Controls over ozone deposition to a high elevation subalpine forest

Turnipseed, Andrew A.; Burns, Sean P.; Moore, David J. P.; Hu, Jia; Guenther, Alex; Monson, Russell K.

doi:10.1016/j.agrformet.2009.04.001

Cited by 41 publications

(50 citation statements)

References 77 publications

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“…The previous studies showed that the main controlling factors of V d were radiation, air temperature, friction velocity, vapor pressure deficit, soil moisture, and phenology (Wesely and Hicks, 2000;Kurpius et al, 2002;Turnipseed et al, 2009;Zona et al, 2014). In our study, we found that there were obvious positive correlations between V d and both global radiation (Q) and friction velocity (u*) during the growing stage (Fig.…”

Section: Relationship Between V D and Environmental Variablessupporting

confidence: 63%

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Ozone concentrations, flux and potential effect on yield during wheat growth in the Northwest-Shandong Plain of China

Zhu

Sun

Zhao

et al. 2015

Journal of Environmental Sciences

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Section: Relationship Between V D and Environmental Variablessupporting

confidence: 63%

“…The diurnal variation of V d is affected by many factors, e.g., radiation, turbulent intensity, and atmospheric humidity (Fowler et al, 2009;Turnipseed et al, 2009;Zona et al, 2014). In the morning, the increasing radiation results in stronger turbulence (described by friction velocity u*) (Fig.…”

Section: Mean Diurnal Variations Of Deposition Velocitymentioning

confidence: 99%

Ozone concentrations, flux and potential effect on yield during wheat growth in the Northwest-Shandong Plain of China

Zhu

Sun

Zhao

et al. 2015

Journal of Environmental Sciences

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“…O 3 can penetrate the tissues of leaves easily through stomatal uptake, causing stomatal occlusion and leaf damage. The direct uptake by vegetation through the stomata is also a major sink of O 3 in the lower troposphere (Turnipseed et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Evaluating the calculated dry deposition velocities of reactive nitrogen oxides and ozone from two community models over a temperate deciduous forest

Wang

Chen

et al. 2011

Atmospheric Environment

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Evaluating the calculated dry deposition velocities of reactive nitrogen oxides and ozone from two community models over a temperate deciduous forest ) is very sensitive to the minimum canopy stomatal resistance (R i ) which is specified for each seasonal category assigned in WDDM. Treating Sep-Oct as autumn in WDDM for this deciduous forest site caused a large underprediction of V d (O 3 ) due to the leafless assumption in 'autumn' seasonal category for which an infinite R i was assigned. Reducing R i to a value of 70 s m À1 , the same as the default value for the summer season category, the modeled and measured V d (O 3 ) agreed reasonably well. HNO 3 was found to dominate the NO y flux during the measurement period; thus the modeled V d (NO y ) was mainly controlled by the aerodynamic and quasi-laminar sublayer resistances (R a and R b ), both being sensitive to the surface roughness length (z 0 ). Using an appropriate value for z 0 (10% of canopy height), WDDM and Noah-GEM agreed well with the observed daytime V d (NO y ). The differences in V d (HNO 3 ) between WDDM and Noah-GEM were small due to the small differences in the calculated R a and R b between the two models; however, the differences in R c of NO 2 and PAN between the two models reached a factor of 1.1e1.5, which in turn caused a factor of 1.1e1.3 differences for V d . Combining the measured concentrations and modeled V d , NO x , PAN and HNO 3 accounted for 19%, 4%, and 70% of the measured NO y fluxes, respectively.

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“…Of the components of ET (evaporation and transpiration), evaporation is generally expected to dominate ET nighttime , so our results suggest that wind speed or u * may drive evaporation. Yet wind speed also decreases leaf surface resistance, providing a possible mechanism for impacting ET nighttime in the presence of positive nighttime transpiration (Dawson et al 2007), which has been reported as relatively frequent in Engelmann spruce and subalpine fir, two of the dominant trees in the Niwot Ridge forest (Turnipseed et al 2009). Any seasonality of a possible interaction between wind speed and transpiration could be difficult to detect because ET nighttime is slightly higher in the winter (Fig.…”

Section: Drivers Of Ecosystem Metabolism Considering All Seasons Togementioning

confidence: 99%

Climate controls over ecosystem metabolism: insights from a fifteen-year inductive artificial neural network synthesis for a subalpine forest

et al. 2017

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deductive studies of the same ecosystem. Here we examined the seasonal climate determinants of NEP and ET by analyzing a 15-year EC time-series from a subalpine forest using an ensemble of Artificial Neural Networks (ANNs) at the half-day (daytime/nighttime) time-step. We extracted relative rankings of climate drivers and driver-response relationships directly from the dataset with minimal a priori assumptions. The ANN analysis revealed temperature variables as primary climate drivers of NEP and daytime ET, when all seasons are considered, consistent with the assembly of past studies. New relations uncovered by the ANN approach include the role of soil moisture in driving daytime NEP during the snowmelt period, the nonlinear response of NEP to temperature across seasons, and the low relevance of summer rainfall for NEP or ET at the same daytime/nighttime time step. These new results offer a more complete perspective of climate-ecosystem interactions at this site than traditional deductive analyses alone.

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Controls over ozone deposition to a high elevation subalpine forest

Cited by 41 publications

References 77 publications

Ozone concentrations, flux and potential effect on yield during wheat growth in the Northwest-Shandong Plain of China

Ozone concentrations, flux and potential effect on yield during wheat growth in the Northwest-Shandong Plain of China

Evaluating the calculated dry deposition velocities of reactive nitrogen oxides and ozone from two community models over a temperate deciduous forest

Climate controls over ecosystem metabolism: insights from a fifteen-year inductive artificial neural network synthesis for a subalpine forest

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