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Abstract: The eddy-covariance method often underestimates fluxes under stable, low-wind conditions at night when turbulence is not well developed. The most common approach to resolve the problem of nighttime flux underestimation is to identify and remove the deficit periods using friction–velocity (u * ) threshold filters (u * Th ). This study modifies an accepted method for u * Th evaluation by incorporating change-point-detection techniques. The original and modified methods are evaluated at 38 sites as part of the North American Carbon Program (NACP) site-level synthesis. At most sites, the modified method produced u * Th estimates that were higher and less variable than the original method. It also provided an objective method to identify sites that lacked a u * Th response. The modified u * Th estimates were robust and comparable among years. Inter-annual u * Th differences were small, so that a single u * Th value was warranted at most sites. No variation in the u * Th was observed by time of day (dusk versus mid or late night), however, a few sites showed significant u * Th variation with time of year. Among-site variation in the u * Th was strongly related to canopy height and the mean annual nighttime u * . The modified u * Th estimates excluded a high fraction of nighttime data – 61% on average. However, the negative impact of the high exclusion rate on annual net ecosystem production (NEP) was small compared to the larger impact of underestimating the u * Th . Compared to the original method, the higher u * Th estimates from the modified method caused a mean 8% reduction in annual NEP across all site-years, and a mean 7% increase in total ecosystem respiration (R e ). The modified method also reduced the u * Th -related uncertainties in annual NEP and R e by more than 50%. These results support the use of u * Th filters as a pragmatic solution to a complex problem. [Copyright &y& Elsevier]

Abstract: Estimates of data uncertainties are required to integrate different observational data streams as model constraints using model–data fusion. We describe an approach with which random and systematic uncertainties in optical measurements of leaf area index [LAI] can be quantified. We use data from a measurement campaign at the spruce-dominated Howland Forest AmeriFlux site for illustrative purposes. We made measurements along two transects (one in a mature stand, one in a recently harvested shelterwood) before sunset on successive days using both the Li-Cor LAI-2000 plant canopy analyzer and digital hemispherical photography (DHP). The random measurement uncertainty (1σ) at a given point for a single measurement is about 5% for LAI-2000 and 10% for DHP. These uncertainties are small compared to potential systematic biases due to instrument calibration errors and data processing decisions, which are estimated to be 10–20% for each instrument. Sampling uncertainty (due to the spatial variability along each transect where we conducted our measurements) is an additional, but again relatively small, uncertainty. Assumptions about clumping parameters, for which standard literature values are typically used, remain large sources of uncertainty. This analysis can also be used to develop strategies to reduce measurement uncertainties. [Copyright &y& Elsevier]

Abstract: The concept of light-use efficiency (LUE) is the underlying basis for estimating carbon exchange in many ecosystem models, especially those models that utilize remote sensing to constrain estimates of canopy photosynthesis. An understanding of the factors that control the efficiency with which forest canopies harvest available light to fix carbon via photosynthesis is therefore necessary for the development of useful production efficiency models. We present an analysis of observations of daily LUE for 2004 in a northern hardwood stand at the Bartlett Experimental Forest CO2 flux tower, White Mountains, New Hampshire (USA). We used eddy covariance measurements to estimate gross carbon exchange (GCE), and radiometric instruments mounted above and below the canopy to estimate the fraction of incident photosynthetically active radiation absorbed by the canopy (fAPAR). Both GCE and fAPAR show strong seasonal and day-to-day variability that contribute to temporal variation in LUE. During the middle of the growing season, when fAPAR is relatively constant, day-to-day variation in LUE is largely explained (r 2 =0.85) by changes in the ratio of diffuse to total downwelling radiation, but is not strongly correlated with any other measured meteorological variable. We also calculated top-of-canopy NDVI based on measurements of reflected radiation at 400–700 and 305–2800nm. Seasonal variation in this broadband NDVI paralleled that of the 500m MODIS pixel containing the flux tower. The relationship between broadband NDVI and fAPAR is approximately linear during green-up, but non-linear during autumn senescence. This seasonal hysteresis has implications for the use of remote sensing indices (such as NDVI or EVI) in satellite estimation of fAPAR for production efficiency modeling. [Copyright &y& Elsevier]