reaction of O3 with biogenically‐emitted hydrocarbons leads to both hydroxyl radical formation and secondary aerosol growth with important effects on atmospheric chemistry and climate.

Ecosystem ozone uptake can occur through stomatal and surface deposition and through gas phase chemical reactions. In a California pine forest, thinning dramatically enhanced both monoterpene emission and ozone uptake. These simultaneous enhancements provide strong evidence that ozone reactions with unmeasured biogenically emitted volatile organic compounds (BVOCs) dominate ozone uptake, and these unmeasured BVOC emissions are approximately 10 times the measured monoterpene flux. Branch enclosure measurements confirm more than 100 BVOCs are emitted but not typically observed above the forest. These BVOCs likely impact tropospheric composition as a previously unquantified source of secondary oxygenated VOCs, organic aerosols, and OH radicals.

As forests age, their structure and productivity change, yet in some cases, annual rates of water loss remain unchanged. To identify mechanisms that might explain such observations, and to determine if widely different age classes of forests differ functionally, we examined young (Y, approximately 25 years), mature (M, approximately 90 years) and old (O, approximately 250 years) ponderosa pine (Pinus ponderosa Dougl. ex P. Laws.) stands growing in a drought-prone region of central Oregon. Although the stands differed in tree leaf area index (LAIT) (Y = 0.9, M = 2.8, O = 2.1), cumulative tree transpiration measured by sap flow did not differ substantially during the growing season (100-112 mm). Yet when water was readily available, transpiration per unit leaf area of the youngest trees was about three times that of M trees and five times that of O trees. These patterns resulted from a nearly sixfold difference in leaf specific conductance (KL) between the youngest and oldest trees. At the time of maximum transpiration in the Y stand in May-June, gross carbon uptake (gross ecosystem production, GEP) was similar for Y and O stands despite an almost twofold difference in stand leaf area index (LAIS). However, the higher rate of water use by Y trees was not sustainable in the drought-prone environment, and between spring and late summer, KL of Y trees declined fivefold compared with a nearly twofold decline for M trees and a < 30% reduction in O trees. Because the Y stand contained a significant shrub understory and more exposed soil, there was no appreciable difference in mean daily latent energy fluxes between the Y stand and the older stands as measured by the eddy-covariance technique. These patterns resulted in 60 to 85% higher seasonal GEP and 55 to 65% higher water-use efficiency at the M and O stands compared with the Y stand.

[1] We investigated the relative importance of climatic versus biotic controls on gross primary production (GPP) and water vapor fluxes in seasonally drought-affected ponderosa pine forests. The study was conducted in young (YS), mature (MS), and old stands (OS) over 4 years at the AmeriFlux Metolius sites. Model simulations showed that interannual variation of GPP did not follow the same trends as precipitation, and effects of climatic variation were smallest at the OS (<10%), largest at the MS (>50%), and intermediate at the YS (<20%). In the young, developing stand, interannual variation in leaf area has larger effects on fluxes than climate, although leaf area is a function of climate in that climate can interact with age-related shifts in carbon allocation and affect whole-tree hydraulic conductance. Older forests, with well-established root systems, appear to be better buffered from effects of seasonal drought and interannual climatic variation. Interannual variation of net ecosystem exchange (NEE) was also lowest at the OS, where NEE is controlled more by interannual variation of ecosystem respiration, 70% of which is from soil, than by the variation of GPP, whereas variation in GPP is the primary reason for interannual changes in NEE at the YS and MS. Across spatially heterogeneous landscapes with high frequency of younger stands resulting from natural and anthropogenic disturbances, interannual climatic variation and change in leaf area are likely to result in large interannual variation in GPP and NEE.