The effect of static pressure-wind covariance on vertical carbon dioxide exchange at a windy subalpine forest site

Burns, Sean P.; Frank, J. M.; Massman, W. J.; Patton, Edward G.; Blanken, Peter D.

doi:10.1016/j.agrformet.2021.108402

Cited by 12 publications

(5 citation statements)

References 96 publications

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“…Discrepancies between simulated and observed within-canopy wind speed are partially attributable to the actual canopy height at GLEES being lower than the Noah-MP default setting of H CAN (20 m) for evergreen needleleaf forests (Burns et al, 2021). M-O and M-O-RSL within-canopy wind speed predictions have smaller biases and higher correlations when H can is set to a more accurate value (12 m) (Burns et al, 2021) (Figure 13b). Discrepancies that persist between simulated and observed within-canopy wind speed are likely affected by limitations imposed by the one-layer bulk canopy model structure of Noah-MP as well as violations of the HF08 dense canopy assumption (Bonan et al, 2021;Burns et al, 2018).…”

Section: Ameriflux Within-canopy Wind Speed and Turbulent Heat Flux Analysismentioning

confidence: 98%

“…At GLEES, M-O and M-O-RSL underestimate within-canopy wind speed by 68% and 60%, respectively, and M-O has a higher correlation with observed within-canopy wind speed than M-O-RSL (Figure 13a). Discrepancies between simulated and observed within-canopy wind speed are partially attributable to the actual canopy height at GLEES being lower than the Noah-MP default setting of H CAN (20 m) for evergreen needleleaf forests (Burns et al, 2021). M-O and M-O-RSL within-canopy wind speed predictions have smaller biases and higher correlations when H can is set to a more accurate value (12 m) (Burns et al, 2021) (Figure 13b).…”

Section: Ameriflux Within-canopy Wind Speed and Turbulent Heat Flux Analysismentioning

confidence: 98%

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Implementation and Evaluation of a Unified Turbulence Parameterization Throughout the Canopy and Roughness Sublayer in Noah‐MP Snow Simulations

Abolafia‐Rosenzweig

Burns

et al. 2021

J Adv Model Earth Syst

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The Noah‐MP land surface model (LSM) relies on the Monin‐Obukhov (M‐O) Similarity Theory (MOST) to calculate land‐atmosphere exchanges of water, energy, and momentum fluxes. However, MOST flux‐profile relationships neglect canopy‐induced turbulence in the roughness sublayer (RSL) and parameterize within‐canopy turbulence in an ad hoc manner. We implement a new physics scheme (M‐O‐RSL) into Noah‐MP that explicitly parameterizes turbulence in RSL. We compare Noah‐MP simulations employing the M‐O‐RSL scheme (M‐O‐RSL simulations) and the default M‐O scheme (M‐O simulations) against observations obtained from 647 Snow Telemetry (SNOTEL) stations and two AmeriFlux stations in the western United States. M‐O‐RSL simulations of snow water equivalent (SWE) outperform M‐O simulations over 64% and 69% of SNOTEL sites in terms of root‐mean‐square‐error (RMSE) and correlation, respectively. The largest improvements in skill for M‐O‐RSL occur over closed shrubland sites, and the largest degradations in skill occur over deciduous broadleaf forest sites. Differences between M‐O and M‐O‐RSL simulated snowpack are primarily attributable to differences in aerodynamic conductance for heat underneath the canopy top, which modulates sensible heat flux. Differences between M‐O and M‐O‐RSL within‐canopy and below‐canopy sensible heat fluxes affect the amount of heat transported into snowpack and hence change snowmelt when temperatures are close to or above the melting point. The surface energy budget analysis over two AmeriFlux stations shows that differences between M‐O and M‐O‐RSL simulations can be smaller than other model biases (e.g., surface albedo). We intend for the M‐O‐RSL physics scheme to improve performance and uncertainty estimates in weather and hydrological applications that rely on Noah‐MP.

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Section: Ameriflux Within-canopy Wind Speed and Turbulent Heat Flux Analysismentioning

confidence: 98%

Section: Ameriflux Within-canopy Wind Speed and Turbulent Heat Flux Analysismentioning

confidence: 98%

Implementation and Evaluation of a Unified Turbulence Parameterization Throughout the Canopy and Roughness Sublayer in Noah‐MP Snow Simulations

Abolafia‐Rosenzweig

Burns

et al. 2021

J Adv Model Earth Syst

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“…One port of the sensors was connected to a pressure reference to attenuate high-frequency P raw fluctuations. The pressure head used in this study was of the quad-disc type [47,48], made of white 3 mm PVC sheets and in-house 3D-printed parts.…”

Section: Air Pressure Measurementsmentioning

confidence: 99%

On the Potential of Using Air Pressure Fluctuations to Estimate Wind-Induced Tree Motion in a Planted Scots Pine Forest

Kolbe

Mohr

Maier³

et al. 2022

Forests

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This paper reports statistical relationships between measured airflow, air pressure fluctuations, and the wind-induced motion of planted Scots pine trees (Pinus sylvestris L.). The results presented illustrate the potential of low-cost, ground-based air pressure measurements for monitoring wind-induced tree response. It is suggested that air pressure fluctuations can be used as surrogate information for above-canopy airflow, often used to estimate wind loads on forest trees. We demonstrate that air pressure fluctuations can be measured representatively at the forest floor and correlate very well with wind speed and direction at mean canopy-top (18 m a.g.l.) and above the 18 m high, 56-year-old forest. Their strong correlation (coefficient of determination R2 > 0.77) allows a good approximation of airflow conditions above the canopy, and, with some limitations, in the below-canopy space. Air pressure fluctuations also correlate very well with wind-induced tree motion with a similar correlation to that between wind speed and tree motion. Furthermore, the main directions of wind-induced tree motion agree very well with the propagation direction of air pressure waves. Above-canopy airflow measurements in forests with a large vertical extent are rare, and often require tall wind measurement towers. Therefore, we consider the estimation of airflow conditions over forests using ground-based air pressure measurements a promising option for monitoring the airflow conditions of relevance for predicting wind-induced tree response over large areas using a minimum of measurement infrastructure.

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“…Because the most energetic turbulence eddies predominantly scale with observation height during daytime (Sun et al, 2016(Sun et al, , 2020, the estimated   w p under convective conditions should not be significantly impacted by the sampling rate. The term,     w p z / , is approximately estimated from the vertical difference of   w p between 1.5 and 30 m. The detailed investigation of pressure fluxes as functions of separation distance between sonic anemometers and pressure sensors as well as atmosphere-instability is conducted by Burns et al (2021), who found pressure fluxes are closely related to wind speed.…”

Section: Instrumentation and Data Processingmentioning

confidence: 99%

“…The term,

\partial \bar{w^{'} p^{'}} / \partial z

, is approximately estimated from the vertical difference of

\bar{w^{'} p^{'}}

between 1.5 and 30 m. The detailed investigation of pressure fluxes as functions of separation distance between sonic anemometers and pressure sensors as well as atmosphere‐instability is conducted by Burns et al. (2021), who found pressure fluxes are closely related to wind speed.…”

Section: Instrumentation and Data Processingmentioning

confidence: 99%

Revisiting the Surface Energy Imbalance

et al. 2021

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The traditional surface energy balance (SEB) is investigated by applying the thermal energy balance based on total energy conservation. Based on total energy conservation, the thermal and the kinetic energy balances are connected through non‐hydrostatic energy transfer (QNH) as a result of the hydrostatic imbalance caused by potential energy changes from vertical density fluxes in response to mechanical and thermal forcing in the stratified atmosphere. Field observations show that both QNH and the surface energy imbalance (SEI) have the same diurnal variation; both are near zero under neutral conditions and increase with increasing atmospheric unstable stratification. Energy dissipation due to enhanced turbulent kinetic energy from QNH is estimated to be the largest energy consumption for potentially explaining the largest SEI under free convective conditions. The relatively large value of energy dissipation in the atmospheric surface layer is due to its exponential increase toward the surface and its increase with atmospheric instability. The other two energy transfers that are traditionally missed are related to water mass flowing through the open soil and air layers, but are relatively small in comparison with the energy dissipation. The new understanding of the energy transfers provides not only a potential explanation for the temporal and magnitude variations of the SEI but also the explanation for different heat/moisture transferring efficiencies between thermally and mechanically generated turbulent mixing. Understanding the SEB is important not only for solving the SEI but also for understanding energy conservation in the atmosphere.

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The effect of static pressure-wind covariance on vertical carbon dioxide exchange at a windy subalpine forest site

Cited by 12 publications

References 96 publications

Implementation and Evaluation of a Unified Turbulence Parameterization Throughout the Canopy and Roughness Sublayer in Noah‐MP Snow Simulations

Implementation and Evaluation of a Unified Turbulence Parameterization Throughout the Canopy and Roughness Sublayer in Noah‐MP Snow Simulations

On the Potential of Using Air Pressure Fluctuations to Estimate Wind-Induced Tree Motion in a Planted Scots Pine Forest

Revisiting the Surface Energy Imbalance

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