For the past five decades, modelers have relied on Monin-Obukhov Similarity Theory (MOST) to model surface exchanges for application in atmospheric models for boundary layer meteorology and weather and climate prediction. During this time, studies have also illuminated some of the limitations of MOST based surface layer parameterizations, particularly when MOST’s foundational assumptions of flat and horizontally homogeneous terrain are violated. Recent work over groups of meteorological towers from Stiperski and Calaf 2023 have provided a promising method to account for these deviations from the ideal, traditional MOST using the anisotropy of turbulence to create new surface exchange relations. These modified relations may be able to capture the deviations from MOST specifically around non-homogeneous surfaces, and non-stationarity. To further assess the validity of the Stiperski relations, we examine them over 7 years of turbulence data from the 47, ecologically diverse eddy-covariance tower sites in the National Ecological Observation Network (NEON) and develop new anisotropy generalized MOST scalings for the scalar variances of moisture and carbon.
The relations from Stiperski and Calaf 2023 show significant improvement over traditional MOST based schemes for predicting the velocity variances as well as the variances of heat, moisture and carbon in the NEON network under both stable and unstable stratification. This extends the work of Stiperski and Calaf to vegetated canopies, where the scaling has not been previously examined. The improvement is consistent across the varied ecosystems present in NEON, including tropical, arctic, and mountainous sites. For the streamwise velocity variance, for example, we see a median improvement (measured with a skill score) of 40% at the NEON sites. Characteristics of anisotropy are also examined across the sites, with an eye towards developing model relations for turbulence anisotropy applicable in large scale schemes (i.e. numerical weather prediction and earth system models. Initial results for the scaling of the gradients of heat and momentum, which can be used to parameterize surface fluxes in the modeling context, are also shown, with promising improvement over traditional MOST despite significant scatter. The route for application of these schemes in surface layer parameterizations in ESMs is also briefly explored, with an eye towards the potential for significant improvements in modeling of surface exchange.
How to cite: Waterman, T., Stiperski, I., and Calaf, M.: Extending Generalized Surface Layer Scaling to Diverse, Complex Terrain and Canopies for Improved Land-Atmosphere Exchange, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11806, https://doi.org/10.5194/egusphere-egu25-11806, 2025.
Spatially organized km-scale surface thermal heterogeneity can lead to the formation of secondary circulations, which, in turn, can influence the boundary layer and the initiation, development, and enhancement of cumulus clouds. While the importance of this process is becoming well recognized, quantitative understanding of the relationship between thermal heterogeneity and the corresponding circulations remains largely confined to modeling studies. In this study, we use observational data from the ARM Southern Great Plains (SGP) site to explore how combining satellite remote sensing of land surface temperature (LST) with a mesoscale network of Doppler lidars can help understand the role of surface thermal heterogeneity in driving secondary circulations.
We analyze data from five Doppler lidars at SGP, which have been continuously measuring vertical profiles of wind components (u, v, w) at high temporal frequency since 2016. The combination of the five time-varying profiles are used to compute vertically integrated dispersive kinetic energy (DKE) at each time step as an indirect measure of circulation strength. LST data from GOES-16/17 is then used to quantify surface thermal heterogeneity, particularly in the morning hours. Our analysis focuses on days with minimal synoptic forcing to isolate local effects. Preliminary results show a statistically significant positive correlation between surface thermal heterogeneity and DKE, suggesting a link to the strength of secondary circulations. This study highlights the potential to improve our understanding of this process and provides a valuable tool for evaluating Earth system models that aim to represent the role of km-scale thermal heterogeneity in the atmosphere.
How to cite: Chaney, N., Germ, P., Calaf, M., Pardyjak, E., and Waterman, T.: Investigating the Role of Kilometer-scale Surface Thermal Heterogeneity in Secondary Circulations Using Satellite Remote Sensing and Doppler Lidars, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14269, https://doi.org/10.5194/egusphere-egu25-14269, 2025.
To better understand and quantify the dynamics of surface thermal heterogeneities and their effect on energy transport in form of dispersive fluxes within the atmospheric boundary layer, we investigate the significance and applicability of the heterogeneity parameter after Margairaz et al. (2020). We aim to overcome this non-dimensional scaling quantity, since it depends on parameters such as the heterogeneity length, scale, and temperature amplitude, which are originally determined for checker-board-type surfaces but may be less suited to describe the complexity of real-world surface structures. To address this goal, we train separate artificial neural networks (ANNs) to predict dispersive sensible and latent heat fluxes for a randomized quadratically shaped heterogeneity distribution, as well as for datasets from the CHEESEHEAD19 campaign representing a real-world complex surface heterogeneity with a broad spectrum of patch sizes and gradual changes in surface characteristics. To investigate the role of the different input variables, we train various ANNs receiving different combinations of variables and compute feature importance weightings afterwards. We scrutinize the role of traditional input variables such as the heterogeneity parameter, temperature or humidity gradients, boundary layer height, and atmospheric stability measures. Further, we consider the incorporation of raw input features, such as horizontal and vertical wind speed, temperatures, and humidities. Finally, we incorporate spatial temperature maps, which we pre-process with a convolutional ANN. We make three core observations. First, the incorporation of raw input features beyond traditional variables improves both the dispersive sensible and latent heat flux diagnosis, suggesting room for improvement in the input variable selection and combination. Second, the inclusion of the spatial temperature map is more meaningful for dispersive latent than for sensible heat flux diagnosis. Third, the heterogeneity parameter after Margairaz et al. (2020) is informative for synthetic randomized quadratically shaped surfaces, but not for real-world complex surface heterogeneity environments, in which case the spatial temperature map processed by a convolutional ANN is most valuable. The results imply that the role of the compressed spatial temperature map should be explored further. We ultimately aim to extract an equation from the neural network characterizing heterogeneous surfaces. Furthermore, the incorporation of the other identified useful raw input features – ideally in form of an equation – needs to be assessed in further depth.
How to cite: Wagner, B., Karlbauer, M., Butz, M., Mauder, M., and Wanner, L.: Discovering new Influences on Dispersive Heat Fluxes over Heterogeneous Surfaces with Machine Learning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15199, https://doi.org/10.5194/egusphere-egu25-15199, 2025.
Since eddy covariance (EC) flux towers are typically mounted within structured landscapes, interpreting EC flux data is complicated due to spatial heterogeneity, which may exhibit sources and sinks simultaneously. This complexity makes it challenging to understand mechanisms and controls determining flux budgets for the individual land cover types that make up the entire ecosystem. Therefore, it complicates the scaling of flux results in space and/or time, or comparing EC fluxes under different environmental conditions.
We present a novel tool to decompose blended flux data from EC towers into individual components emitted by different land cover types within the tower’s footprint. The tool has two key components: 1) an exceptionally efficient algorithm that solves the steady-state transport equation, and 2) a linear optimizer to solve the inversion problem. This design allows for the analysis of years of continuous EC data on a typical desktop computer in a short time, with output consisting of half-hourly flux data for each land cover type individually.
The approach is entirely data-driven and can be applied to the fluxes of energy and scalars such as methane, N2O, or CO2. The model takes as input a land cover map containing the footprint and the standard output from the raw eddy data processing software, EddyPro. The accuracy of the flux attribution tool was validated using two EC towers in close proximity, sharing the same ecosystem and meteorological conditions, but with different land cover structures in the footprint. The agreement between the inversion results for each of the towers proves its applicability for a wide range of research questions.
How to cite: Schlutow, M., Chew, R., Yazbeck, T., and Göckede, M.: Spatial source attribution of eddy covariance flux data by inversion optimization, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12704, https://doi.org/10.5194/egusphere-egu25-12704, 2025.
Eddy covariance (EC) flux observations deviate from the fluxes at the ecosystem-atmosphere interface when the turbulent flow is decoupled from the surface. This problem severely limits the applicability of the EC technique to monitor ecosystem-atmosphere interactions including trace gas exchange. Despite some progress on understanding vertical coupling processes over the past years, the role and interplay of dynamic stability, canopy drag, and the strength of vertical turbulent mixing remains insufficiently understood. Furthermore, the commonly used metric to identify decoupling, friction velocity, does not represent these processes.
In this work we use the recently developed decoupling metric Omega to detect decoupling at 45 contrasting EC sites across a broad range of canopy architectures and biomes (Peltola et al. 2025, https://doi.org/10.1016/j.agrformet.2024.110326). Omega encapsulates the main processes controlling decoupling in a single dimensionless metric, thus providing a unified framework for studying coupling at all sites. We focus on evaluating the applicability of Omega to detect decoupling at these sites and use it to evaluate the processes controlling decoupling across sites.
The results show that Omega was able to identify coupling at all tested sites satisfactorily. The vertical turbulent carbon dioxide flux showed a similar Omega dependence at all sites, although there was some site-to-site variability. In contrast, when the change in storage flux term was added to the analysis, the similarity between sites disappeared. This suggests that the storage flux term depends on parameters other than those controlling vertical turbulent mixing. Canopy drag played an important role in the formation of decoupling at dense forest sites, and at such sites decoupling was observed even during the day.
Based on these findings, we delineate different Omega regimes in which different mass balance terms (vertical turbulent flux, storage flux and advective components) are important, and discuss improved approaches for detecting the regime where the sum of vertical turbulent flux and storage flux equals the surface gas exchange.
How to cite: Peltola, O., Aslan, T., Aurela, M., Lohila, A., Mammarella, I., Papale, D., Thomas, C. K., and Vesala, T.: Towards improved understanding on flow decoupling at eddy covariance sites with the aid of a universal coupling metric, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6444, https://doi.org/10.5194/egusphere-egu25-6444, 2025.
The Amazon Rainforest plays a vital role in the global carbon and water cycle, yet responses of old growth tropical rainforests to climate change and rising CO2 concentrations remain poorly understood. Especially the western part of the Amazon is underrepresented in ecohydrological studies. At the Panguana research station, as part of the AndesFlux Network, fluxes of CO2, water vapor and the dynamics of the CO2, CH4 and water vapor profile inside the forest and above the 35 m tall canopy have been continuously monitored since December 2023 to fill this gap and determine whether this site acts as a net source or sink for carbon. Building on this objective, our focus extends to understanding the timescales and ecosystem drivers responsible for flux variability, a crucial step toward predicting ecosystem responses to future changes.
As the main objective, we aim at understanding what are the main drivers for ecosystem flux variability, e.g. incoming solar radiation, water availability, or water vapor deficit and on which timescale we can detect the highest variability of ecosystem fluxes. In a tropical region the highest variability in an annual dataset would be expected to occur on a seasonal timescale. However, we did not observe the expected difference in latent heat flux when comparing the mean dial course on a seasonal basis. Surprisingly, we found the highest variability of latent heat flux to occur on much shorter timescales of up to ten days, coinciding with variability of incoming shortwave radiation for which a timescale of highest variability of eight days was detected. Understanding the processes causing this periodicity in latent heat flux in a tropical region and resulting effects on CO2 flux is the primary objective of this analysis.
A further objective of this study presented here is to calculate a CO2-based carbon budget, with the inclusion of the storage term change to understand the effect of ecosystem respiration at night. While the methane exchange to the carbon budget may be significant at this site, it is outside the scope of the current study. Additional objectives of this project include calculating the energy balance of this site and analysing at the surface water balance to better understand seasonal differences and their impact on the carbon cycle.
After calculating the 4h-daytime energy balance closure with different perturbation time scales, we selected a perturbation timescale of 20 min as the best compromise between reducing the systematic and random flux errors. This choice leads to a high energy balance closure of 75% over the course of one year maximizing to 80% when calculated for the rainy season.
These analyses contribute to a deeper understanding of the driving processes of ecosystem exchange in the tropical rainforest near the Andes and help to assess how this part of the Amazon basin may respond to future changes in water availability and atmospheric circulation.
How to cite: Heidemann, L., Cosio, E., Cruz, R., Diller, J., Niessner, A., Olesch, J., Salinas, N., Stern, R., and Thomas, C.: Analysing the time scales of variability in carbon dioxide and energy balance components of a tropical Amazon rainforest in central Peru, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10813, https://doi.org/10.5194/egusphere-egu25-10813, 2025.
Gaining a deeper understanding of dynamic interactions between cities and the atmospheric boundary layer (ABL) and ABL processes in general is crucial for, e.g., the development and application of next-generation numerical weather prediction and climate modelling. In this context, detailed ABL observations provide essential information to identify potential spatial heterogeneity in urban and rural environments with respect to surface-atmosphere exchanges and resulting ABL characteristics such as ABL clouds.
As part of the year-long urbisphere-Berlin measurement campaign in Berlin, Germany (October 2021-September 2022), a wide range of ABL observations were carried out to study impacts of the city on the ABL. Central to the deployed systematic network were 25 sites with ground-based Automatic Lidar and Ceilometers (ALC) to measure aerosol backscatter for investigation of intra-urban, urban-rural, and upwind-city-downwind effects of ABL clouds and detection of the mixed layer.
Here, we present a systematic investigation of year-round effects of the city on ABL cloud-base height and cloud-cover fraction, mixed-layer height, and near-surface fog conditions, exploiting the dense ALC network. The comprehensive data set allows studies along diurnal and annual cycles in high temporal resolution, as well as obtaining robust statistical results for groups of sites, considering spatial heterogeneity due to local effects around the sites. Our analyses show city effects on ABL clouds along the diurnal cycle including upwind-city-downwind effects, yet also depending on cloud type and season. Mixed-layer height undergoes a distinctive annual cycle, being systematically higher above the city and with intra-urban differentiation. Over the year, the occurrence of ground-based fog is on average 1,5 times more frequently found at rural sites compared to city sites, most prominent differences are found during autumn and winter. These results are the first that are based on the complete year-long urbisphere-Berlin ALC data and highlight potentials and benefits of such high-resolution observational data sets from ground-based remote sensing for future investigations.
How to cite: Fenner, D., Christen, A., Grimmond, S., Kotthaus, S., Meier, F., and Zeeman, M.: Urban effects on atmospheric boundary-layer clouds, mixed-layer height and fog detected by a dense network of ceilometers in Berlin, Germany, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15275, https://doi.org/10.5194/egusphere-egu25-15275, 2025.
The uncertainty evaluation of eddy covariance flux measurements has been thoroughly developed in the last two decades. However, the various methods proposed are not yet fully compliant with the internationally accepted metrological guidelines, e.g. those indicated in the Guide to expression of uncertainty in measurement and related supplements issued by the Joint Committee for Guides in Metrology and internationally adopted as reference in metrology. Scope of this presentation is to implement the formal methodology for the determination of a combined standard uncertainty for the estimated fluxes through the law of propagation of uncertainty, assuming independent variables. Compared to previous methods, this approach considers the complete flux equation, including the coordinate rotations and the physical conversions and, most importantly, provides an easy to implement analytical tool to quantify the individual contributions to the full measurement uncertainty arising from all the variables actually included in the calculation (turbulent wind components, scalar of interest, air temperature and pressure). The linear method adopted for uncertainty propagation has been also validated through a Monte Carlo simulation, which is the gold standard for propagating probability distributions. The methodology has been applied to a full year of carbon dioxide fluxes measured in the San Rossore 2 ICOS Ecosystem Station, a Mediterranean forest, but it is valid for most of the common eddy covariance systems, being based on theoretical principles. The median of the estimated relative uncertainty of the flux over the considered year is 13.5%, assuming an instrumental uncertainty of 30 Pa for the barometer, 0.5 °C for the thermometer, 4 ppm for the CO2 analyzer and 0.4 m/s for the three components of the sonic anemometer. The main uncertainty contributions come from the analyzer and the vertical component of the anemometer, with medians of the evaluated relative uncertainties equal to 11.9% and 3.25%, respectively. Preliminary results suggest that the method is robust and confirm expectations about the relative contribution of the different instruments used for flux determination, but at the same time constitute a tool for a sounder metrological assessment of all eddy covariance measurements and applications.
How to cite: Arriga, N. and Bottacin, A.: Metrology for fluxes: eddy covariance measurement uncertainty, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17589, https://doi.org/10.5194/egusphere-egu25-17589, 2025.
The O2:CO2 exchange ratio of land-atmosphere fluxes (ER) can be used to identify sources and sinks of CO2 in land ecosystems. During photosynthesis, the O2:CO2 ER at the leaf level is approximately -1 mol mol-1, reflecting the uptake of one mole of CO2 associated with the release of one mole of O2. However, the ER at the level of entire ecosystems is largely unknown.
Here we present a unique dataset of two years of continuous O2 and CO2 flux measurements at the agricultural FLUXNET site Reinshof (51°29'24.0"N, 9°55'55.2"E, DE-Rns) near Göttingen, Germany, in 2023 and 2024. Fluxes were calculated using flux-gradient approaches with air sampled from three inlets situated at 0.5, 1.0 and 3.0 m above ground. Dry mole fractions of O2 and CO2 were measured using a modified Oxzilla II differential oxygen analyzer (Sable Systems, USA) and a Li-820 CO2 infrared gas analyser (LiCor Biosciences, USA), respectively.
The results show that O2 and CO2 mole fractions and net O2 and CO2 fluxes were strongly anticorrelated. The O2:CO2 flux ER showed a distinct annual cycle, with values around -1.5 mol mol-1 under bare soil conditions and -1.1 mol mol-1 during the main growing season when sugar beet (2023) and winter wheat (2024) was grown, respectively. An influence from anthropogenic emissions was observed during the winter with stable atmospheric stratification, when winds originated from the city centre of Göttingen or the nearby road. The longer vegetation period of sugar beet in 2023 was well reflected by extended O2 release and CO2 uptake, as well as ERs at around -1.1 mol mol-1.
In conclusion, the O2:CO2 ER of a cropland showed considerable seasonal variability, which offers the opportunity to use O2 flux measurements as a tracer of the carbon cycle.
How to cite: Markwitz, C., Tunsch, E., Manning, A., Pickers, P., and Knohl, A.: Continuous measurements of O2:CO2 flux exchange ratios above a cropland in central Germany, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1695, https://doi.org/10.5194/egusphere-egu25-1695, 2025.
Land-atmosphere (L-A) feedback plays a key role in the evolution of Earth’s weather and climate system. However, the understanding and simulation of land-atmosphere interaction still suffers from severe limitations and errors. For instance, Abramowitz et al. (2024) demonstrated that the simulation of surface fluxes by land-atmosphere models, irrespective of their complexity, strongly deviates from observations. Similarly, Monin-Obukhov Similarity Theory (MOST) seems to be inadequate (Wulfmeyer et al. 2023) for the parameterization of evapotranspiration, but is nevertheless used in almost all coupled land-atmosphere system models.
The overarching goal of LAFI is to understand and quantify L-A feedbacks via unique synergistic observations and model simulations from the micro-gamma (» 2 m) to the meso-gamma (» 2 km) scales across diurnal to seasonal time scales. In this presentation, we give an overview of the objectives and the current results of LAFI with respect to the understanding of surface-layer flow and fluxes, the energy balance closure (EBC), and entrainment over heterogenous agricultural terrain. More insight will be gained by the LAFI field campaign, which will be performed from Spring to Autumn 2025 at the Land-Atmosphere Feedback Observatory (LAFO) of the University of Hohenheim. The LAFI field campaign will enhance the current sensor synergy at LAFO, in order to capture key variables more fully within the soil, vegetation, and atmosphere compartments (Späth et al. 2023). Highlights of the new LAFI instrumentation include water-vapour isotope sensors, sap-flow sensors, fiber-optical distributed sensors (FODS, Thomas and Selker, 2021),unmanned aerial vehicles (UAVs), and scanning water-vapor, temperature, and wind lidar systems. We demonstrate how these measurements complement each other to gain new insights into flux-driver relationships, soil evaporation, crop transpiration, and entrainment, as well as the impact of land-surface heterogeneities and dispersive fluxes on the EBC. The very first results of this campaign will also be presented.
References:
Abramowitz et al. 2024: https://bg.copernicus.org/articles/21/5517/2024
Späth et al. 2023: https://doi.org/10.5194/gi-12-25-2023
Thomas, C.K., Selker, J.S., 2021. https://doi.org/10.1007/978-3-030-52171-4_20
Wulfmeyer et al. 2023: https://link.springer.com/article/10.1007/s10546-022-00761-2
How to cite: Wulfmeyer, V.: The Land-Atmosphere Feedback Initiative (LAFI): Field observations, modeling approaches, and first results, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19157, https://doi.org/10.5194/egusphere-egu25-19157, 2025.
Ground heat flux may play an important role in surface energy balance. In this study we evaluate the performance of the objective hysteresis model (OHM) for estimating ground heat flux from net radiation and compare it with the linear regression model. The experimental sites include residential roofs (concrete), campus grassland, agricultural grassland, and peat bog. Our field measurements show that the mean partition coefficient from net radiation to ground heat flux varied from 0.47 (concrete roof) to 0.079 (agricultural grassland). The mean hysteresis (lag) factors for residential roof, campus grassland, and peat bog were 0.55, 0.26, and 0.11 h, respectively; and the hysteresis factor at the agricultural site was only 0.032 h. However, the partition and hysteresis coefficients in the OHM were found to vary with time for the same surface. Our measurements and analysis show that when the hysteresis factor is larger than 0.11 h, ground heat flux estimates from net radiation can be improved (17–37% reduction in the root mean square error) by using OHM instead of a simple linear regression model.
How to cite: Hsieh, C.-I. and Visessri, S.: Estimating Ground Heat Flux from Net Radiation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1437, https://doi.org/10.5194/egusphere-egu25-1437, 2025.
Soil heat flux (SHF) is a key component of the surface energy balance and a driver of soil physiochemical and biological processes. Despite its importance accurate estimation of soil heat flux is hindered due to variations in soil composition, overlying vegetation density and phenology, and highly variable environmental forcings. These factors have challenged the development of robust models of SHF, with modeling studies focused on mid-day conditions corresponding to satellite overpass times, missing the significant variability that occurs throughout diurnal periods across a growing season. Here we assess the performance of ensemble machine learning modeling for predicting soil heat flux at half-hourly resolution for multiple agro-ecosystems. Observations span a wide range of phenological and climatological variability over a complete growing season. We utilized the random forest machine learning (ML) approach to develop a wide range of models utilizing combinations of predictor variables that include widely-available meteorological conditions and proximal remote sensing observations of reflectance indices and land surface temperature (LST). The performance of the ML models developed here was compared to a set of six semi-empirical soil heat flux models developed around the use of remote sensing information. The random forest ML ensembles demonstrated a general ability to significantly outperform the six semi-empirical models in capturing diurnal variations across the growing season for each of the four crops examined here (soybean, corn, sorghum and miscanthus). We found ML models using the complete set of meteorological and remote sensing predictors captured over 90% of the variability in SHF across all crops. ML models using only LST and NDVI as predictors were able to capture over 82% of SHF variability across all crops. Shapley additive explanations (SHAP) methods were examined to allow for model interpretability, providing insights into the typically opaque ML modeling process. From a set of seven observation variables an exhaustive search was performed to identify predictor attributions for each of the four crops examined here. Models trained with fewer input variables tended to display more linear and interpretable feature attribution, suggestive of physical consistency. LST and air temperature were often the most crucial predictors when present due to high correlation with soil heat flux, with NDVI the next most crucial predictor due to its ability to quantify canopy density and phenological status. These results suggest that robust and accurate soil heat flux estimations can be made at high-temporal resolution purely through simple proximal remote sensing observations and widely available meteorological observations.
How to cite: Drewry, D. and Cross, J.: Ensemble machine learning for interpretable soil heat flux estimation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2935, https://doi.org/10.5194/egusphere-egu25-2935, 2025.
We present first results of a new BROOK90 hydrological model version. This new version includes a closed energy and water balance for subdaily time steps based on an adapted Shuttleworth-Wallace model for the description of energy and water fluxes for different evaporation components, like interception, soil evaporation and transpiration. The simulation results have been compared to ICOS eddy-covariance measurements from the Anchor Station Tharandt for the year 2022.
The comparison shows considerable good result for 30-minute estimates of latent and sensible heat fluxes from dry surfaces, whiles simulated fluxes from wet surfaces perform worse. Snow conditions seem to be almost random, but rainy conditions might possess a certain correlation between measured and simulated fluxes. Reason for these results can be found on the one hand in the choice of model parameters for vegetation like maximal canopy resistances, leaf area index or canopy height in the model and on the other hand, limitations of the eddy-covariance measurements under wet conditions.
How to cite: Kronenberg, R., Vorobevskii, I., and Luong, T. T.: First results of an extended BROOK90 hydrological model to estimate subdaily water and energy fluxes. A case study of ICOS Anchor station in Tharandt, Germany, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9419, https://doi.org/10.5194/egusphere-egu25-9419, 2025.
The land surface plays a crucial role in the climate system, significantly influencing the exchanges of energy, mass, and momentum among the atmosphere, biosphere, and lithosphere. While land-surface processes in homogeneous terrains are well understood and effectively integrated into the parameterization schemes of existing weather models, our understanding of these processes in extremely heterogeneous regions remains insufficient. This gap in knowledge limits our capacity to accurately parameterize land-surface interactions in such areas. Extremely heterogeneous surfaces are characterized by a variety of soil types and pronounced orographic features, such as mountains or steep slopes.
State-of-the-art weather models commonly utilize the Monin-Obukhov similarity theory (MOST) for parameterizing surface momentum, heat, and moisture fluxes. However, these similarity functions are based on empirical data obtained from field campaigns conducted in homogeneous environments. When these functions are applied to extremely heterogeneous regions, they can produce large biases between modeled and observed surface sensible or latent heat fluxes. Furthermore, in large-eddy simulations (LES), the underlying assumptions of MOST - such as horizontal homogeneity and stationarity - are often violated. Additionally, inconsistencies arise between the fluxes calculated using subgrid closure schemes and those derived from MOST in the surface layer.
To tackle these challenges, we propose an alternative approach that circumvents the use of MOST for parameterizing surface fluxes. In land-surface-parameterization schemes, surface fluxes are often determined using resistance networks. Instead of estimating these resistances using MOST, our aerodynamic resistance approach (ARA) uses the eddy viscosity/diffusivity calculated by the subgrid closure schemes.
First tests in idealized large-eddy simulations (LES) using the Weather Research and Forecasting model (WRF) show that the ARA-calculated surface fluxes are more consistent with the subgrid closure calculations than the MOST-derived fluxes. Next, the ARA will be tested in real-case simulations of the Tengchong site (China) on the Tibetan plateau which is known for its heterogeneous landscape. Moreover, the simulation results will be compared to observational data which has been available at the site for more than 12 years.
How to cite: Wedemeyer, C. and Shao, Y.: Parametrization of extremely heterogeneous land-surface processes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9912, https://doi.org/10.5194/egusphere-egu25-9912, 2025.
Irrigation is triggered through climatic conditions, but reversely affects the climate itself. A model sensitivity analysis of the irrigation impacts on the severe summer 2003 drought and heat wave event in Central Europe is carried out here. For this purpose, the Weather Research and Forecasting (WRF) model is employed with a newly developed and modified irrigation scheme. A two-domain nested setup with 12 km horizontal grid resolution in the outer domain and convection-resolving 3 km in the inner domain is selected. Two ensembles, one with and one without irrigation, are initialized to assess the irrigation impacts with greater security. Four subregions are defined: a region containing all of Germany, two small regions with locally higher irrigation amounts within Germany and an area in the Po Valley, the region with highest irrigation quantities in Central Europe. This way, the influence of different irrigation amounts is investigated. Impacts on the following variables are examined in different temporal scales: air temperature, soil moisture, planetary boundary layer height (PBLH), sensible and latent heat flux, moisture flux divergence, convective available potential energy (CAPE), and convective inhibition (CIN). The results indicate that the overall influence of irrigation during the extreme event is rather small. This is related to the comparatively low irrigation amounts and the extreme conditions. A partially significant increase in soil moisture in the topsoil layer occurs in the Po Valley. Generally, irrigation is found to reduce PBLH and sensible heat flux as well as increasing the latent heat flux. In addition, a cooling effect is partly found in the daily mean cycle of temperature. Furthermore, there are visible effects on moisture flux divergence (tendency to decrease or convergence), on CAPE (increase) and on CIN (less increase). These effects are most pronounced in the Po Valley due to the higher irrigation amounts.
How to cite: Petrovic, D., Fersch, B., and Kunstmann, H.: Irrigation impacts on the severe summer 2003 drought and heat wave event in Central Europe, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12023, https://doi.org/10.5194/egusphere-egu25-12023, 2025.
Field crops can emit or store carbon depending on the season and cropping practices. A process-based modeling approach allowed us to predict the transfer pattern of the CO2 fluxes and energy balance between soil, vegetation, and atmosphere. In this study, the SURFATM-CO2 model was developed to simulate distinctly the CO2 exchanges between soil, plants, and the atmosphere. The model couples soil respiration, taking into account its temperature sensitivity, with photosynthesis and plant respiration process-based, taking into account the plant's CO2 compensation point. The SURFATM-CO2 process model was evaluated using field measurements obtained from a novel multiport profile system consisting of 4 vertical measurement heights to monitor the spatial and temporal variation of CO2, water, and temperature within and above the maize canopy in east Tennessee. The 5Hz frequency raw data were averaged into 15-minute runs and used as input for the SURFATM model. The model satisfactorily simulates the energy balance, and we are currently testing the model for the CO2 fluxes. The main objective of this study is to understand the exchanges of CO2 between the soil, vegetation and atmosphere compartments. The finding of the SURFATM-CO2 model will highlight the ability of the SURFATM-model to capture the canopy-atmosphere interaction as well as provide a base for model application in the studies of carbon dynamics, and cropland ecosystem management.
How to cite: Raza, T., Personne, E., Lichiheb, N., Eash, N., and Oetting, J.: Evaluation of CO2 and energy balance fluxes from a maize canopy in east Tennessee using the SURFATM model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19174, https://doi.org/10.5194/egusphere-egu25-19174, 2025.
Accurately quantifying surface energy budgets in croplands is essential for efficient water resource allocation and sustainable agricultural practices. However, the representativeness of eddy covariance (EC) measurements in hilly agricultural fields remains less examined. In this study, we conducted an experiment employing three EC flux towers to assess the consistency of surface energy budget components across a hilly agricultural field (~90 acres). The experimental field was divided into three zones, each equipped with an EC tower positioned at its central location to ensure that 90% of the flux footprint fell within the corresponding zone (i.e., US-SZ1, US-SZ2 and US-SZ3). The meteorological conditions and energy fluxes were found to be significantly influenced by various agricultural activities, including both growing and non-growing periods, as well as cropland management practices. Despite relatively similar meteorological conditions observed across the three sites during the wheat growing period (WGP), substantial discrepancies were evident in the primary energy budget components, with the exception of net radiation, at both diurnal and seasonal scales. During WGP, the sensible, latent, and ground heat fluxes exhibited differences within 10%, 27%, and 29%, respectively, leading toconsiderable disparities in the energy balance closure. The closure ratios (CRs) for US-SZ1, US-SZ2, and US-SZ3 were approximately 93%, 84%, and 85% respectively. The influence of environmental variables on the discrepancies in their CRs were also investigated. The relationships between CRs and friction velocity, atmospheric stability, turbulent kinetic energy, as well as heat transport efficiency exhibited certain distinctions among the three sites. Our findings indicate that factors like site elevation, topography, and measurement uncertainty differentially affect energy flux components in sloping landscapes. Employing multiple tower/point measurements is crucial for reducing uncertainties in energy flux estimates under sloping terrain conditions.
How to cite: Yao, J., Gao, Z., Li, L., Russell, E., Pressley, S., and Dai, Y.: Assessing the discrepancy of energy fluxes over spring wheat under sloping topography conditionsbased on eddy covariance measurements, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20222, https://doi.org/10.5194/egusphere-egu25-20222, 2025.
Terrestrial ecosystems play a crucial role in carbon sequestration and provide vital ecosystem services such as food, energy, and raw materials. Climate change, through rising temperatures, altered precipitation patterns, and extreme events, threatens the carbon sink potential of these ecosystems, with forests and grasslands particularly at risk. Long-term data from flux tower networks offer valuable insights into how different ecosystems respond to climate change and management interventions, helping to develop strategies to mitigate greenhouse gas emissions and maintain ecosystem resilience. In this study, we present such data from a <10 km cluster of long-term FLUXNET/ICOS sites in Central Europe, comprising an old spruce forest (DE-Tha), a young oak plantation after a cleared windthrow (DE-Hzd), a permanent grassland site (DE-Gri), and an agricultural site with a crop rotation typical for this region (DE-Kli). By analysing decades of data from these four eddy covariance measurement sites, the research highlights the influence of drought, management, and land cover changes on CO2 and H2O fluxes. The interannual variability of evapotranspiration depends less on land use than the CO2 exchange. Our findings show that forests without terminal disturbances can act as larger carbon sinks than previously estimated. DE-Tha is a consistent carbon sink, with thinning helping to maintain the CO2 sequestration at a stable level of 350 gC m−2 a−1. In contrast, disturbances like clear cutting or windthrow can cause ecosystems to become carbon sources for several years, with recovery delayed due to soil carbon losses from increased respiration (DE-Hzd). While DE-Hzd was resilient to drought, the carbon uptake of DE-Tha was significantly reduced by around 50% during dry years compared to wet years. Furthermore, sustainable management maintains carbon sequestration and land-use practices, such as crop selection, significantly impact net ecosystem productivity. These insights are valuable for optimizing land management strategies to enhance carbon sinks in similar regions.
How to cite: Grünwald, T., Mauder, M., Wanner, L., Hehn, M., Moderow, U., Queck, R., Prasse, H., and Bernhofer, C.: Carbon fluxes controlled by land management and disturbances at a cluster of long-term ecosystem monitoring sites in Central Europe, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6957, https://doi.org/10.5194/egusphere-egu25-6957, 2025.
Open-path (OP) infrared gas analyzers (IRGA) are widely used for CO2 eddy covariance flux measurements in diverse ecosystems, including in arid desert environments. These high sensible heat and low CO2 flux conditions can lead to a systematic bias in the estimation of the carbon exchange. Numerous studies using both open- and closed-path IRGAs report large overestimates of CO2 uptake in the OP measurements, which persists for all seasons and is not driven by biological activity, but rather by instrumentation artefacts. Despite the attempts to address these biases, their origin and the appropriate correction approaches remain unresolved. Sensor-path heat exchange has been considered as a potential source of the bias. Consequently, later models OP gas analyzers have eliminated the self-heating effects, yet they still exhibit apparent CO2 uptake. In this study we consider the influence of ambient air temperature on the absorption in the CO2 spectral band typically used in non-dispersive broadband IRGAs as the source of the bias. We show the results from simulations of infrared transmission in the CO2 spectral band using high resolution molecular transmission (HITRAN) database. We evaluated the temperature sensitivity of an IRGA by simulating integrated absorption spectra for a typical interference optical filter with a 100 nm passband where the CO2 density was kept constant, and the gas mixture temperature was varied between 244 and 385 K. The data show that if the absorption is not corrected for temperature of the air in the optical sensing path a bias is introduced. The bias causes underestimation of CO2 density at warmer temperatures and overestimation of CO2 density at low temperatures. We conclude that OP gas analyzer measurements need to be corrected for the effects of changes in air temperature in the sensing path. We demonstrate that the correction is not universal, but rather instrument specific and depends on the actual pass band of the specific interference filter used.
How to cite: Bogoev, I.: Addressing a sensible heat bias in open-path eddy covariance carbon dioxide flux measurements, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4594, https://doi.org/10.5194/egusphere-egu25-4594, 2025.
Understory eddy-covariance measurements provide valuable insights into ecosystem CO2 exchange processes, particularly in understanding the interplay between understory and overstory exchange processes. However, their placement deep within the canopy presents some methodological challenges not typically encountered in standard eddy-covariance measurements above the canopy, where surface layer assumptions are generally applicable.
Key challenges arise from the violation of these surface layer assumptions in common flux correction and quality control procedures. Traditional frequency response corrections for flux calculations often rely on idealized cospectra derived from surface layer theory. These assumptions do not hold within the canopy, where spectra and cospectra exhibit distinct characteristics. Furthermore, commonly used turbulence-based quality control metrics, like the integral turbulence test, rely on surface layer scaling relationships to compare measured and modeled fluxes. The application of these relationships within the canopy is questionable due to the altered turbulence structure. For net ecosystem exchange (NEE) measurements, conventional filtering methods, such as friction velocity (u*) filtering, aim to identify periods when measured fluxes are expected to closely represent the true NEE. However, the low fluxes and turbulence characteristic of the understory environment complicate the reliable application of these filtering approaches.
This study critically examines and revises established correction and quality control procedures specifically for understory eddy-covariance measurements. We investigate the impact of these revised methods on understory CO2 exchange estimates using data from an understory site in Tyrol, Austria (At-Mmg). Our results are further compared with the total net ecosystem exchange estimated by an above-canopy eddy-covariance system over the past three years.
How to cite: Platter, A., Hammerle, A., and Wohlfahrt, G.: Methodological challenges for understory eddy-covariance measurements, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6639, https://doi.org/10.5194/egusphere-egu25-6639, 2025.
As demographic trends continue to point towards urbanisation and urban climate change-related health risks are increasing, a fundamental understanding of the processes that shape the urban boundary layer climate is becoming increasingly important. While previous studies have used mobile measurement devices to measure instantaneous physical weather elements in high spatial resolution in an urban environment, high-resolution measurement data on atmospheric flux densities in cities is scarce.
We present an innovative approach to measure latent and sensible heat fluxes, as well as CO2 fluxes and further flow statistics as TKE in a mid-sized city (75,000 citizens) in Central Europe using a mobile eddy-covariance (EC) system on a cargo bike with first measurements executed during a radiation night and three consecutive heat days in August 2024. Our goal was to gain flux density data for several street transects in a heterogeneous urban environment during the hottest and coldest time periods of the day. To compare the measured temperature and humidity used for the eddy-covariance calculations, we set up eight weather stations mounted on streetlights along our measurement route, at which we stopped for two minutes each. Motion data was observed with an integrated high precision inertial navigation system (INS) to adjust the EC observation for bicycle movements. To ensure nearly steady-state conditions were fulfilled, the perturbation and averaging periods were fitted to calculate flux densities along approximately homogeneous street transects. As the bike velocity of 4 to 6 m s-1 only allows for relatively short averaging periods of up to a minute in the heterogenous environment, only the high-frequency fraction of the turbulence spectrum can be quantified. Assuming a similar distribution of the inertial subrange turbulence across the research area, this choice still allowed for comparison of the fluxes along the route.
With our route traversing a range of land surface conditions from a densely built-up district centre to a floodplain valley adjacent to the city, we were able to determine a strong heterogeneity in the expression of the urban heat and park cool islands within our study area. First results of the EC calculations indicate the capability of our mobile flux system to detect fine differences in flux densities within the heterogeneous urban environment.
Our flux measurements together with the additionally measured weather elements of solar radiation, temperature, humidity, wind direction and wind speed from the eight stationary micro weather stations within the study area provide the foundation for the development of a heat adaption strategy in the city district aiming at creating an environment with diminished health risks and urban heat island effects.
How to cite: Spakowski, L., Resch, S., Olesch, J., and Thomas, C.: Measuring urban surface fluxes using a mobile eddy-covariance system at a fine resolution to develop a heat mitigation strategy in a mid-sized European city , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10940, https://doi.org/10.5194/egusphere-egu25-10940, 2025.
The Eddy Covariance (EC) technique is widely used to quantify carbon dioxide (CO2) fluxes between the atmosphere and terrestrial ecosystems, playing a crucial role in climate research and carbon cycle studies. To maximize the impact and the meaningfulness of these measurements, they have to be comparable in time and space. The reliability and comparability of EC data critically depend on ensuring metrological traceability to SI units through national standards or internationally agreed references by means of rigorous calibration practices.
This study examines the traceability chain for key EC components (air temperature and pressure, wind components and CO2 concentration in air), emphasizing calibration processes for gas analyzers. Gas analyzers, which measure CO2 amount fractions, are calibrated using traceable gas mixtures, such as Certified Reference Materials, linked to primary national standards, ensuring accuracy and minimizing biases. We assess the impact of the calibration uncertainty on overall flux estimates and propose a methodology for periodic recalibration of the analysers to account for their drift and response to environmental influences.
By establishing robust links to national metrology standards, this work enhances the traceability and reliability of EC data across diverse ecosystems and temporal scales. The outcomes provide a foundation for harmonizing EC networks globally, improving confidence in CO2 flux measurements and their role in shaping evidence-based climate policies. This focus on calibration underscores the importance of metrology in advancing the precision and usefulness of environmental measurements.
How to cite: Bottacin, A., Sega, M., Durbiano, F., Rolle, F., and Arriga, N.: Metrological Traceability in Eddy Covariance Measurements of CO2 Flux, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9629, https://doi.org/10.5194/egusphere-egu25-9629, 2025.
In complex and heterogeneous landscapes, determining the spatial origin of measured fluxes is critical for interpreting eddy covariance (EC) data accurately. To address this, footprint models are used to simulate the transport of turbulence and quantify the contribution of different areas within the source region. These models rely on theoretical assumptions, such as homogeneous and stationary atmospheric conditions, which often deviate significantly from real-world conditions particularly in terrains with uneven topography or land cover. This discrepancy may lead to substantial uncertainties, as the models may fail to accurately represent the true flux contributions under these non-ideal conditions.
To evaluate the reliability of the Flux Footprint Prediction (FFP) model (Kljun et al., 2015) and its performance under real-world conditions, we conducted three tracer release campaigns in the upwind region of a tall tower EC greenhouse gas observation system located at Hove (55.7169°N, 12.2375°E), a rural area west of Copenhagen, Denmark. The experiments utilized acetylene (C₂H₂) as the tracer gas, released at a controlled and precisely known emission rate. The FFP model were assessed using data from different averaging intervals, enabling a detailed comparison of temporal resolutions and their impact on flux estimates.
The observed fluxes were systematically compared with the model predictions, allowing us to identify discrepancies and provide critical insights into the strengths and limitations of the FFP model, particularly in rural and heterogeneous landscapes. Moreover, the analysis highlights the influence of averaging intervals on the agreement between measured and modelled fluxes. This work also provides a reference for applying tracer release experiments in heterogeneous terrain using the tall tower EC system, contributing to the understanding of experimental design and model validation in such environments.
How to cite: Wang, Z., Kissas, K., Scheutz, C., and Ibrom, A.: Evaluating a Flux Footprint Model Using Tracer Release Experiments and Tall Tower Eddy Covariance Measurements, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11746, https://doi.org/10.5194/egusphere-egu25-11746, 2025.
The Tibetan Plateau (TP) greatly affects climate and environment systems over Asian through the lower atmospheric mass/energy transfer processes. However, the lower atmospheric processes were not clearly understood due to the limitation of observational data, especially over the TP mountain regions. Observations and model simulations suggested a distinguished land-air transfer and vertical structure over the TP mountain regions, which largely differ from those over plateau flat regions. An inhomogeneous distributions are also found in the land-air exchange processes over the whole TP regions, and a new high-resolution dataset are consequently constructed and developed, under the consideration of different TP climate classification.
How to cite: Zhou, L.: Observational Studies on the Land-air Exchange Processes over the Tibetan Mountain, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1539, https://doi.org/10.5194/egusphere-egu25-1539, 2025.
The Earth's surface forms a dynamic boundary characterized by continuous gas exchanges between the critical zone and the overlying atmosphere. As global concern grows over climate change driven by increasing levels of greenhouse gases – such as carbon dioxide (CO2) and methane (CH4) – abandoned oil, gas, and even groundwater wells can be significant sources of these emissions. Here, we monitor CO2 and oxygen (O2) and quantify the CO2 flux in two different recharge wells – one that extends below the groundwater level (wet well) and one that reaches into the unsaturated zone above the groundwater level (dry well). Novel monitoring systems that measure CO2, O2, temperature, and relative humidity were installed at the top and bottom of each well, enabling high-resolution, continuous data collection at 1-min time intervals. Using atmospheric measurements taken from a nearby meteorological station, we investigate the mechanisms that influence the air transport between the wells and the atmosphere. The high-resolution measurements indicate different air transport mechanisms between the two wells. In the wet well, there was stratification during the summer, with consistently high CO2 values measured at the bottom of the well while low values were measured at the top of the well. In the dry well, two daily outflow cycles were observed, with high CO2 concentrations and fluxes from the well to the atmosphere. These findings highlight the potential contribution of recharge wells to CO₂ emissions and the importance of understanding their transport mechanisms.
How to cite: Lavner, E., Gross, A., and Levintal, E.: High-resolution monitoring of CO2/O2 transport in recharge wells, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5075, https://doi.org/10.5194/egusphere-egu25-5075, 2025.
While covering only about 3% of the global land surface, peatlands store approximately one-third of all terrestrial carbon (C) and 12–21% of global soil organic nitrogen (N). Pristine peatland soils typically function as minor sinks for carbon dioxide (CO2), moderate sources of methane (CH4), and minor to moderate sources of nitrous oxide (N2O). However, over the past century, extensive drainage of peatlands for forestry, particularly in temperate and boreal regions, has substantially altered the dynamics of greenhouse gases (GHG).
The lowering of the groundwater table has a crucial impact on soil GHG exchange with aerobic conditions inhibiting methanogenesis, thereby reducing CH4 flux, while simultaneously increasing N2O flux and accelerating peat decomposition. These changes transform peatlands from carbon sinks to net carbon sources and intensify their N2O emissions. However, actively growing tree stands may partially offset soil carbon losses through sequestration and indirectly modulate CH4 and N2O fluxes by altering soil moisture and microbial activity.
While the net ecosystem exchange of drained peatland forest soils is relatively well studied, there's limited knowledge regarding ecosystem-scale GHG fluxes, especially in the transitional hemiboreal forest zone. In this study, we present the first years of eddy-covariance measurements of CO2, CH4, and N2O fluxes from a drained peatland forest in Eastern Estonia. The site, drained in the early 1970s via an open-ditch network, is dominated by Downy Birch (Betula pubescens, 64%) and Norway Spruce (Picea abies, 36%). The current soil profile, classified as Drainic Eutric Histosol, features a peat layer approximately one meter thick and a moderate C:N ratio (15.1) in the upper soil horizon. Our findings contribute to the growing body of knowledge on peatland forest GHG fluxes, offering valuable data for managing forested peatlands in a changing climate.
How to cite: Krasnova, A., Soosaar, K., and Mander, Ü.: The greenhouse gas exchange of a drained peatland forest: first insights from eddy-covariance measurements of CO2, CH4 and N2O fluxes in Estonia, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13303, https://doi.org/10.5194/egusphere-egu25-13303, 2025.
The potential effects of desert plantations, such as those used for climate change mitigation, during extreme heat waves remain an important and unresolved question. While the influence of large-scale surface heterogeneity, such as land-sea distribution and mountain ranges on weather, is well established and incorporated in operational numerical weather prediction models, the impact of smaller-scale heterogeneities remains uncertain. Specifically, the interplay between the synoptic forcing and the arising effects of mesoscale interactions is not yet fully understood.
The Eastern Mediterranean and the Middle East face intensified heat and drought due to climate change, impacting regional weather and local ecosystems. Semi-arid forests, such as the Yatir pine forest on the edge of the Negev Desert, provide a unique lens through which to study land surface-atmosphere feedback, particularly under extreme heat events.
Ongoing studies show that due to high incoming solar radiation and its low albedo, the Yatir Forest net radiation is higher than in any other eco-regions, balanced by a large sensible heat flux. Thus, the resulting cooler surface suppresses the emission of longwave radiation compared with the surrounding warmer shrubland. The thermal contrast between the forest and the surrounding shrubland can also result in the development of secondary circulations within the PBL. The combined effects of these processes significantly modify the surface-atmosphere energy exchange, can affect the forest microclimate, and, if extended to a larger scale, could potentially impact regional weather and climate.
This research investigates the interactions between the Yatir Forest and the atmosphere under dry heat extremes, focusing on mechanisms driving radiation dynamics, energy fluxes, and local circulations. Our approach combines in-situ measurements from the Yatir Forest, atmospheric reanalysis data, Lagrangian analysis, and high-resolution simulations using the ICON numerical weather prediction model. Through a series of numerical forest configuration experiments incorporating forest-atmosphere feedback, we examine the potential of semi-arid afforestation to influence boundary layer dynamics, exploring the implications for local and potentially regional moderation of extreme climatic events and sustainable land use. We incorporate the concept of the canopy convector effect for semi-arid regions to demonstrate the sensitivity of the numerical results to surface parameters and synoptic conditions causing heat waves.
- Department of Earth and Planetary Sciences, Weizmann Institute of Science, Rehovot, Israel (yotam.menachem@weizmann.ac.il)
How to cite: Menachem, Y., Magaritz-Ronen, L., Rotenberg, E., Hochman, L., Raveh-Rubin, S., and Yakir, D.: Exploring Forest-Atmosphere Interactions Under Heat Extremes in a Semi-Arid Region , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3154, https://doi.org/10.5194/egusphere-egu25-3154, 2025.
