Uncertainty estimates for 1-h averaged turbulence fluxes of carbon dioxide, latent heat and sensible heat

Tellus B ◽  
2010 ◽  
Vol 62 (2) ◽  
pp. 87-99 ◽  
Author(s):  
Dean Vickers ◽  
Mathias Göckede ◽  
Beverly Law
Keyword(s):  
Carbon Dioxide ◽  
Latent Heat ◽  
Sensible Heat ◽  
Uncertainty Estimates

scholarly journals Low-level jets and above-canopy drainage as causes of turbulent exchange in the nocturnal boundary layer

2014 ◽  
Vol 11 (16) ◽  
pp. 4507-4519 ◽  
Author(s):  
T. S. El-Madany ◽  
H. F. Duarte ◽  
D. J. Durden ◽  
B. Paas ◽  
M. J. Deventer ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Wind Speed ◽  
Eddy Covariance ◽  
Latent Heat ◽  
Heat Fluxes ◽  
Vertical Wind ◽  
Sensible Heat ◽  
Intermittent Turbulence ◽  
Low Level Jets ◽  
Vertical Wind Velocity

Abstract. Sodar (SOund Detection And Ranging), eddy-covariance, and tower profile measurements of wind speed and carbon dioxide were performed during 17 consecutive nights in complex terrain in northern Taiwan. The scope of the study was to identify the causes for intermittent turbulence events and to analyze their importance in nocturnal atmosphere–biosphere exchange as quantified with eddy-covariance measurements. If intermittency occurs frequently at a measurement site, then this process needs to be quantified in order to achieve reliable values for ecosystem characteristics such as net ecosystem exchange or net primary production. Fourteen events of intermittent turbulence were identified and classified into above-canopy drainage flows (ACDFs) and low-level jets (LLJs) according to the height of the wind speed maximum. Intermittent turbulence periods lasted between 30 and 110 min. Towards the end of LLJ or ACDF events, positive vertical wind velocities and, in some cases, upslope flows occurred, counteracting the general flow regime at nighttime. The observations suggest that the LLJs and ACDFs penetrate deep into the cold air pool in the valley, where they experience strong buoyancy due to density differences, resulting in either upslope flows or upward vertical winds. Turbulence was found to be stronger and better developed during LLJs and ACDFs, with eddy-covariance data presenting higher quality. This was particularly indicated by spectral analysis of the vertical wind velocity and the steady-state test for the time series of the vertical wind velocity in combination with the horizontal wind component, the temperature, and carbon dioxide. Significantly higher fluxes of sensible heat, latent heat, and shear stress occurred during these periods. During LLJs and ACDFs, fluxes of sensible heat, latent heat, and CO2 were mostly one-directional. For example, exclusively negative sensible heat fluxes occurred while intermittent turbulence was present. Latent heat fluxes were mostly positive during LLJs and ACDFs, with a median value of 34 W m−2, while outside these periods the median was 2 W m−2. In conclusion, intermittent turbulence periods exhibit a strong impact on nocturnal energy and mass fluxes.

Concerning the Measurement and Magnitude of Heat, Water Vapor, and Carbon Dioxide Exchange from a Semiarid Grassland

2009 ◽  
Vol 48 (5) ◽  
pp. 982-996 ◽  
Author(s):  
Joseph G. Alfieri ◽  
Peter D. Blanken ◽  
David Smith ◽  
Jack Morgan
Keyword(s):  
Carbon Dioxide ◽  
Energy Balance ◽  
Latent Heat ◽  
Land Surface ◽  
Turbulent Fluxes ◽  
Carbon Dioxide Exchange ◽  
Sensible Heat ◽  
Seasonal Behavior ◽  
Eddy Diffusivities ◽  
Using Data

Abstract Grassland environments constitute approximately 40% of the earth’s vegetated surface, and they play a key role in a number of processes linking the land surface with the atmosphere. To investigate these linkages, a variety of techniques, including field and modeling studies, are required. Using data collected at the Central Plains Experimental Range (CPER) in northeastern Colorado from 25 March to 10 November 2004, this study compares two common ways of measuring turbulent fluxes of latent heat, sensible heat, and carbon dioxide in the field: the eddy covariance (EC) and Bowen ratio energy balance (BREB) methods. The turbulent fluxes measured by each of these methods were compared in terms of magnitude and seasonal behavior and were combined to calculate eddy diffusivities and examine turbulent transport. Relative to the EC method, the BREB method tended to overestimate the magnitude of the sensible heat, latent heat, and carbon dioxide fluxes. As a result, substantial differences in both the diurnal pattern and long-term magnitudes of the water and carbon budgets were apparent depending on which method was used. These differences arise from (i) the forced closure of the surface energy balance and (ii) the assumption of similarity between the eddy diffusivities required by the BREB method. An empirical method was developed that allows the BREB and EC datasets to be reconciled; this method was tested successfully using data collected at the CPER site during 2005. Ultimately, however, the BREB and EC methods show important differences that must be recognized and taken into account when analyzing issues related to the energy, water, or carbon cycles.

scholarly journals Intercomparison of Nine Micrometeorological Stations during the BEAREX08 Field Campaign

2011 ◽  
Vol 28 (11) ◽  
pp. 1390-1406 ◽  
Author(s):  
Joseph G. Alfieri ◽  
William P. Kustas ◽  
John H. Prueger ◽  
Lawrence E. Hipps ◽  
José L. Chávez ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Measurement Uncertainty ◽  
Critical Role ◽  
Carbon Dioxide Flux ◽  
Sensible Heat ◽  
Environmental Processes ◽  
Uncertainty Estimates ◽  
Flux Measurements ◽  
Uncertainty Analyses ◽  
Using Data

Abstract Land–atmosphere interactions play a critical role in regulating numerous meteorological, hydrological, and environmental processes. Investigating these processes often requires multiple measurement sites representing a range of surface conditions. Before these measurements can be compared, however, it is imperative that the differences among the instrumentation systems are fully characterized. Using data collected as a part of the 2008 Bushland Evapotranspiration and Agricultural Remote Sensing Experiment (BEAREX08), measurements from nine collocated eddy covariance (EC) systems were compared with the twofold objective of 1) characterizing the interinstrument variation in the measurements, and 2) quantifying the measurement uncertainty associated with each system. Focusing on the three turbulent fluxes (heat, water vapor, and carbon dioxide), this study evaluated the measurement uncertainty using multiple techniques. The results of the analyses indicated that there could be substantial variability in the uncertainty estimates because of the advective conditions that characterized the study site during the afternoon and evening hours. However, when the analysis was limited to nonadvective, quasi-normal conditions, the response of the nine EC stations were remarkably similar. For the daytime period, both the method of Hollinger and Richardson and the method of Mann and Lenschow indicated that the uncertainty in the measurements of sensible heat, latent heat, and carbon dioxide flux were approximately 13 W m−2, 27 W m−2, and 0.10 mg m−2 s−1, respectively. Based on the results of this study, it is clear that advection can greatly increase the uncertainty associated with EC flux measurements. Since these conditions, as well as other phenomena that could impact the measurement uncertainty, are often intermittent, it may be beneficial to conduct uncertainty analyses on an ongoing basis.

scholarly journals Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms

2016 ◽  
Author(s):  
Gianluca Tramontana ◽  
Martin Jung ◽  
Gustau Camps-Valls ◽  
Kazuhito Ichii ◽  
Botond Raduly ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Latent Heat ◽  
Seasonal Cycle ◽  
Ecosystem Respiration ◽  
Gross Primary Production ◽  
Remotely Sensed ◽  
Temperate Climate ◽  
Sensible Heat ◽  
Net Radiation ◽  
Validation Experiment

Abstract. Spatial-temporal fields of land-atmosphere fluxes derived from data-driven models can complement simulations by process-based Land Surface Models. While a number of strategies for empirical models with eddy covariance flux data have been applied, a systematic intercomparison of these methods has been missing so far. In this study, we perform a cross-validation experiment for predicting carbon dioxide (CO2), latent heat, sensible heat and net radiation fluxes, in different ecosystem types with eleven machine learning (ML) methods from four different classes (kernel methods, neural network, tree methods, and regression splines). We employ two complementary setups: (1) eight days average fluxes based on remotely sensed data, and (2) daily mean fluxes based on meteorological data and mean seasonal cycle of remotely sensed variables. The pattern of predictions from different ML and setups were very consistent. There were systematic differences in performance among the fluxes, with the following ascending order: net ecosystem exchange (R2 < 0.5), ecosystem respiration (R2 > 0.6), gross primary production (R2 > 0.7), latent heat (R2 > 0.7), sensible heat (R2 > 0.7), net radiation (R2 > 0.8). ML methods predicted very well the across sites variability and the seasonal cycle (R2 > 0.7) of the observed fluxes, while the eight days deviations from the mean seasonal cycle were not well predicted (R2 < 0.5). Fluxes were better predicted at forested and temperate climate sites than at ones growing in extreme climates or less representated in training data (e.g. the tropics). The large ensemble of ML based models evaluated will be the basis of new global flux products.

scholarly journals Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms

2016 ◽  
Vol 13 (14) ◽  
pp. 4291-4313 ◽  
Author(s):  
Gianluca Tramontana ◽  
Martin Jung ◽  
Christopher R. Schwalm ◽  
Kazuhito Ichii ◽  
Gustau Camps-Valls ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Latent Heat ◽  
Seasonal Cycle ◽  
Ecosystem Respiration ◽  
Remotely Sensed ◽  
Temperate Climate ◽  
Sensible Heat ◽  
Net Radiation ◽  
Validation Experiment ◽  
The Mean

Abstract. Spatio-temporal fields of land–atmosphere fluxes derived from data-driven models can complement simulations by process-based land surface models. While a number of strategies for empirical models with eddy-covariance flux data have been applied, a systematic intercomparison of these methods has been missing so far. In this study, we performed a cross-validation experiment for predicting carbon dioxide, latent heat, sensible heat and net radiation fluxes across different ecosystem types with 11 machine learning (ML) methods from four different classes (kernel methods, neural networks, tree methods, and regression splines). We applied two complementary setups: (1) 8-day average fluxes based on remotely sensed data and (2) daily mean fluxes based on meteorological data and a mean seasonal cycle of remotely sensed variables. The patterns of predictions from different ML and experimental setups were highly consistent. There were systematic differences in performance among the fluxes, with the following ascending order: net ecosystem exchange (R2 < 0.5), ecosystem respiration (R2 > 0.6), gross primary production (R2> 0.7), latent heat (R2 > 0.7), sensible heat (R2 > 0.7), and net radiation (R2 > 0.8). The ML methods predicted the across-site variability and the mean seasonal cycle of the observed fluxes very well (R2 > 0.7), while the 8-day deviations from the mean seasonal cycle were not well predicted (R2 < 0.5). Fluxes were better predicted at forested and temperate climate sites than at sites in extreme climates or less represented by training data (e.g., the tropics). The evaluated large ensemble of ML-based models will be the basis of new global flux products.

scholarly journals Effect of Urban Heat Island and Global Warming Countermeasures on Heat Release and Carbon Dioxide Emissions from a Detached House

Atmosphere ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 572
Author(s):  
Daisuke Narumi ◽  
Ronnen Levinson ◽  
Yoshiyuki Shimoda
Keyword(s):  
Carbon Dioxide ◽  
Global Warming ◽  
Heat Release ◽  
Urban Heat Island ◽  
Co2 Emissions ◽  
Heat Island ◽  
Sensible Heat ◽  
Heating Season ◽  
Urban Heat ◽  
Cooling Effects

Urban air temperature rises induced by the urban heat island (UHIE) effect or by global warming (GW) can be beneficial in winter but detrimental in summer. The SCIENCE-Outdoor model was used to simulate changes to sensible heat release and CO2 emissions from buildings yielded by four UHIE countermeasures and five GW countermeasures. This model can evaluate the thermal condition of building envelope surfaces, both inside and outside. The results showed that water-consuming UHIE countermeasures such as evaporative space cooling and roof water showering provided positive effects (decreasing sensible heat release and CO2 emissions related to space conditioning) in summer. Additionally, they had no negative (unwanted cooling) effects in winter since they can be turned off in the heating season. Roof greening can provide the greatest space- conditioning CO2 emissions reductions among four UHIE countermeasures, and it reduces the amount of heat release slightly in the heating season. Since the effect on reducing carbon dioxide (CO2) emissions by UHIE countermeasures is not very significant, it is desirable to introduce GW countermeasures in order to reduce CO2 emissions. The significance of this study is that it constructed the new simulation model SCIENCE-Outdoor and applied it to show the influence of countermeasures upon both heat release and CO2 emissions.

scholarly journals Challenges in Modeling Turbulent Heat Fluxes to Snowpacks in Forest Clearings

2018 ◽  
Vol 19 (10) ◽  
pp. 1599-1616 ◽  
Author(s):  
Jonathan P. Conway ◽  
John W. Pomeroy ◽  
Warren D. Helgason ◽  
Nicholas J. Kinar
Keyword(s):  
Heat Exchange ◽  
Surface Temperature ◽  
Latent Heat ◽  
Turbulent Fluxes ◽  
Heat Fluxes ◽  
Turbulent Heat ◽  
Snow Surface ◽  
Turbulent Heat Exchange ◽  
Uncertainty Estimates ◽  
Model Tuning

Abstract Forest clearings are common features of evergreen forests and produce snowpack accumulation and melt differing from that in adjacent forests and open terrain. This study has investigated the challenges in specifying the turbulent fluxes of sensible and latent heat to snowpacks in forest clearings. The snowpack in two forest clearings in the Canadian Rockies was simulated using a one-dimensional (1D) snowpack model. A trade-off was found between optimizing against measured snow surface temperature or snowmelt when choosing how to specify the turbulent fluxes. Schemes using the Monin–Obukhov similarity theory tended to produce negatively biased surface temperature, while schemes that enhanced turbulent fluxes, to reduce the surface temperature bias, resulted in too much melt. Uncertainty estimates from Monte Carlo experiments showed that no realistic parameter set could successfully remove biases in both surface temperature and melt. A simple scheme that excludes atmospheric stability correction was required to successfully simulate surface temperature under low wind speed conditions. Nonturbulent advective fluxes and/or nonlocal sources of turbulence are thought to account for the maintenance of heat exchange in low-wind conditions. The simulation of snowmelt was improved by allowing enhanced latent heat fluxes during low-wind conditions. Caution is warranted when snowpack models are optimized on surface temperature, as model tuning may compensate for deficiencies in conceptual and numerical models of radiative, conductive, and turbulent heat exchange at the snow surface and within the snowpack. Such model tuning could have large impacts on the melt rate and timing of the snow-free transition in simulations of forest clearings within hydrological and meteorological models.

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