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Systematic biases in eddy covariance measurements of net ecosystem-atmosphere carbon dioxide exchange (NEE) are ubiquitous in forests when turbulence is low at night. We propose an alternative to the conventional bias correction, the friction velocity (u*) filter, by hypothesizing that these biases have two separate, concurrent causes: (1) a subcanopy CO2 storage pool that eludes typical storage measurements, creating a turbulence-dependent bias, and (2) advective divergence loss of CO2, creating a turbulence-independent bias. We correct for (1) using a simple parametric model of missing storage (MS). Prior experiments have inferred (2) directly from atmospheric measurements (DRAINO). For sites at which DRAINO experiments have not been performed or are infeasible, we estimate (2) empirically using a PAR-extrapolated advective respiration loss (PEARL) approach. We compare u* filter estimates of advection and NEE to MS-PEARL estimates at one temperate forest and two tropical forest sites. We find that for tropical forests, u* filters can produce a range of extreme NEE estimates, from long-term forest carbon emission to sequestration, that diverge from independent assessments and are not physically sustainable. Our MS model eliminates the dependence of nighttime NEE on u*, consistent with findings from DRAINO studies that nighttime advective losses of CO2 are often not dependent on the strength of turbulence. Our PEARL estimates of mean advective loss agree with available DRAINO measurements. The MS-PEARL correction to long-term NEE produces better agreement with forest inventories at all three sites. Moreover, the correction retains all nighttime eddy covariance data and is therefore more widely applicable than the u* filter approach, which rejects substantial nighttime data—up to 93% at one of the tropical sites. The full MS-PEARL NEE correction is therefore an equally defensible and more practical alternative to the u* filter, but leads to different conclusions about the resulting carbon balance. Our results therefore highlight the need to investigate which approach's underlying hypotheses are more physically realistic.

Spatiotemporal variability in ozone dry deposition is often overlooked despite its implications for interpreting and modeling tropospheric ozone concentrations accurately. Understanding the influences of stomatal versus nonstomatal deposition processes on ozone deposition velocity is important for attributing observed changes in the ozone depositional sink and associated damage to ecosystems. Here, we aim to identify the stomatal versus nonstomatal deposition processes driving observed variability in ozone deposition velocity over the northeastern United States during June–September. We use ozone eddy covariance measurements from Harvard Forest in Massachusetts, which span a decade, and from Kane Experimental Forest in Pennsylvania and Sand Flats State Forest in New York, which span one growing season each, along with observation‐driven modeling. Using a cumulative precipitation indicator of soil wetness, we infer that high soil uptake during dry years and low soil uptake during wet years may contribute to the twofold interannual variability in ozone deposition velocity at Harvard Forest. We link stomatal deposition and humidity to variability in ozone deposition velocity on daily timescales. The humidity dependence may reflect higher uptake by leaf cuticles under humid conditions, noted in previous work. Previous work also suggests that uptake by leaf cuticles may be enhanced after rain, but we find that increases in ozone deposition velocity on rainy days are instead mostly associated with increases in stomatal conductance. Our analysis highlights a need for constraints on subseasonal variability in ozone dry deposition to soil and fast in‐canopy chemistry during ecosystem stress. Key Points: Stomatal and cuticular deposition are inferred as drivers of day‐to‐day variability in deposition velocityHigher stomatal uptake increases deposition velocity on rainy daysObserved interannual variability at Harvard Forest may reflect enhanced ozone deposition to dry soil [ABSTRACT FROM AUTHOR]

Stable carbon isotopes can be used to partition the net ecosystem–atmosphere exchange (NEE) of carbon dioxide (CO 2 ) into its photosynthetic and respiratory components, but the method has not been generally adopted due to instrumental and theoretical limitations. Here, motivated by recently improved instrumentation, we extend the theory of isotopic flux partitioning to include photorespiration, foliar daytime ‘dark’ respiration, and other refinements, arriving at a general yet practical formulation from which all previous formulations can be derived as simplifying approximations. We use a full growing season of isotopic eddy covariance flux data from a temperate deciduous forest to demonstrate the method, quantify its uncertainties, and determine biases associated with previously published formulations. We find that when δ 13 C of CO 2 is acquired with high precision (0.02‰ RMSE for 100 s integration times), the statistical uncertainty in the partitioned fluxes is comparable to that in NEE itself—i.e., as good as practicably possible. Assessable systematic uncertainty is ±17% of gross ecosystem production (GEP), due mostly to uncertainty in the isotopic fractionation by carboxylation. Additional, currently unquantifiable systematic uncertainty is associated with treating the canopy as a single “big leaf”. Both sources of systematic uncertainty could be greatly reduced by feasible supporting leaf-level measurements. Our extended theory corrects systematic biases in previous isotopic approaches, including overestimation (by 13%) of GEP due to the omission of photorespiration. The partitioning determines the isotopic signature of photosynthesis, which we find to vary seasonally between −24 and −28‰ such that the isotopic disequilibrium between ecosystem carbon input and output remains stable at approximately −0.5‰ through most of the growing season. The key advantage of isotopic partitioning over standard, regression-based partitioning is that it enables controls on the ecosystem-scale photosynthetic and respiratory fluxes to emerge from observations, without having to assume functional relations to environmental drivers a priori . As an example, we show how isotopic partitioning reveals certain large variations in daytime NEE to be caused by shifts in the flux tower sampling footprint between regions of high and low respiratory flux—a finding unobtainable by standard partitioning. For this reason, isotopic partitioning can be more precise than standard partitioning for quantifying environmental controls on NEE. [ABSTRACT FROM AUTHOR]

Highlights: [•] We directly measure the isotopic composition of NEE in a forest by eddy covariance. [•] Precision is limited both by instrument noise and by horizontal heterogeneity. [•] Precision is sufficient to partition NEE into photosynthesis and respiration. [•] The ‘EC/flask’ indirect flux measurement method can be biased by 2‰. [•] Laser spectrometer accuracy is on par with isotope ratio mass spectrometry. [ABSTRACT FROM AUTHOR]