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Wetland methane responses to temperature and precipitation are studied in a boreal wetland-rich region in northern Europe using ecosystem process models. Six ecosystem models (JSBACH-HIMMELI, LPX-Bern, LPJ-GUESS, JULES, CLM4.5, and CLM5) are compared to multi-model means of ecosystem models and atmospheric inversions from the Global Carbon Project and upscaled eddy covariance flux results for their temperature and precipitation responses and seasonal cycles of the regional fluxes. Two models with contrasting response patterns, LPX-Bern and JSBACH-HIMMELI, are used as priors in atmospheric inversions with Carbon Tracker Europe–CH4 (CTE-CH4) in order to find out how the assimilation of atmospheric concentration data changes the flux estimates and how this alters the interpretation of the flux responses to temperature and precipitation. Inversion moves wetland emissions of both models towards co-limitation by temperature and precipitation. Between 2000 and 2018, periods of high temperature and/or high precipitation often resulted in increased emissions. However, the dry summer of 2018 did not result in increased emissions despite the high temperatures. The process models show strong temperature and strong precipitation responses for the region (51 %–91 % of the variance explained by both). The month with the highest emissions varies from May to September among the models. However, multi-model means, inversions, and upscaled eddy covariance flux observations agree on the month of maximum emissions and are co-limited by temperature and precipitation. The setup of different emission components (peatland emissions, mineral land fluxes) has an important role in building up the response patterns. Considering the significant differences among the models, it is essential to pay more attention to the regional representation of wet and dry mineral soils and periodic flooding which contribute to the seasonality and magnitude of methane fluxes. The realistic representation of temperature dependence of the peat soil fluxes is also important. Furthermore, it is important to use process-based descriptions for both mineral and peat soil fluxes to simulate the flux responses to climate drivers. [ABSTRACT FROM AUTHOR]

Accurate representation of the turbulent exchange of carbon, water, and heat between the land surface and the atmosphere is critical for modelling global energy, water, and carbon cycles in both future climate projections and weather forecasts. Evaluation of models' ability to do this is performed in a wide range of simulation environments, often without explicit consideration of the degree of observational constraint or uncertainty and typically without quantification of benchmark performance expectations. We describe a Model Intercomparison Project (MIP) that attempts to resolve these shortcomings, comparing the surface turbulent heat flux predictions of around 20 different land models provided with in situ meteorological forcing evaluated with measured surface fluxes using quality-controlled data from 170 eddy-covariance-based flux tower sites. Predictions from seven out-of-sample empirical models are used to quantify the information available to land models in their forcing data and so the potential for land model performance improvement. Sites with unusual behaviour, complicated processes, poor data quality, or uncommon flux magnitude are more difficult to predict for both mechanistic and empirical models, providing a means of fairer assessment of land model performance. When examining observational uncertainty, model performance does not appear to improve in low-turbulence periods or with energy-balance-corrected flux tower data, and indeed some results raise questions about whether the energy balance correction process itself is appropriate. In all cases the results are broadly consistent, with simple out-of-sample empirical models, including linear regression, comfortably outperforming mechanistic land models. In all but two cases, latent heat flux and net ecosystem exchange of CO2 are better predicted by land models than sensible heat flux, despite it seeming to have fewer physical controlling processes. Land models that are implemented in Earth system models also appear to perform notably better than stand-alone ecosystem (including demographic) models, at least in terms of the fluxes examined here. The approach we outline enables isolation of the locations and conditions under which model developers can know that a land model can improve, allowing information pathways and discrete parameterisations in models to be identified and targeted for future model development. [ABSTRACT FROM AUTHOR]

The recent development of low-cost optical particle counters (OPCs) presents new opportunities for improving spatial coverage of particle concentration in the atmosphere as they are more affordable, compact, and consume less power than traditional OPCs. In particular, these OPCs could improve our ability to quantify dust emissions in complex environments during aeolian soil erosion. The high frequency sampling capacity (1 Hz) of some sensors may make them suitable for estimating dust emissions using the eddy-covariance method. Here, the capability of the low-cost OPC-N3 from Alfasense to estimate the size-resolved dust flux using the eddy-covariance method is evaluated. During the Jordan Wind Erosion and Dust Investigation (J-WADI) experiment, we tested one OPC-N3 against traditional reference OPCs. The OPCs were located in close proximity to a sonic anemometer, enabling the correlation of dust concentration and vertical velocity fluctuations for estimating dust fluxes. Despite the high temperature and dusty wind conditions of the campaign, the N3 monitored the dynamics and magnitude of dust concentration with reasonable precision. The turbulence characteristics of the dust concentration fluctuations measured by the N3 were similar to those from the reference OPC. After calibrating the N3 dust concentration, the N3 accurately estimated the dust emission flux, with differences of less than 30 % compared to the reference OPC. Our results confirm the potential of low-cost OPCs for dust-erosion research. Nonetheless, further evaluation of low-cost OPCs is still needed across different environments and weather conditions. [ABSTRACT FROM AUTHOR]

Greenhouse gas monitoring is important to ensure climate goals are being achieved. This study unveils the potential of using atmospheric tall towers in direct flux measurements, bridging the gap between atmospheric and ecosystem monitoring networks. The ICOS Cities (PAUL) project aims to monitor CO2 emissions in urban areas, where concentrated emissions make them key targets for climate change mitigation. This study explores the synergy between ICOS atmospheric and ecosystem networks by utilizing slow-response analysers (∼ 3 s) on tall atmospheric towers for ecosystem studies using the eddy covariance method. A standard setup with an ultrasonic anemometer and an infrared (IR) fast-response CO2 analyser was installed and compared with measurements from an existing cavity ring-down spectroscopy (CRDS) analyser measuring CO2, CO, and CH4. Deployed on the 100 m Saclay tower near Paris, covering a 43.9 km2 80 % footprint with heavy traffic roads, a nearby heating plant, and a forest, the setup addressed technical challenges and height-induced complexities. Corrections for flux attenuation by high-frequency losses were limited to < 20 % on average for all stabilities and around 11 % for unstable conditions. Elevated mean fluxes for CO2 (10 µmolm-2s-1) and CH4 (200 µmolm-2s-1) were observed from the heating plant wind direction during December and January. Conversely, the forest direction exhibited the strongest sink among all wind directions, with - 4 µmolm-2s-1 during July and August. Storage and vertical advection were estimated using the routine three-level profile measurements done in ICOS atmospheric towers. Storage term was of the same magnitude as turbulent flux, increasing at night and de-stocking during the first half of the day. Vertical advection averaged zero on a monthly basis. These results demonstrate the feasibility and versatility of utilizing atmospheric towers for urban emission monitoring, offering valuable insights for emission monitoring strategies worldwide. [ABSTRACT FROM AUTHOR]

The eddy covariance (EC) technique is currently the most widely used method for measuring carbon exchange between terrestrial ecosystems and the atmosphere at the ecosystem scale. Using this technique, a regional carbon flux network comprising a total of 34 sites has been established in the Heihe River Basin (HRB) in Northwest China. This network has been measuring the net ecosystem exchange (NEE) of CO2 for a variety of vegetation types. In this study, we compiled and post-processed half-hourly flux data from these 34 EC flux sites in the HRB to create a continuous, homogenized time series dataset. We employed standardized processing procedures to fill data gaps in meteorological and NEE measurements at half-hourly intervals. NEE measurements were also partitioned into gross primary production (GPP) and ecosystem respiration (Reco). Furthermore, half-hourly meteorological and NEE data were aggregated to daily, weekly, monthly, and yearly timescales. As a result, we produced a continuous carbon flux and auxiliary meteorological dataset, which includes 18 sites with continuous multi-year observations and 16 sites observed only during the 2012 growing season, amounting to a total of 1,513 site-months. Using the post-processed dataset, we explored the temporal and spatial characteristics of carbon exchange in the HRB. In the diurnal variation curve, GPP, NEE, and Reco peak later for ecosystems in the artificial oasis (cropland and wetland) compared to those outside the artificial oasis (grassland, forest, woodland, and Gobi/desert). Seasonal NEE, GPP, and Reco peak in early July for grassland, forest, woodland, and cropland but remain close to zero throughout the year for gobi/desert. In the last decade, NEE of wetlands significantly increased, while NEE for other ecosystems did not exhibit significant trends. Annual NEE, GPP, and Reco are significantly higher for sites inside the artificial/natural oasis compared to those outside the oasis. This post-processed carbon flux dataset has numerous applications, including exploring the carbon exchange characteristics of alpine and arid ecosystems, analyzing ecosystem responses to climate extremes, conducting cross-site synthesis from regional to global scales, supporting regional and global upscaling studies, interpreting and calibrating remote sensing products, and evaluating and calibrating carbon cycle models. [ABSTRACT FROM AUTHOR]

The air–sea exchange of ozone (O 3) is controlled by chemistry involving halogens, dissolved organic carbon, and sulfur in the sea surface microlayer. Calculations also indicate faster ozone photolysis at aqueous surfaces, but the role of clouds as an ozone sink is currently not well established. Fast-response ozone sensors offer opportunities to measure eddy covariance (EC) ozone fluxes in the marine boundary layer. However, intercomparisons of fast airborne O 3 sensors and EC O 3 fluxes measured on aircraft have not been conducted before. In April 2022, the Technological Innovation Into Iodine and GV Environmental Research (TI 3 GER) field campaign deployed three fast ozone sensors (gas chemiluminescence and a combination of UV absorption with coumarin chemiluminescence detection, CID) together with a fast water vapor sensor and anemometer to study iodine chemistry in the troposphere and stratosphere over Colorado and over the Pacific Ocean near Hawaii and Alaska. Here, we present an instrument comparison between the NCAR Fast O 3 instrument (FO3, gas-phase CID) and two KIT Fast AIRborne Ozone instruments (FAIRO, UV absorption and coumarin CID). The sensors have comparable precision < 0.4 % Hz -0.5 (0.15 ppbv Hz -0.5), and ozone volume mixing ratios (VMRs) generally agreed within 2 % over a wide range of environmental conditions: 10 < O 3 < 1000 ppbv, below detection < NO x < 7 ppbv, and 2 ppmv < H 2 O < 4 % VMR. Both instrument designs are demonstrated to be suitable for EC flux measurements and were able to detect O 3 fluxes with exchange velocities (defined as positive for upward) as slow as - 0.010 ± 0.004 cm s -1 , which is in the lower range of previously reported measurements. Additionally, we present two case studies. In one, the direction of ozone and water vapor fluxes was reversed (vO3 = + 0.134 ± 0.005 cm s -1), suggesting that overhead evaporating clouds could be a strong ozone sink. Further work is needed to better understand the role of clouds as a possibly widespread sink of ozone in the remote marine boundary layer. In the second case study, vO3 values are negative (varying by a factor of 6–10 from - 0.036 ± 0.006 to - 0.003 ± 0.004 cm s -1), while the water vapor fluxes are consistently positive due to evaporation from the ocean surface and spatially homogeneous. This case study demonstrates that the processes governing ozone and water vapor fluxes can become decoupled and illustrates the need to elucidate possible drivers (physical, chemical, or biological) of the variability in ozone exchange velocities on fine spatial scales (∼ 20 km) over remote oceans. [ABSTRACT FROM AUTHOR]

Direct measurement of carbon and water fluxes at high frequencies make eddy covariance (EC) the technique most preferred to characterize water use efficiency (WUE). However, reliability of EC fluxes largely hinges on the energy balance ratio (EBR) and inclusion of low-frequency fluxes. This study is aimed at investigating the role of the averaging period in representing EC fluxes and its propagation into WUE dynamics. Carbon and water fluxes were monitored in a drip-irrigated maize field at 10 Hz frequency and were averaged over 1, 5, 10, 15, 30, 45, 60, and 120 min, considering daytime unstable conditions. The optimal averaging period to simulate WUE fluxes for each growth stage is obtained by considering cumulative frequency (Ogive) curves. A clear departure of EBR from unity was observed during the dough and maturity stages of the crop due to ignorance of canopy heat storage, low-frequency flux losses, and an inadequate averaging period. Deviations in representing water (carbon) fluxes relative to the conventional 30 min average are within ±3 % (±10 %) for 10–120 min averaging and beyond ±3 % (±10 %) for other time averages. Ogive plots show that the optimal averaging period to represent carbon, water, and WUE fluxes is 15–30 min for the sixth leaf and silking stages and is 45–60 min for the dough and maturity stages. Dynamics of WUE considering optimal averaging periods are in the range of μ ± σ : 1.49 ± 0.95, 1.37 ± 0.74, 1.39 ± 0.79, and 3.06 ± 0.69 µmolmmol-1 for the sixth leaf, silking, dough, and maturity stages, respectively. The error in representing WUE by conventional 30 min averaging is marginal (< 1.5 %) throughout the crop period except for the dough stage (12.12 %). We conclude that the conventional 30 min averaging of EC fluxes is not appropriate for representing WUE throughout the crop period. Our findings can help to develop efficient water management strategies by accurately characterizing WUE fluxes from the EC measurements. [ABSTRACT FROM AUTHOR]

CO 2 storage (Fs) is the cumulation or depletion in CO 2 amount over a period in an ecosystem. Along with the eddy covariance flux and wind-stream advection of CO 2 , it is a major term in the net ecosystem CO 2 exchange (NEE) equation. The CO 2 storage dominates the NEE equation under a stable atmospheric stratification when the equation is used for forest ecosystems over complex terrains. However, estimating Fs remains challenging due to the frequent gusts and random fluctuations in boundary-layer flows that lead to tremendous difficulties in capturing the true trend of CO 2 changes for use in storage estimation from eddy covariance along with atmospheric profile techniques. Using measurements from Qingyuan Ker Towers equipped with NEE instrument systems separately covering mixed broad-leaved, oak, and larch forest towers in a mountain watershed, this study investigates gust periods and CO 2 fluctuation magnitudes and examines their impact on Fs estimation in relation to the terrain complexity index (TCI). The gusts induce CO 2 fluctuations for numerous periods of 1 to 10 min over 2 h. Diurnal, seasonal, and spatial differences (P < 0.01) in the maximum amplitude of CO 2 fluctuations (Am) range from 1.6 to 136.7 ppm, and these differences range from 140 to 170 s in a period (Pm) at the same significance level. Am and Pm are significantly correlated to the magnitude of and random error in Fs with diurnal and seasonal differences. These correlations decrease as CO 2 averaging time windows become longer. To minimize the uncertainties in Fs , a constant [CO 2 ] averaging time window for the Fs estimates is not ideal. Dynamic averaging time windows and a decision-level fusion model can reduce the potential underestimation of Fs by 29 %–33 % for temperate forests in complex terrain. In our study, the relative contribution of Fs to the 30 min NEE observations ranged from 17 % to 82 % depending on turbulent mixing and the TCI. The study's approach is notable as it incorporates the TCI and utilizes three flux towers for replication, making the findings relevant to similar regions with a single tower. [ABSTRACT FROM AUTHOR]

Conventional and recently developed approaches for estimating turbulent scalar fluxes under stable atmospheric conditions are evaluated, with a focus on gases for which fast sensors are not readily available. First, the relaxed eddy accumulation (REA) classical approach and a recently proposed mixing length parameterization, labeled A22, are tested against eddy-covariance computations. Using high-frequency measurements collected from two contrasting sites (the frozen tundra near Utqiaġvik, Alaska, and a sparsely vegetated grassland in Wendell, Idaho, during winter), it is shown that the REA and A22 models outperform the conventional Monin–Obukhov similarity theory (MOST) utilized widely to infer fluxes from mean gradients. Second, scenarios where slow trace gas sensors are the only viable option in field measurements are investigated using digital filtering applied to fast-response sensors to simulate their slow-response counterparts. With a filtered scalar signal, the observed filtered eddy-covariance fluxes are referred to here as large-eddy-covariance (LEC) fluxes. A virtual eddy accumulation (VEA) approach, akin to the REA model but not requiring a mechanical apparatus to separate the gas flows, is also formulated and tested. A22 outperforms VEA and LEC in predicting the observed unfiltered (total) eddy-covariance (EC) fluxes; however, VEA can still capture the LEC fluxes well. This finding motivates the introduction of a sensor response time correction into the VEA formulation to offset the effect of sensor filtering on the underestimated net averaged fluxes. The only needed parameter for this correction is the mean velocity at the instrument height, a surrogate of the advective timescale. The VEA approach is very suitable and simple to use with gas sensors of intermediate speed (∼ 0.5 to 1 Hz) and with conventional open- or closed-path setups. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) plays a crucial role in the regional water-energy cycle, illustrating intricate interactions among climate, vegetation and soil. Eddy covariance (EC) technology is a primary method for measuring ET. However, incomplete data due to adverse weather conditions and equipment malfunctions are common. This study utilizes the full-factorial method to address the ET gaps at 339 sites across multiple global flux networks, and the filled ET data are compared with three ET products: the Land component of the Fifth Generation of European Reanalysis (EAR5-Land), Global Land Evaporation Amsterdam Model (GLEAM), and Breathing Earth System Simulator (BESS). Results indicate high consistency between filled ET data and three ET products at 264 out of 339 sites. The absolute average mean error (|MAE|) and root mean system error (|RMSE|) are 0.32 mm/d and 0.92 mm/d, respectively. Among the remaining 75 sites, 49 sites exhibit better agreement between filled ET and measured ET data than ET products in terms of seasonal variations and numerical ranges. Further verification is needed for the reliability of filled ET data at the other 26 sites due to a scarcity of measured ET data. Overall, the gap-filled ET data from 313 sites (2210 site-years) demonstrate high-quality. These sites exhibit a strong correlation between available energy and turbulent fluxes, with R2, MAE, and RMSE for different surface types ranging from 0.84 to 0.94, 21.49 to 28.67 W/m2, and 28.37 to 36.91 W/m2, respectively. And the average energy balance closure rate is 0.73, indicating a relatively high degree of closure in the energy balance. These 313 sites with high-quality filled ET data can be utilized for ET model validation, ET product verification, water demand assessment, and other related tasks. The filled ET dataset can be publicly accessed at https://doi.org/10.57760/sciencedb.11651 (Wang & Jiang, 2024). [ABSTRACT FROM AUTHOR]

The dissipation rates of the basic second-order moments are the key parameters playing a vital role in turbulence modelling and controlling turbulence energetics and spectra and turbulent fluxes of momentum and heat. In this paper, we use the results of direct numerical simulations (DNSs) to evaluate dissipation rates of the basic second-order moments and revise the energy and flux budget (EFB) turbulence closure theory for stably stratified turbulence. We delve into the theoretical implications of this approach and substantiate our closure hypotheses through DNS data. We also show why the concept of down-gradient turbulent transport becomes incomplete when applied to the vertical turbulent flux of potential temperature under stable stratification. We reveal essential feedback between the turbulent kinetic energy (TKE), the vertical turbulent flux of buoyancy, and the turbulent potential energy (TPE), which is responsible for maintaining shear-produced stably stratified turbulence for any Richardson number. [ABSTRACT FROM AUTHOR]

Earth system models (ESMs) typically represent surface heterogeneity on scales smaller than the atmospheric grid, while land–atmosphere coupling is based on grid mean values. Here we present a general approach allowing subgrid surface heterogeneity to influence the updraft thermodynamic properties in a multi-plume mass flux parameterization. The approach is demonstrated in single column experiments with an eddy diffusivity–mass flux (EDMF) boundary layer scheme. Instead of triggering based on grid mean surface values, updrafts are explicitly assigned to individual surface tiles with positive buoyancy flux. Joint distributions of near-surface vertical velocities and thermodynamic variables are defined over individual surface tiles, and updraft properties are drawn from the positive tails of the distributions. The approach allows updraft properties to covary with surface heterogeneity, and updrafts from different tiles maintain distinct properties to heights of several hundred meters. Mass flux contributions to subgrid variances are increased near the surface, but impacts on mean state variables are relatively small. We suggest that larger impacts might be obtained by adding a specialized plume to represent the effects of secondary circulations. [ABSTRACT FROM AUTHOR]

The anthropogenic and biogenic contributions of isoprene, monoterpenes, sesquiterpenes and methanol in an urban area were estimated based on direct eddy covariance flux observations during four campaigns between 2018 and 2021. While these compounds are typically thought to be dominated by biogenic sources on regional and global scales, the role of potentially significant anthropogenic emissions in urban areas has been recently debated. Typical fluxes of isoprene, monoterpenes and sesquiterpenes were on the order of 0.07 ± 0.02, 0.09 and 0.003 nmol m -2 s -1 during spring. During summer, emission fluxes of isoprene, monoterpenes and sesquiterpenes were higher on the order of 0.85 ± 0.09, 0.11 and 0.004 nmol m -2 s -1. It was found that the contribution of the anthropogenic part is strongly seasonally dependent. For isoprene, the anthropogenic fraction can be as high as 64 % in spring but is typically very low < 18 % during the summer season. For monoterpenes, the anthropogenic fraction was estimated to be between 43 % in spring and less than 20 % in summer. With values of 2.8 nmol m -2 s -1 in spring and 3.2 nmol m -2 s -1 in summer, methanol did not exhibit a significant seasonal variation of observed surface fluxes. However, there was a difference in emissions between weekdays and weekends (about 2.3 times higher on weekdays in spring). This suggests that methanol emissions are likely influenced by anthropogenic activities during all seasons. [ABSTRACT FROM AUTHOR]

The climate of the Tibetan Plateau (TP) has experienced substantial changes in recent decades as a result of the location's susceptibility to global climate change. The changes observed across the TP are closely associated with regional land–atmosphere interactions. Current models and satellites struggle to accurately depict the interactions; therefore, critical field observations on land–atmosphere interactions outlined here provide necessary independent validation data and fine-scale process insights for constraining reanalysis products, remote sensing retrievals, and land surface model parameterizations. Scientific data sharing is crucial for the TP since in situ observations are rarely available under these harsh conditions. However, field observations are currently dispersed among individuals or groups and have not yet been integrated for comprehensive analysis. This has prevented a better understanding of the interactions, the unprecedented changes they generate, and the substantial ecological and environmental consequences they bring about. In this study, we collaborated with different agencies and organizations to present a comprehensive dataset for hourly measurements of surface energy balance components, soil hydrothermal properties, and near-surface micrometeorological conditions spanning up to 17 years (2005–2021). This dataset, derived from 12 field stations covering a variety of typical TP landscapes, provides the most extensive in situ observation data available for studying land–atmosphere interactions on the TP to date in terms of both spatial coverage and duration. Three categories of observations are provided in this dataset: meteorological gradient data (met), soil hydrothermal data (soil), and turbulent flux data (flux). To assure data quality, a set of rigorous data-processing and quality control procedures are implemented for all observation elements (e.g., wind speed and direction at different height) in this dataset. The operational workflow and procedures are individually tailored to the varied types of elements at each station, including automated error screening, manual inspection, diagnostic checking, adjustments, and quality flagging. The hourly raw data series; the quality-assured data; and supplementary information, including data integrity and the percentage of correct data on a monthly scale, are provided via the National Tibetan Plateau Data Center (10.11888/Atmos.tpdc.300977, Ma et al., 2023a). With the greatest number of stations covered, the fullest collection of meteorological elements, and the longest duration of observations and recordings to date, this dataset is the most extensive hourly land–atmosphere interaction observation dataset for the TP. It will serve as the benchmark for evaluating and refining land surface models, reanalysis products, and remote sensing retrievals, as well as for characterizing fine-scale land–atmosphere interaction processes of the TP and underlying influence mechanisms. [ABSTRACT FROM AUTHOR]

Estimating gross primary productivity (GPP) over space and time is fundamental for understanding the response of the terrestrial biosphere to climate change. Eddy covariance flux towers provide in situ estimates of GPP at the ecosystem scale, but their sparse geographical distribution limits larger-scale inference. Machine learning (ML) techniques have been used to address this problem by extrapolating local GPP measurements over space using satellite remote sensing data. However, the accuracy of the regression model can be affected by uncertainties introduced by model selection, parameterization, and choice of explanatory features, among others. Recent advances in automated ML (AutoML) provide a novel automated way to select and synthesize different ML models. In this work, we explore the potential of AutoML by training three major AutoML frameworks on eddy covariance measurements of GPP at 243 globally distributed sites. We compared their ability to predict GPP and its spatial and temporal variability based on different sets of remote sensing explanatory variables. Explanatory variables from only Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data and photosynthetically active radiation explained over 70 % of the monthly variability in GPP, while satellite-derived proxies for canopy structure, photosynthetic activity, environmental stressors, and meteorological variables from reanalysis (ERA5-Land) further improved the frameworks' predictive ability. We found that the AutoML framework Auto-sklearn consistently outperformed other AutoML frameworks as well as a classical random forest regressor in predicting GPP but with small performance differences, reaching an r2 of up to 0.75. We deployed the best-performing framework to generate global wall-to-wall maps highlighting GPP patterns in good agreement with satellite-derived reference data. This research benchmarks the application of AutoML in GPP estimation and assesses its potential and limitations in quantifying global photosynthetic activity. [ABSTRACT FROM AUTHOR]

It is known from arid and semi-arid ecosystems that atmospheric water vapor can directly be adsorbed by the soil matrix. Soil water vapor adsorption was typically neglected and only recently received attention because of improvements in measurement techniques. One technique rarely explored for the measurement of soil water vapor adsorption is eddy covariance (EC). Soil water vapor adsorption may be detectable as downwardly directed (i.e., negative) EC latent heat (λE) flux measurements under dry conditions, but a systematic assessment of the use of negative λE fluxes from EC flux stations to characterize adsorption is missing. We propose a classification method to characterize soil water vapor adsorption, excluding conditions of dew and fog when λE derived from EC is not trustworthy due to stable atmospheric conditions. We compare downwardly directed λE fluxes from EC with measurements from weighing lysimeters for 4 years in a Mediterranean savanna ecosystem and 3 years in a temperate agricultural site. Our aim is to assess if overnight water inputs from soil water vapor adsorption differ between ecosystems and how well they are detectable by EC. At the Mediterranean site, the lysimeters measured soil water vapor adsorption each summer, whereas at the temperate site, soil water vapor adsorption was much rarer and was measured predominantly under an extreme drought event in 2018. During 30 % of nights in the 4-year measurement period at the Mediterranean site, the EC technique detected downwardly directed λE fluxes of which 88.8 % were confirmed to be soil water vapor adsorption by at least one lysimeter. At the temperate site, downwardly directed λE fluxes were only recorded during 15 % of the nights, with only 36.8 % of half hours matching simultaneous lysimeter measurement of soil water vapor adsorption. This relationship slightly improved to 61 % under bare-soil conditions and extreme droughts. This underlines that soil water vapor adsorption is likely a much more relevant process in arid ecosystems compared to temperate ones and that the EC method was able to capture this difference. The comparisons of the amounts of soil water vapor adsorption between the two methods revealed a substantial underestimation of the EC compared to the lysimeters. This underestimation was, however, comparable with the underestimation in evaporation by the eddy covariance and improved in conditions of higher turbulence. Based on a random-forest-based feature selection, we found the mismatch between the methods being dominantly related to the site's inherent variability in soil conditions, namely soil water status, and soil (surface) temperature. We further demonstrate that although the water flux is very small with mean values of 0.04 or 0.06 mm per night for EC or lysimeter, respectively, it can be a substantial fraction of the diel soil water balance under dry conditions. Although the two instruments substantially differ with regard to the measured ratio of adsorption to evaporation over 24 h with 64 % and 25 % for the lysimeter and EC methods, they are in either case substantial. Given the usefulness of EC for detecting soil water vapor adsorption as demonstrated here, there is potential for investigating adsorption in more climate regions thanks to the greater abundance of EC measurements compared to lysimeter observations. [ABSTRACT FROM AUTHOR]

Land surface models (LSMs) should have reliable forcing, validation, and surface attribute data as the foundation for effective model development and improvement. Eddy covariance flux tower data are considered the benchmarking data for LSMs. However, currently available flux tower datasets often require multiple aspects of processing to ensure data quality before application to LSMs. More importantly, these datasets lack site-observed attribute data, limiting their use as benchmarking data. Here, we conducted a comprehensive quality screening of the existing reprocessed flux tower dataset, including the proportion of gap-filled data, external disturbances, and energy balance closure (EBC), leading to 90 high-quality sites. For these sites, we collected vegetation, soil, topography information, and wind speed measurement height from literature, regional networks, and Biological, Ancillary, Disturbance, and Metadata (BADM) files. Then we obtained the final flux tower attribute dataset by global data product complement and plant functional types (PFTs) classification. This dataset is provided in NetCDF format complete with necessary descriptions and reference sources. Model simulations revealed substantial disparities in output between the attribute data observed at the site and the defaults of the model, underscoring the critical role of site-observed attribute data and increasing the emphasis on flux tower attribute data in the LSM community. The dataset addresses the lack of site attribute data to some extent, reduces uncertainty in LSMs data source, and aids in diagnosing parameter as well as process deficiencies. The dataset is available at https://doi.org/10.5281/zenodo.10939725 (Shi et al., 2024). [ABSTRACT FROM AUTHOR]

The complexity of aerosol particle properties and the diversity of characterizations make aerosol vertical transport flux measurements and analysis difficult. Although there are different methods, such as aerosol particle number concentration flux and aerosol mass flux based on the eddy covariance principle as well as aerosol mass flux measurements based on the light-propagated large-aperture scintillation principle, there is a lack of mutual validation among the different methods. In this paper, a comparison of aerosol mass flux measurements based on the eddy covariance principle and aerosol mass flux measurements based on the light-propagated large-aperture scintillation principle is carried out. The key idea of aerosol mass flux measurements based on the light-propagated large-aperture scintillation principle is the determination of the imaginary part of the atmospheric equivalent refractive index structure parameter (AERISP). In this paper, we first compare the AERISPs measured by two different methods and then compare the aerosol mass vertical transport fluxes obtained by different methods. The experiments were conducted on the campus of the University of Science and Technology of China (USTC). A light propagation experiment was carried out between two tall buildings to obtain the imaginary and real parts of the AERISPs for the whole path, which in turn can be used to obtain the aerosol vertical transport flux. An updated visibility meter is installed on the meteorological tower in the middle of the light path, which is utilized to obtain the single-point visibility, which is then converted to the imaginary part of the atmospheric equivalent refractive index (AERI). The imaginary parts of the AERISP were obtained via spectral analysis with visibility data. The results show that the imaginary parts of the AERISPs obtained by different methods are in good agreement. The imaginary part of the AERI measured by the visibility meter and the vertical wind speed obtained by the ultrasonic anemometer were used for covariance calculations to obtain the aerosol vertical transport flux. The trends in aerosol vertical transport fluxes obtained by the different methods are consistent, and there are differences in some details, which may be caused by the inhomogeneity in the vertical transport of aerosol fluxes. The experimental results also showed that urban green land is a sink area for aerosol particles. [ABSTRACT FROM AUTHOR]

We estimate the surface energy balance (SEB) of the Murtèl rock glacier, a seasonally snow-covered permafrost landform with a ventilated coarse blocky active layer (AL) located in the eastern Swiss Alps. We focus on the parameterisation of the turbulent heat fluxes. Seasonally contrasting atmospheric conditions occur in the Murtèl cirque, with downslope katabatic jets in winter and a strongly unstable atmosphere over the heated blocky surface in summer. We use a novel comprehensive sensor array both above the ground surface and in the coarse blocky AL to track the rapid coupling by convective heat and moisture fluxes between the atmosphere, the snow cover, and the AL for the time period September 2020–September 2022. The in situ sensor array includes a sonic anemometer for eddy-covariance flux above-ground and sub-surface long-wave radiation measurements in a natural cavity between the AL blocks. During the thaw seasons, the measurements suggest an efficient (∼ 90 %) export of the available net radiation by sensible and latent turbulent fluxes, thereby strongly limiting the heat available for melting ground ice. Turbulent export of heat and moisture drawn from the permeable AL contributes to the well-known insulating effect of the coarse blocky AL and partly explains the climate resiliency of rock glaciers. This self-cooling capacity is counteracted by an early snow melt-out date, exposing the low-albedo blocky surface to the intense June–July insolation and causing reduced evaporative cooling due to exacerbated moisture scarcity in the near-surface AL during dry spells. With climate change, earlier snowmelt and increased frequency, duration, and intensity of heat waves and droughts are projected. Regarding the parameterisation of the turbulent fluxes, we estimated the year-round turbulent fluxes using a modified scheme. The monthly SEB is closed within 20 Wm-2 except during the snowmelt months and under katabatic drainage winds in winter. Detected sensible turbulent fluxes from nocturnal ventilation processes, although a potentially important ground cooling mechanism, are within our 20 Wm-2 uncertainty because nighttime wind speeds are low. Wintertime katabatic wind speeds needed to be scaled to close the SEB, which hints at the limits of parameterisations based on the Monin–Obukhov similarity theory in complex mountain terrain and katabatic drainage winds. The present work contributes to the process understanding of the SEB and climate sensitivity of coarse blocky landforms. [ABSTRACT FROM AUTHOR]

In recent years, urban canopy models (UCMs) have been used as fully coupled components of mesoscale atmospheric models as well as offline tools to estimate temperature and surface fluxes using atmospheric forcings. Examples include multi-layer urban canopy models (MLUCMs), where the vertical variability of turbulent fluxes is calculated by solving prognostic momentum and turbulent kinetic energy (TKE, k) using mixing length scale (l) and drag parameterizations. These parameterizations are based on the well-established 1.5-order k-l turbulence closure theory and are often informed by microscale fluid dynamics simulations. However, this approach can include simplifications such as assuming the same diffusion coefficient for momentum, TKE, and scalars. In addition, the dispersive stresses arising from spatially averaged flow properties have been parameterized together with the turbulent fluxes despite being controlled by different mechanisms. Both of these assumptions impact the quantification of the turbulent exchange of flow properties and subsequent air temperature predictions in urban canopies. To assess these assumptions and improve corresponding parameterization, we analyzed 49 large-eddy simulations (LES) for idealized urban arrays, encompassing variable building height distributions and a comprehensive range of urban densities (λp∈[0.0625,0.64]) seen in global cities. We find that the efficiency of turbulent transport (numerically described via diffusion coefficients) is similar for scalars and momentum but is 3.5 times higher for TKE. Additionally, parameterizing the dispersive momentum flux using the k-l closure was a source of error, while scaling with the pressure gradient and urban morphological parameters appears more appropriate. In response to these findings, we propose two changes to the previous version of MLUCM: (a) separate characterization for turbulent diffusion coefficient for momentum and TKE and (b) introduction of an explicit physics-based "mass-flux" term to represent the fraction of the dispersive momentum transport directly induced from buildings as an amendment to the existing "eddy-diffusivity" framework. The updated one-dimensional model, after being tuned for building height variability, is further compared against the original LES results and demonstrates improved performance in predicting vertical turbulent exchange in urban canopies. [ABSTRACT FROM AUTHOR]

Due to large losses of seagrass meadows worldwide, restoration is proposed as a key strategy for increasing coastal resilience and recovery. The emergence of a seagrass meadow is expected to substantially amplify biodiversity and enhance benthic metabolism by increasing primary productivity and respiration. Nevertheless, open questions remain regarding the metabolic balance of aging seagrass meadows and the roles benthic communities within the seagrass ecosystem play in overall metabolism. To address these questions, we investigated a chronosequence of bare sediments and adjacent Zostera marina meadows of 3 and 7 years since restoration alongside a natural meadow located within a high-temperate marine embayment in Gåsö, Sweden. We combined continuous measurements of O2 fluxes using underwater eddy covariance with dissolved inorganic carbon (DIC) and O2 fluxes from benthic chambers during the productive season (July). Based on the ratio between O2 and DIC, we derived site-specific photosynthetic and respiratory quotients, enabling the conversion of eddy covariance fluxes to DIC. We assessed benthic diversity parameters as potential drivers of metabolic flux variability. We observed high rates of gross primary productivity (GPP) spanning - 18 to - 82 mmolDICm-2d-1 , which increased progressively with meadow age. Community respiration (CR) mirrored the GPP trend, and all meadows were net heterotrophic (GPP < CR), with net community productivity (NCP) ranging from 16 to 28 mmolDICm-2d-1. While autotrophic biomass did not increase with meadow age, macrophyte diversity did, elucidating potential effects of niche complementarity among macrophytes on community metabolism. These findings provide valuable insights into how community composition and meadow development relate to ecosystem functioning, highlighting potential tradeoffs between carbon uptake and biodiversity. [ABSTRACT FROM AUTHOR]

An extreme warming event near the North Pole, with 2 m temperature rising above 0 °C , was observed in late December 2015. This specific event has been attributed to cyclones and their associated moisture intrusions. However, little is known about the characteristics and drivers of similar events in the historical record. Here, using data from European Centre for Medium-Range Weather Forecasts Reanalysis, version 5 (ERA5), we study these winter extreme warming events with 2 m temperature over a grid point above 0 °C over the high Arctic (poleward of 80° N) that occurred during 1980–2021. In ERA5, such wintertime extreme warming events can only be found over the Atlantic sector. They occur rarely over many grid points, with a total absence during some winters. Furthermore, even when occurring, they tend to be short-lived, with the majority of the events lasting for less than a day. By examining their surface energy budget, we found that these events transition with increasing latitude from a regime dominated by turbulent heat flux into the one dominated by downward longwave radiation. Positive sea level pressure anomalies which resemble blocking over northern Eurasia are identified as a key ingredient in driving these events, as they can effectively deflect the eastward propagating cyclones poleward, leading to intense moisture and heat intrusions into the high Arctic. Using an atmospheric river (AR) detection algorithm, the roles of ARs in contributing to the occurrence of these extreme warming events defined at the grid-point scale are explicitly quantified. The importance of ARs in inducing these events increases with latitude. Poleward of about 83° N, 100 % of these events occurred under AR conditions, corroborating that ARs were essential in contributing to the occurrence of these events. Over the past 4 decades, both the frequency, duration, and magnitude of these events have been increasing significantly. As the Arctic continues to warm, these events are likely to increase in both frequency, duration, and magnitude, with great implications for the local sea ice, hydrological cycle, and ecosystem. [ABSTRACT FROM AUTHOR]

The Amazon basin rainforest is a critical component of the climate system, currently representing 25 % of terrestrial carbon gains and storing 150 to 200 billion tonnes of carbon. If and by which extent the Amazon rainforest will remain a net carbon sink is an open scientific question, motivated by the unexplained diversity across Earth System Model (ESM) results. Specifically, divergent responses are observed in Amazon vegetation productivity projections, especially under sustained global warming scenarios. We explore this inter-model diversity in projected Amazon vegetation in CMIP6 historical and ssp585 scenario simulations with thirteen ESM by explicitly accounting for the relative contributions of changes in the El Niño-Southern Oscillation (ENSO) and local mean-state climate changes. Our results demonstrate the dominant role of local mean-state climatic changes in shaping the response of the Amazon carbon cycle for 7 out of 13 ESM, with only a minor role for changes in ENSO and its teleconnection despite the strong inter-model diversity in representing ENSO. While temperature and water availability influence displays a high inter-model agreement, the most critical local processes determining uncertainty and divergence across ESM responses within the Amazon basin are the surface energy balance components, in particular shortwave incoming radiation and latent heat fluxes. We identify the main sources of model specificities in land scheme parameterizations, especially the incorporation of Phosphorous limitation, which leads to a stronger reduction of vegetation productivity under strong warming scenarios. We therefore advocate for increased focus from modelling groups towards a more accurate and consistent representation of surface radiative and turbulent fluxes in the Amazon region. Additionally, we hypothesize that a uniform incorporation of Phosphorous limitation across all the ESM may contribute to minimize the uncertainties. This dual approach can lead to more robust estimates of vegetation productivity within the Amazon basin across different climate change scenarios. [ABSTRACT FROM AUTHOR]

Diagnostic terrestrial biosphere models (TBMs) forced by remote sensing observations have been a principal tool for providing benchmarks on global gross primary productivity (GPP) and evapotranspiration (ET). However, these models often estimate GPP and ET at coarse daily or monthly steps, hindering analysis of ecosystem dynamics at the diurnal (hourly) scales, and prescribe some essential parameters (i.e., the Ball–Berry slope (m) and the maximum carboxylation rate at 25 °C (Vcmax25)) as constant, inducing uncertainties in the estimates of GPP and ET. In this study, we present hourly estimations of global GPP and ET datasets at a 0.25° resolution from 2001 to 2020 simulated with a widely used diagnostic TBM – the Biosphere–atmosphere Exchange Process Simulator (BEPS). We employed eddy covariance observations and machine learning approaches to derive and upscale the seasonally varied m and Vcmax25 for carbon and water fluxes. The estimated hourly GPP and ET are validated against flux observations, remote sensing, and machine learning-based estimates across multiple spatial and temporal scales. The correlation coefficients (R2) and slopes between hourly tower-measured and modeled fluxes are R2=0.83 , regression slope =0.92 for GPP, and R2=0.72 , regression slope =1.04 for ET. At the global scale, we estimated a global mean GPP of 137.78±3.22 Pg C yr -1 (mean ± 1 SD) with a positive trend of 0.53 Pg C yr -2 (p<0.001), and an ET of 89.03±0.82×103 km 3 yr -1 with a slight positive trend of 0.10×103 km 3 yr -2 (p<0.001) from 2001 to 2020. The spatial pattern of our estimates agrees well with other products, with R2=0.77 –0.85 and R2=0.74 –0.90 for GPP and ET, respectively. Overall, this new global hourly dataset serves as a "handshake" among process-based models, remote sensing, and the eddy covariance flux network, providing a reliable long-term estimate of global GPP and ET with diurnal patterns and facilitating studies related to ecosystem functional properties, global carbon, and water cycles. The hourly GPP and ET estimates are available at 10.57760/sciencedb.ecodb.00163 (Leng et al., 2023a) and the accumulated daily datasets are available at 10.57760/sciencedb.ecodb.00165 (Leng et al., 2023b). [ABSTRACT FROM AUTHOR]

Landscapes are often assumed to be homogeneous when interpreting eddy covariance fluxes, which can lead to biases when gap-filling and scaling up observations to determine regional carbon budgets. Tundra ecosystems are heterogeneous at multiple scales. Plant functional types, soil moisture, thaw depth, and microtopography, for example, vary across the landscape and influence net ecosystem exchange (NEE) of carbon dioxide (CO2) and methane (CH4) fluxes. With warming temperatures, Arctic ecosystems are changing from a net sink to a net source of carbon to the atmosphere in some locations, but the Arctic's carbon balance remains highly uncertain. In this study we report results from growing season NEE and CH4 fluxes from an eddy covariance tower in the Yukon–Kuskokwim Delta in Alaska. We used footprint models and Bayesian Markov chain Monte Carlo (MCMC) methods to unmix eddy covariance observations into constituent land-cover fluxes based on high-resolution land-cover maps of the region. We compared three types of footprint models and used two land-cover maps with varying complexity to determine the effects of these choices on derived ecosystem fluxes. We used artificially created gaps of withheld observations to compare gap-filling performance using our derived land-cover-specific fluxes and traditional gap-filling methods that assume homogeneous landscapes. We also compared resulting regional carbon budgets when scaling up observations using heterogeneous and homogeneous approaches. Traditional gap-filling methods performed worse at predicting artificially withheld gaps in NEE than those that accounted for heterogeneous landscapes, while there were only slight differences between footprint models and land-cover maps. We identified and quantified hot spots of carbon fluxes in the landscape (e.g., late growing season emissions from wetlands and small ponds). We resolved distinct seasonality in tundra growing season NEE fluxes. Scaling while assuming a homogeneous landscape overestimated the growing season CO2 sink by a factor of 2 and underestimated CH4 emissions by a factor of 2 when compared to scaling with any method that accounts for landscape heterogeneity. We show how Bayesian MCMC, analytical footprint models, and high-resolution land-cover maps can be leveraged to derive detailed land-cover carbon fluxes from eddy covariance time series. These results demonstrate the importance of landscape heterogeneity when scaling carbon emissions across the Arctic. [ABSTRACT FROM AUTHOR]

In this study, we use a data assimilation framework based on the Adaptive Markov Chain Monte Carlo (MCMC) algorithm to constrain process parameters in LPJ-GUESS using CH4 eddy covariance flux observations from 14 different natural boreal and temperate wetlands. The objective is to derive a single set of calibrated parameter values. These parameters are then used in the model to validate its CH4 flux output against 5 different types of natural wetlands situated in different locations, assessing their generality for simulating CH4 fluxes from different boreal and temperate wetlands. The results show that the MCMC framework has substantially reduced the cost function (measuring the misfit between simulated and observed CH4 fluxes) and facilitated detailed characterisation of the posterior distribution. A reduction of around 95 % in the cost function and approximately 50 % in RMSE were observed. The validation experiment results indicate that four out of 5 sites successfully reduced RMSE, demonstrating the effectiveness of the framework for estimating CH4 emissions from wetlands not included in the study. [ABSTRACT FROM AUTHOR]

We present a comprehensive analysis of Arctic surface energy budget (SEB) components during atmospheric river (AR) events identified by integrated water vapor transport exceeding the monthly 85th percentile climatological threshold in 3-hourly ERA5 reanalysis data from January 1980 to December 2015. Analysis of average anomalies in SEB components, net SEB, and the overall AR contribution to the total seasonal SEB reveals clear seasonality and distinct land – sea – sea ice contrast patterns. Over the sea ice-covered central Arctic Ocean, ARs significantly impact net SEB, inducing substantial surface warming in fall, winter, and spring, primarily driven by large anomalies in surface downward longwave radiation. We find that ARs make a substantial relative contribution to the mean SEB in spring (32 %), exceeding their corresponding occurrence frequency (11 %). However, in other seasons, ARs contribute relatively less to the mean SEB than their frequency, indicating a diminished role compared to their occurrence frequency. Over sub-polar oceans, ARs have the most substantial positive impact on net SEB in cold seasons, mainly attributed to significant positive turbulent heat flux anomalies, with a maximum contribution to the mean SEB in spring averaging 65 %. In summer, ARs induce negative impacts on net SEB, primarily due to reduced shortwave radiation from increased cloud cover during AR events. Over continents, ARs generate smaller absolute impacts on net SEB but contribute significantly to the mean SEB in cold seasons, far surpassing their corresponding frequency, highlighting their crucial role in determining the net SEB over continents during cold seasons. Greenland, especially western Greenland, exhibits significant downward longwave radiation anomalies associated with ARs, which drive large net SEB anomalies and contribute >54 % to mean SEB, and induce amplified surface warming year-round. This holds significance for melt events, particularly during summer. This study quantifies the role of ARs on surface energy budget, contributing to our understanding of the Arctic warming and sea ice decline in ongoing Arctic amplification. [ABSTRACT FROM AUTHOR]

Land–atmosphere (L–A) interactions are important for understanding convective processes, climate feedbacks, the development and perpetuation of droughts, heatwaves, pluvials, and other land-centered climate anomalies. Local L–A coupling (LoCo) metrics capture relevant L–A processes, highlighting the impact of soil and vegetation states on surface flux partitioning and the impact of surface fluxes on boundary layer (BL) growth and development and the entrainment of air above the BL. A primary goal of the Climate Process Team in the Coupling Land and Atmospheric Subgrid Parameterizations (CLASP) project is parameterizing and characterizing the impact of subgrid heterogeneity in global and regional Earth system models (ESMs) to improve the connection between land and atmospheric states and processes. A critical step in achieving that aim is the incorporation of L–A metrics, especially LoCo metrics, into climate model diagnostic process streams. However, because land–atmosphere interactions span timescales of minutes (e.g., turbulent fluxes), hours (e.g., BL growth and decay), days (e.g., soil moisture memory), and seasons (e.g., variability in behavioral regimes between soil moisture and latent heat flux), with multiple processes of interest happening in different geographic regions at different times of year, there is not a single metric that captures all the modes, means, and methods of interaction between the land and the atmosphere. And while monthly means of most of the LoCo-relevant variables are routinely saved from ESM simulations, data storage constraints typically preclude routine archival of the hourly data that would enable the calculation of all LoCo metrics. Here, we outline a reasonable data request that would allow for adequate characterization of sub-daily coupling processes between the land and the atmosphere, preserving enough sub-daily output to describe, analyze, and better understand L–A coupling in modern climate models. A secondary request involves embedding calculations within the models to determine mean properties in and above the BL to further improve characterization of model behavior. Higher-frequency model output will (i) allow for more direct comparison with observational field campaigns on process-relevant timescales, (ii) enable demonstration of inter-model spread in L–A coupling processes, and (iii) aid in targeted identification of sources of deficiencies and opportunities for improvement of the models. [ABSTRACT FROM AUTHOR]

Land surface models (LSMs) are an important tool for advancing our knowledge of the Earth system. LSMs are constantly improved to represent the various terrestrial processes in more detail. High-quality data, freely available from various observation networks, are being used to improve the prediction of terrestrial states and fluxes of water and energy. To optimize LSMs with observations, data assimilation methods and tools have been developed in the past decades. We apply the coupled Community Land Model version 5 (CLM5) and Parallel Data Assimilation Framework (PDAF) system (CLM5-PDAF) for 13 forest field sites throughout Europe covering different climate zones. The goal of this study is to assimilate in situ soil moisture measurements into CLM5 to improve the modeled evapotranspiration fluxes. The modeled fluxes will be evaluated using the predicted evapotranspiration fluxes with eddy covariance (EC) systems. Most of the sites use point-scale measurements from sensors placed in the ground; however, for three of the forest sites we use soil water content data from cosmic-ray neutron sensors, which have a measurement scale closer to the typical land surface model grid scale and EC footprint. Our results show that while data assimilation reduced the root-mean-square error for soil water content on average by 56 % to 64 %, the root-mean-square error for the evapotranspiration estimation is increased by 4 %. This finding indicates that only improving the soil water content (SWC) estimation of state-of-the-art LSMs such as CLM5 is not sufficient to improve evapotranspiration estimates for forest sites. To improve evapotranspiration estimates, it is also necessary to consider the representation of leaf area index (LAI) in magnitude and timing, as well as uncertainties in water uptake by roots and vegetation parameters. [ABSTRACT FROM AUTHOR]

Direct measurement of carbon and water fluxes at high frequency makes eddy covariance (EC) as the most preferred technique to characterize water use efficiency (WUE). However, reliability of EC fluxes is hinged on energy balance ratio (EBR) and inclusion of low-frequency fluxes. This study is aimed at investigating the role of averaging period to represent EC fluxes and its propagation into WUE dynamics. Carbon and water fluxes were monitored in a drip-irrigated Maize field at 10 Hz frequency and are averaged over 1, 5, 10, 15, 30, 45, 60, and 120 minutes considering daytime unstable conditions. Optimal averaging period to simulate WUE fluxes for each growth stage is obtained by considering cumulative frequency (ogive) curves. A clear departure of EBR from unity was observed during dough stage of the crop due to ignorance of canopy heat storage. Error in representing water (carbon) fluxes relative to the conventional 30 min. average is within ± 3 % (± 10 %) for 10-120 min. averaging and is beyond ± 3 % (± 10 %) for other time-averages. Ogive plots conclude that optimal averaging period to represent carbon and water fluxes is 15-30 min. for 6th leaf and silking stages, and is 45-60 min. for dough and maturity stages. Dynamics of WUE considering optimal averaging periods are in the range of 1.49 ± 0.95, 1.37 ± 0.74, 1.39 ± 0.79, and 3.06 ± 0.69 µmol mmol-1 for the 6th leaf, silking, dough, and maturity stages respectively. Error in representing WUE with conventional 30 min. averaging is marginal (< 1.5 %) except during the dough stage (12.12 %). Our findings can help in developing efficient water management strategies by accurately characterizing WUE fluxes from the EC measurements. [ABSTRACT FROM AUTHOR]

A prerequisite for understanding the local, regional, and hemispherical impacts of Arctic sea-ice decline on the atmosphere is to quantify the effects of sea-ice concentration (SIC) on the turbulent surface fluxes of sensible and latent heat in the Arctic. We analyse these effects utilising four global atmospheric reanalyses, ERA5, JRA-55, MERRA-2, and NCEP/CFSR (including both the NCEP Climate Forecast System Reanalysis (CFSR) and the NCEP Climate Forecast System Version 2 (CFSv2)), and evaluate their uncertainties arising from inter-reanalysis differences in SIC and in the sensitivity of the turbulent surface fluxes to SIC. The magnitude of the differences in SIC is up to 0.15 but typically around 0.05 in most of the Arctic over all four seasons. Orthogonal-distance regression and ordinary-least-squares regression analyses indicate that the greatest sensitivity of both the latent and the sensible heat flux to SIC occurs in the cold season, November to April. For these months, using daily means of data, the average sensitivity is 400 Wm-2 for the latent heat flux and over 800 Wm-2 for the sensible heat flux per unit of SIC (change in SIC from 0 to 1), with differences between reanalyses that are as large as 300 Wm-2 for the latent heat flux and 600 Wm-2 for the sensible heat flux per unit of SIC. The sensitivity is highest for the NCEP/CFSR reanalysis. Comparing the periods 1980–2000 and 2001–2021, we find that the effect of SIC on turbulent surface fluxes has weakened owing to the increasing surface temperature of sea ice and sea-ice decline. The results also indicate signs of a decadal-scale improvement in the mutual agreement between reanalyses. The effect of SIC on turbulent surface fluxes arises mostly via the effect of SIC on atmosphere–surface differences in temperature and specific humidity, whereas the effect of SIC on wind speed (via surface roughness and atmospheric-boundary-layer stratification) partly cancels out in the turbulent surface fluxes, as the wind speed increases the magnitudes of both upward and downward fluxes. [ABSTRACT FROM AUTHOR]

To compare in situ and laboratory estimates of sea spray aerosol (SSA) production fluxes, we conducted two research campaigns in the vicinity of an eddy covariance (EC) flux tower on the island of Östergarnsholm in the Baltic Sea during May and August 2021. To accomplish this, we performed EC flux measurements for particles with diameters between 0.25 and 2.5 µm simultaneously with laboratory measurements using a plunging jet sea spray simulation chamber containing local seawater sampled close to the footprint of the flux tower. We observed a log-linear relationship between wind speed and EC-derived SSA emission fluxes, a power-law relationship between significant wave height and EC-derived SSA emission fluxes, and a linear relationship between wave Reynolds number and EC-derived SSA emission fluxes, all of which are consistent with earlier studies. Although we observed a weak negative relationship between particle production in the sea spray simulation chamber and seawater chlorophyll- α concentration and a weak positive relationship with the concentration of fluorescent dissolved organic matter in seawater, we did not observe any significant impact of dissolved oxygen on particle production in the chamber. To obtain an estimate of the size-resolved emission spectrum for particles with dry diameters between 0.015 and 10 µm , we combined the estimates of SSA particle production fluxes obtained using the EC measurements and the chamber measurements in three different ways: (1) using the traditional continuous whitecap method, (2) using air entrainment measurements, and (3) simply scaling the chamber data to the EC fluxes. In doing so, we observed that the magnitude of the EC-derived emission fluxes compared relatively well to the magnitude of the fluxes obtained using the chamber air entrainment method as well as the previous flux measurements of and the parameterizations of and. As a result of these measurements, we have derived a wind-speed-dependent and wave-state-dependent SSA parameterization for particles with dry diameters between 0.015 and 10 µm for low-salinity waters such as the Baltic Sea, thus providing a more accurate estimation of SSA production fluxes. [ABSTRACT FROM AUTHOR]

Abstract. Accurate representation of the turbulent exchange of carbon, water, and heat between the land surface and the atmosphere is critical for modelling global energy, water, and carbon cycles, both in future climate projections and weather forecasts. We describe a Model Intercomparison Project (MIP) that compares the surface turbulent heat flux predictions of around 20 different land models provided with in-situ meteorological forcing, evaluated with measured surface fluxes using quality-controlled data from 170 eddy-covariance based flux tower sites. Several out-of-sample empirical model predictions of site fluxes are used as benchmarks to quantify the degree to which land model performance could improve across a broad range of metrics. The performance discrepancy between empirical and mechanistic model predictions also provides a potential pathway to understand sources of model error. Sites with unusual behaviour, complicated processes, poor data quality or uncommon flux magnitude will be more difficult to predict for both mechanistic and empirical models. Results suggest that latent heat flux and net ecosystem exchange of CO2 are better predicted by land models than sensible heat flux, which at least conceptually would appear to have fewer physical processes controlling it. Land models that are implemented in Earth System Models also appear to perform notably better than stand-alone ecosystem (including demographic) models, at least in terms of the fluxes examined here. Flux tower data quality is also explored as an uncertainty source, with the difference between energy-balance corrected versus raw fluxes examined, as well as filtering for low wind speed periods. Land model performance does not appear to improve with energy-balance corrected data, and indeed some results raised questions about whether the correction process itself was appropriate. In both cases results were broadly consistent, with simple out-of-sample empirical models, including linear regression, comfortably outperforming mechanistic land models. The PLUMBER2 approach, and its openly available data, enable precise isolation of the locations and conditions in which model developers can know that a given land model can improve, allowing information pathways and discrete parametrisations in models to be identified and targeted for model development. [ABSTRACT FROM AUTHOR]

Wetland methane responses to temperature and precipitation were studied in a boreal wetland-rich region in Northern Europe using ecosystem process models. Six ecosystem models (JSBACH-HIMMELI, LPX-Bern, LPJ-GUESS, JULES, CLM4.5 and CLM5) were compared to multi-model mean of ecosystem models and atmospheric inversions from the Global Carbon Project and up-scaled eddy covariance flux results for their temperature and precipitation responses and seasonal cycles of the regional fluxes. Two models with contrasting response patterns, LPX-Bern and JSBACH-HIMMELI, were used as priors in atmospheric inversions with Carbon Tracker Europe -- CH4 in order to find out how the inversion attempts to change the prior fluxes in the posterior and how this alters the interpretation of the flux responses to temperature and precipitation. The inversion attempted to move emissions of both models in posterior towards co-limitation by temperature and precipitation. In general high temperature and/or high precipitation periods often resulted in high posterior emissions. This was not the case for the warm and dry period of summer 2018. The process models showed strong temperature as well as strong precipitation responses for the region (51-91 % of the variance explained by both), and the month of maximum emissions varied from May to September. However, multi-model means, inversions and up-scaled eddy covariance flux observations agreed on the month of maximum emissions, and had rather balanced temperature and precipitation responses. The set-up of different emission components (peatland emissions, mineral land fluxes) had a significant role in building up the response patterns. Considering the significant differences among the models, it is essential to pay more attention to the magnitude, composition, annual cycle and climate driver responses of wetland emissions in different regions. [ABSTRACT FROM AUTHOR]

The dissipation rates of the basic turbulent second-order moments are the key parameters controlling turbulence energetics and spectra, turbulent fluxes of momentum and heat, and playing a vital role in turbulence modelling. In this paper, we use the results of Direct Numerical Simulations (DNS) to evaluate dissipation rates of the basic turbulent second-order moments and revise the energy and flux-budget turbulence closure model for stably stratified turbulence. We delve into the theoretical implications of this approach and substantiate our closure hypotheses through DNS data. We also show why the concept of down-gradient turbulent transport becomes incomplete when applied to the vertical turbulent flux of potential temperature under very stable stratification. We reveal essential feedback between turbulent kinetic energy, the vertical flux of buoyancy and turbulent potential energy, which is responsible for maintaining shear-produced stably stratified turbulence up to extreme static stability. [ABSTRACT FROM AUTHOR]

The anthropogenic and biogenic contributions of isoprene, monoterpenes, sesquiterpenes and methanol in an urban area were estimated based on direct eddy covariance flux observations during four campaigns between 2018 and 2021. While these compounds are typically thought to be dominated by biogenic sources on regional and global scales, the role of potentially significant anthropogenic emissions in urban areas has been recently debated. Typical fluxes of isoprene, monoterpenes and sesquiterpenes were on the order of 0.09 nmol m−2 s−1, 0.09 nmol m−2 s−1 and 0.003 nmol m−2 s−1 during spring. During summer, emission fluxes of isoprene, monoterpenes and sesquiterpenes were higher on the order of 0.85 nmol m−2 s−1, 0.11 nmol m−2 s−1, 0.004 nmol m−2 s−1. It was found that the contribution of the anthropogenic part is strongly seasonally dependent. For isoprene the anthropogenic fraction can be as high as 64 % in spring, but is typically very low < 18 % during the summer season. For monoterpenes the anthropogenic fraction was estimated between 43 % in spring and less than 20 % in summer. With values of 2.8 nmol m−2 s−1 in spring and 3.2 nmol m−2 s−1 in summer, methanol did not exhibit a significant seasonal variation of observed surface fluxes. However, there was a difference in emissions between weekdays and weekends (about 2.3 times higher on weekdays in spring). This suggests that methanol emissions are likely influenced by anthropogenic activities during all seasons. [ABSTRACT FROM AUTHOR]

Based on the turbulent heat flux from the third scientific expedition to the Qinghai-Tibet Plateau in 2012, imputation evaluations were conducted using algorithms like Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and the Transformer model with deep self-attention mechanism. Results indicated that the Transformer model performed optimally. To further enhance imputation accuracy, a combined model of Transformer and Convolutional Neural Network (CNN), termed as Transformer_CNN, was proposed. Herein, while the Transformer primarily focused on global attention, the convolution operations in the CNN provided the model with local attention. Experimental outcomes revealed that the imputations from Transformer_CNN surpassed the traditional single artificial intelligence model approaches. The coefficient of determination (R2) reached 0.949 in the sensible heat flux test set and 0.894 in the latent heat flux test set, thereby confirming the applicability of the Transformer_CNN model for data imputation of turbulent heat flux in the Qinghai-Tibet Plateau. Ultimately, the turbulent heat flux observational database from 2007 to 2016 at the station was imputed using the Transformer_CNN model. [ABSTRACT FROM AUTHOR]

A seminal study by Hoskins and Karoly (1981) explored the atmospheric circulation response to tropospheric heating perturbations at low and mid latitudes. Here we revisit and extend their study by investigating the circulation and temperature response to low, mid and high latitude surface heating using an idealised moist, gray radiation model. Our results corroborate previous findings showing that heating perturbations at low and mid latitudes are balanced by different mean circulation responses - upward motion and horizontal temperature advection, respectively. Transient eddy heat flux divergence plays an increasingly important role with latitude, becoming the main circulation response at high latitudes. However, this mechanism is less efficient at balancing heating perturbations than temperature advection, leading to greater reliance on an additional contribution from radiative cooling. These dynamical and radiative adjustments promote stronger lower tropospheric warming for surface heating at high latitudes compared with lower latitudes, suggesting a mechanism by which sea ice loss promotes a polar-amplified temperature signal of climate change. [ABSTRACT FROM AUTHOR]

The snowpack has a major influence on the land surface energy budget. Accurate simulation of the snowpack energy and radiation budget is challenging due to, e.g., effects of vegetation and topography, as well as limitations in the theoretical understanding of turbulent transfer in the stable boundary layer. Studies that evaluate snow, hydrology and land surface models against detailed observations of all surface energy balance components at high latitudes are scarce. In this study, we compared different configurations of the SURFEX land surface model against surface energy flux, snow depth and soil temperature observations from four eddy-covariance stations in Finland. The sites cover two different climate and snow conditions, representing the southern and northern subarctic zones, as well as the contrasting forest and peatland ecosystems typical for the boreal landscape. We tested different turbulent flux parameterizations implemented in the Crocus snowpack model. In addition, we examined common alternative approaches to conceptualize soil and vegetation, and we assessed their performance in simulating surface energy fluxes, snow conditions and soil thermal regime. Our results show that a stability correction function that increases the turbulent exchange under stable atmospheric conditions is imperative to simulate sensible heat fluxes over the peatland snowpacks and that realistic peat soil texture (soil organic content) parameterization greatly improves the soil temperature simulations. For accurate simulations of surface energy fluxes, snow and soil conditions in forests, an explicit vegetation representation is necessary. Moreover, we demonstrate the high sensitivity of surface fluxes to a poorly documented parameter involved in snow cover fraction computation. Although we focused on models within the SURFEX platform, the results have broader implications for choosing suitable turbulent flux parameterization and model structures depending on the potential use cases for high-latitude land surface modeling. [ABSTRACT FROM AUTHOR]

Arctic sea ice has been declining rapidly in recent decades. We investigate how the poleward transport of moisture and heat from lower latitudes through atmospheric rivers (ARs) influences Arctic sea ice variations. We use hourly ERA5 (fifth-generation European Reanalysis) data for 1981–2020 at 0.25 ∘ × 0.25 ∘ resolution to examine the meteorological conditions and sea ice changes associated with ARs in the Arctic. In the years 2012 and 2020, which had an extremely low summer Arctic sea ice extent, we show that the individual AR events associated with large cyclones initiate a rapid sea ice decrease through turbulent heat fluxes and winds. We carry out further statistical analysis of the meteorological conditions and sea ice variations for 1981–2020 over the entire Arctic Ocean. We find that on weather timescales the atmospheric moisture content anticorrelates significantly with the sea ice concentration tendency almost everywhere in the Arctic Ocean, while the dynamic sea ice motion driven by northward winds further reduces the sea ice concentration. [ABSTRACT FROM AUTHOR]

Surface net radiation (SNR) is a vital input for many land surface and hydrological models. However, most of the current remote sensing datasets of SNR come mostly at coarse resolutions or have large gaps due to cloud cover that hinder their use as input in models. Here, we present a downscaled and continuous daily SNR product across Europe for 2018–2019. Long-wave outgoing radiation is computed from a merged land surface temperature (LST) product in combination with Meteosat Second Generation emissivity data. The merged LST product is based on all-sky LST retrievals from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard the geostationary Meteosat Second Generation (MSG) satellite and clear-sky LST retrievals from the Sea and Land Surface Temperature Radiometer (SLSTR) onboard the polar-orbiting Sentinel-3A satellite. This approach makes use of the medium spatial (approx. 5–7 km) but high temporal (30 min) resolution, gap-free data from MSG along with the low temporal (2–3 d) but high spatial (1 km) resolution of the Sentinel-3 LST retrievals. The resulting 1 km and daily LST dataset is based on an hourly merging of both datasets through bias correction and Kalman filter assimilation. Short-wave outgoing radiation is computed from the incoming short-wave radiation from MSG and the downscaled albedo using 1 km PROBA-V data. MSG incoming short-wave and long-wave radiation and the outgoing radiation components at 1 km spatial resolution are used together to compute the final daily SNR dataset in a consistent manner. Validation results indicate an improvement of the mean squared error by ca. 7 % with an increase in spatial detail compared to the original MSG product. The resulting pan-European SNR dataset, as well as the merged LST product, can be used for hydrological modelling and as input to models dedicated to estimating evaporation and surface turbulent heat fluxes and will be regularly updated in the future. The datasets can be downloaded from 10.5281/zenodo.8332222 and 10.5281/zenodo.8332128. [ABSTRACT FROM AUTHOR]

A widely applicable parameterisation of turbulent heat and momentum fluxes at sea has been developed for the SURFEX v8.1 surface model. This wave-age-dependent stress parameterisation (WASP) combines a close fit to available in situ observations at sea up to wind speed of 60 m s -1 with the possibility of activating the impact of wave growth on the wind stress. It aims in particular at representing the effect of surface processes that depend on the surface wind according to the state of the art. It can be used with the different atmospheric models coupled with the surface model SURFEX, including the CNRM-CM climate model, the operational (numerical weather prediction) systems in use at Météo-France, and the research model Meso-NH. Designed to be used in coupled or forced mode with a wave model, it can also be used in an atmosphere-only configuration. It has been validated and tested in several case studies covering different surface conditions known to be sensitive to the representation of surface turbulent fluxes: (i) the impact of a sea surface temperature (SST) front on low-level flow by weak wind, (ii) the simulation of a Mediterranean heavy precipitating event where waves are known to influence the low-level wind and displace precipitation, (iii) several tropical cyclones, and (iv) a climate run over 35 years. It shows skills comparable to or better than the different parameterisations in use in SURFEX v8.1 so far. [ABSTRACT FROM AUTHOR]

Large-eddy simulations (LESs) have been increasingly used for studying atmosphere and land surface interactions over heterogeneous areas. However, parameterizations based on Monin–Obukhov Similarity Theory (MOST) often violate the basic assumptions of the very theory, generate inconsistencies with the LES turbulence closures, and produce surface flux estimates depending on LES model resolutions. Here, we propose a novel scheme for turbulent flux estimates in LES models. It computes the fluxes locally using the LES subgrid closure, which is then constrained on the macroscopic scale using MOST. Compared with several other schemes, the new scheme performs better for the various types of land surfaces tested. We validate our scheme by comparing surface flux estimates with field measurements obtained over an oasis surface at various height levels. Additionally, we scrutinize other quantities related to the surface energy balance, including net radiation, ground heat flux, and surface skin temperature, all of which align well with observational data. Our sensitivity experiments, focusing on model horizontal resolution, underscore the robustness of our scheme, as it maintains its corrective efficacy despite changes in horizontal grid spacing. We find that the macroscopic constraint imposed by MOST on LES-estimated fluxes strengthens as the horizontal grid spacing decreases, with a more pronounced influence on sensible than latent heat fluxes. These findings collectively highlight the promise and adaptability of our scheme for improved surface flux estimates in LES models. [ABSTRACT FROM AUTHOR]

This study evaluates methods to derive the surface mixing layer (SML) height of the Arctic atmospheric boundary layer (ABL) using in situ measurements inside the Arctic ABL during winter and the transition period to spring. An instrumental payload carried by a tethered balloon was used for the measurements between December 2019 and May 2020 during the year-long Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. Vertically highly resolved (centimeter scale) in situ profile measurements of mean and turbulent parameters were obtained, reaching from the sea ice to several hundred meters above ground. Two typical conditions of the Arctic ABL over sea ice were identified: cloudless situations with a shallow surface-based inversion and cloudy conditions with an elevated inversion. Both conditions are associated with significantly different SML heights whose determination as accurately as possible is of great importance for many applications. We used the measured turbulence profile data to define a reference of the SML height. With this reference, a more precise critical bulk Richardson number of 0.12 was derived, which allows an extension of the SML height determination to regular radiosoundings. Furthermore, we have tested the applicability of the Monin–Obukhov similarity theory to derive SML heights based on measured turbulent surface fluxes. The application of the different approaches and their advantages and disadvantages are discussed. [ABSTRACT FROM AUTHOR]

In recent years, urban canopy models (UCMs) have been used as fully coupled components of mesoscale atmospheric models as well as offline tools to estimate temperature and surface fluxes using atmospheric forcings. Examples include multi-layer urban canopy models (MLUCMs), where the vertical variability of turbulent fluxes is calculated by solving prognostic momentum and turbulent kinetic energy (TKE, k) equations using length scale (l) and drag parameterizations. These parameterizations are based on the well-established 1.5-order k − l turbulence closure theory and are often informed by microscale fluid dynamics simulations. However, this approach can include simplifications such as the assumption of the same diffusion coefficient for momentum, TKE, and scalars. In addition, the dispersive stresses arising from spatially-averaged flow properties have been parameterized together with the turbulent fluxes while being controlled by different mechanisms. Both of these assumptions impact the quantification of turbulent exchange of flow properties and subsequent air temperature prediction in urban canopies. To assess these assumptions and improve corresponding parameterization, we conducted 49 large-eddy simulations (LES) for idealized urban arrays, encompassing variable building height distributions and a comprehensive range of urban densities (λ p ∈ [0.0625,0.64]) seen in global cities. We find that the efficiency of turbulent transport (numerically described via diffusion coefficients) is similar for scalars and momentum but 3.5 times higher for TKE. Additionally, the parameterization of the dispersive momentum flux using the k − l closure was a source of error, while scaling with the pressure gradient and urban morphological parameters appears more appropriate. In response to these findings, we propose two changes to MLUCM v2.0: (a) separate characterization for turbulent diffusion coefficient for momentum and TKE; and (b) introduction of an explicit physics-based 'mass flux' term to represent the non-Gaussian component of the dispersive momentum transport as an amendment to the existing 'eddy diffusivity' framework. The updated one-dimensional model, after being tuned for building height variability, is further compared against the original LES results and demonstrates improved performance in predicting vertical turbulent exchange in urban canopies. [ABSTRACT FROM AUTHOR]

Conventional and recently developed approaches for estimating turbulent scalar fluxes under stable conditions are evaluated. The focus is on methods that do not require fast scalar sensors such as the relaxed eddy accumulation (REA) approach, the disjunct eddy-covariance (DEC) approach, and a novel mixing length parametrization labelled as A22. Using high-frequency measurements collected from two contrasting sites (Utqiagvik, Alaska and Wendell, Idaho 'during winter'), it is shown that the REA and A22 models outperform the conventional Monin-Obukhov Similarity Theory (MOST) utilized in Earth System Models. With slow trace gas sensors used in disjunct eddy-covariance (DEC) approaches and the more complex signal filtering associated with REA devices (here simulated using filtered signals from fast-response sensors), A22 outperforms REA and DEC in predicting the observed unfiltered (total) eddy-covariance (EC) fluxes. However, REA and DEC can still capture the observed filtered EC fluxes computed with the filtered scalar signal. This finding motivates the development of a correction, blending the REA and DEC methods, for the underestimated net averaged fluxes to incorporate the effect of sensor filtering. The only needed parameter for this correction is the mean velocity at the instrument height, a surrogate of the advective timescale. [ABSTRACT FROM AUTHOR]

Current understanding of land–atmosphere exchange fluxes is limited by the fact that available observational techniques mainly quantify net fluxes, which are the sum of generally larger, bidirectional fluxes that partially cancel out. As a consequence, validation of gas exchange fluxes applied in models is challenging due to the lack of ecosystem-scale exchange flux measurements partitioned into soil, plant, and atmospheric components. One promising experimental method to partition measured turbulent fluxes uses the exchange-process-dependent isotopic fractionation of molecules like CO2 and H2O. When applying this method at a field scale, an isotope flux (δ flux) needs to be measured. Here, we present and discuss observations made during the LIAISE (Land surface Interactions with the Atmosphere over the Iberian Semi-arid Environment) 2021 field campaign using an eddy covariance (EC) system coupled to two laser spectrometers for high-frequency measurement of the isotopic composition of H2O and CO2. This campaign took place in the summer of 2021 in the irrigated Ebro River basin near Mollerussa, Spain, embedded in a semi-arid region. We present a systematic procedure to scrutinise and analyse measurements of the δ -flux variable, which plays a central role in flux partitioning. Our experimental data indicated a larger relative signal loss in the δ fluxes of H2O compared to the net ecosystem flux of H2O , while this was not true for CO2. Furthermore, we find that mole fractions and isotope ratios measured with the same instrument can be offset in time by more than a minute for the H2O isotopologues due to the isotopic memory effect. We discuss how such artefacts can be detected and how they impact flux partitioning. We argue that these effects are likely due to condensation of water on a cellulose filter in our inlet system. Furthermore, we show that these artefacts can be resolved using physically sound corrections for inlet delays and high-frequency loss. Only after such corrections and verifications are made can ecosystem-scale fluxes be partitioned using isotopic fluxes as constraints, which in turn allows for conceptual land–atmosphere exchange models to be validated. [ABSTRACT FROM AUTHOR]

The zonal momentum budget of the deep upper tropics is studied in the context of present and future climates. In the zonal mean, as is known, a robust balance exists between the acceleration by the horizontal eddy momentum flux convergence and the deceleration by the mean meridional momentum advection. During summer, climatological stationary Rossby waves over the Asian monsoon longitudes converge westerly momentum into the tropics and are the primary contributors to the eddy term. During winter, anomalous westerly winds over the tropical East Pacific allow extratropical waves to propagate into the deep tropics, where they tend to break and decelerate the flow. When integrated over all longitudes, eddies from these two regions sum constructively in summer and destructively in winter, always yielding a net positive momentum forcing that balances the mean flow term. The state-of-the-art CMIP6 suite qualitatively captures these features in the historical run and shows that the momentum fluxes change due to global warming. In summer, stationary eddy circulations in the Asian monsoon zone weaken in the upper troposphere (UT) but strengthen in the lower stratosphere (LS). Greater upward mass flux from the troposphere forces a stronger divergence and a more intense circulation in the LS following a Sverdrup vorticity balance. This strengthening of summertime tropical and subtropical stationary waves in the LS is observed over all longitudes and is verified in an idealized aquaplanet general circulation model experiment. In winter, eddy westerlies over the East Pacific longitudes decrease in strength due to the expected weakening of the subtropical stationary waves with warming. This causes a significant decrease in the propagation of extratropical waves into this region, along with a drop in eddy potential vorticity fluxes associated with these waves. Thus, apart from the mean meridional flux, which weakens due to the projected weakening of the Hadley Cells, our analysis of warming simulations clearly suggests significant and robust changes in the eddy momentum fluxes in the deep tropics. Potential implications of these changes in the context of the zonal mean flow and regional circulations are discussed. [ABSTRACT FROM AUTHOR]

Instrumentation packages for eddy covariance (EC) measurements have been developed for unoccupied aerial vehicles (UAVs) to measure the turbulent fluxes of latent heat (LE), sensible heat (H), and CO2 (Fc) in the atmospheric boundary layer. This study aims to evaluate the performance of this UAV-based EC system. First, the measurement precision (1σ) of georeferenced wind was estimated to be 0.07 ms-1. Then, the effect of the calibration parameter and aerodynamic characteristics of the UAV on wind measurement was examined by conducting a set of calibration flights. The results showed that the calibration improved the quality of the measured wind field, and the influence of upwash and the leverage effect can be ignored in wind measurement by the UAV. Third, for the measurements of turbulent fluxes, the error caused by instrumental noise was estimated to be 0.03 µmolm-2s-1 for Fc , 0.02 Wm-2 for H , and 0.08 Wm-2 for LE. Fourth, data from the standard operational flights were used to assess the influence of resonance on the measurements and to test the sensitivity of the measurement under the variation (±30 %) in the calibration parameters around their optimum value. The results showed that the effect of resonance mainly affected the measurement of CO2 (∼5 %). The pitch offset angle (εθ) significantly affected the measurement of vertical wind (∼30 %) and turbulent fluxes (∼15 %). The heading offset angle (εψ) mainly affected the measurement of horizontal wind (∼15 %), and other calibration parameters had no significant effect on the measurements. The results lend confidence to the use of the UAV-based EC system and suggest future improvements for the optimization of the next-generation system. [ABSTRACT FROM AUTHOR]