Surface Energy Budget Closure Research Articles

Snow thermal conductivity and conductive flux in the Central Arctic: Estimates from observations and implications for models

During the Arctic winter, the conductive heat flux through the sea ice and snow balances the radiative and turbulent heat fluxes at the surface. Snow on sea ice is a thermal insulator that reduces the magnitude of the conductive flux. The thermal conductivity of snow, that is, how readily energy is conducted, is known to vary significantly in time and space from observations, but most forecast and climate models use a constant value. This work begins with a demonstration of the importance of snow thermal conductivity in a regional coupled forecast model. Varying snow thermal conductivity impacts the magnitudes of all surface fluxes, not just conduction, and their responses to atmospheric forcing. Given the importance of snow thermal conductivity in models, we use observations from sea ice mass balance buoys installed during the Multidisciplinary drifting Observatory for the Study of Arctic Climate expedition to derive the profiles of thermal conductivity, density, and conductive flux. From 13 sites, median snow thermal conductivity ranges from 0.33 W m−1 K−1 to 0.47 W m−1 K−1 with a median from all data of 0.39 W m−1 K−1 from October to February. In terms of surface energy budget closure, estimated conductive fluxes are generally smaller than the net atmospheric flux by as much as 20 W m−2, but the average residual during winter is −6 W m−2, which is within the uncertainties. The spatial variability of conductive heat flux is highest during clear and cold time periods. Higher surface temperature, which often occurs during cloudy conditions, and thicker snowpacks reduce temporal and spatial variability. These relationships are compared between observations and the coupled forecast model, emphasizing both the importance and challenge of describing thermodynamic parameters of snow cover for modeling the Arctic as a coupled system.

A Hybrid Bulk Algorithm to Predict Turbulent Fluxes over Dry and Wet Bare Soils

Abstract Measurements made in the Columbia River basin (Oregon) in an area of irregular terrain during the second Wind Forecast Improvement Project (WFIP2) field campaign are used to develop an optimized hybrid bulk algorithm to predict the surface turbulent fluxes from readily measured or modeled quantities over dry and wet bare or lightly vegetated soil surfaces. The hybrid (synthetic) algorithm combines (i) an aerodynamic method for turbulent flow, which is based on the transfer coefficients (drag coefficient and Stanton number), roughness lengths, and Monin–Obukhov similarity; and (ii) a modified Priestley–Taylor (P-T) algorithm with physically based ecophysiological constraints, which is essentially based on the surface energy budget (SEB) equation. Soil heat flux in the latter case was estimated from measurements of soil temperature and soil moisture. In the framework of the hybrid algorithm, bulk estimates of the momentum flux and the sensible heat flux are derived from a traditional aerodynamic approach, whereas the latent heat flux (or moisture flux) is evaluated from a modified P-T model. Direct measurements of the surface fluxes (turbulent and radiative) and other ancillary atmospheric/soil parameters made during WFIP2 for different soil conditions (dry and wet) are used to optimize and tune the hybrid bulk algorithm. The bulk flux estimates are validated against the measured eddy-covariance fluxes. We also discuss the SEB closure over dry and wet surfaces at various time scales based on the modeled and measured fluxes. Although this bulk flux algorithm is optimized for the data collected during the WFIP2, a hybrid approach can be used for similar flux-tower sites and field campaigns.

On the surface energy balance closure at different temporal scales

Measurements of the surface energy fluxes (turbulent and radiative) and other ancillary atmospheric/soil parameters made in the Columbia River Basin (Oregon) in an area of complex terrain during a 10-month long portion of the second Wind Forecast Improvement Project (WFIP 2) field campaign are used to study the surface energy budget (SEB) and surface fluxes over different temporal scales. This study analyzes and discusses SEB closure based on half-hourly, daily, monthly, seasonal, and sub-annual (~10-month) temporal averages. The data were collected over all four seasons for different states of the underlying ground surface (dry, wet, and frozen). Our half-hourly direct measurements of energy balance show that the sum of the turbulent sensible and latent heat fluxes systematically underestimate positive net radiation by around 20–30% during daytime and overestimate negative net radiation at night. This imbalance of the surface energy budget is comparable to other terrestrial sites. However, on average, the residual energy imbalance is significantly reduced at daily, weekly, and monthly averaging timescales, and moreover, the SEB can be closed for this site within reasonable limits on seasonal and sub-annual timescales (311-day averaging for the entire field campaign dataset). Increasing the averaging time to daily and longer time intervals substantially reduces the ground heat flux and storage terms, because energy locally entering the soil, air column, and vegetation in the morning is released in the afternoon and evening. Averaging on daily to sub-annual timescales also reduces random instrumental measurement errors and other uncertainties as well as smooths out a hysteresis effect (phase lag) in the SEB relationship between different components. This study shows that SEB closure is better for dry soils compared to wet soils and the statistical dependence of the turbulent fluxes and net radiation for freezing soil surfaces appears weak, if not non-existent, apparently due to lack of the latent heat of fusion term in the traditional SEB equation.

Seasonal variation of evapotranspiration and its effect on the surface energy budget closure at a tropical forest over north-east India

This study uses 1 yr of eddy covariance (EC) flux observations to investigate seasonal variations in evapotranspiration (ET) and surface energy budget (SEB) closure at a tropical semi-deciduous forest located in north-east India. The annual cycle is divided into four seasons, namely, pre-monsoon, monsoon, post-monsoon and winter. The highest energy balance closure (76%) is observed during pre-monsoon, whereas the lowest level of closure (62%) is observed during winter. Intermediate closure of 68% and 72% is observed during the monsoon and post-monsoon seasons, respectively. Maximum latent heat flux during winter (\(\hbox {150 W m}^{-2}\)) is half of the maximum latent heat (\(\hbox {300 W m}^{-2}\)) flux during the monsoon. ET is a controlling factor of SEB closure, with the highest rates of closure corresponding to the periods of the highest ET. The Bowen ratio ranges from 0.93 in winter to 0.27 during the monsoon. This is the first time the role of ET in the seasonal variation of SEB closure has been reported for any ecosystem in north-east India using EC measurements.

Time dependency of eddy covariance site energy balance

Energy flow through ecosystems plays a critical role in processes at multiple spatial and temporal scales, from phonologically driven growing season or monthly temporal scales of landscapes to sub-diurnal responses of soil respiration to temperature, photosynthesis and water inputs. The interaction of short and longwave radiation and their partitioning through ecosystems is complex with terrestrial canopies and aquatic structure both connecting above- and below-ground processes via energy fluxes. Previous work has shown that at 30-min timescales, only 8% of eddy covariance sites in the La Thuile dataset observe energy closure and when averaged to 24-h timescales, this goes up to 45%. This work examines the effect of temporal lags in energy storage in both terrestrial and aquatic ecosystems. Analyses show energy storage terms have unique temporal lags that vary between ecosystem and time of year, from having zero lag to several hour timelags within terrestrial ecosystems, depending primarily on water content. Large differences between ecosystem types are also highlighted as aquatic ecosystems have lags that range between daily and monthly timescales. Furthermore, ecosystem disturbance can alter time-lags as well and results from a native bark beetle disturbance show both vegetation and soil lag increasing following changes to ecosystem processes from tree mortality. Considering energy storage lags can improve site energy closure in 20% of site-days in the FLUXNET2015 dataset and these results will lead to a better understanding of surface energy budget closure as well as highlighting the importance of time-dependency of ecosystem energy fluxes as a unique method to infer ecosystem processes.

Failure of Taylor's hypothesis in the atmospheric surface layer and its correction for eddy‐covariance measurements

AbstractTaylors' frozen turbulence hypothesis suggests that all turbulent eddies are advected by the mean streamwise velocity, without changes in their properties. This hypothesis has been widely invoked to compute Reynolds averaging using temporal turbulence data measured at a single point in space. However, in the atmospheric surface layer, the exact relationship between convection velocity and wave number k has not been fully revealed since previous observations were limited by either their spatial resolution or by the sampling length. Using Distributed Temperature Sensing (DTS), acquiring turbulent temperature fluctuations at high temporal and spatial frequencies, we computed convection velocities across wave numbers using a phase spectrum method. We found that convection velocity decreases as k−1/3 at the higher wave numbers of the inertial subrange instead of being independent of wave number as suggested by Taylor's hypothesis. We further corroborated this result using large eddy simulations. Applying Taylor's hypothesis thus systematically underestimates turbulent spectrum in the inertial subrange. A correction is proposed for point‐based eddy‐covariance measurements, which can improve surface energy budget closure and estimates of CO2 fluxes.

A‐posteriori analysis of surface energy budget closure to determine missed energy pathways

The residual of the surface energy budget is represented as the linearized sum of energy losses due to storage, advection and flux underestimation. Individual contributions to the residual can be quantified through constrained multiple linear regression which identifies the site specific processes that are responsible for the lack of energy budget closure. This residual decomposition approach is applied to energy balance data from the Surface Layer Turbulence and Environmental Science Test (SLTEST) site at the Dugway Proving Grounds in the Utah Salt Flats. In this case, energy storage in the soil and underestimation of the soil heat flux accounted for 89% of the residual variance. Underestimation of the sensible and latent heat fluxes had no apparent contribution to the residual, and the contribution of advection to the residual was not statistically significant.

Measurements Of Turbulence Transfer In The Near-Surface Layer Over The Southeastern Tibetan Plateau

Turbulent flux measurements at Qamdo site over the Tibetan Plateau during TIPEX from May 18 to June 30, 1998 are presented. Sensible heat dominated,accounting for about 66% of the available energy (the sum of net radiation and soil heat flux) prior to the monsoon(dry period), reducing to about 31%, with latent heat increased to about 56% of available energy,in the monsoon season (wet period). Surface energy budget closure on average was about 0.80 (0.85)prior to the monsoon and 0.89 (0.76) during the monsoon using eddy correlation (profile) methods. The sum of latent and sensible heat fluxes calculated from the flux-profilemethod was smaller by about 15% than that from eddy correlation. Martano's method is used toestimate the surface aerodynamic roughness length z0 and zero plane displacement d from singlelevel sonic anemometer data, giving d = 0.12 m and z0 = 0.08 m. The overall neutral dragcoefficient (CDN) and scalar coefficient (CHN) were found to be CDN = 0.0055and CHN = 0.0059 in the southeastern area of Tibet. Their variations with the mean wind speed at 10 m are discussed.