Partitioning Of Surface Energy Fluxes Research Articles

A study of the impact of landscape heterogeneity on surface energy fluxes in a tropical climate using SUEWS

This study employs an evaluated Surface Urban Energy and Water Balance Scheme (SUEWS) model driven by Local Climate Zone (LCZ)-derived urban canopy parameters to explore landscape heterogeneity's impacts on the partitioning of surface energy fluxes and heat stress variations in Lagos, Nigeria. The results reveal that LCZ-based SUEWS effectively replicates the diurnal patterns of air temperature (Tair), relative humidity (RH), and land surface temperature (LST). The root mean square error (RMSE) for simulated Tair and RH ranges from 0.4 °C to 1.2 °C and 1.8% to 8.0%, respectively. While a nighttime warm bias in LST is observed, it reduces during the daytime. Significant spatial variability in turbulent heat fluxes and LST among LCZs is noted, with core urban LCZs experiencing notable increases in sensible heat flux and LST, while suburban LCZs exhibit higher latent heat flux values. Anthropogenic heat flux peaks in high compact low-rise LCZ 3, reaching a maximum of 95 W/m2. Remarkably, no significant difference is found in the diurnal heat stress cycle among LCZs, despite consistently elevated heat stress levels in high compact LCZs. These findings offer valuable insights into quantifying surface energy flux variabilities and spatio-temporal heat stress patterns in a densely populated tropical city- Lagos Nigeria.

Deciphering the relationship between vegetation and Indian summer monsoon rainfall

Land surface utilization in the Indian subcontinent has undergone dramatic transformations over the years, altering the region’s surface energy flux partitioning. The resulting changes in moisture availability and atmospheric stability can be critical in determining the season’s monsoon rainfall. This study uses fully coupled global climate model simulations with idealized land cover to elucidate the consequences of land surface alterations. We find that an increase in forest cover, in general, increases precipitation in India. However, precipitation is not a linear function of forest-covered-area due to the spatially heterogeneous nature of the impact. A fully forest-covered India receives less precipitation than when the forest covers only the eastern side of India, occupying just about half the area. This signifies the importance of the east-west gradient in vegetation cover observed over India. Using an energy balance model, we diagnose that the diverse nature of this precipitation response results from three different pathways: evaporation from the surface, the net energy input into the atmosphere, and moist stability. Evaporation exhibits a linear relationship with forest-covered-area and reveals minimal spatial heterogeneity. On the contrary, the influence through the other two pathways is found to be region specific. Rainfall modulation via changes in net energy input is dominant in the head Bay of Bengal region, which is susceptible to convective systems. Whereas impact through stability changes is particularly significant south of 20∘ N. In addition, we find that moisture advection modulates the significance of these pathways over northwest India. Thus, the impact of land cover changes act via three effective mechanisms and are region dependent. The findings in this study have broader ramifications since the dominant region-specific mechanisms identified are expected to be valid for other forcings and are not just limited to the scenarios considered here.

Understanding and reducing the uncertainties of land surface energy flux partitioning within CMIP6 land models

Land surfaces dissipate energy through latent (LE) and sensible (H) heat fluxes that modulate atmospheric temperature and humidity, which in return affect land surface vegetation and soil processes. Within this two-way land-atmosphere coupling, surface energy partitioning (LE versus H) plays a central role in connecting the land and atmosphere states and fluxes. However, considerably large uncertainties still exist in earth system land models, i.e. the phase 6 of the Coupled Model Intercomparison Project (CMIP6). Further, the underlying controls from climate and biological factors on surface energy partitioning over different biome types are not well understood. In this study, we combined machine learning (ML) and causal inference models to investigate and reduce the uncertainties (i.e., parametric, structural, and forcing uncertainties) of CMIP6 simulated evaporative fraction (defined as LE / (LE + H)) across 64 FLUXNET sites that cover five major biomes. We found that CMIP6 model ensembles overestimated evaporative fraction with considerable spread at deciduous broadleaf forest, evergreen needleleaf forest, and savanna sites. Accounting for the biases from all related surface climate and biological driving variables, the CMIP6 model simulated EF could be largely improved (e.g., R between model and observed EF improved from 0.47 to 0.66), with leaf area index, vapor pressure deficit, and precipitation dominated the model improvement. Furthermore, ML-based parameterization generally showed a promising opportunity to further reduce model biases (e.g., R improved from 0.66 to 0.80) in spite of the limited improvement at evergreen broadleaf forest sites where model bias may be dominated by structural imperfection. This study provided an effective framework to understand and reduce model uncertainties in simulating land surface energy flux partitioning and, more importantly, highlighted the need of effective model structure improvement for the next generation earth system land model development.

Grid resolution dependency of land surface heterogeneity effects on boundary‐layer structure

AbstractLand surface heterogeneity exerts a strong control on atmospheric boundary‐layer (ABL) evolution by spatially varying the distribution and partitioning of surface energy fluxes and triggering secondary circulations. The representation of this physical process in numerical weather prediction (NWP) models is especially affected in the terra incognita as the model grid resolution approaches the length‐scale of the largest eddies in the boundary layer. We explore these effects for a mesoscale strip‐like land surface inhomogeneity in land cover, soil moisture or a superposition of both embedded in an elsewhere homogeneous landscape. The study is conducted with the numerical weather prediction model ICON (ICOsahedral Nonhydrostatic), using the default operational level 2.5 Mellor–Yamada turbulence closure (MY) and a large‐eddy simulation (LES) configuration as a benchmark. While simulations with the default ABL scheme approach the LES reference when refining the spatial grid towards finer resolution, the model generates artificial circulations leading to ABL height oscillations when the horizontal grid resolution approaches the ABL height . The effect of these model‐induced circulations on the state of the boundary layer is even present with weak thermal heterogeneity under low background wind speed ( but diminishes with increasing background wind speed. The tuning of the asymptotic turbulent mixing length‐scale in the operational ABL scheme helps in reducing the amplitude of the oscillations, thereby reducing the artificially induced circulations due to thermal heterogeneity which might act as unintentional trigger for clouds and precipitation. Based on the tuned synthetic model data from sensitivity runs, we propose a new parametrization for a 2‐D as a function of , and , which is otherwise held as a constant in the ABL scheme.

Radiative feedbacks on land surface change and associated tropical precipitation shifts

AbstractChanges in land surface albedo and land surface evaporation modulate the atmospheric energy budget by changing temperatures, water vapor, clouds, snow and ice cover, and the partitioning of surface energy fluxes. Here idealized perturbations to land surface properties are imposed in a global model to understand how such forcings drive shifts in zonal mean atmospheric energy transport and zonal mean tropical precipitation. For a uniform decrease in global land albedo, the albedo forcing and a positive water vapor feedback contribute roughly equally to increased energy absorption at the top of the atmosphere (TOA), while radiative changes due to the temperature and cloud cover response provide a negative feedback and energy loss at TOA. Decreasing land albedo causes a northwards shift in the zonal mean intertropical convergence zone (ITCZ). The combined effects on ITCZ location of all atmospheric feedbacks roughly cancel for the albedo forcing; the total ITCZ shift is comparable to that predicted for the albedo forcing alone. For an imposed increase in evaporative resistance that reduces land evaporation, low cloud cover decreases in the northern mid-latitudes and more energy is absorbed at TOA there; longwave loss due to warming provides a negative feedback on the TOA energy balance and ITCZ shift. Imposed changes in land albedo and evaporative resistance modulate fundamentally different aspects of the surface energy budget. However, the pattern of TOA radiation changes due to the water vapor and air temperature responses are highly correlated for these two forcings because both forcings lead to near-surface warming.

Closure in Surface Flux Estimation by Energy Balance Model: Comparison of Priestly-Taylor and Penman-Monteith Computations for a Tropical Site in Ibadan

Increasing demand to further understand complexity in surface energy flux partitioning necessitates the adaptation of numerous estimation methods to fit the site of observation. This is useful for reducing the uncertainty in physically measurable parameters especially those in tropical regions with high human interference in the atmospheric boundary layer. In this study, we used computations from two methods - the Priestley-Taylor (PT) and the Penman-Monteith (PM), based on the Energy Balance model to ascertain closure performance in the surface flux estimations. The study was carried out at the Nigerian Meteorological Experiment III site (7.38 o N and 3.93 o E, 224.2m) located in Ibadan, Southwest Nigeria. Thirty days of a year (2006) dataset were examined using the Bowen ratio (BR) energy balance model to validate the PT and PM methods. The systems were examined across daily and diurnal cycles to better understand the differences in energy partitioning. Results showed that both systems generally favored latent heat flux compared to sensible heat flux perhaps due to above-normal rainfall during the period. The PM method performed better than the PT method with a period average for the sensible heat and latent heat fluxes as 32.05 Wm -2 and 67.66 Wm -2 respectively, accounting for 29.22% and 61.39% of the total net radiation. The PT method underestimates the sensible heat flux by as much as 19.70 Wm -2 compared to the PM method, with a period average of 12.36 Wm -2 representing 11.26% of total net radiation. The PM method also gives a period average Bowen ratio estimate of 0.55, consistent with the standard range for grasslands. The study suggests that the performance of the PM method is related to its response to heat and water vapor transfer over humid regions and would contribute to further research on land-surface interactions over the tropics. Finally, we propose that the measurement of available energy, net radiation, and ground heat flux should be separated for different collocated systems in order to reduce the forcing of closure and aid in proper partitioning of the fluxes. Keywords: surface energy flux, energy balance model, Priestly-Taylor, Penman-Monteith, West Africa, latent heat, sensible heat, NIMEX_3 DOI: 10.7176/JEES/11-5-05 Publication date: May 31 st 2021

Deforestation reshapes land-surface energy-flux partitioning

Land-use and land-cover change significantly modify local land-surface characteristics and water/energy exchanges, which can lead to atmospheric circulation and regional climate changes. In particular, deforestation accounts for a large portion of global land-use changes, which transforms forests into other land cover types, such as croplands and grazing lands. Many previous efforts have focused on observing and modeling land–atmosphere–water/energy fluxes to investigate land–atmosphere coupling induced by deforestation. However, interpreting land–atmosphere–water/energy-flux responses to deforestation is often complicated by the concurrent impacts from shifts in land-surface properties versus background atmospheric forcings. In this study, we used 29 paired FLUXNET sites, to improve understanding of how deforested land surfaces drive changes in surface-energy-flux partitioning. Each paired sites included an intact forested and non-forested site that had similar background climate. We employed transfer entropy, a method based on information theory, to diagnose directional controls between coupling variables, and identify nonlinear cause–effect relationships. Transfer entropy is a powerful tool to detective causal relationships in nonlinear and asynchronous systems. The paired eddy covariance flux measurements showed consistent and strong information flows from vegetation activity (gross primary productivity (GPP)) and physical climate (e.g. shortwave radiation, air temperature) to evaporative fraction (EF) over both non-forested and forested land surfaces. More importantly, the information transfers from radiation, precipitation, and GPP to EF were significantly reduced at non-forested sites, compared to forested sites. We then applied these observationally constrained metrics as benchmarks to evaluate the Energy Exascale Earth System Model (E3SM) land model (ELM). ELM predicted vegetation controls on EF relatively well, but underpredicted climate factors on EF, indicating model deficiencies in describing the relationships between atmospheric state and surface fluxes. Moreover, changes in controls on surface energy flux partitioning due to deforestation were not detected in the model. We highlight the need for benchmarking model simulated surface-energy fluxes and the corresponding causal relationships against those of observations, to improve our understanding of model predictability on how deforestation reshapes land surface energy fluxes.

Modeled Land Atmosphere Coupling Response to Soil Moisture Changes with Different Generations of Land Surface Models

An idealized study with two land surface models (LSMs): TERRA-Multi Layer (TERRA-ML) and Community Land Model (CLM) alternatively coupled to the same atmospheric model COSMO (Consortium for Small-Scale Modeling), reveals differences in the response of the LSMs to initial soil moisture. The bulk parameterization of evapotranspiration pathways, which depends on the integrated soil moisture of active layers rather than on each discrete layer, results in a weaker response of the surface energy flux partitioning to changes in soil moisture for TERRA-ML, as compared to CLM. The difference in the resulting surface energy flux partitioning also significantly affects the model response in terms of the state of the atmospheric boundary layer. For vegetated land surfaces, both models behave quite differently for drier regimes. However, deeper reaching root fractions in CLM align both model responses with each other. In general, differences in the parameterization of the available root zone soil moisture, evapotranspiration pathways, and the soil-vegetation structure in the two LSMs are mainly responsible for the diverging tendencies of the simulated land atmosphere coupling responses.

Impact of a Multi‐Layer Snow Scheme on Near‐Surface Weather Forecasts

AbstractSnow cover properties have a large impact on the partitioning of surface energy fluxes and thereby on near‐surface weather parameters. Snow schemes of intermediate complexity have been widely used for hydrological and climate studies, whereas their impact on typical weather forecast time scales has received less attention. A new multilayer snow scheme is implemented in the European Centre for Medium‐range Weather Forecasts Integrated Forecasting System and its impact on snow and 2‐m temperature forecasts is evaluated. The new snow scheme is evaluated offline at well‐instrumented field sites and compared to the current single‐layer scheme. The new scheme largely improves the representation of snow depth for most of the sites considered, reducing the root‐mean‐square error averaged over all sites by more than 30%. The improvements are due to a better description of snow density in thick and cold snowpacks, but also due to an improved representation of sporadic melting episodes because of the inclusion of a thin top snow layer with a low thermal inertia. The evaluation of coupled 10‐day weather forecasts shows an improved representation of snow depth at all lead times, demonstrating a positive impact at the global scale. Regarding the impact on weather parameters, the multilayer snow scheme improves the simulated minimum 2‐m temperature, by decreasing the positive bias and improving the amplitude of the diurnal cycle over snow‐covered regions. However, the increased variability of the 2‐m temperature can have a detrimental impact in regions characterized by preexisting errors in the daily mean temperature, associated with errors in cloud processes or surface albedo.

A Recent Systematic Increase in Vapor Pressure Deficit over Tropical South America

We show a recent increasing trend in Vapor Pressure Deficit (VPD) over tropical South America in dry months with values well beyond the range of trends due to natural variability of the climate system defined in both the undisturbed Preindustrial climate and the climate over 850–1850 perturbed with natural external forcing. This trend is systematic in the southeast Amazon but driven by episodic droughts (2005, 2010, 2015) in the northwest, with the highest recoded VPD since 1979 for the 2015 drought. The univariant detection analysis shows that the observed increase in VPD cannot be explained by greenhouse-gas-induced (GHG) radiative warming alone. The bivariate attribution analysis demonstrates that forcing by elevated GHG levels and biomass burning aerosols are attributed as key causes for the observed VPD increase. We further show that There is a negative trend in evaporative fraction in the southeast Amazon, where lack of atmospheric moisture, reduced precipitation together with higher incoming solar radiation (~7% decade−1 cloud-cover reduction) influences the partitioning of surface energy fluxes towards less evapotranspiration. The VPD increase combined with the decrease in evaporative fraction are the first indications of positive climate feedback mechanisms, which we show that will continue and intensify in the course of unfolding anthropogenic climate change.

Measuring full-range soil hydraulic properties for the prediction of crop water availability using gamma-ray attenuation and inverse modeling

Accurate knowledge of soil hydraulic properties (K-θ-h) for the entire range of crop available water is essential for the prediction of soil water movement and related processes by mechanistic models, including the partitioning of surface energy fluxes into transpiration and evaporation and the dynamics of root water uptake, mandatory processes for adjustments of crop water use efficiency. We implemented an experimental and numerical protocol to obtain K-θ-h of eleven soils with a broad spectrum of texture and land use. Measurements of the soil water content during evaporation experiments using gamma-ray beam attenuation, a non-invasive technique, were adopted as an alternative approach to conventional measurements of the soil water pressure head. Inverse parameter optimization was performed using Hydrus-1D. The optimized K-θ-h functions were interpreted with respect to crop available water, where results calculated by a proposed “dynamic” method were compared with those determined using the conventional “static” criteria with standardized pressure heads. The evaporation experiment protocol allowed the determination of the K-θ-h relationships by inverse modeling from near-saturation to the dry range (∼ −150 m) with satisfactory accuracy. Soil water retention curves of the fine-textured soils determined by the conventional method (pressure plates) deviated from those estimated by the inverse optimization near saturation and in the dry range, with the conventional method predicting larger water content values. In terms of crop available water, the “dynamic” method allowed incorporating system characteristics (atmospheric demand and crop properties) and K-θ-h in a process-based way, contrarily to the “static” method. Considering a specific scenario, for the fine-textured soils the “static” and “dynamic” approaches performed similarly, however, for the coarse-textured soils, they diverged significantly. No tendency could be revealed for crop water availability under different land uses, and, in general, crop available water for soils under forest use was very similar to their counterparts under agricultural use.

Connection Between Root Zone Soil Moisture and Surface Energy Flux Partitioning Using Modeling, Observations, and Data Assimilation for a Temperate Grassland Site in Germany

AbstractLand surface models (LSMs) with different degrees of complexity are in use as lower boundary conditions for atmospheric models with the simpler LSMs preferentially used in numerical weather forecasting. This study evaluates the second‐generation TERRA Multi‐Layer and the third‐generation Community Land Model (CLM) to better understand the connection between root zone soil moisture and surface energy fluxes, which is important for predictions. Both LSMs were compared in multiyear, observation‐driven simulations at the Falkenberg grassland site (Germany), and their results were compared to observations. With their default settings for the site, both LSMs tend to overestimate the Bowen ratio, while CLM additionally exhibited a wet bias and a too low soil moisture variance. With modified photosynthetic parameters in CLM, the Bowen ratio improved considerably, but the soil moisture bias and its too low variance remained. Joint data assimilation with soil parameter update significantly improved the soil moisture variance but degraded the Bowen ratio. We could identify the default shallow root fraction distribution to be responsible for the overestimated Bowen ratio, which could be largely reduced by increasing the root fractions in deeper layers. This study demonstrates how observations and data assimilation with joint state‐parameter updating can be used to improve the realism of third‐generation LSMs and thus our understanding of the connection between root zone soil moisture and surface energy flux partitioning.

Estimation of surface energy fluxes in the Arctic tundra using the remote sensing thermal-based Two-Source Energy Balance model

Abstract. The Arctic has become generally a warmer place over the past decades leading to earlier snow melt, permafrost degradation and changing plant communities. Increases in precipitation and local evaporation in the Arctic, known as the acceleration components of the hydrologic cycle, coupled with land cover changes, have resulted in significant changes in the regional surface energy budget. Quantifying spatiotemporal trends in surface energy flux partitioning is key to forecasting ecological responses to changing climate conditions in the Arctic. An extensive local evaluation of the Two-Source Energy Balance model (TSEB) – a remote-sensing-based model using thermal infrared retrievals of land surface temperature – was performed using tower measurements collected over different tundra types in Alaska in all sky conditions over the full growing season from 2008 to 2012. Based on comparisons with flux tower observations, refinements in the original TSEB net radiation, soil heat flux and canopy transpiration parameterizations were identified for Arctic tundra. In particular, a revised method for estimating soil heat flux based on relationships with soil temperature was developed, resulting in significantly improved performance. These refinements result in mean turbulent flux errors generally less than 50 W m−2 at half-hourly time steps, similar to errors typically reported in surface energy balance modeling studies conducted in more temperate climatic regimes. The MODIS leaf area index (LAI) remote sensing product proved to be useful for estimating energy fluxes in Arctic tundra in the absence of field data on the local biomass amount. Model refinements found in this work at the local scale build toward a regional implementation of the TSEB model over Arctic tundra ecosystems, using thermal satellite remote sensing to assess response of surface fluxes to changing vegetation and climate conditions.

Modeling the Partitioning of Turbulent Fluxes at Urban Sites with Varying Vegetation Cover

Abstract Inclusion of vegetation is critical for urban land surface models (ULSM) to represent reasonably the turbulent sensible and latent heat flux densities in an urban environment. Here the Joint UK Land Environment Simulator (JULES), a ULSM, is used to simulate the Bowen ratio at a number of urban and rural sites with vegetation cover varying between 1% and 98%. The results show that JULES is able to represent the observed Bowen ratios, but only when the additional anthropogenic water supplied into the urban ecosystem is considered. The impact of the external water use (e.g., through irrigation or street cleaning) on the surface energy flux partitioning can be as substantial as that of the anthropogenic heat flux on the sensible and latent heat fluxes. The Bowen ratio varies from 1 to 2 when the plan area vegetation fraction is between 30% and 70%. However, when the vegetation fraction is less than 20%, the Bowen ratios increase substantially (2–10) and have greater sensitivity to assumptions about external water use. As there are few long-term observational sites with vegetation cover less than 30%, there is a clear need for more measurement studies in such environments.

Crop growth and irrigation interact to influence surface fluxes in a regional climate-cropland model (WRF3.3-CLM4crop)

In this study, we coupled Version 4.0 of the Community Land Model that includes crop growth and management (CLM4crop) into the Weather Research and Forecasting (WRF) model Version 3.3 to better represent interactions between climate and agriculture. We evaluated the performance of the coupled model (WRF3.3-CLM4crop) by comparing simulated crop growth and surface climate to multiple observational datasets across the continental United States. The results showed that although the model with dynamic crop growth overestimated leaf area index (LAI) and growing season length, interannual variability in peak LAI was improved relative to a model with prescribed crop LAI and growth period, which has no environmental sensitivity. Adding irrigation largely improved daily minimum temperature but the RMSE is still higher over irrigated land than non-irrigated land. Improvements in climate variables were limited by an overall model dry bias. However, with addition of an irrigation scheme, soil moisture and surface energy flux partitioning were largely improved at irrigated sites. Irrigation effects were sensitive to crop growth: the case with prescribed crop growth underestimated irrigation water use and effects on temperature and overestimated soil evaporation relative to the case with dynamic crop growth in moderately irrigated regions. We conclude that studies examining irrigation effects on weather and climate using coupled climate–land surface models should include dynamic crop growth and realistic irrigation schemes to better capture land surface effects in agricultural regions.

The Effects of Great Plains Irrigation on the Surface Energy Balance, Regional Circulation, and Precipitation

Irrigation provides a needed source of water in regions of low precipitation. Adding water to a region that would otherwise see little natural precipitation alters the partitioning of surface energy fluxes, the evolution of the planetary boundary layer, and the atmospheric transport of water vapor. The effects of irrigation are investigated in this paper through the employment of the Advanced Research (ARW) Weather Research and Forecasting Model (WRF) using a pair of simulations representing the extremes of an irrigated and non-irrigated U.S. Great Plains region. In common with previous studies, irrigation in the Great Plains alters the radiation budget by increasing latent heat flux and cooling the surface temperatures. These effects increase the net radiation at the surface, channeling that energy into additional latent heat flux, which increases convective available potential energy and provides downstream convective systems with additional energy and moisture. Most noteworthy in this study is the substantial influence of irrigation on the structure of the Great Plains Low-level Jet (GPLLJ). The simulation employing irrigation is characterized by a positive 850-mb geopotential height anomaly, a result interpreted by quasi-geostrophic theory to be a response to low-level irrigation-induced cooling. The modulation of the regional-scale height pattern associated with the GPLLJ results in weaker flow southeast of the 850-mb anomaly and stronger flow to the northwest. Increased latent heat flux in the irrigated simulation is greater than the decrease in regional transport, resulting in a net increase in atmospheric moisture and a nearly 50% increase in July precipitation downstream of irrigated regions without any change to the number of precipitation events.

Development and verification of a new wind speed forecasting system using an ensemble Kalman filter data assimilation technique in a fully coupled hydrologic and atmospheric model

Wind power is rapidly gaining prominence as a major source of renewable energy. Harnessing this promising energy source is challenging because of the chaotic nature of wind and its inherently intermittent nature. Accurate forecasting tools are critical to support the integration of wind energy into power grids and to maximize its impact on renewable energy portfolios. We have adapted the Data Assimilation Research Testbed (DART), a community software facility which includes the ensemble Kalman filter (EnKF) algorithm, to expand our capability to use observational data to improve forecasts produced with a fully coupled hydrologic and atmospheric modeling system, the ParFlow (PF) hydrologic model and the Weather Research and Forecasting (WRF) mesoscale atmospheric model, coupled via mass and energy fluxes across the land surface, and resulting in the PF.WRF model. Numerous studies have shown that soil moisture distribution and land surface vegetative processes profoundly influence atmospheric boundary layer development and weather processes on local and regional scales. We have used the PF.WRF model to explore the connections between the land surface and the atmosphere in terms of land surface energy flux partitioning and coupled variable fields including hydraulic conductivity, soil moisture, and wind speed and demonstrated that reductions in uncertainty in these coupled fields realized through assimilation of soil moisture observations propagate through the hydrologic and atmospheric system. The sensitivities found in this study will enable further studies to optimize observation strategies to maximize the utility of the PF.WRF‐DART forecasting system.

The impact of soil freezing/thawing processes on water and energy balances

A frozen soil parameterization coupling of thermal and hydrological processes is used to investigate how frozen soil processes affect water and energy balances in seasonal frozen soil. Simulation results of soil liquid water content and temperature using soil model with and without the inclusion of freezing and thawing processes are evaluated against observations at the Rosemount field station. By comparing the simulated water and heat fluxes of the two cases, the role of phase change processes in the water and energy balances is analyzed. Soil freezing induces upward water flow towards the freezing front and increases soil water content in the upper soil layer. In particular, soil ice obviously prevents and delays the infiltration during rain at Rosemount. In addition, soil freezing/thawing processes alter the partitioning of surface energy fluxes and lead the soil to release more sensible heat into the atmosphere during freezing periods.

The contribution of C3 and C4 plants to the carbon cycle of a tallgrass prairie: an isotopic approach.

The photosynthetic pathway composition (C(3):C(4) mixture) of an ecosystem is an important controller of carbon exchanges and surface energy flux partitioning, and therefore represents a fundamental ecophysiological distinction. To assess photosynthetic mixtures at a tallgrass prairie pasture in Oklahoma, we collected nighttime above-canopy air samples along concentration and isotopic gradients throughout the 1999 and 2000 growing seasons. We analyzed these samples for their CO(2) concentration and carbon isotopic composition and calculated C(3):C(4) proportions with a two-source mixing model. In 1999, the C(4) percentage increased from 38% in spring (late April) to 86% in early fall (mid-September). The C(4) percentages inferred from ecosystem respiration measurements in 2000 indicate a smaller shift, from 67% in spring (early May) to 77% in mid-summer (late July). We also sampled daytime CO(2 )concentration and carbon isotope gradients above the canopy to determine ecosystem discrimination against (13)CO(2) during net uptake. These discrimination values were always lower than corresponding nighttime ecosystem respiration isotopic signatures would suggest. After accounting for the isotopic disequilibria between respiration and photosynthesis resulting from seasonal variations in the C(3):C(4) mixture, we estimated canopy photosynthetic discrimination. The C(4) percentage calculated from this approach agrees with the percentage determined from nighttime respiration for sampling periods in both growing seasons. Isotopic imbalances between photosynthesis and respiration are likely to be common in mixed C(3):C(4 )ecosystems and must be considered when using daytime isotopic measurements to constrain ecosystem physiology. Given the global extent of such ecosystems, isotopic imbalances likely contribute to global variations in the carbon isotopic composition of atmospheric CO(2).

Mapping surface energy flux partitioning at large scales with optical and microwave remote sensing data from Washita '92

A model evaluating the energy balance of the soil/substrate and vegetation (i.e., two‐source) was applied to remotely sensed near‐surface soil moisture maps generated from passive microwave data collected during the Washita '92 experiment. Model parameters were derived from a soil texture and a land‐use/land cover database along with a normalized difference vegetation index map created from a SPOT satellite image. The Bowen ratio (BO, ratio of sensible to latent heat flux) was used for investigating the temporal and spatial variability in model output. Comparisons between predicted and observed heat fluxes were made with values summed over the daytime period. Daily maps of midday BO indicated areas with low vegetation cover or bare soil and senescent vegetation were drying out significantly (i.e., dramatic increases in BO), while other areas with higher vegetation cover showed smaller increases in BO in response to a drying soil surface. This result agrees with the profile soil moisture and surface flux observations indicating adequate moisture was available to the vegetation for meeting atmospheric demand. The predicted daytime fluxes agreed to within 1 mm of the observations with ≈25% difference between modeled and observed daytime evapotranspiration. Differences between modeled and measured surface temperatures averaged ≈2 K. The discrepancies between model output and observations are similar to the uncertainty in these measurements, indicating that the model provided reliable daytime energy flux maps for the Washita '92 study area using remotely sensed near‐surface soil moisture.