Partitioning Of Energy Fluxes Research Articles

Consistency assessment of latent heat flux and observational datasets over the Amazon basin

The Amazon basin plays a crucial role in the global hydrological cycle and the climate system. Removal of latent heat from the surface covered by the tropical forest through evapotranspiration is a key process that still requires further research due to the complex nature of the involved processes, lack of observations and different model assumptions. Here we present an assessment of the consistency between different latent heat fluxes datasets through an indirect comparison against the daily amplitude of surface temperature and vegetation status estimated from satellite observations. Our study is based on the hypothesis that the observational satellite data can be used to provide hints on how realistically fluxes are represented in different datasets. Results evidence that datasets diverge inside the basin in both space and time, but it is possible to figure out areas under water-limited conditions, especially around the borders of the basin and some regions over eastern/southeastern Amazonia. In despite of these differences, a clear link between daily amplitude of surface temperature, leaf area index and latent heat flux can be observed over particular areas and seasons, where also correlations reach values closer to −0.98 (0.94) for surface temperature (leaf area index) indicating that satellite observations are suitable for assessing the representation of the partitioning of energy fluxes in models and widely used datasets.

Unraveling phenological and stomatal responses to flash drought and implications for water and carbon budgets

Abstract. In recent years, extreme droughts in the United States have increased in frequency and severity, underlining a need to improve our understanding of vegetation resilience and adaptation. Flash droughts are extreme events marked by the rapid dry down of soils due to lack of precipitation, high temperatures, and dry air. These events are also associated with reduced preparation, response, and management time windows before and during drought, exacerbating their detrimental impacts on people and food systems. Improvements in actionable information for flash drought management are informed by atmospheric and land surface processes, including responses and feedbacks from vegetation. Phenologic state, or growth stage, is an important metric for modeling how vegetation modulates land–atmosphere interactions. Reduced stomatal conductance during drought leads to cascading effects on carbon and water fluxes. We investigate how uncertainty in vegetation phenology and stomatal regulation propagates through vegetation responses during drought and non-drought periods by coupling a land surface hydrology model to a predictive phenology model. We assess the role of vegetation in the partitioning of carbon, water, and energy fluxes during flash drought and carry out a comparison against drought and non-drought periods. We selected study sites in Kansas, USA, that were impacted by the flash drought of 2012 and that have AmeriFlux eddy covariance towers which provide ground observations to compare against model estimates. Results show that the compounding effects of reduced precipitation and high vapor pressure deficit (VPD) on vegetation distinguish flash drought from other drought and non-drought periods. High VPD during flash drought shuts down modeled stomatal conductance, resulting in rates of evapotranspiration (ET), gross primary productivity (GPP), and water use efficiency (WUE) that fall below those of average drought conditions. Model estimates of GPP and ET during flash drought decrease to rates similar to what is observed during the winter, indicating that plant function during drought periods is similar to that of dormant months. These results have implications for improving predictions of drought impacts on vegetation.

Development of inter-grid-cell lateral unsaturated and saturated flow model in the E3SM Land Model (v2.0)

Abstract. The lateral transport of water in the subsurface is important in modulating terrestrial water energy distribution. Although a few land surface models have recently included lateral saturated flow within and across grid cells, it is not a default configuration in the Climate Model Intercomparison Project version 6 experiments. In this work, we developed the lateral subsurface flow model within both unsaturated and saturated zones in the Energy Exascale Earth System Model (E3SM) Land Model version 2 (ELMv2.0). The new model, called ELMlat, was benchmarked against PFLOTRAN, a 3D subsurface flow and transport model, for three idealized hillslopes that included a convergent hillslope, divergent hillslope, and tilted V-shaped hillslope with variably saturated initial conditions. ELMlat showed comparable performance against PFLOTRAN in terms of capturing the dynamics of soil moisture and groundwater table for the three benchmark hillslope problems. Specifically, the mean absolute errors (MAEs) of the soil moisture in the top 10 layers between ELMlat and PFLOTRAN were within 1 %±3 %, and the MAEs of water table depth were within ±0.2 m. Next, ELMlat was applied to the Little Washita experimental watershed to assess its prediction of groundwater table, soil moisture, and soil temperature. The spatial pattern of simulated groundwater table depth agreed well with the global groundwater table benchmark dataset generated from a global model calibrated with long-term observations. The effects of lateral groundwater flow on the energy flux partitioning were more prominent in lowland areas with shallower groundwater tables, where the difference in simulated annual surface soil temperature could reach 0.3–0.4 ∘C between ELMv2.0 and ELMlat. Incorporating lateral subsurface flow in ELM improves the representation of the subsurface hydrology, which will provide a good basis for future large-scale applications.

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.

Surface flux equilibrium estimates of evaporative fraction and evapotranspiration at global scale: Accuracy evaluation and performance comparison

Accurate estimation of terrestrial evapotranspiration (ET) is crucial to understand the water cycle and partitioning of turbulent energy fluxes at the land surface. However, large-scale ET estimation is always difficult to achieve due to the complexity of controlling factors and the heterogeneity of landscapes. A recent model, referred to as the surface flux equilibrium (SFE) model, proposes that evaporative fraction (EF, the ratio of ET to available energy at the surface) can be estimated accurately from near-surface specific humidity and air temperature, which makes it applicable to common weather stations and climate reanalysis datasets. However, previous validation only focused on the evaluation of this model at site and basin scale for ET estimation, making the real performance of the SFE model for EF estimation heretofore unknown. There also exists a gap in the comparison between the SFE model and other existing products in the representation of global ET patterns and variation trend. Against this background, a comprehensive validation was performed in this study to evaluate the accuracy of the SFE model for both EF and ET estimation. The validation against 136 flux towers worldwide shows that the ground-based SFE model achieved EF estimation with a root-mean-square-error (RMSE) of 0.22 and 0.16 at daily and monthly scale, respectively. The corresponding RMSE of ET estimation was 0.94 mm/day and 16.37 mm/month. The application of the SFE model on a global scale was achieved by using two reanalysis products (ERA5-Land and GLDAS-CLSM) as inputs respectively. The results indicate that the SFE model did hold potential to reduce the RMSE of these two products for ET estimation, but the cost was the decrease in correlation coefficient (r). The comparison with five existing global ET products (BESS, GLEAM, AVHRR, MOD16, and SSEBop) shows that the SFE model performed on the basis of ERA5-Land product has achieved moderate accuracy among these products. Judged from r, mean absolute error, RMSE, and bias, its accuracy was ranked as fourth, fourth, second, and second, respectively. However, a further evaluation over different land cover types suggests that the ERA-based SFE model has significant superiority over cropland, relative superiority over forest and savannas, and significant inferiority over shrubland and grassland. This analysis is the first global validation of the SFE model for EF estimation as well as its comparison with existing global ET products. The validation analysis proves that this simple model has generally achieved comparable accuracy to existing complex models in the representation of global ET patterns and variation trend.

A Methodology for Estimating the Energy and Moisture Budget of the Convective Boundary Layer Using Continuous Ground-Based Infrared Spectrometer Observations

Abstract Land–atmosphere interactions play a critical role in both the atmospheric water and energy cycles. Changes in soil moisture and vegetation alter the partitioning of surface water and energy fluxes, influencing diurnal evolution of the planetary boundary layer (PBL). The mixing-diagram framework has proven useful in understanding the evolution of the heat and moisture budget within the convective boundary layer (CBL). We demonstrate that observations from the Department of Energy Atmospheric Radiation Measurement (ARM) Southern Great Plains site provide all of the needed inputs needed for the mixing-diagram framework, allowing us to quantify the impact from the surface fluxes, advection, radiative heating, encroachment, and entrainment on the evolution of the CBL. Profiles of temperature and humidity retrieved from the ground-based infrared spectrometer [Atmospheric Emitted Radiance Interferometer (AERI)] are a critical component in this analysis. Large-eddy simulation results demonstrate that mean mixed-layer values derived are shown to be critical to close the energy and moisture budgets. A novel approach demonstrated here is the use of network of AERIs and Doppler lidars to quantify the advective fluxes of heat and moisture. The framework enables the estimation of the entrainment fluxes as a residual, providing a way to observe the entrainment fluxes without using multiple lidar systems. The high temporal resolution of the AERI observations enables the morning, midday, and afternoon evolution of the CBL to be quantified. This work provides a new way to use observations in this framework to evaluate weather and climate models. Significance Statement The energy and moisture budget of the planetary boundary layer (PBL) is influenced by multiple sources, and accurately representing this evolution in numerical models is critical for weather forecasts and climate predictions. The mixing-diagram approach, driven by profiling observations as illustrated here, provides a powerful way to quantify the contributions from each of these sources. In particular, the energy and moisture mixed into the PBL from above the PBL can be determined accurately from ground-based remote sensors using this approach.

Evapotranspiration regulates leaf temperature and respiration in dryland vegetation

Evapotranspiration regulates energy flux partitioning at the leaf surface, which in turn regulates leaf temperature. However, the mechanistic relationship between evapotranspiration and leaf temperature remains poorly constrained. In this study, we present a novel mechanistic model to predict leaf temperature as a linearized function of the evaporative fraction. The model is validated using measurements from infrared radiometers mounted on two flux towers in Arizona, USA, which measure canopies of Prosopis velutina with contrasting water availability. Both the observations and model predictions reveal that leaf temperature equilibrates with air temperature when latent heat flux consumes all of the energy incident on the leaf surface. Leaf temperature exceeds air temperature when there is a net input of energy into the leaf tissue. The flux tower observations revealed that evaporative cooling reduced canopy leaf temperature by ca. 1–5 °C, depending on water availability. Evaporative cooling also enhanced net carbon uptake by reducing leaf respiration by ca. 15% in the middle of the growing season. The regulation of leaf temperature by evapotranspiration and the resulting impact on net carbon uptake represents an important link between plant water and carbon cycles that has received little attention in literature. The model presented here provides a mechanistic framework to quantify leaf evaporative cooling and examine its impacts on plant physiological function.

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.

Evaluating the impacts of three-dimensional building morphology on urban near-surface energy fluxes: A case study in Beijing, China

Large-scale urbanization is associated with three-dimensional (3D) urban morphology variations worldwide. 3D building morphology alters the partitioning of urban near-surface energy fluxes and varies both temporally and spatially. Taking Beijing in China, as a case study, we modeled 64 scenarios to explore the relationships between 3D building morphology and urban ground-surface energy fluxes by adopting the ENVI-met model. We found that (1) the dimension of 3D fractal (3DF) significantly influences all the urban energy balance indicators. (2) Large values of 3DF cause decreases in the incoming shortwave radiation of 85.0 W/m2, 50.0 W/m2, 200.0 W/m2, and 125.0 W/m2 in summer, winter, summer daytime, and winter daytime, respectively. Correspondingly, the energy balance pattern varies with season and daytime/nighttime. (3) The explanatory power of 3DSI for the atmosphere–soil energy processes was weak and did not exceed 15 %. These findings can enhance our understanding of the effects of 3D building morphology on energy exchanges and provide information relevant to the management of 3D building morphology at a fine scale.

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.

Surface energy balance and flux partitioning of annual crops in southwestern France

In the micrometeorology community, it is well known that the turbulent fluxes measured with eddy covariance (EC) systems do not usually equal the available energy. Hence, qualitative knowledge of the impact of different vegetation types, and climatic variables on this ‘nonclosure’ is essential. This study analyzed a unique database of EC flux measurements covering 8 growing seasons of 3 crops (maize, wheat, and rapeseed) cultivated over two close agricultural sites (FR-Lam and FR-Aur) in southwestern France. For data analysis, some dry and wet cropping seasons of the same crop type were selected; then, their phenological stages were identified to investigate their effect on the energy balance closure (EBC), and flux partitioning. The results showed that the systematic effect of each site on the EBC was stronger than the influence of crop type and stage, as EBC was generally higher at FR-Aur (82%) than at FR-Lam (67%), even for the same crop type. The assessed effect of rainfall, and phenological stages on energy partitioning revealed that during the wet seasons, over 42% of the net radiation (Rn) was accounted for by the latent heat flux (LE), which was 9% higher than the recorded LE in the dry year during the active vegetation period. Similarly, the ground heat flux (G) was observed to be very sensitive to vegetation; G accounted for 30% of Rn when vegetation was low, whereas at the peak of vegetation, it fell below 16% due to canopy shading. Closure was also assessed under various atmospheric stability conditions and wind sectors, and it was observed to be higher under unstable conditions, and in prevailing wind directions. Analysis of the sensible heat advection (AH) revealed that AH accounts for more than half of the imbalance at both sites.

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.

Surface and Atmospheric Driven Variability of the Single‐Layer Urban Canopy Model Under Clear‐Sky Conditions Over London

AbstractUrban canopy models (UCMs) are parametrization schemes that are used to improve weather forecasts in urban areas. The performance of UCMs depends on understanding potential uncertainty sources that can generally originate from the (a) urban surface parameters, (b) atmospheric forcing, and (c) physical description. Here, we investigate the relative importance of surface and atmospheric driven model sensitivities of the single‐layer urban canopy model when fully interactive with a 1‐D configuration of the Weather Research and Forecasting model (WRF). The impact of different physical descriptions in UCMs and other key parameterization schemes of WRF is considered. As a case study, we use a 54‐hr period with clear‐sky conditions over London. Our analysis is focused on the surface radiation and energy flux partitioning and the intensity of turbulent mixing. The impact of changes in atmospheric forcing and surface parameter values on model performance appears to be comparable in magnitude. The advection of potential temperature, aerosol optical depth, exchange coefficient and roughness length for heat, surface albedo, and the anthropogenic heat flux are the most influential. Some atmospheric forcing variations have similar impact on the key physical processes as changes in surface parameters. Hence, error compensation may occur if one optimizes model performance using a single variable or combinations that have potential for carryover effects (e.g., temperature). Process diagrams help differences to be understood in the physical description of different UCMs, boundary layer, and radiation schemes and between the model and the observations.

Assessment of an Automated Calibration of the SEBAL Algorithm to Estimate Dry-Season Surface-Energy Partitioning in a Forest–Savanna Transition in Brazil

Evapotranspiration ( E T ) provides a strong connection between surface energy and hydrological cycles. Advancements in remote sensing techniques have increased our understanding of energy and terrestrial water balances as well as the interaction between surface and atmosphere over large areas. In this study, we computed surface energy fluxes using the Surface Energy Balance Algorithm for Land (SEBAL) algorithm and a simplified adaptation of the CIMEC (Calibration using Inverse Modeling at Extreme Conditions) process for automated endmember selection. Our main purpose was to assess and compare the accuracy of the automated calibration of the SEBAL algorithm using two different sources of meteorological input data (ground measurements from an eddy covariance flux tower and reanalysis data from Modern-Era Reanalysis for Research and Applications version 2 (MERRA-2)) to estimate the dry season partitioning of surface energy and water fluxes in a transitional area between tropical rainforest and savanna. The area is located in Brazil and is subject to deforestation and cropland expansion. The SEBAL estimates were validated using eddy covariance measurements (2004 to 2006) from the Large-Scale Biosphere-Atmosphere Experiment in the Amazon (LBA) at the Bananal Javaés (JAV) site. Results indicated a high accuracy for daily ET, using both ground measurements and MERRA-2 reanalysis, suggesting a low sensitivity to meteorological inputs. For daily ET estimates, we found a root mean square error (RMSE) of 0.35 mm day−1 for both observed and reanalysis meteorology using accurate quantiles for endmembers selection, yielding an error lower than 9% (RMSE compared to the average daily ET). Overall, the ET rates in forest areas were 4.2 mm day−1, while in grassland/pasture and agricultural areas we found average rates between 2.0 and 3.2 mm day−1, with significant changes in energy partitioning according to land cover. Thus, results are promising for the use of reanalysis data to estimate regional scale patterns of sensible heat (H) and latent heat (LE) fluxes, especially in areas subject to deforestation.

The topographic control on land surface energy fluxes: A statistical approach to bias correction

Subsurface hydrodynamics are an important component of the hydrological cycle and a key factor in the partitioning of land surface water and energy fluxes. Because of computational reasons they are often neglected, or strongly simplified, in numerical weather prediction and climate models. Particularly in regions where the water table is shallow, soil moisture acts as a link between groundwater, land and atmosphere. Because of its long-term memory, groundwater represents a buffer for the effects of climate variability. To dynamically model this system we study the outputs of a variably saturated groundwater flow model (ParFlow) coupled with a surface model (CLM) and propose an empirical approach for the statistical correction of the bias of the energy fluxes in a highly-parametrized simulation. Corrections are based on a comparison of the simple scheme with the fully-coupled subsurface-land-atmosphere simulations. This simple dynamical scheme computes the potential latent heat flux in case of near saturation, while the fully-coupled simulations approximate the reality. Our statistical model examines the evapotranspiration surplus, i.e., the difference in latent heat flux between the two runs, and aims to correct the bias in the latent heat flux of the simple scheme, based on the characteristics of each point of the domain. In particular, we focus on the ability of topography-related indices, such as the topographic wetness index and the depth-to-water index, to provide information on the availability of water for evapotranspiration. Our results confirm that topographic indices are good predictors for moisture availability. While small-scale structures cannot be accurately reproduced by our model, large-scale biases of latent heat flux over the domain are effectively removed. Moreover, the bias corrected fluxes show a better agreement with the fluxes of the full modelling system than commonly used free-drainage simulations. Thus, the proposed approach can be useful in approximating the effect of groundwater on land surface water and energy fluxes in, e.g., regional climate models.

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.